{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b251c804",
   "metadata": {},
   "source": [
    "# Un'app per la sostenibilità: dove conviene lanciarla?\n",
    "\n",
    "## Analisi esplorativa del Google Play Store\n",
    "\n",
    "**Esame:** Data Manipulation and Visualization · Master in AI e Agenti AI per il Business (start2impact University)\n",
    "**Autore:** Fabio Mencio · **Data:** settembre 2026, **rivisto a ottobre 2026** dopo la correzione\n",
    "**Dataset:** [Google Play Store Apps](https://www.kaggle.com/datasets/lava18/google-play-store-apps) — Kaggle, licenza CC BY 3.0 · i dati si fermano all'**8 agosto 2018**\n",
    "\n",
    "---\n",
    "\n",
    "### La traccia\n",
    "\n",
    "> Ho deciso di creare la mia prima app a tema sostenibilità, ma prima voglio capire quale\n",
    "> settore sia il più adatto. Scarico i dati di tutto il Google Play Store e li analizzo.\n",
    "\n",
    "### La domanda guida\n",
    "\n",
    "> **Se dovessi lanciare oggi una nuova app legata alla sostenibilità, quali caratteristiche\n",
    "> del mercato Google Play dovrei considerare per scegliere il segmento e il modello di\n",
    "> business più promettenti?**\n",
    "\n",
    "### Cosa cambia in questa revisione\n",
    "\n",
    "La correzione ha segnalato cinque problemi e un dettaglio. Li ho affrontati tutti, e due\n",
    "hanno cambiato i risultati.\n",
    "\n",
    "| | Osservazione | Cosa ho fatto | Dove |\n",
    "|---|---|---|---|\n",
    "| 1 | Il testo cita numeri che il notebook non mostra | il notebook è salvato **con tutti gli output**, ogni tabella citata è stampata, e la sezione 11 **ricalcola ogni numero del testo** e lo confronta con quello scritto | tutto, sez. 11 |\n",
    "| 2 | La somma di posizioni perde l'ampiezza delle differenze e dà pesi uguali senza dirlo | misure grezze accanto alle posizioni, un punteggio che conserva le distanze, 36 combinazioni di pesi diversi | 8.1 |\n",
    "| 3 | Del classificatore misuro la precisione, non la copertura | ho letto una per una 1.000 app estratte a caso **fuori** dai candidati | 7.1 |\n",
    "| 4 | La difficoltà di ingresso sovrascrive la classifica senza dirlo | una sola tabella con «posizione secondo i dati» e «difficoltà stimata», e la regola scritta: prevale la seconda | 9 |\n",
    "| 5 | La cautela su correlazione e causalità scritta in 4.1 viene contraddetta nelle conclusioni | una regola in 4.2, applicata a ipotesi, diagnosi e strategia; ogni scelta finale dice su che tipo di prova poggia | 4.2, 6, 9 |\n",
    "| — | Nomi ripetuti: categoria e installazioni coincidono? | controllato: 15 nomi appartengono ad app diverse, che ora tengo separate | 1.3 |\n",
    "\n",
    "**Cosa è cambiato nei risultati.**\n",
    "\n",
    "1. **Le parole chiave trovano circa un'app sostenibile su tre.** Il segmento non è «lo\n",
    "   0,35% del catalogo»: vale fra lo 0,35% (il minimo certo) e il 2,3%, con una stima centrale\n",
    "   dell'1,0%.\n",
    "2. **La classifica delle categorie dipende dal metodo.** Sommando le posizioni vince Food &\n",
    "   Drink; con un punteggio che conserva le distanze vince Shopping. La scelta di Food & Drink\n",
    "   resta, ma ora è dichiarata per quello che è: un giudizio sulla difficoltà di ingresso, che\n",
    "   decide fra due candidate che i dati non separano.\n",
    "\n",
    "### Prima di eseguire\n",
    "\n",
    "1. Su Google Colab trascino i 2 file CSV nel pannello dei file a sinistra: finiscono nella cartella `/content`, da cui il notebook li legge. In locale li cerca nella cartella `data/`.\n",
    "2. Servono `pandas`, `numpy`, `matplotlib`, `seaborn`, già presenti su Colab.\n",
    "3. `plotly` serve per un grafico interattivo, `ydata-profiling` per un riepilogo automatico: su Colab la prima cella li installa, altrove sono facoltativi e il notebook va avanti anche senza.\n",
    "\n",
    "### Indice\n",
    "\n",
    "| Sezione | Contenuto |\n",
    "|---|---|\n",
    "| 1 | I dati: importazione e pulizia |\n",
    "| 2 | Com'è fatto il mercato |\n",
    "| 3 | Valori anomali |\n",
    "| 4 | Correlazioni, e la regola su correlazione e causalità |\n",
    "| 5 | Le categorie: concorrenza e interesse |\n",
    "| 6 | Cinque ipotesi, verificate |\n",
    "| 7 | Il segmento sostenibilità: precisione e copertura |\n",
    "| 8 | Quale categoria scegliere, e quanto è solida la classifica |\n",
    "| 9 | La strategia |\n",
    "| 10 | Limiti |\n",
    "| 11 | Registro dei numeri citati |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1402bd1a",
   "metadata": {
    "id": "1402bd1a"
   },
   "source": [
    "---\n",
    "\n",
    "## 1. I dati: importazione e pulizia\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "820b0286",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:10.383777Z",
     "iopub.status.busy": "2026-10-07T04:52:10.383590Z",
     "iopub.status.idle": "2026-10-07T04:52:11.595502Z",
     "shell.execute_reply": "2026-10-07T04:52:11.594573Z"
    },
    "executionInfo": {
     "elapsed": 8049,
     "status": "ok",
     "timestamp": 1790952638232,
     "user": {
      "displayName": "Fabio mencio",
      "userId": "05860154059512044184"
     },
     "user_tz": -120
    },
    "id": "820b0286",
    "outputId": "8675eb7e-6417-4c62-be55-4df411415b08"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas 3.0.6 | numpy 2.5.3 | seaborn 0.13.2\n",
      "plotly: presente\n",
      "ydata-profiling: assente (salterò un riepilogo)\n"
     ]
    }
   ],
   "source": [
    "# su Colab installo le due librerie facoltative; in locale non installo nulla\n",
    "# (ydata-profiling farebbe retrocedere pandas alla versione 2)\n",
    "import sys\n",
    "if \"google.colab\" in sys.modules:\n",
    "    !pip install -q plotly ydata-profiling\n",
    "\n",
    "import os\n",
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline\n",
    "\n",
    "# librerie facoltative\n",
    "try:\n",
    "    import plotly.express as px\n",
    "    PLOTLY = True\n",
    "except ImportError:\n",
    "    PLOTLY = False\n",
    "\n",
    "try:\n",
    "    from ydata_profiling import ProfileReport\n",
    "    YDATA = True\n",
    "except ImportError:\n",
    "    YDATA = False\n",
    "\n",
    "pd.set_option(\"display.max_columns\", 30)\n",
    "pd.set_option(\"display.width\", 170)\n",
    "\n",
    "# stile dei grafici, impostato una volta per tutte\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.rcParams.update({\"figure.figsize\": (9, 4), \"figure.dpi\": 110,\n",
    "                     \"axes.titlesize\": 12, \"axes.titleweight\": \"bold\",\n",
    "                     \"axes.titlelocation\": \"left\", \"grid.linewidth\": 0.5})\n",
    "BLU, ARANCIONE, VERDE, GRIGIO = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#9a9a96\"\n",
    "\n",
    "print(\"pandas\", pd.__version__, \"| numpy\", np.__version__, \"| seaborn\", sns.__version__)\n",
    "print(\"plotly:\", \"presente\" if PLOTLY else \"assente (salterò un grafico)\")\n",
    "print(\"ydata-profiling:\", \"presente\" if YDATA else \"assente (salterò un riepilogo)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c145a1d5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 178
    },
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:11.597371Z",
     "iopub.status.busy": "2026-10-07T04:52:11.597076Z",
     "iopub.status.idle": "2026-10-07T04:52:11.707403Z",
     "shell.execute_reply": "2026-10-07T04:52:11.706406Z"
    },
    "executionInfo": {
     "elapsed": 419,
     "status": "ok",
     "timestamp": 1790952638653,
     "user": {
      "displayName": "Fabio mencio",
      "userId": "05860154059512044184"
     },
     "user_tz": -120
    },
    "id": "c145a1d5",
    "outputId": "06dec8fd-29a9-4a0a-ff7a-7735a30399a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "App        : 10841 righe x 13 colonne\n",
      "Recensioni : 64295 righe x 5 colonne\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>159</td>\n",
       "      <td>19M</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>January 7, 2018</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>967</td>\n",
       "      <td>14M</td>\n",
       "      <td>500,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Pretend Play</td>\n",
       "      <td>January 15, 2018</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>87510</td>\n",
       "      <td>8.7M</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App        Category  Rating Reviews  Size    Installs  Type Price Content Rating                     Genres  \\\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook  ART_AND_DESIGN     4.1     159   19M     10,000+  Free     0       Everyone               Art & Design   \n",
       "1                                Coloring book moana  ART_AND_DESIGN     3.9     967   14M    500,000+  Free     0       Everyone  Art & Design;Pretend Play   \n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...  ART_AND_DESIGN     4.7   87510  8.7M  5,000,000+  Free     0       Everyone               Art & Design   \n",
       "\n",
       "       Last Updated Current Ver   Android Ver  \n",
       "0   January 7, 2018       1.0.0  4.0.3 and up  \n",
       "1  January 15, 2018       2.0.0  4.0.3 and up  \n",
       "2    August 1, 2018       1.2.4  4.0.3 and up  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- lettura dei due file CSV -------------------------------------------\n",
    "# I file sono caricati a mano nel pannello dei file di Colab: stanno in /content\n",
    "PERCORSO_APP = \"/content/googleplaystore.csv\"\n",
    "PERCORSO_REC = \"/content/googleplaystore_user_reviews.csv\"\n",
    "\n",
    "# se non sono in /content sto girando in locale: li cerco in data/\n",
    "if not os.path.exists(PERCORSO_APP):\n",
    "    PERCORSO_APP = \"data/googleplaystore.csv\"\n",
    "    PERCORSO_REC = \"data/googleplaystore_user_reviews.csv\"\n",
    "\n",
    "grezzo = pd.read_csv(PERCORSO_APP)\n",
    "recensioni_grezze = pd.read_csv(PERCORSO_REC)\n",
    "\n",
    "print(\"App        :\", grezzo.shape[0], \"righe x\", grezzo.shape[1], \"colonne\")\n",
    "print(\"Recensioni :\", recensioni_grezze.shape[0], \"righe x\", recensioni_grezze.shape[1], \"colonne\")\n",
    "grezzo.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "51c3b796",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:11.709553Z",
     "iopub.status.busy": "2026-10-07T04:52:11.709258Z",
     "iopub.status.idle": "2026-10-07T04:52:11.720918Z",
     "shell.execute_reply": "2026-10-07T04:52:11.720147Z"
    },
    "executionInfo": {
     "elapsed": 29,
     "status": "ok",
     "timestamp": 1790952638698,
     "user": {
      "displayName": "Fabio mencio",
      "userId": "05860154059512044184"
     },
     "user_tz": -120
    },
    "id": "51c3b796",
    "outputId": "8257b448-df06-4571-ea93-b83ebba29a34"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 10841 entries, 0 to 10840\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   App             10841 non-null  str    \n",
      " 1   Category        10841 non-null  str    \n",
      " 2   Rating          9367 non-null   float64\n",
      " 3   Reviews         10841 non-null  str    \n",
      " 4   Size            10841 non-null  str    \n",
      " 5   Installs        10841 non-null  str    \n",
      " 6   Type            10840 non-null  str    \n",
      " 7   Price           10841 non-null  str    \n",
      " 8   Content Rating  10840 non-null  str    \n",
      " 9   Genres          10841 non-null  str    \n",
      " 10  Last Updated    10841 non-null  str    \n",
      " 11  Current Ver     10833 non-null  str    \n",
      " 12  Android Ver     10838 non-null  str    \n",
      "dtypes: float64(1), str(12)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "# ---utilizzo la funzione info() per vedere che tipo di dati ha letto pandas ----------------------------------\n",
    "grezzo.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14ee7d17",
   "metadata": {
    "id": "14ee7d17"
   },
   "source": [
    "**Cosa ho trovato.**\n",
    "\n",
    "1. **Quasi tutte le colonne sono testo.** L'unica numerica è `Rating`. `Reviews`, `Installs` e\n",
    "   `Price` contengono numeri ma sono scritte come testo, per via del `+`, delle virgole e del\n",
    "   `$`. Vanno convertite.\n",
    "2. **`Rating` ha valori mancanti**: 9.367 validi su 10.841.\n",
    "3. **`Installs` non è un numero, è una fascia**: `10,000+` vuol dire «tra 10.000 e 49.999».\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b8280e5",
   "metadata": {
    "id": "9b8280e5"
   },
   "source": [
    "### 1.1 Utilizzo per una ulteriore analisi esplorativa automatica dei dati YData Profiling da https://docs.profiling.ydata.ai/\n",
    "\n",
    "`ydata-profiling` genera da sola un report completo e una lista di anomalie. È il modo più\n",
    "rapido di farsi un'idea di un dataset nuovo. Il report viene salvato in un file HTML a parte,\n",
    "perché è troppo grande per stare nel notebook.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "43470d8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:11.722954Z",
     "iopub.status.busy": "2026-10-07T04:52:11.722715Z",
     "iopub.status.idle": "2026-10-07T04:52:11.750176Z",
     "shell.execute_reply": "2026-10-07T04:52:11.749340Z"
    },
    "id": "43470d8c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ydata-profiling non installato: salto il report e vado avanti.\n",
      "\n",
      "Righe diverse che compaiono più di una volta: 410\n",
      "Copie in più da eliminare (pandas)         : 483\n"
     ]
    }
   ],
   "source": [
    "# --- riepilogo automatico con ydata-profiling ---------------------------\n",
    "if YDATA:\n",
    "    import contextlib, io\n",
    "    profilo = ProfileReport(grezzo, title=\"Google Play Store\", progress_bar=False,\n",
    "                            duplicates={\"head\": 5},\n",
    "                            correlations={\"auto\": {\"calculate\": True}})\n",
    "    with contextlib.redirect_stdout(io.StringIO()), contextlib.redirect_stderr(io.StringIO()):\n",
    "        profilo.to_file(\"report_ydata.html\")\n",
    "    print(\"Report salvato in report_ydata.html\\n\")\n",
    "    print(\"Anomalie segnalate dallo strumento:\")\n",
    "    for avviso in profilo.description_set.alerts:\n",
    "        print(\"  -\", avviso)\n",
    "else:\n",
    "    print(\"ydata-profiling non installato: salto il report e vado avanti.\")\n",
    "\n",
    "# lo stesso conteggio dei duplicati, fatto con pandas: si vede sempre\n",
    "righe_ripetute = grezzo[grezzo.duplicated(keep=False)]\n",
    "print(\"\\nRighe diverse che compaiono più di una volta:\", len(righe_ripetute.drop_duplicates()))\n",
    "print(\"Copie in più da eliminare (pandas)         :\", grezzo.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fcb0126",
   "metadata": {
    "id": "9fcb0126"
   },
   "source": [
    "**Cosa ho trovato.** Lo strumento segnala righe duplicate, i valori mancanti di `Rating` e\n",
    "il fatto che `Type` e `Content Rating` siano molto sbilanciate (quasi tutte le app sono\n",
    "gratuite e per tutti).\n",
    "\n",
    "Il report conta **410** righe duplicate,\n",
    "ma `pandas` ne conta **483**. La differenza sta nel modo di contare: YData Profiling conta quante righe diverse\n",
    "si ripetono (410), mentre pandas conta quante copie in più ci sono (483). Uno strumento automatico come YData Profiling è utile in quanto ci dice\n",
    "dove guardare possibili anomalie e lascia a noi l'approfondimento e le relative conclusioni.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "_oaPHA0qlb1_",
   "metadata": {
    "id": "_oaPHA0qlb1_"
   },
   "source": [
    "### 1.2 Verifico i voti delle app\n",
    "\n",
    "Il voto di un'app va da 1 a 5. Controllo se vale per tutte."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0177b809",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:11.751804Z",
     "iopub.status.busy": "2026-10-07T04:52:11.751599Z",
     "iopub.status.idle": "2026-10-07T04:52:11.756573Z",
     "shell.execute_reply": "2026-10-07T04:52:11.755836Z"
    },
    "id": "0177b809"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Righe con voto fuori dall'intervallo 1-5: 1\n",
      "-------------------------------------------------------\n",
      "                                                  10472\n",
      "App             Life Made WI-Fi Touchscreen Photo Frame\n",
      "Category                                            1.9\n",
      "Rating                                             19.0\n",
      "Reviews                                            3.0M\n",
      "Size                                             1,000+\n",
      "Installs                                           Free\n",
      "Type                                                  0\n",
      "Price                                          Everyone\n",
      "Content Rating                                      NaN\n",
      "Genres                                February 11, 2018\n",
      "Last Updated                                     1.0.19\n",
      "Current Ver                                  4.0 and up\n",
      "Android Ver                                         NaN\n"
     ]
    }
   ],
   "source": [
    "# --- controllo: il voto deve stare tra 1 e 5 ----------------------------\n",
    "fuori_scala = grezzo[(grezzo[\"Rating\"] > 5) | (grezzo[\"Rating\"] < 1)] # verifico che il voto non sia maggiore di 5 o minore di 1\n",
    "print(\"Righe con voto fuori dall'intervallo 1-5:\", len(fuori_scala))\n",
    "print(\"-------------------------------------------------------\")\n",
    "print(fuori_scala.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff86d58b",
   "metadata": {
    "id": "ff86d58b"
   },
   "source": [
    "**Cosa ho trovato.** La riga 10472 è **sfalsata di una colonna**: `1.9` è\n",
    "finito in `Category`, `19` in `Rating`, `3.0M` in `Reviews`. Probabilmente il nome dell'app\n",
    "conteneva una virgola non protetta e tutti i campi sono slittati.\n",
    "\n",
    "Non è un voto anomalo da analizzare è un **errore**, e lo elimino. Il punto è che `19` è un valore impossibile nel range di votazione, non estremo di cui tener conto. Un controllo in una riga di codice lo trova, mentre nelle\n",
    "statistiche descrittive passerebbe per un normale outlier, falsando i valori.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac16bf33",
   "metadata": {},
   "source": [
    "### 1.3 Pulizia dataset\n",
    "\n",
    "| Problema | Decisione | Perché |\n",
    "|---|---|---|\n",
    "| riga 10472 sfalsata | elimino | irrecuperabile senza inventare la categoria |\n",
    "| 483 righe identiche | elimino | nessuna informazione persa |\n",
    "| 698 righe con un nome già visto (523 nomi) | **stesso nome e stessa fascia di installazioni** = stessa app: tengo la riga con **più recensioni**; fascia diversa = app diverse, le tengo tutte | il controllo qui sotto |\n",
    "| `Installs` con `+` e virgole | converto al **minimo** della fascia | non inventa nulla |\n",
    "| `Size` = «Varies with device» | lascio **vuoto** | non è un valore, è un'assenza |\n",
    "| `Rating` mancante (13,6% delle righe grezze) | **non riempio** | vedi il controllo dopo la pulizia |\n",
    "\n",
    "Aggiungo anche due colonne che nel dataset non ci sono e che mi serviranno:\n",
    "**`giorni_da_aggiornamento`** e **`recensioni_per_1000`** (quante recensioni ogni mille\n",
    "installazioni: misura il coinvolgimento ed è confrontabile tra app grandi e piccole).\n",
    "\n",
    "#### Prima di togliere i doppioni: stesso nome vuol dire stessa app?\n",
    "\n",
    "Nella prima versione tenevo, per ogni nome, la riga con più recensioni. Funziona solo se le\n",
    "righe con lo stesso nome sono letture della **stessa** app fatte in momenti diversi: in quel\n",
    "caso categoria e installazioni devono coincidere. Lo controllo prima di decidere."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84475b67",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:11.758412Z",
     "iopub.status.busy": "2026-10-07T04:52:11.758198Z",
     "iopub.status.idle": "2026-10-07T04:52:11.792878Z",
     "shell.execute_reply": "2026-10-07T04:52:11.792139Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Righe con un nome già visto: 698 (su 523 nomi)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nomi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>stessa categoria, stesse installazioni</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>categoria diversa, stesse installazioni</th>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installazioni diverse</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         nomi\n",
       "stessa categoria, stesse installazioni    432\n",
       "categoria diversa, stesse installazioni    76\n",
       "installazioni diverse                      15"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2513</th>\n",
       "      <td>Blood Pressure</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>10</td>\n",
       "      <td>2.4M</td>\n",
       "      <td>March 14, 2015</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2310</th>\n",
       "      <td>Blood Pressure</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>33033</td>\n",
       "      <td>7.4M</td>\n",
       "      <td>July 24, 2018</td>\n",
       "      <td>3.27.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1711</th>\n",
       "      <td>Bubble Shooter</td>\n",
       "      <td>GAME</td>\n",
       "      <td>10,000,000+</td>\n",
       "      <td>148895</td>\n",
       "      <td>46M</td>\n",
       "      <td>July 17, 2018</td>\n",
       "      <td>1.20.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>Bubble Shooter</td>\n",
       "      <td>GAME</td>\n",
       "      <td>10,000,000+</td>\n",
       "      <td>148897</td>\n",
       "      <td>46M</td>\n",
       "      <td>July 17, 2018</td>\n",
       "      <td>1.20.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>Bubble Shooter</td>\n",
       "      <td>GAME</td>\n",
       "      <td>10,000,000+</td>\n",
       "      <td>148945</td>\n",
       "      <td>46M</td>\n",
       "      <td>July 17, 2018</td>\n",
       "      <td>1.20.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>Bubble Shooter</td>\n",
       "      <td>GAME</td>\n",
       "      <td>10,000,000+</td>\n",
       "      <td>148990</td>\n",
       "      <td>46M</td>\n",
       "      <td>July 17, 2018</td>\n",
       "      <td>1.20.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3894</th>\n",
       "      <td>Bubble Shooter</td>\n",
       "      <td>GAME</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>43576</td>\n",
       "      <td>50M</td>\n",
       "      <td>September 13, 2017</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2041</th>\n",
       "      <td>Bubble Shooter</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>59843</td>\n",
       "      <td>20M</td>\n",
       "      <td>January 9, 2018</td>\n",
       "      <td>2.3.3122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3242</th>\n",
       "      <td>Calculator</td>\n",
       "      <td>TOOLS</td>\n",
       "      <td>100,000,000+</td>\n",
       "      <td>40770</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>November 21, 2017</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>Calculator</td>\n",
       "      <td>DATING</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>57</td>\n",
       "      <td>6.2M</td>\n",
       "      <td>October 25, 2017</td>\n",
       "      <td>1.1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>Call Blocker</td>\n",
       "      <td>COMMUNICATION</td>\n",
       "      <td>1,000,000+</td>\n",
       "      <td>17529</td>\n",
       "      <td>10M</td>\n",
       "      <td>July 26, 2018</td>\n",
       "      <td>5.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>Call Blocker</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>188841</td>\n",
       "      <td>3.2M</td>\n",
       "      <td>June 21, 2018</td>\n",
       "      <td>1.1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5044</th>\n",
       "      <td>Cardiac diagnosis (heart rate, arrhythmia)</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>4559</td>\n",
       "      <td>6.4M</td>\n",
       "      <td>July 27, 2018</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2269</th>\n",
       "      <td>Cardiac diagnosis (heart rate, arrhythmia)</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>100+</td>\n",
       "      <td>8</td>\n",
       "      <td>6.5M</td>\n",
       "      <td>July 25, 2018</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9641</th>\n",
       "      <td>Chess Free</td>\n",
       "      <td>GAME</td>\n",
       "      <td>50,000,000+</td>\n",
       "      <td>1375988</td>\n",
       "      <td>15M</td>\n",
       "      <td>June 7, 2018</td>\n",
       "      <td>2.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>Chess Free</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>23772</td>\n",
       "      <td>17M</td>\n",
       "      <td>August 2, 2017</td>\n",
       "      <td>1.15.3028.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             App       Category      Installs  Reviews                Size        Last Updated         Current Ver\n",
       "2513                              Blood Pressure        MEDICAL        1,000+       10                2.4M      March 14, 2015                 1.0\n",
       "2310                              Blood Pressure        MEDICAL    5,000,000+    33033                7.4M       July 24, 2018              3.27.3\n",
       "1711                              Bubble Shooter           GAME   10,000,000+   148895                 46M       July 17, 2018              1.20.1\n",
       "1657                              Bubble Shooter           GAME   10,000,000+   148897                 46M       July 17, 2018              1.20.1\n",
       "1875                              Bubble Shooter           GAME   10,000,000+   148945                 46M       July 17, 2018              1.20.1\n",
       "1972                              Bubble Shooter           GAME   10,000,000+   148990                 46M       July 17, 2018              1.20.1\n",
       "3894                              Bubble Shooter           GAME    5,000,000+    43576                 50M  September 13, 2017                 4.4\n",
       "2041                              Bubble Shooter         FAMILY    5,000,000+    59843                 20M     January 9, 2018            2.3.3122\n",
       "3242                                  Calculator          TOOLS  100,000,000+    40770  Varies with device   November 21, 2017  Varies with device\n",
       "477                                   Calculator         DATING        1,000+       57                6.2M    October 25, 2017               1.1.6\n",
       "436                                 Call Blocker  COMMUNICATION    1,000,000+    17529                 10M       July 26, 2018                5.86\n",
       "206                                 Call Blocker       BUSINESS    5,000,000+   188841                3.2M       June 21, 2018              1.1.13\n",
       "5044  Cardiac diagnosis (heart rate, arrhythmia)        MEDICAL      100,000+     4559                6.4M       July 27, 2018                 117\n",
       "2269  Cardiac diagnosis (heart rate, arrhythmia)        MEDICAL          100+        8                6.5M       July 25, 2018                   7\n",
       "9641                                  Chess Free           GAME   50,000,000+  1375988                 15M        June 7, 2018                2.72\n",
       "2156                                  Chess Free         FAMILY    5,000,000+    23772                 17M      August 2, 2017         1.15.3028.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Controllo: le righe con lo stesso nome sono davvero la stessa app? ---\n",
    "\n",
    "# 1) tolgo la riga sfalsata e le righe identiche, come farà la pulizia\n",
    "base = grezzo[~((grezzo[\"Rating\"] > 5) | (grezzo[\"Rating\"] < 1))].drop_duplicates()\n",
    "ripetute = base[base.duplicated(\"App\", keep=False)]\n",
    "\n",
    "# 2) per ogni nome ripetuto conto quante categorie e quante fasce di installazioni diverse ha\n",
    "per_nome = ripetute.groupby(\"App\").agg(righe=(\"App\", \"size\"),\n",
    "                                       categorie=(\"Category\", \"nunique\"),\n",
    "                                       fasce=(\"Installs\", \"nunique\"))\n",
    "esito = pd.Series(\"stessa categoria, stesse installazioni\", index=per_nome.index)\n",
    "esito[(per_nome[\"categorie\"] > 1) & (per_nome[\"fasce\"] == 1)] = \"categoria diversa, stesse installazioni\"\n",
    "esito[per_nome[\"fasce\"] > 1] = \"installazioni diverse\"\n",
    "\n",
    "print(f\"Righe con un nome già visto: {len(ripetute) - ripetute['App'].nunique()} \"\n",
    "      f\"(su {ripetute['App'].nunique()} nomi)\\n\")\n",
    "display(esito.value_counts().rename(\"nomi\").to_frame())\n",
    "\n",
    "# 3) i casi con installazioni diverse, per vederli in faccia\n",
    "diverse = ripetute[ripetute[\"App\"].isin(esito[esito == \"installazioni diverse\"].index)]\n",
    "display(diverse.sort_values([\"App\", \"Reviews\"])[\n",
    "    [\"App\", \"Category\", \"Installs\", \"Reviews\", \"Size\", \"Last Updated\", \"Current Ver\"]].head(16))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0feff86a",
   "metadata": {},
   "source": [
    "**Cosa ho trovato.**\n",
    "\n",
    "- **432 nomi su 523** hanno sempre la stessa categoria e le stesse installazioni: sono la\n",
    "  stessa app letta più volte, e tenere la lettura con più recensioni (la più recente) è\n",
    "  corretto.\n",
    "- **76 nomi** cambiano categoria ma non installazioni: è la stessa app esposta su due\n",
    "  scaffali (quasi sempre GAME e FAMILY, o EDUCATION e FAMILY), con recensioni quasi identiche.\n",
    "  Ne tengo una sola riga, quella con più recensioni, e la sua categoria.\n",
    "- **15 nomi** hanno fasce di installazioni diverse, e lì il nome inganna: «Calculator» da 100\n",
    "  milioni di installazioni in TOOLS e «Calculator» da 1.000 in DATING sono due app diverse, con\n",
    "  dimensione, versione e data diverse. Con la regola vecchia ne sopravviveva una sola.\n",
    "\n",
    "**Decisione:** considero la stessa app le righe con **stesso nome e stessa fascia di\n",
    "installazioni**. Le app analizzate passano da 9.659 a 9.674. La regola non è perfetta in un\n",
    "caso su 15 («QR Scanner & Barcode Scanner 2018», stessa versione e stessa data, è la stessa app\n",
    "che cambia fascia fra due letture e resta contata due volte), ma sbaglia molto meno della\n",
    "precedente, che fondeva 13 coppie di app diverse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dc4ffc23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:11.794901Z",
     "iopub.status.busy": "2026-10-07T04:52:11.794667Z",
     "iopub.status.idle": "2026-10-07T04:52:11.849115Z",
     "shell.execute_reply": "2026-10-07T04:52:11.848152Z"
    },
    "id": "dc4ffc23"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passaggio</th>\n",
       "      <th>righe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dataset grezzo</td>\n",
       "      <td>10841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>meno la riga sfalsata</td>\n",
       "      <td>10840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>meno le righe duplicate</td>\n",
       "      <td>10357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>meno le letture ripetute della stessa app</td>\n",
       "      <td>9674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dataset pulito</td>\n",
       "      <td>9674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   passaggio  righe\n",
       "0                             dataset grezzo  10841\n",
       "1                      meno la riga sfalsata  10840\n",
       "2                    meno le righe duplicate  10357\n",
       "3  meno le letture ripetute della stessa app   9674\n",
       "4                             dataset pulito   9674"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "App analizzate: 9,674  (89.2% delle righe di partenza)\n",
      "Voto disponibile per 8,210 app (84.9%)\n",
      "Controlli: voti tra 1 e 5 OK | coppie nome + installazioni uniche OK\n"
     ]
    }
   ],
   "source": [
    "def pulisci(df):\n",
    "    \"Pulisce il dataset e restituisce anche il registro delle righe eliminate.\"\n",
    "    registro = [(\"dataset grezzo\", len(df))]\n",
    "\n",
    "    d = df[~((df[\"Rating\"] > 5) | (df[\"Rating\"] < 1))].copy()   # tolgo la riga sfalsata e la registro comunque\n",
    "    registro.append((\"meno la riga sfalsata\", len(d)))\n",
    "\n",
    "    d = d.drop_duplicates()                                      # via le righe identiche e le registro comunque\n",
    "    registro.append((\"meno le righe duplicate\", len(d)))\n",
    "\n",
    "    # stessa app = stesso nome E stessa fascia di installazioni (vedi il controllo sopra):\n",
    "    # della stessa app tengo la lettura con più recensioni (la più recente)\n",
    "    d[\"Reviews\"] = pd.to_numeric(d[\"Reviews\"], errors=\"coerce\")\n",
    "    d = (d.sort_values(\"Reviews\", ascending=False, kind=\"stable\")\n",
    "           .drop_duplicates(subset=[\"App\", \"Installs\"], keep=\"first\").sort_index())\n",
    "    registro.append((\"meno le letture ripetute della stessa app\", len(d)))\n",
    "\n",
    "    # conversione da stringa +10000 a valore numero intero in formato int64\n",
    "    d[\"installazioni\"] = (d[\"Installs\"].str.replace(\",\", \"\", regex=False)\n",
    "                                       .str.replace(\"+\", \"\", regex=False).astype(\"int64\"))\n",
    "    # conversione da stringa $4.99 tolgo $ e converto in valore in numero in virgola mobile formato float\n",
    "    d[\"prezzo\"] = d[\"Price\"].str.replace(\"$\", \"\", regex=False).astype(float)\n",
    "    # dal prezzo convertito trovo se l'app è gratis  o a pagamento attraverso la condizione >0\n",
    "    d[\"tipo\"] = np.where(d[\"prezzo\"] > 0, \"A pagamento\", \"Gratuita\")\n",
    "\n",
    "    #Ripulisco i valori perchè sono formati e unità di misura diverse e porto tutto in megabyte.\n",
    "    #Alcuni valore sono tipo testo Varies with device  e li sostituisco con NaN\n",
    "    dimensione = d[\"Size\"].replace(\"Varies with device\", np.nan)\n",
    "    numero = pd.to_numeric(dimensione.str.rstrip(\"MkG\"), errors=\"coerce\")\n",
    "    d[\"dimensione_mb\"] = np.select([dimensione.str.endswith(\"k\", na=False),\n",
    "                                    dimensione.str.endswith(\"G\", na=False)],\n",
    "                                   [numero / 1024, numero * 1024], numero)\n",
    "\n",
    "    # converto la stringa data aggiornamento in un vero oggetto data utilizzabile\n",
    "    d[\"data_aggiornamento\"] = pd.to_datetime(d[\"Last Updated\"], format=\"%B %d, %Y\")\n",
    "    # calcolo per ogni app quanti giorni sono passati dall'ultimo aggiornamento rispetto all'app più recente\n",
    "    d[\"giorni_da_aggiornamento\"] = (d[\"data_aggiornamento\"].max()\n",
    "                                    - d[\"data_aggiornamento\"]).dt.days\n",
    "    # Costruisco una misura di \"quanto le persone si esprimono\", per confrontare le recensioni di app\n",
    "    #molto scaricate e quindi con molte recensioni , con app meno scaricate. Moltiplico per mille per rendere più\n",
    "    #leggibile il risultato a colpo d'occhio.\n",
    "    d[\"recensioni_per_1000\"] = d[\"Reviews\"] / d[\"installazioni\"].replace(0, np.nan) * 1000\n",
    "\n",
    "    registro.append((\"dataset pulito\", len(d)))\n",
    "    return d, pd.DataFrame(registro, columns=[\"passaggio\", \"righe\"])\n",
    "\n",
    "# la funzione restituisce due valori (una tupla),dividendoli in due variabili separate:\n",
    "# app per il dataframe pulito e pronto all'uso, con tutte le colonne nuove appena viste.\n",
    "# registro per la tabella che riassume quante righe c'erano a ogni passaggio di pulizia,\n",
    "# utile per documentare/verificare il processo.\n",
    "app, registro = pulisci(grezzo)\n",
    "\n",
    "# rinomino in italiano e tengo solo le colonne che uso\n",
    "app = app.rename(columns={\"App\": \"nome\", \"Category\": \"categoria\", \"Rating\": \"voto\",\n",
    "                          \"Reviews\": \"recensioni\", \"Content Rating\": \"eta_minima\",\n",
    "                          \"Genres\": \"generi\"})\n",
    "app = app[[\"nome\", \"categoria\", \"voto\", \"recensioni\", \"installazioni\", \"tipo\", \"prezzo\",\n",
    "           \"eta_minima\", \"generi\", \"dimensione_mb\", \"data_aggiornamento\",\n",
    "           \"giorni_da_aggiornamento\", \"recensioni_per_1000\"]]\n",
    "\n",
    "display(registro)\n",
    "print(f\"App analizzate: {len(app):,}  ({len(app) / len(grezzo):.1%} delle righe di partenza)\")\n",
    "print(f\"Voto disponibile per {app['voto'].notna().sum():,} app ({app['voto'].notna().mean():.1%})\")\n",
    "print(\"Controlli:\",\n",
    "      \"voti tra 1 e 5 OK\" if app[\"voto\"].dropna().between(1, 5).all() else \"VOTI FUORI SCALA\",\n",
    "      \"| coppie nome + installazioni uniche OK\"\n",
    "      if not app.duplicated([\"nome\", \"installazioni\"]).any() else \"| DOPPIONI\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3031b731",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:11.850822Z",
     "iopub.status.busy": "2026-10-07T04:52:11.850586Z",
     "iopub.status.idle": "2026-10-07T04:52:11.895536Z",
     "shell.execute_reply": "2026-10-07T04:52:11.894713Z"
    },
    "id": "3031b731"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numero_app</th>\n",
       "      <th>recensioni_mediane</th>\n",
       "      <th>installazioni_mediane</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>senza voto</th>\n",
       "      <td>1464</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>con voto</th>\n",
       "      <td>8210</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            numero_app  recensioni_mediane  installazioni_mediane\n",
       "senza voto        1464                 1.0                  100.0\n",
       "con voto          8210              3020.0               100000.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "App senza voto con meno di 10 recensioni: 83.5%\n",
      "\n",
      "Dataset pulito salvato in playstore_pulito.csv\n"
     ]
    }
   ],
   "source": [
    "# --- in questa parte di codice provo a verificare se le app senza voti sono un caso o è sistematico------------------\n",
    "\n",
    "# Divido le app in due gruppi, con voto e senza voto,\n",
    "# e per ciascun gruppo calcolo le relative statistiche\n",
    "confronto = app.groupby(app[\"voto\"].notna()).agg(\n",
    "    numero_app=(\"nome\", \"count\"),\n",
    "    recensioni_mediane=(\"recensioni\", \"median\"),\n",
    "    installazioni_mediane=(\"installazioni\", \"median\"),\n",
    ")\n",
    "# Rinomino le righe per renderle leggibili\n",
    "confronto.index = [\"senza voto\", \"con voto\"]\n",
    "display(confronto)\n",
    "\n",
    "#confronto che restituisce True/False per ogni riga del sottoinsieme \"senza voto\": True se ha meno di 10 recensioni\n",
    "# poi calcolo la media di una serie di True/False\n",
    "senza_voto = app[app[\"voto\"].isna()]\n",
    "print(f\"App senza voto con meno di 10 recensioni: {(senza_voto['recensioni'] < 10).mean():.1%}\")\n",
    "\n",
    "# salvo il dataset pulito in un nuovo file CSV\n",
    "app.to_csv(\"playstore_pulito.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "print(\"\\nDataset pulito salvato in playstore_pulito.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75dbc17e",
   "metadata": {
    "id": "75dbc17e"
   },
   "source": [
    "**Risultato** i due gruppi non c'entrano nulla l'uno con l'altro: un'app senza\n",
    "voto ha 1 recensione mediana contro 3.020, e 100 installazioni contro 100.000.\n",
    "L'83,5% delle app senza voto ha meno di 10 recensioni: il voto manca perché quasi nessuno le ha recensite.\n",
    "\n",
    "Mettere la media nella parti mancanti creerebbe un picco artificiale.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2470b2cd",
   "metadata": {
    "id": "2470b2cd"
   },
   "source": [
    "---\n",
    "\n",
    "## 2. Com'è fatto il mercato delle app?\n",
    "\n",
    "La domanda non è \"quanto vale la media\", ma quale misura\n",
    "posso usare: con i dati di mercato la media è spesso fuorviante e c'è un modo semplice per il confronto cioè con la mediana.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "441869dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:11.897166Z",
     "iopub.status.busy": "2026-10-07T04:52:11.896974Z",
     "iopub.status.idle": "2026-10-07T04:52:11.914996Z",
     "shell.execute_reply": "2026-10-07T04:52:11.914303Z"
    },
    "id": "441869dc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>voto</th>\n",
       "      <th>recensioni</th>\n",
       "      <th>installazioni</th>\n",
       "      <th>dimensione_mb</th>\n",
       "      <th>giorni_da_aggiornamento</th>\n",
       "      <th>recensioni_per_1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8210.00</td>\n",
       "      <td>9674.00</td>\n",
       "      <td>9.674000e+03</td>\n",
       "      <td>8446.00</td>\n",
       "      <td>9674.00</td>\n",
       "      <td>9659.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.17</td>\n",
       "      <td>216525.14</td>\n",
       "      <td>7.789048e+06</td>\n",
       "      <td>20.38</td>\n",
       "      <td>281.12</td>\n",
       "      <td>38.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.54</td>\n",
       "      <td>1830025.70</td>\n",
       "      <td>5.372864e+07</td>\n",
       "      <td>21.82</td>\n",
       "      <td>406.73</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>4.50</td>\n",
       "      <td>22.00</td>\n",
       "      <td>7.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.30</td>\n",
       "      <td>972.00</td>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>12.00</td>\n",
       "      <td>96.00</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.50</td>\n",
       "      <td>29457.75</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>28.00</td>\n",
       "      <td>366.75</td>\n",
       "      <td>38.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.00</td>\n",
       "      <td>78158306.00</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>100.00</td>\n",
       "      <td>3001.00</td>\n",
       "      <td>4000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          voto   recensioni  installazioni  dimensione_mb  giorni_da_aggiornamento  recensioni_per_1000\n",
       "count  8210.00      9674.00   9.674000e+03        8446.00                  9674.00              9659.00\n",
       "mean      4.17    216525.14   7.789048e+06          20.38                   281.12                38.10\n",
       "std       0.54   1830025.70   5.372864e+07          21.82                   406.73                99.99\n",
       "min       1.00         0.00   0.000000e+00           0.01                     0.00                 0.00\n",
       "25%       4.00        25.00   1.000000e+03           4.50                    22.00                 7.38\n",
       "50%       4.30       972.00   1.000000e+05          12.00                    96.00                17.00\n",
       "75%       4.50     29457.75   1.000000e+06          28.00                   366.75                38.16\n",
       "max       5.00  78158306.00   1.000000e+09         100.00                  3001.00              4000.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>media</th>\n",
       "      <th>mediana</th>\n",
       "      <th>media / mediana</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>recensioni</th>\n",
       "      <td>216525.14</td>\n",
       "      <td>972.0</td>\n",
       "      <td>222.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installazioni</th>\n",
       "      <td>7789047.81</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>77.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>giorni_da_aggiornamento</th>\n",
       "      <td>281.12</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recensioni_per_1000</th>\n",
       "      <td>38.10</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dimensione_mb</th>\n",
       "      <td>20.38</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voto</th>\n",
       "      <td>4.17</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              media   mediana  media / mediana\n",
       "recensioni                216525.14     972.0           222.76\n",
       "installazioni            7789047.81  100000.0            77.89\n",
       "giorni_da_aggiornamento      281.12      96.0             2.93\n",
       "recensioni_per_1000           38.10      17.0             2.24\n",
       "dimensione_mb                 20.38      12.0             1.70\n",
       "voto                           4.17       4.3             0.97"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- statistiche descrittive, e il rapporto media/mediana --------------\n",
    "NUMERICHE = [\"voto\", \"recensioni\", \"installazioni\", \"dimensione_mb\",\n",
    "             \"giorni_da_aggiornamento\", \"recensioni_per_1000\"]\n",
    "display(app[NUMERICHE].describe().round(2))\n",
    "\n",
    "# il prezzo lo lascio fuori: la sua mediana è 0 (il 92% delle app è gratuito)\n",
    "riepilogo = pd.DataFrame({\"media\": app[NUMERICHE].mean(),\n",
    "                          \"mediana\": app[NUMERICHE].median()})\n",
    "riepilogo[\"media / mediana\"] = riepilogo[\"media\"] / riepilogo[\"mediana\"]\n",
    "riepilogo.sort_values(\"media / mediana\", ascending=False).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23b0627b",
   "metadata": {
    "id": "23b0627b"
   },
   "source": [
    "**Risultato** La colonna `media / mediana` è la spia dell'ipotesi iniziale. Per il voto vale 0,97: media e mediana coincidono, la media si può usare. Per le recensioni vale\n",
    "223, per le installazioni 78: la media è duecento volte la mediana, quindi non\n",
    "descrive nessuna app reale, questo dipende dalle app Facebook e WhatsApp che trascinano in alto la media per ovvie ragioni.\n",
    "\n",
    "**Decisione valida per tutto il notebook:** per recensioni e installazioni utilizzo mediana e utilizzo per le rappresentazioni una scala logaritmica per comprimere la dinamica dei grafici. Per il voto vanno bene gli strumenti\n",
    "consueti.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0e4a0f19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:11.916475Z",
     "iopub.status.busy": "2026-10-07T04:52:11.916290Z",
     "iopub.status.idle": "2026-10-07T04:52:12.334105Z",
     "shell.execute_reply": "2026-10-07T04:52:12.333369Z"
    },
    "id": "0e4a0f19"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1485x374 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voto      mediana 4.3 | >= 4,0: 76.7% | >= 4,5: 31.1% | < 3,0: 3.4%\n",
      "Recensioni    mediana 972 | massimo 78,158,306\n",
      "Installazioni mediana 100,000 | fasce distinte 20\n"
     ]
    }
   ],
   "source": [
    "# --- Distribuzione delle tre variabili più rilevanti: voto, recensioni ed installazioni ---\n",
    "\n",
    "# 1) preparo una figura con 3 grafici affiancati\n",
    "fig, axes = plt.subplots(1, 3, figsize=(13.5, 3.4))\n",
    "\n",
    "# 2) Voto: istogramma, con la mediana evidenziata da una linea ARANCIONE\n",
    "voti = app[\"voto\"].dropna()\n",
    "axes[0].hist(voti, bins=np.arange(1, 5.11, 0.1), color=BLU, edgecolor=\"white\", linewidth=0.5)\n",
    "axes[0].axvline(voti.median(), color=ARANCIONE, linewidth=2)\n",
    "axes[0].set_xlabel(\"voto\")\n",
    "axes[0].set_ylabel(\"numero di app\")\n",
    "axes[0].set_title(\"Il voto è compresso verso l'alto\")\n",
    "axes[0].text(voti.median() - 0.1, axes[0].get_ylim()[1] * 0.9, f\"mediana {voti.median()}\",\n",
    "             color=ARANCIONE, ha=\"right\", fontweight=\"bold\", fontsize=9)\n",
    "\n",
    "# 3) RECENSIONI : istogramma in scala logaritmica\n",
    "axes[1].hist(app.loc[app[\"recensioni\"] > 0, \"recensioni\"], bins=np.logspace(0, 8, 40),\n",
    "             color=BLU, edgecolor=\"white\", linewidth=0.5)\n",
    "axes[1].set_xscale(\"log\")\n",
    "axes[1].set_xlabel(\"recensioni (scala logaritmica)\")\n",
    "axes[1].set_ylabel(\"numero di app\")\n",
    "axes[1].set_title(\"Le recensioni in scala log\")\n",
    "\n",
    "# 4) INSTALLAZIONI — barre orizzontali, una per ciascuna delle 20 fasce\n",
    "fasce = app[\"installazioni\"].value_counts().sort_index()\n",
    "axes[2].barh(range(len(fasce)), fasce.values, color=BLU)\n",
    "axes[2].set_yticks(range(len(fasce)), [f\"{v:,}\" for v in fasce.index], fontsize=7)\n",
    "axes[2].set_xlabel(\"numero di app\")\n",
    "axes[2].set_title(\"Le installazioni a fasce\")\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "# 5) riepilogo numerico delle tre variabili più rilevanti date come argomento al print\n",
    "print(f\"Voto      mediana {voti.median()} | >= 4,0: {(voti >= 4).mean():.1%} | \"\n",
    "      f\">= 4,5: {(voti >= 4.5).mean():.1%} | < 3,0: {(voti < 3).mean():.1%}\")\n",
    "print(f\"Recensioni    mediana {app['recensioni'].median():,.0f} | \"\n",
    "      f\"massimo {app['recensioni'].max():,.0f}\")\n",
    "print(f\"Installazioni mediana {app['installazioni'].median():,.0f} | \"\n",
    "      f\"fasce distinte {app['installazioni'].nunique()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6065465b",
   "metadata": {
    "id": "6065465b"
   },
   "source": [
    "\n",
    "**Risultato**. Il voto è compresso verso l'alto: il 76,7% delle app ha un voto sopra 4,0, mentre solo il 3,4% sta sotto 3,0. Questo cambia il modo di leggere un voto: 4,2 non è un buon risultato, è sotto la mediana del mercato (4,3). Per considerarsi davvero buoni bisogna arrivare al quartile superiore, che parte da 4,5.\n",
    "\n",
    "Le installazioni, invece, non sono un numero continuo ma venti fasce fisse (es. 10.000+, 50.000+, 100.000+...). Da qui in poi, quando parlerò di \"installazioni mediane\" di una categoria, farò sempre riferimento a uno di questi venti valori e una differenza inferiore a 5 volte tra due categorie non è significativa, perché può dipendere semplicemente dal salto tra una fascia e l'altra.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d074c415",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:12.335703Z",
     "iopub.status.busy": "2026-10-07T04:52:12.335506Z",
     "iopub.status.idle": "2026-10-07T04:52:12.490473Z",
     "shell.execute_reply": "2026-10-07T04:52:12.489672Z"
    },
    "id": "d074c415"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1265x352 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "App a pagamento: 756 (7.81%) | prezzo mediano 2.99 $ | il 90.2% costa <= 10 $\n",
      "Dimensione mediana 12 MB | età 'Everyone' 81.8% | giorni dall'ultimo aggiornamento (mediana) 96\n"
     ]
    }
   ],
   "source": [
    "# --- Concentrazione del mercato e modello di business (gratis o a pagamento) ---\n",
    "\n",
    "# 1) CONCENTRAZIONE per l'1%, 10% e 50% delle app più scaricate,\n",
    "#    calcolo quale quota delle installazioni totali si prendono\n",
    "totale = app[\"installazioni\"].sum()\n",
    "concentrazione = pd.DataFrame([\n",
    "    {\"gruppo\": f\"top {q:.0%}\",\n",
    "     \"numero di app\": int(len(app) * q),\n",
    "     \"quota delle installazioni\": app[\"installazioni\"].nlargest(int(len(app) * q)).sum() / totale}\n",
    "    for q in (0.01, 0.10, 0.50)\n",
    "])\n",
    "\n",
    "# 2) preparo una figura con 2 grafici affiancati\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11.5, 3.2))\n",
    "\n",
    "# 3) GRAFICO 1:  barre orizzontali della concentrazione, col gruppo \"top 1%\" in evidenza\n",
    "colori = [ARANCIONE if g == \"top 1%\" else BLU for g in concentrazione[\"gruppo\"]]\n",
    "ax1.barh(concentrazione[\"gruppo\"], concentrazione[\"quota delle installazioni\"] * 100,\n",
    "         color=colori)\n",
    "ax1.invert_yaxis()\n",
    "ax1.set_xlabel(\"% delle installazioni totali\")\n",
    "ax1.set_title(\"L'1% delle app si prende metà del mercato\")\n",
    "ax1.set_xlim(0, 118)\n",
    "for i, v in enumerate(concentrazione[\"quota delle installazioni\"]):\n",
    "    ax1.text(v * 100 + 1.5, i, f\"{v:.1%}\", va=\"center\", fontweight=\"bold\", fontsize=9)\n",
    "\n",
    "# 4) GRAFICO 2:  quante app sono gratuite e quante a pagamento\n",
    "conteggio_tipo = app[\"tipo\"].value_counts()\n",
    "ax2.bar(conteggio_tipo.index, conteggio_tipo.values, color=[BLU, ARANCIONE], width=0.5)\n",
    "ax2.set_ylabel(\"numero di app\")\n",
    "ax2.set_title(\"Il 92,2% del catalogo è gratuito\")\n",
    "ax2.set_ylim(0, conteggio_tipo.max() * 1.2)\n",
    "for i, v in enumerate(conteggio_tipo.values):\n",
    "    ax2.text(i, v + 180, f\"{v:,}\\n{v / len(app):.1%}\", ha=\"center\", fontsize=9)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 5) riepilogo testuale: prezzo delle app a pagamento\n",
    "prezzi = app.loc[app[\"tipo\"] == \"A pagamento\", \"prezzo\"]\n",
    "print(f\"App a pagamento: {len(prezzi):,} ({len(prezzi) / len(app):.2%}) | \"\n",
    "      f\"prezzo mediano {prezzi.median():.2f} $ | il {(prezzi <= 10).mean():.1%} costa <= 10 $\")\n",
    "\n",
    "# 6) riepilogo testuale: altre caratteristiche del catalogo (dimensione, età, aggiornamenti)\n",
    "print(f\"Dimensione mediana {app['dimensione_mb'].median():.0f} MB | \"\n",
    "      f\"età 'Everyone' {(app['eta_minima'] == 'Everyone').mean():.1%} | \"\n",
    "      f\"giorni dall'ultimo aggiornamento (mediana) {app['giorni_da_aggiornamento'].median():.0f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6797d119",
   "metadata": {
    "id": "6797d119"
   },
   "source": [
    "**Risultato** Il mercato è dominato da pochi: l'1% delle app detiene il 49,4% delle installazioni, il 10% ne detiene l'88,1%. Lanciare un'app significa, per default statistico,\n",
    "finire nella metà che si divide le briciole e la domanda utile diventa: esistono categorie dove questa regola è meno brutale? (sezione 5).\n",
    "\n",
    "Il mercato ha anche già scelto il modello: 92,2% gratuito, e tra le app a pagamento il\n",
    "prezzo tipico è 2,99 $. L'app mediana occupa 12 MB, è per tutti, ed è stata aggiornata 96\n",
    "giorni fa.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4bae245",
   "metadata": {
    "id": "a4bae245"
   },
   "source": [
    "---\n",
    "\n",
    "## 3. I valori anomali\n",
    "\n",
    "Il metodo classico è la **regola dell'intervallo interquartile (IQR)**: è anomalo ciò che sta\n",
    "oltre `Q1 − 1,5 × IQR` o `Q3 + 1,5 × IQR`. Lo applico a tutte le variabili per un risultato più intuitivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9a3490a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:12.492139Z",
     "iopub.status.busy": "2026-10-07T04:52:12.491916Z",
     "iopub.status.idle": "2026-10-07T04:52:12.503429Z",
     "shell.execute_reply": "2026-10-07T04:52:12.502751Z"
    },
    "id": "9a3490a8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n outlier</th>\n",
       "      <th>su n valori</th>\n",
       "      <th>% outlier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>recensioni</th>\n",
       "      <td>1661.0</td>\n",
       "      <td>9674.0</td>\n",
       "      <td>17.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recensioni (in log)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9674.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installazioni</th>\n",
       "      <td>1985.0</td>\n",
       "      <td>9674.0</td>\n",
       "      <td>20.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installazioni (in log)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9674.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voto</th>\n",
       "      <td>492.0</td>\n",
       "      <td>8210.0</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        n outlier  su n valori  % outlier\n",
       "recensioni                 1661.0       9674.0      17.17\n",
       "recensioni (in log)           0.0       9674.0       0.00\n",
       "installazioni              1985.0       9674.0      20.52\n",
       "installazioni (in log)        0.0       9674.0       0.00\n",
       "voto                        492.0       8210.0       5.99"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Applicazione della regola IQR ---\n",
    "def conta_outlier(serie):\n",
    "    serie = serie.dropna()\n",
    "    q1, q3 = serie.quantile([0.25, 0.75])\n",
    "    iqr = q3 - q1\n",
    "    anomali = ((serie < q1 - 1.5 * iqr) | (serie > q3 + 1.5 * iqr)).sum()\n",
    "    return pd.Series({\"n outlier\": anomali, \"su n valori\": len(serie),\n",
    "                      \"% outlier\": 100 * anomali / len(serie)})\n",
    "\n",
    "da_controllare = {\n",
    "    \"recensioni\": app[\"recensioni\"],\n",
    "    \"recensioni (in log)\": np.log10(app[\"recensioni\"].clip(lower=1)),\n",
    "    \"installazioni\": app[\"installazioni\"],\n",
    "    \"installazioni (in log)\": np.log10(app[\"installazioni\"].clip(lower=1)),\n",
    "    \"voto\": app[\"voto\"],\n",
    "}\n",
    "pd.DataFrame({n: conta_outlier(s) for n, s in da_controllare.items()}).T.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4074513",
   "metadata": {
    "id": "a4074513"
   },
   "source": [
    "**Risultato**\n",
    "\n",
    "Applico la regola IQR alla colonna delle recensioni, e il risultato sono 1.661 app segnalate come anomale: il 17,2% del totale. Un numero così alto può rappresentare un campanello d'allarme: se un criterio marca come \"anomalo\" un valore su sei, probabilmente il problema non sono i dati, ma il criterio applicato a una distribuzione per cui non era pensato.\n",
    "\n",
    "La controprova arriva subito dopo: applico lo stesso identico criterio alla stessa colonna, ma calcolata in scala logaritmica. Il risultato cambia radicalmente: zero outlier. I dati sono rimasti gli stessi, è cambiata solo la scala su cui li misuro a conferma che il problema era nella scelta della scala, non nei dati.\n",
    "\n",
    "Sul voto, invece, la regola IQR funziona correttamente anche in scala normale: segnala 492 app (6,0%), tutte con voto basso. Qui le anomalie sono genuine, perché il voto è una variabile che si comporta bene (in gergo statistico: è distribuita in modo più regolare, senza la forte asimmetria delle recensioni).\n",
    "\n",
    "In conclusione: prima di cercare gli outlier bisogna scegliere la scala giusta per la variabile che stai analizzando. Lo stesso criterio può dare risultati completamente diversi e uno dei due sarà quasi sempre fuorviante.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "70fc071d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:12.505484Z",
     "iopub.status.busy": "2026-10-07T04:52:12.505303Z",
     "iopub.status.idle": "2026-10-07T04:52:12.516222Z",
     "shell.execute_reply": "2026-10-07T04:52:12.515781Z"
    },
    "id": "70fc071d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "App con più recensioni che installazioni: 11 (impossibile!)\n",
      "È un effetto della conversione: 'Installs' diceva '1+' e io l'ho reso 1.\n",
      "\n",
      "App che costano 100 $ o più: 20, di cui 15 con 'rich' nel nome\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nome</th>\n",
       "      <th>categoria</th>\n",
       "      <th>prezzo</th>\n",
       "      <th>installazioni</th>\n",
       "      <th>voto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4367</th>\n",
       "      <td>I'm Rich - Trump Edition</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>400.00</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4197</th>\n",
       "      <td>most expensive app (H)</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>399.99</td>\n",
       "      <td>100</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4362</th>\n",
       "      <td>💎 I'm rich</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>399.99</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5351</th>\n",
       "      <td>I am rich</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>399.99</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5354</th>\n",
       "      <td>I am Rich Plus</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>399.99</td>\n",
       "      <td>10000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          nome  categoria  prezzo  installazioni  voto\n",
       "4367  I'm Rich - Trump Edition  LIFESTYLE  400.00          10000   3.6\n",
       "4197    most expensive app (H)     FAMILY  399.99            100   4.3\n",
       "4362                💎 I'm rich  LIFESTYLE  399.99          10000   3.8\n",
       "5351                 I am rich  LIFESTYLE  399.99         100000   3.8\n",
       "5354            I am Rich Plus     FAMILY  399.99          10000   4.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Media dei prezzi con quelle 20 app:  14.05 $\n",
      "Media dei prezzi senza quelle 20:    4.74 $\n",
      "Mediana, in entrambi i casi:         2.99 $\n"
     ]
    }
   ],
   "source": [
    "# --- Due casi di valori anomali, spiegati uno per uno -------------------\n",
    "\n",
    "# 1) CASO 1: app con più recensioni che installazioni (fisicamente impossibile)\n",
    "impossibili = app[app[\"recensioni_per_1000\"] > 1000]\n",
    "print(f\"App con più recensioni che installazioni: {len(impossibili)} (impossibile!)\")\n",
    "print(\"È un effetto della conversione: 'Installs' diceva '1+' e io l'ho reso 1.\\n\")\n",
    "\n",
    "# 2) CASO 2: app che costano 100 $ o più quanto quelle 20 app distorcono la media dei prezzi\n",
    "care = app[app[\"prezzo\"] >= 100]\n",
    "print(f\"App che costano 100 $ o più: {len(care)}, \"\n",
    "      f\"di cui {care['nome'].str.contains('rich', case=False).sum()} con 'rich' nel nome\")\n",
    "display(care.nlargest(5, \"prezzo\")[[\"nome\", \"categoria\", \"prezzo\", \"installazioni\", \"voto\"]])\n",
    "\n",
    "a_pagamento = app[app[\"tipo\"] == \"A pagamento\"]\n",
    "print(f\"Media dei prezzi con quelle 20 app:  {a_pagamento['prezzo'].mean():.2f} $\")\n",
    "print(f\"Media dei prezzi senza quelle 20:    {a_pagamento.loc[a_pagamento['prezzo'] < 100, 'prezzo'].mean():.2f} $\")\n",
    "print(f\"Mediana, in entrambi i casi:         {a_pagamento['prezzo'].median():.2f} $\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f969a512",
   "metadata": {
    "id": "f969a512"
   },
   "source": [
    "**Risultato** Le app sopra i 100 dollari sono i cloni di I am rich: app che non fanno nulla e il cui unico scopo è essere costose. Venti app su 756 spostano la media dei prezzi da 4,74 dollari a 14,05 dollari, mentre la mediana non si muove di un centesimo. È il motivo pratico per cui sui dati di mercato la mediana è più affidabile della media.\n",
    "\n",
    "Non le elimino dal dataset, ma le escludo dalle analisi sul prezzo e lo dichiaro ogni\n",
    "volta. Gli altri outlier (Facebook, WhatsApp), sono il mercato, non rumore: vanno tenuti.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e78a3c",
   "metadata": {
    "id": "f0e78a3c"
   },
   "source": [
    "---\n",
    "\n",
    "## 4. Le correlazioni\n",
    "\n",
    "La correlazione dice se due variabili si muovono insieme. È un numero che va da **−1** a **+1**:\n",
    "\n",
    "- vicino a **+1**: quando una cresce, cresce anche l'altra;\n",
    "- vicino a **−1**: quando una cresce, l'altra cala;\n",
    "- vicino a **0**: le due variabili non sono legate.\n",
    "\n",
    "La calcolo in **due modi diversi**, perché ognuno vede cose diverse:\n",
    "\n",
    "- il primo controlla se le due variabili crescono **in proporzione**, cioè se i punti del grafico stanno su una retta;\n",
    "- il secondo controlla solo se crescono **nella stessa direzione**, anche quando la relazione disegna una curva e non una retta.\n",
    "\n",
    "`pandas` li calcola entrambi con la stessa funzione `.corr()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9e8643bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:12.518076Z",
     "iopub.status.busy": "2026-10-07T04:52:12.517938Z",
     "iopub.status.idle": "2026-10-07T04:52:12.917936Z",
     "shell.execute_reply": "2026-10-07T04:52:12.917168Z"
    },
    "id": "9e8643bb"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1375x462 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Due modi di misurare quanto le variabili si muovono insieme -------\n",
    "COLONNE = [\"voto\", \"recensioni\", \"installazioni\", \"prezzo\", \"dimensione_mb\",\n",
    "           \"giorni_da_aggiornamento\"]\n",
    "ETICHETTE = [\"voto\", \"recensioni\", \"installazioni\", \"prezzo\", \"dimensione\", \"giorni da agg.\"]\n",
    "\n",
    "# i due metodi di calcolo: il primo misura relazioni \"su una retta\",\n",
    "# il secondo misura relazioni \"per posizione in classifica\" (anche se curve)\n",
    "METODI = [\"pearson\", \"spearman\"]\n",
    "TITOLI = [\"Relazioni lineari: le due variabili crescono in proporzione?\",\n",
    "          \"Relazioni per tendenza: le due variabili crescono insieme,\\nanche se non in proporzione?\"]\n",
    "\n",
    "# 1) preparo una figura con 2 grafici affiancati\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12.5, 4.2))\n",
    "\n",
    "# 2) per ciascuno dei due metodi, calcolo la matrice e la disegno come heatmap\n",
    "for ax, metodo, titolo in zip(axes, METODI, TITOLI):\n",
    "    matrice = app[COLONNE].corr(method=metodo)\n",
    "    sns.heatmap(matrice, mask=np.triu(np.ones_like(matrice, dtype=bool), k=1),\n",
    "                annot=True, fmt=\"+.2f\", cmap=\"coolwarm_r\", center=0, vmin=-1, vmax=1,\n",
    "                square=True, linewidths=2, linecolor=\"white\", cbar=False, ax=ax,\n",
    "                xticklabels=ETICHETTE, yticklabels=ETICHETTE, annot_kws={\"size\": 9})\n",
    "    ax.set_title(titolo)\n",
    "    plt.setp(ax.get_xticklabels(), rotation=35, ha=\"right\")\n",
    "\n",
    "# 3) didascalia comune, per leggere i colori\n",
    "fig.suptitle(\"Blu = crescono insieme  ·  rosso = vanno in direzioni opposte\",\n",
    "             fontsize=9, x=0.01, ha=\"left\", y=1.02)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cfb46fd2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:12.919903Z",
     "iopub.status.busy": "2026-10-07T04:52:12.919659Z",
     "iopub.status.idle": "2026-10-07T04:52:13.194689Z",
     "shell.execute_reply": "2026-10-07T04:52:13.193154Z"
    },
    "id": "cfb46fd2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recensioni ~ installazioni, sui valori originali : 0.625\n",
      "recensioni ~ installazioni, sui logaritmi         : 0.958\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1210x418 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Lo stesso confronto, letto in due scale diverse --------------------\n",
    "\n",
    "# 1) calcolo quanto sono legate \"recensioni\" e \"installazioni\",\n",
    "#    prima sui valori originali, poi sui loro logaritmi\n",
    "coefficiente_grezzo = app[[\"recensioni\", \"installazioni\"]].corr().iloc[0, 1]\n",
    "coefficiente_log = np.log10(app[[\"recensioni\", \"installazioni\"]].clip(lower=1)).corr().iloc[0, 1]\n",
    "\n",
    "print(f\"recensioni ~ installazioni, sui valori originali : {coefficiente_grezzo:.3f}\")\n",
    "print(f\"recensioni ~ installazioni, sui logaritmi         : {coefficiente_log:.3f}\")\n",
    "\n",
    "# 2) preparo due grafici affiancati: a sinistra scala normale, a destra scala logaritmica\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 3.8))\n",
    "\n",
    "# 3) disegno lo stesso grafico a punti due volte, cambiando solo la scala degli assi\n",
    "for ax, log, titolo in [(ax1, False, f\"Scala normale: indice = {coefficiente_grezzo:.2f}\"),\n",
    "                        (ax2, True, f\"Scala logaritmica: indice = {coefficiente_log:.2f}\")]:\n",
    "    ax.scatter(app[\"installazioni\"], app[\"recensioni\"], s=6, alpha=0.3, color=BLU,\n",
    "               linewidths=0)\n",
    "    if log:\n",
    "        ax.set_xscale(\"log\")\n",
    "        ax.set_yscale(\"log\")\n",
    "    ax.set_xlabel(\"installazioni\")\n",
    "    ax.set_ylabel(\"recensioni\")\n",
    "    ax.set_title(titolo)\n",
    "\n",
    "# 4) cosa si vede nel primo grafico\n",
    "ax1.text(0.3, 0.55, \"i punti non stanno\\nsu una retta,\\nstanno su una curva\",\n",
    "         transform=ax1.transAxes, fontsize=9)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f66dd2",
   "metadata": {
    "id": "89f66dd2"
   },
   "source": [
    "**Risultato**\n",
    "\n",
    "Recensioni e installazioni dicono praticamente la stessa cosa. Il legame tra le due è fortissimo (quasi il massimo possibile): in pratica, sapere quante recensioni ha un'app equivale a sapere quante installazioni ha. Misurano la stessa cosa di fondo soprattutto quanto è grande il pubblico di quell'app.\n",
    "\n",
    "Il voto non dipende da quanto un'app è scaricata. Il legame tra voto e installazioni è quasi zero, qualunque metodo uso per calcolarlo. In parole semplici: un'app molto scaricata non è né più né meno probabile che sia ben votata di una scaricata poco. Sono due cose indipendenti e quindi scorrelate.\n",
    "\n",
    "Lo stesso identico legame può sembrare debole o forte, a seconda di come lo misuri e qui la differenza conta. Nella sezione precedente abbiamo visto che lo stesso confronto (recensioni e installazioni) dà un risultato basso se calcolato sui numeri \"grezzi\", e un risultato molto più alto se calcolato sui logaritmi. Il numero basso non va letto come \"più prudente\" o più affidabile: è semplicemente sbagliato, perché calcolato sulla scala sbagliata (dove i punti disegnano una curva, non una retta). Quando due modi diversi di misurare lo stesso legame danno risultati molto distanti tra loro, è un segnale che la relazione non è una semplice proporzione, e va guardata cambiando punto di vista utilizzando una scala diversa.\n",
    "\n",
    "Le app aggiornate di recente hanno, in media, voti più alti e più pubblico. Più tempo è passato dall'ultimo aggiornamento, più tendono a scendere sia il voto sia il numero di recensioni e installazioni. Questo non dimostra che aggiornare spesso faccia salire il voto, potrebbe anche essere il contrario (le app di maggior successo vengono aggiornate più spesso, non il contrario).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "-ZbNGGkh2A6C",
   "metadata": {
    "id": "-ZbNGGkh2A6C"
   },
   "source": [
    "## 4.1 Da Cosa dipende il voto?\n",
    "Abbiamo appena visto che il voto non dipende da quante persone scaricano l'app. Proviamo con un'altra misura, costruita apposta: quante recensioni ogni mille installazioni riceve un'app cioè, tra tutte le persone che la usano, quante si prendono davvero la briga di scrivere qualcosa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "918d2f21",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:13.196597Z",
     "iopub.status.busy": "2026-10-07T04:52:13.196390Z",
     "iopub.status.idle": "2026-10-07T04:52:13.304995Z",
     "shell.execute_reply": "2026-10-07T04:52:13.304266Z"
    },
    "id": "918d2f21"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numero_app</th>\n",
       "      <th>voto_medio</th>\n",
       "      <th>recensioni_per_1000</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gruppo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Q1 meno recensite</th>\n",
       "      <td>1650</td>\n",
       "      <td>3.96</td>\n",
       "      <td>4.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q2</th>\n",
       "      <td>1634</td>\n",
       "      <td>4.05</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q3</th>\n",
       "      <td>1642</td>\n",
       "      <td>4.16</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q4</th>\n",
       "      <td>1646</td>\n",
       "      <td>4.26</td>\n",
       "      <td>33.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q5 più recensite</th>\n",
       "      <td>1638</td>\n",
       "      <td>4.44</td>\n",
       "      <td>80.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   numero_app  voto_medio  recensioni_per_1000\n",
       "gruppo                                                        \n",
       "Q1 meno recensite        1650        3.96                 4.73\n",
       "Q2                       1634        4.05                10.34\n",
       "Q3                       1642        4.16                18.62\n",
       "Q4                       1646        4.26                33.00\n",
       "Q5 più recensite         1638        4.44                80.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 825x330 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indice di correlazione tra voto e coinvolgimento: +0.379\n"
     ]
    }
   ],
   "source": [
    "# --- La domanda : le app più recensite hanno anche un voto più alto? -----------------\n",
    "\n",
    "# 1) tengo solo le app che hanno sia il voto sia il dato sul coinvolgimento,\n",
    "#    e le divido in 5 gruppi di uguale numerosità (quintili), dal meno al più recensito\n",
    "confronto = app.dropna(subset=[\"voto\", \"recensioni_per_1000\"]).copy()\n",
    "confronto[\"gruppo\"] = pd.qcut(confronto[\"recensioni_per_1000\"], 5,\n",
    "                              labels=[\"Q1 meno recensite\", \"Q2\", \"Q3\", \"Q4\",\n",
    "                                      \"Q5 più recensite\"])\n",
    "\n",
    "# 2) per ciascun gruppo calcolo: quante app contiene, il voto medio,\n",
    "#    e il livello di coinvolgimento tipico (valore centrale del gruppo)\n",
    "per_gruppo = confronto.groupby(\"gruppo\", observed=True).agg(\n",
    "    numero_app=(\"nome\", \"count\"),\n",
    "    voto_medio=(\"voto\", \"mean\"),\n",
    "    recensioni_per_1000=(\"recensioni_per_1000\", \"median\"),\n",
    ")\n",
    "display(per_gruppo.round(2))\n",
    "\n",
    "# 3) grafico a barre: un voto medio per ciascuno dei 5 gruppi\n",
    "fig, ax = plt.subplots(figsize=(7.5, 3))\n",
    "ax.bar(range(5), per_gruppo[\"voto_medio\"], color=sns.color_palette(\"Blues\", 5), width=0.6)\n",
    "ax.set_xticks(range(5), [e.replace(\" \", \"\\n\", 1) for e in per_gruppo.index], fontsize=9)\n",
    "ax.set_ylim(3.8, 4.55)\n",
    "ax.set_ylabel(\"voto medio\")\n",
    "ax.set_title(\"Voto e coinvolgimento salgono insieme (legame, non causa)\")\n",
    "for i, v in enumerate(per_gruppo[\"voto_medio\"]):\n",
    "    ax.text(i, v + 0.012, f\"{v:.2f}\", ha=\"center\", fontweight=\"bold\", fontsize=10)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 4) un indice numerico che conferma la tendenza vista nel grafico\n",
    "indice = confronto[[\"voto\", \"recensioni_per_1000\"]].corr(method=\"spearman\").iloc[0, 1]\n",
    "print(f\"Indice di correlazione tra voto e coinvolgimento: {indice:+.3f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "90cbccb3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:13.306615Z",
     "iopub.status.busy": "2026-10-07T04:52:13.306422Z",
     "iopub.status.idle": "2026-10-07T04:52:13.382046Z",
     "shell.execute_reply": "2026-10-07T04:52:13.381245Z"
    },
    "id": "90cbccb3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recensioni con sentiment: 37,432 | app coperte: 819 (8.5% del dataset)\n",
      "Correlazione voto ~ tono del testo scritto:    +0.242\n",
      "Correlazione voto ~ % di recensioni positive:  +0.317\n"
     ]
    }
   ],
   "source": [
    "# --- Controprova con il secondo file: il tono delle recensioni scritte ---\n",
    "\n",
    "# 1) tengo solo le recensioni che hanno il sentiment (positivo, negativo o neutro)\n",
    "recensioni = recensioni_grezze.dropna(subset=[\"Sentiment\"])\n",
    "\n",
    "# 2) per ogni app calcolo due misure del tono delle recensioni:\n",
    "#    la polarità tipica (da -1 = negativa a +1 = positiva)\n",
    "#    e la percentuale di recensioni positive\n",
    "per_app = recensioni.groupby(\"App\").agg(\n",
    "    polarita_mediana=(\"Sentiment_Polarity\", \"median\"),\n",
    "    quota_positive=(\"Sentiment\", lambda s: (s == \"Positive\").mean()),\n",
    ")\n",
    "\n",
    "# 3) unisco i due file (merge) usando il nome dell'app come chiave comune,\n",
    "#    e tengo solo le app che hanno anche il voto\n",
    "unite = app.merge(per_app, left_on=\"nome\", right_index=True, how=\"inner\").dropna(subset=[\"voto\"])\n",
    "\n",
    "# 4) controllo se il voto in numeri e il tono delle recensioni scritte vanno d'accordo\n",
    "print(f\"Recensioni con sentiment: {len(recensioni):,} | app coperte: {len(unite):,} \"\n",
    "      f\"({len(unite) / len(app):.1%} del dataset)\")\n",
    "print(f\"Correlazione voto ~ tono del testo scritto:    \"\n",
    "      f\"{unite[['voto', 'polarita_mediana']].corr().iloc[0, 1]:+.3f}\")\n",
    "print(f\"Correlazione voto ~ % di recensioni positive:  \"\n",
    "      f\"{unite[['voto', 'quota_positive']].corr().iloc[0, 1]:+.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d29454b4",
   "metadata": {
    "id": "d29454b4"
   },
   "source": [
    "**Risultato**\n",
    "Scaricare un'app non basta a farla votare bene: il legame tra voto e numero di installazioni è quasi nullo. Quello che conta davvero è quante persone, tra chi la usa, decidono di lasciare una recensione. Qui il legame è molto più forte, ed è anche ordinato: passando dal gruppo di app meno recensite a quello più recensito, il voto medio sale in modo costante, da 3,96 a 4,44, un gradino alla volta, senza salti strani.\n",
    "\n",
    "Una seconda fonte di dati, indipendente dalla prima, va nella stessa direzione: confrontando il voto numerico con il tono delle recensioni scritte (quanto sono positive o negative), i due si confermano a vicenda. Il controllo riguarda 819 app, un campione più piccolo ma utile come riprova.\n",
    "\n",
    "Cosa non dimostra questo risultato. Non dice perché succede. Potrebbe essere che le app piacciono e per questo le persone si sentono spinte a scriverne bene, oppure che le app che riescono a far scrivere recensioni (magari perché più curate,più coinvolgenti o promemoria di lasciare recenzione durante l'utilizzo) finiscono anche per piacere di più. Da questi dati non si può distinguere la causa dall'effetto.\n",
    "\n",
    "Cosa invece ci dice con sicurezza. Smentisce un'idea intuitiva ma sbagliata: \"più persone usano un'app, meglio viene votata\". E indica dove guardare per migliorare: non conta quanto è grande il pubblico, ma quanta parte di quel pubblico trova un motivo per sentirsi coinvolta e farsi sentire sia in maniera positiva che negativa.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d7dd81",
   "metadata": {},
   "source": [
    "### 4.2 Correlazione non è causalità: la regola che tengo fino in fondo\n",
    "\n",
    "Nella prima versione scrivevo questa cautela qui, e poi la dimenticavo nelle conclusioni:\n",
    "usavo «il voto segue il coinvolgimento» per diagnosticare un concorrente, e chiamavo «leva»\n",
    "una cosa che forse è un effetto. Da qui in avanti vale una regola: **quando un legame osservato\n",
    "entra in una decisione, accanto c'è scritto che è un legame, e quali altre spiegazioni non\n",
    "posso escludere.**\n",
    "\n",
    "Per ogni legame che uso, le spiegazioni alternative sono sempre tre:\n",
    "\n",
    "| Spiegazione alternativa | Esempio in questo notebook |\n",
    "|---|---|\n",
    "| **la causa va al contrario** | non è l'aggiornamento frequente a far salire il voto: sono le app con più successo (e più utenti che segnalano problemi) a essere aggiornate di più |\n",
    "| **una terza variabile muove entrambe** | il prezzo non «segnala qualità»: in ogni fascia di prezzo stanno tipi di app diversi (temi grafici in basso, app mediche in alto), e il tipo di app muove sia il prezzo sia le installazioni |\n",
    "| **il caso**, o un artefatto dei dati | una categoria piccola può avere percentuali estreme per pochi elementi; le installazioni sono fasce, non numeri |\n",
    "\n",
    "Due letture utili: [Correlation and causation](https://www.abs.gov.au/statistics/understanding-statistics/statistical-terms-and-concepts/correlation-and-causation)\n",
    "dell'Australian Bureau of Statistics, e [Spurious correlations](https://www.tylervigen.com/spurious-correlations)\n",
    "di Tyler Vigen, che raccoglie correlazioni fortissime e prive di senso.\n",
    "\n",
    "Nel testo segno con **⚠ legame, non causa** ogni punto in cui una decisione poggia su una\n",
    "correlazione."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41677b96",
   "metadata": {
    "id": "41677b96"
   },
   "source": [
    "---\n",
    "\n",
    "## 5. Le categorie: dove c'è concorrenza e dove c'è interesse\n",
    "\n",
    "La Concorrenza non è una cosa sola. Il numero di app da solo può ingannare: una categoria con\n",
    "pochi concorrenti può essere impossibile da attaccare se uno di loro si prende tutto. Uso\n",
    "quattro misure:\n",
    "\n",
    "| Misura | Domanda a cui risponde |\n",
    "|---|---|\n",
    "| numero di app | quanti concorrenti ci sono? |\n",
    "| quota installazioni ÷ quota app | la categoria riceve più attenzione di quanto spazio occupa? |\n",
    "| **quota della prima app** | quanta parte del mercato si prende già il leader? |\n",
    "| % di app oltre il milione | quanto è probabile arrivare a un pubblico grande? |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f02fb5cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:13.383552Z",
     "iopub.status.busy": "2026-10-07T04:52:13.383359Z",
     "iopub.status.idle": "2026-10-07T04:52:13.406545Z",
     "shell.execute_reply": "2026-10-07T04:52:13.405763Z"
    },
    "id": "f02fb5cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33 categorie, 9,674 app\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numero_app</th>\n",
       "      <th>quota_app</th>\n",
       "      <th>quota_installazioni</th>\n",
       "      <th>domanda_su_offerta</th>\n",
       "      <th>quota_prima_app</th>\n",
       "      <th>quota_oltre_1M</th>\n",
       "      <th>voto_mediano</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>categoria</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FAMILY</th>\n",
       "      <td>1877</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.302</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAME</th>\n",
       "      <td>947</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.179</td>\n",
       "      <td>1.826</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.571</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOOLS</th>\n",
       "      <td>829</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.108</td>\n",
       "      <td>1.255</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.332</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUSINESS</th>\n",
       "      <td>420</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.164</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDICAL</th>\n",
       "      <td>399</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.048</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERSONALIZATION</th>\n",
       "      <td>376</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.293</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRODUCTIVITY</th>\n",
       "      <td>375</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.077</td>\n",
       "      <td>1.985</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.395</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIFESTYLE</th>\n",
       "      <td>369</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.228</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 numero_app  quota_app  quota_installazioni  domanda_su_offerta  quota_prima_app  quota_oltre_1M  voto_mediano\n",
       "categoria                                                                                                                     \n",
       "FAMILY                 1877      0.194                0.083               0.426            0.160           0.302           4.3\n",
       "GAME                    947      0.098                0.179               1.826            0.074           0.571           4.3\n",
       "TOOLS                   829      0.086                0.108               1.255            0.123           0.332           4.2\n",
       "BUSINESS                420      0.043                0.009               0.213            0.143           0.164           4.2\n",
       "MEDICAL                 399      0.041                0.001               0.013            0.127           0.048           4.3\n",
       "PERSONALIZATION         376      0.039                0.020               0.523            0.065           0.293           4.4\n",
       "PRODUCTIVITY            375      0.039                0.077               1.985            0.172           0.395           4.3\n",
       "LIFESTYLE               369      0.038                0.007               0.175            0.198           0.228           4.2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Costruisco un profilo per ciascuna categoria dello store -----------\n",
    "\n",
    "# 1) per ogni categoria calcolo alcuni numeri di base: quante app contiene,\n",
    "#    quante installazioni ha in totale, quante ne ha la sua app più scaricata,\n",
    "#    il voto mediano, il coinvolgimento mediano, e da quanto non si aggiorna\n",
    "per_categoria = app.groupby(\"categoria\").agg(\n",
    "    numero_app=(\"nome\", \"count\"),\n",
    "    installazioni_totali=(\"installazioni\", \"sum\"),\n",
    "    installazioni_prima_app=(\"installazioni\", \"max\"),\n",
    "    voto_mediano=(\"voto\", \"median\"),\n",
    "    recensioni_per_1000=(\"recensioni_per_1000\", \"median\"),\n",
    "    giorni_da_aggiornamento=(\"giorni_da_aggiornamento\", \"median\"),\n",
    ")\n",
    "\n",
    "# 2) che percentuale del catalogo totale rappresenta questa categoria,\n",
    "#    sia in numero di app sia in installazioni\n",
    "per_categoria[\"quota_app\"] = per_categoria[\"numero_app\"] / per_categoria[\"numero_app\"].sum()\n",
    "per_categoria[\"quota_installazioni\"] = (per_categoria[\"installazioni_totali\"]\n",
    "                                        / per_categoria[\"installazioni_totali\"].sum())\n",
    "\n",
    "# 3) la categoria riceve più o meno attenzione di quanto \"pesa\" nel catalogo?\n",
    "#    sopra 1 = ha più installazioni di quante gliene spetterebbero in proporzione\n",
    "#    sotto 1 = ne ha meno\n",
    "per_categoria[\"domanda_su_offerta\"] = (per_categoria[\"quota_installazioni\"]\n",
    "                                       / per_categoria[\"quota_app\"])\n",
    "\n",
    "# 4) quanto è dominata la categoria da una singola app di successo:\n",
    "#    che percentuale delle sue installazioni totali prende solo la prima app\n",
    "per_categoria[\"quota_prima_app\"] = (per_categoria[\"installazioni_prima_app\"]\n",
    "                                    / per_categoria[\"installazioni_totali\"])\n",
    "\n",
    "# 5) due indicatori in più: che percentuale di app supera il milione di\n",
    "#    installazioni, e che percentuale è a pagamento\n",
    "per_categoria[\"quota_oltre_1M\"] = app.groupby(\"categoria\")[\"installazioni\"].apply(\n",
    "    lambda s: (s >= 1_000_000).mean())\n",
    "per_categoria[\"quota_a_pagamento\"] = app.groupby(\"categoria\")[\"tipo\"].apply(\n",
    "    lambda s: (s == \"A pagamento\").mean())\n",
    "\n",
    "# 6) riepilogo generale, e tabella delle 8 categorie con più app\n",
    "print(f\"{len(per_categoria)} categorie, {per_categoria['numero_app'].sum():,} app\")\n",
    "per_categoria.sort_values(\"numero_app\", ascending=False)[\n",
    "    [\"numero_app\", \"quota_app\", \"quota_installazioni\", \"domanda_su_offerta\",\n",
    "     \"quota_prima_app\", \"quota_oltre_1M\", \"voto_mediano\"]].head(8).round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bfad9590",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:13.408263Z",
     "iopub.status.busy": "2026-10-07T04:52:13.408019Z",
     "iopub.status.idle": "2026-10-07T04:52:13.958582Z",
     "shell.execute_reply": "2026-10-07T04:52:13.957926Z"
    },
    "id": "bfad9590"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1540x990 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La prima app nelle sei categorie più concentrate:\n",
      "\n",
      "  Books & Reference      Google Play Books                         60.0%\n",
      "  Art & Design           Sketch - Draw & Paint                     44.2%\n",
      "  Health & Fitness       Samsung Health                            43.7%\n",
      "  News & Magazines       Google News                               42.2%\n",
      "  Beauty                 Beauty Camera - Selfie Camera             36.8%\n",
      "  Travel & Local         Maps - Navigate & Explore                 34.5%\n"
     ]
    }
   ],
   "source": [
    "# --- Tre modi di guardare la concorrenza nelle categorie ----------------\n",
    "\n",
    "# 1) una funzione che trasforma i nomi tecnici delle categorie\n",
    "#    (es. \"FOOD_AND_DRINK\") in nomi più semplici da leggere (es. \"Food & Drink\")\n",
    "def nome_leggibile(categoria):\n",
    "    return categoria.replace(\"_AND_\", \" & \").replace(\"_\", \" \").title()\n",
    "\n",
    "# 2) ordino le categorie dalla più affollata alla meno affollata,\n",
    "#    e preparo i nomi leggibili da usare nei grafici\n",
    "ordinate = per_categoria.sort_values(\"numero_app\", ascending=False)\n",
    "nomi = [nome_leggibile(c) for c in ordinate.index]\n",
    "righe = range(len(nomi))\n",
    "\n",
    "# 3) preparo tre grafici affiancati e ravvicinati (wspace piccolo),\n",
    "#    tutti con le stesse categorie sull'asse verticale\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 9), sharey=True,\n",
    "                                    gridspec_kw={\"wspace\": 0.06})\n",
    "\n",
    "# 4) per seguire una categoria con lo sguardo da sinistra a destra:\n",
    "#    una riga sì e una no ha uno sfondo grigio chiaro, uguale nei tre grafici,\n",
    "#    e le righe orizzontali della griglia sono tolte per non fare confusione\n",
    "for ax in (ax1, ax2, ax3):\n",
    "    for r in righe:\n",
    "        if r % 2 == 0:\n",
    "            ax.axhspan(r - 0.5, r + 0.5, color=\"#eeeeec\", zorder=0)\n",
    "    ax.grid(axis=\"y\", visible=False)\n",
    "    ax.set_ylim(len(nomi) - 0.5, -0.5)   # stesso ordine dall'alto in basso\n",
    "\n",
    "# 5) primo grafico: quanti concorrenti ci sono in ogni categoria\n",
    "ax1.barh(righe, ordinate[\"numero_app\"], height=0.65, zorder=2,\n",
    "         color=[ARANCIONE if c in (\"FAMILY\", \"GAME\", \"TOOLS\") else BLU for c in ordinate.index])\n",
    "ax1.set_yticks(righe, nomi, fontsize=9.5)\n",
    "ax1.set_xlim(0, ordinate[\"numero_app\"].max() * 1.15)\n",
    "ax1.set_xlabel(\"numero di app\")\n",
    "ax1.set_title(\"Quanti concorrenti\")\n",
    "for r, v in zip(righe, ordinate[\"numero_app\"]):\n",
    "    ax1.text(v + 15, r, f\"{v:,}\".replace(\",\", \".\"), va=\"center\", fontsize=7.5)\n",
    "\n",
    "# 6) secondo grafico: la categoria riceve più o meno attenzione di quanto \"pesa\"?\n",
    "#    oltre la linea nera (valore 1) = più attenzione del previsto, prima = meno\n",
    "ax2.barh(righe, ordinate[\"domanda_su_offerta\"], height=0.65, zorder=2,\n",
    "         color=[VERDE if v >= 1 else GRIGIO for v in ordinate[\"domanda_su_offerta\"]])\n",
    "ax2.axvline(1, color=\"black\", linewidth=1.1, zorder=3)\n",
    "ax2.set_xlim(0, ordinate[\"domanda_su_offerta\"].max() * 1.15)\n",
    "ax2.set_xlabel(\"quota installazioni ÷ quota app\")\n",
    "ax2.set_title(\"Quanta attenzione ricevono\")\n",
    "for r, v in zip(righe, ordinate[\"domanda_su_offerta\"]):\n",
    "    ax2.text(v + 0.05, r, f\"{v:.2f}\".replace(\".\", \",\"), va=\"center\", fontsize=7.5)\n",
    "\n",
    "# 7) terzo grafico: quanto è forte il leader di ciascuna categoria\n",
    "ax3.barh(righe, ordinate[\"quota_prima_app\"] * 100, height=0.65, zorder=2,\n",
    "         color=[ARANCIONE if v > 0.30 else BLU for v in ordinate[\"quota_prima_app\"]])\n",
    "ax3.set_xlim(0, ordinate[\"quota_prima_app\"].max() * 100 * 1.15)\n",
    "ax3.set_xlabel(\"% del mercato presa dalla prima app\")\n",
    "ax3.set_title(\"Quanto è forte il leader\\n(ARANCIONE = oltre il 30%)\")\n",
    "for r, v in zip(righe, ordinate[\"quota_prima_app\"]):\n",
    "    ax3.text(v * 100 + 0.8, r, f\"{v:.0%}\", va=\"center\", fontsize=7.5)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# 8) elenco testuale: chi è l'app leader nelle 6 categorie più dominate da un solo prodotto\n",
    "print(\"La prima app nelle sei categorie più concentrate:\\n\")\n",
    "for categoria in per_categoria.nlargest(6, \"quota_prima_app\").index:\n",
    "    dentro = app[app[\"categoria\"] == categoria]\n",
    "    prima = dentro.loc[dentro[\"installazioni\"].idxmax()]\n",
    "    quota = prima[\"installazioni\"] / dentro[\"installazioni\"].sum()\n",
    "    print(f\"  {nome_leggibile(categoria):22s} {prima['nome'][:40]:40s} {quota:6.1%}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f766faf",
   "metadata": {
    "id": "2f766faf"
   },
   "source": [
    "**Risultato** I tre grafici raccontano tre storie diverse, ed è il confronto tra loro a dare l'informazione utile.\n",
    "\n",
    "**Primo grafico quanti concorrenti.** La categoria più affollata è FAMILY, con 1.877 app: quasi una su cinque di tutto lo store.\n",
    "\n",
    "**Secondo grafico quanta attenzione ricevono.** FAMILY però raccoglie solo l'8,3% delle installazioni. Il suo indice è **0,43**: riceve meno della metà del pubblico che le spetterebbe per il numero di app che ha. Tante app, poco pubblico per ciascuna. All'opposto, **COMMUNICATION** ha l'indice più alto (**4,49**), ma è un'illusione: quelle installazioni sono quasi tutte di WhatsApp, Messenger e Skype.\n",
    "\n",
    "**Terzo grafico : quanto è forte il leader.** Nelle categorie più concentrate, l'app che domina è spesso un'**app già presente sul telefono** quando lo compri: Google Play Books ha il **60%** delle installazioni della sua categoria, Samsung Health il **43,7%**, Google News il 42,2%, Google Maps il 34,5%. Contro queste app non basta fare un prodotto migliore: sono già lì prima che l'utente cerchi un'alternativa.\n",
    "\n",
    "All'estremo opposto c'è la categoria che interessa nel progetto: **Food & Drink**. Qui l'app più scaricata si prende solo il **4,7%** del pubblico, il valore più basso di tutte le 33 categorie. Nessuno domina.\n",
    "\n",
    "**In sintesi** pochi concorrenti non vuol dire mercato facile. La situazione peggiore è *poche app con un leader fortissimo*; la più aperta è *tante app senza un leader*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2a2ecfcd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:13.960112Z",
     "iopub.status.busy": "2026-10-07T04:52:13.959918Z",
     "iopub.status.idle": "2026-10-07T04:52:14.400302Z",
     "shell.execute_reply": "2026-10-07T04:52:14.399441Z"
    },
    "id": "2a2ecfcd"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1320x990 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Dove si arriva a un pubblico grande, e dove gli utenti partecipano di più ---\n",
    "\n",
    "# 1) ordino le categorie dalla domanda più alta alla più bassa.\n",
    "#    Lo stesso ordine vale per tutti e due i grafici, così ogni categoria\n",
    "#    sta alla stessa altezza a sinistra e a destra\n",
    "ordinate = per_categoria.sort_values(\"quota_oltre_1M\", ascending=False)\n",
    "nomi = [nome_leggibile(c) for c in ordinate.index]\n",
    "righe = range(len(nomi))\n",
    "\n",
    "# categorie da evidenziare in arancione in ciascun grafico\n",
    "DA_NOTARE_DOMANDA = [\"FOOD_AND_DRINK\", \"HOUSE_AND_HOME\", \"EDUCATION\", \"SHOPPING\"]\n",
    "DA_NOTARE_COINVOLGIMENTO = [\"GAME\", \"EDUCATION\", \"HEALTH_AND_FITNESS\"]\n",
    "\n",
    "# i due valori di riferimento dello store, disegnati come linea nera\n",
    "media_oltre_1M = (app[\"installazioni\"] >= 1e6).mean() * 100\n",
    "mediana_coinvolgimento = app[\"recensioni_per_1000\"].median()\n",
    "\n",
    "# 2) preparo due grafici affiancati e ravvicinati, con le stesse categorie in verticale\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 9), sharey=True,\n",
    "                               gridspec_kw={\"wspace\": 0.06})\n",
    "\n",
    "# 3) una riga sì e una no con lo sfondo grigio chiaro, uguale nei due grafici,\n",
    "#    per seguire una categoria con lo sguardo da sinistra a destra\n",
    "for ax in (ax1, ax2):\n",
    "    for r in righe:\n",
    "        if r % 2 == 0:\n",
    "            ax.axhspan(r - 0.5, r + 0.5, color=\"#eeeeec\", zorder=0)\n",
    "    ax.grid(axis=\"y\", visible=False)\n",
    "    ax.set_ylim(len(nomi) - 0.5, -0.5)   # stesso ordine dall'alto in basso\n",
    "\n",
    "# 4) primo grafico — DOMANDA: percentuale di app che supera il milione di installazioni\n",
    "ax1.barh(righe, ordinate[\"quota_oltre_1M\"] * 100, height=0.65, zorder=2,\n",
    "         color=[ARANCIONE if c in DA_NOTARE_DOMANDA else BLU for c in ordinate.index])\n",
    "ax1.axvline(media_oltre_1M, color=\"black\", linewidth=1.1, zorder=3)\n",
    "ax1.set_yticks(righe, nomi, fontsize=9.5)\n",
    "ax1.set_xlim(0, ordinate[\"quota_oltre_1M\"].max() * 100 * 1.15)\n",
    "ax1.set_xlabel(\"% di app che superano il milione di installazioni\")\n",
    "ax1.set_title(f\"Domanda\\n(linea = media dello store, {media_oltre_1M:.1f}%)\".replace(\".\", \",\"))\n",
    "for r, v in zip(righe, ordinate[\"quota_oltre_1M\"]):\n",
    "    ax1.text(v * 100 + 0.8, r, f\"{v:.0%}\", va=\"center\", fontsize=7.5)\n",
    "\n",
    "# 5) secondo grafico — COINVOLGIMENTO: recensioni ogni 1.000 installazioni\n",
    "ax2.barh(righe, ordinate[\"recensioni_per_1000\"], height=0.65, zorder=2,\n",
    "         color=[ARANCIONE if c in DA_NOTARE_COINVOLGIMENTO else BLU for c in ordinate.index])\n",
    "ax2.axvline(mediana_coinvolgimento, color=\"black\", linewidth=1.1, zorder=3)\n",
    "ax2.set_xlim(0, ordinate[\"recensioni_per_1000\"].max() * 1.15)\n",
    "ax2.set_xlabel(\"recensioni ogni 1.000 installazioni\")\n",
    "ax2.set_title(f\"Coinvolgimento\\n(linea = valore tipico dello store, {mediana_coinvolgimento:.0f})\")\n",
    "for r, v in zip(righe, ordinate[\"recensioni_per_1000\"]):\n",
    "    ax2.text(v + 0.3, r, f\"{v:.1f}\".replace(\".\", \",\"), va=\"center\", fontsize=7.5)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "408412bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:14.401965Z",
     "iopub.status.busy": "2026-10-07T04:52:14.401748Z",
     "iopub.status.idle": "2026-10-07T04:52:14.723982Z",
     "shell.execute_reply": "2026-10-07T04:52:14.723243Z"
    },
    "id": "408412bd"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "customdata": [
          [
           0.4415475003554457,
           "ART_AND_DESIGN"
          ],
          [
           0.1882168320393081,
           "AUTO_AND_VEHICLES"
          ],
          [
           0.3676869366346718,
           "BEAUTY"
          ],
          [
           0.6002510576459651,
           "BOOKS_AND_REFERENCE"
          ],
          [
           0.14343809480416084,
           "BUSINESS"
          ],
          [
           0.22231534765118277,
           "COMICS"
          ],
          [
           0.09058564866599968,
           "COMMUNICATION"
          ],
          [
           0.07095917295153835,
           "DATING"
          ],
          [
           0.05250666834688005,
           "EDUCATION"
          ],
          [
           0.10104480326576804,
           "ENTERTAINMENT"
          ],
          [
           0.31302508000764534,
           "EVENTS"
          ],
          [
           0.16047136203160006,
           "FAMILY"
          ],
          [
           0.2196118986025335,
           "FINANCE"
          ],
          [
           0.04721463159147714,
           "FOOD_AND_DRINK"
          ],
          [
           0.07425049095807537,
           "GAME"
          ],
          [
           0.4370543365671112,
           "HEALTH_AND_FITNESS"
          ],
          [
           0.10286747086878091,
           "HOUSE_AND_HOME"
          ],
          [
           0.18869380674848305,
           "LIBRARIES_AND_DEMO"
          ],
          [
           0.19848219120226537,
           "LIFESTYLE"
          ],
          [
           0.19869580445264978,
           "MAPS_AND_NAVIGATION"
          ],
          [
           0.12721294227837412,
           "MEDICAL"
          ],
          [
           0.42208023968214725,
           "NEWS_AND_MAGAZINES"
          ],
          [
           0.31724771113707606,
           "PARENTING"
          ],
          [
           0.06525307699220603,
           "PERSONALIZATION"
          ],
          [
           0.2146315322131597,
           "PHOTOGRAPHY"
          ],
          [
           0.1724705487303265,
           "PRODUCTIVITY"
          ],
          [
           0.0714107807077506,
           "SHOPPING"
          ],
          [
           0.18222012953984548,
           "SOCIAL"
          ],
          [
           0.09120139153478059,
           "SPORTS"
          ],
          [
           0.1234145562148654,
           "TOOLS"
          ],
          [
           0.34543660929295517,
           "TRAVEL_AND_LOCAL"
          ],
          [
           0.2543233235606714,
           "VIDEO_PLAYERS"
          ],
          [
           0.1384655995510613,
           "WEATHER"
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>numero di app (scala log)  →  più concorrenza=%{x:,}<br>% oltre il milione  →  più domanda=%{y:.1%}<br>recensioni_per_1000=%{marker.size}<br>quota della<br>prima app=%{marker.color:.1%}<extra></extra>",
         "hovertext": [
          "Art & Design",
          "Auto & Vehicles",
          "Beauty",
          "Books & Reference",
          "Business",
          "Comics",
          "Communication",
          "Dating",
          "Education",
          "Entertainment",
          "Events",
          "Family",
          "Finance",
          "Food & Drink",
          "Game",
          "Health & Fitness",
          "House & Home",
          "Libraries & Demo",
          "Lifestyle",
          "Maps & Navigation",
          "Medical",
          "News & Magazines",
          "Parenting",
          "Personalization",
          "Photography",
          "Productivity",
          "Shopping",
          "Social",
          "Sports",
          "Tools",
          "Travel & Local",
          "Video Players",
          "Weather"
         ],
         "legendgroup": "",
         "marker": {
          "color": [
           0.4415475003554457,
           0.1882168320393081,
           0.3676869366346718,
           0.6002510576459651,
           0.14343809480416084,
           0.22231534765118277,
           0.09058564866599968,
           0.07095917295153835,
           0.05250666834688005,
           0.10104480326576804,
           0.31302508000764534,
           0.16047136203160006,
           0.2196118986025335,
           0.04721463159147714,
           0.07425049095807537,
           0.4370543365671112,
           0.10286747086878091,
           0.18869380674848305,
           0.19848219120226537,
           0.19869580445264978,
           0.12721294227837412,
           0.42208023968214725,
           0.31724771113707606,
           0.06525307699220603,
           0.2146315322131597,
           0.1724705487303265,
           0.0714107807077506,
           0.18222012953984548,
           0.09120139153478059,
           0.1234145562148654,
           0.34543660929295517,
           0.2543233235606714,
           0.1384655995510613
          ],
          "coloraxis": "coloraxis",
          "line": {
           "color": "white",
           "width": 1
          },
          "size": [
           10.85,
           14.042,
           6.6,
           15.8,
           11.31,
           19.451999999999998,
           18.1,
           15.081000000000001,
           21.092799999999997,
           19.7018,
           13.5,
           17.267000000000003,
           19.869999999999997,
           13.568449999999999,
           28.4616,
           20.851,
           10.2085,
           9.615,
           12.85,
           15.0,
           12.84,
           19.8,
           7.1850000000000005,
           18.9,
           15.630099999999999,
           15.1611,
           17.16923,
           22.599999999999998,
           21.6,
           13.4203,
           11.61419,
           12.949344,
           18.194
          ],
          "sizemode": "area",
          "sizeref": 0.071154,
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "",
         "orientation": "v",
         "showlegend": false,
         "type": "scatter",
         "x": [
          61,
          85,
          53,
          222,
          420,
          56,
          316,
          171,
          108,
          87,
          64,
          1877,
          345,
          112,
          947,
          288,
          74,
          84,
          369,
          131,
          399,
          254,
          60,
          376,
          282,
          375,
          202,
          239,
          325,
          829,
          219,
          165,
          79
         ],
         "xaxis": "x",
         "y": [
          0.19672131147540983,
          0.21176470588235294,
          0.18867924528301888,
          0.23873873873873874,
          0.16428571428571428,
          0.2857142857142857,
          0.4462025316455696,
          0.19298245614035087,
          0.5740740740740741,
          0.8390804597701149,
          0.09375,
          0.30207778369738947,
          0.26956521739130435,
          0.4375,
          0.5712777191129884,
          0.3888888888888889,
          0.40540540540540543,
          0.15476190476190477,
          0.22764227642276422,
          0.4122137404580153,
          0.047619047619047616,
          0.33070866141732286,
          0.25,
          0.2925531914893617,
          0.6170212765957447,
          0.39466666666666667,
          0.5247524752475248,
          0.41841004184100417,
          0.3630769230769231,
          0.33172496984318456,
          0.4246575342465753,
          0.5151515151515151,
          0.5316455696202531
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "coloraxis": {
         "colorbar": {
          "title": {
           "text": "quota della<br>prima app"
          }
         },
         "colorscale": [
          [
           0.0,
           "rgb(0,104,55)"
          ],
          [
           0.1,
           "rgb(26,152,80)"
          ],
          [
           0.2,
           "rgb(102,189,99)"
          ],
          [
           0.3,
           "rgb(166,217,106)"
          ],
          [
           0.4,
           "rgb(217,239,139)"
          ],
          [
           0.5,
           "rgb(255,255,191)"
          ],
          [
           0.6,
           "rgb(254,224,139)"
          ],
          [
           0.7,
           "rgb(253,174,97)"
          ],
          [
           0.8,
           "rgb(244,109,67)"
          ],
          [
           0.9,
           "rgb(215,48,39)"
          ],
          [
           1.0,
           "rgb(165,0,38)"
          ]
         ]
        },
        "height": 500,
        "legend": {
         "itemsizing": "constant",
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "rgb(36,36,36)"
            },
            "error_y": {
             "color": "rgb(36,36,36)"
            },
            "marker": {
             "line": {
              "color": "white",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "white",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "rgb(36,36,36)",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "rgb(36,36,36)"
            },
            "baxis": {
             "endlinecolor": "rgb(36,36,36)",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "rgb(36,36,36)"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 1,
             "tickcolor": "rgb(36,36,36)",
             "ticks": "outside"
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 1,
             "tickcolor": "rgb(36,36,36)",
             "ticks": "outside"
            },
            "colorscale": [
             [
              0.0,
              "#440154"
             ],
             [
              0.1111111111111111,
              "#482878"
             ],
             [
              0.2222222222222222,
              "#3e4989"
             ],
             [
              0.3333333333333333,
              "#31688e"
             ],
             [
              0.4444444444444444,
              "#26828e"
             ],
             [
              0.5555555555555556,
              "#1f9e89"
             ],
             [
              0.6666666666666666,
              "#35b779"
             ],
             [
              0.7777777777777778,
              "#6ece58"
             ],
             [
              0.8888888888888888,
              "#b5de2b"
             ],
             [
              1.0,
              "#fde725"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 1,
             "tickcolor": "rgb(36,36,36)",
             "ticks": "outside"
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 1,
             "tickcolor": "rgb(36,36,36)",
             "ticks": "outside"
            },
            "colorscale": [
             [
              0.0,
              "#440154"
             ],
             [
              0.1111111111111111,
              "#482878"
             ],
             [
              0.2222222222222222,
              "#3e4989"
             ],
             [
              0.3333333333333333,
              "#31688e"
             ],
             [
              0.4444444444444444,
              "#26828e"
             ],
             [
              0.5555555555555556,
              "#1f9e89"
             ],
             [
              0.6666666666666666,
              "#35b779"
             ],
             [
              0.7777777777777778,
              "#6ece58"
             ],
             [
              0.8888888888888888,
              "#b5de2b"
             ],
             [
              1.0,
              "#fde725"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 1,
             "tickcolor": "rgb(36,36,36)",
             "ticks": "outside"
            },
            "colorscale": [
             [
              0.0,
              "#440154"
             ],
             [
              0.1111111111111111,
              "#482878"
             ],
             [
              0.2222222222222222,
              "#3e4989"
             ],
             [
              0.3333333333333333,
              "#31688e"
             ],
             [
              0.4444444444444444,
              "#26828e"
             ],
             [
              0.5555555555555556,
              "#1f9e89"
             ],
             [
              0.6666666666666666,
              "#35b779"
             ],
             [
              0.7777777777777778,
              "#6ece58"
             ],
             [
              0.8888888888888888,
              "#b5de2b"
             ],
             [
              1.0,
              "#fde725"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "line": {
              "color": "white",
              "width": 0.6
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 1,
             "tickcolor": "rgb(36,36,36)",
             "ticks": "outside"
            },
            "colorscale": [
             [
              0.0,
              "#440154"
             ],
             [
              0.1111111111111111,
              "#482878"
             ],
             [
              0.2222222222222222,
              "#3e4989"
             ],
             [
              0.3333333333333333,
              "#31688e"
             ],
             [
              0.4444444444444444,
              "#26828e"
             ],
             [
              0.5555555555555556,
              "#1f9e89"
             ],
             [
              0.6666666666666666,
              "#35b779"
             ],
             [
              0.7777777777777778,
              "#6ece58"
             ],
             [
              0.8888888888888888,
              "#b5de2b"
             ],
             [
              1.0,
              "#fde725"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 1,
             "tickcolor": "rgb(36,36,36)",
             "ticks": "outside"
            },
            "colorscale": [
             [
              0.0,
              "#440154"
             ],
             [
              0.1111111111111111,
              "#482878"
             ],
             [
              0.2222222222222222,
              "#3e4989"
             ],
             [
              0.3333333333333333,
              "#31688e"
             ],
             [
              0.4444444444444444,
              "#26828e"
             ],
             [
              0.5555555555555556,
              "#1f9e89"
             ],
             [
              0.6666666666666666,
              "#35b779"
             ],
             [
              0.7777777777777778,
              "#6ece58"
             ],
             [
              0.8888888888888888,
              "#b5de2b"
             ],
             [
              1.0,
              "#fde725"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 1,
             "tickcolor": "rgb(36,36,36)",
             "ticks": "outside"
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 1,
              "tickcolor": "rgb(36,36,36)",
              "ticks": "outside"
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 1,
              "tickcolor": "rgb(36,36,36)",
              "ticks": "outside"
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 1,
              "tickcolor": "rgb(36,36,36)",
              "ticks": "outside"
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 1,
              "tickcolor": "rgb(36,36,36)",
              "ticks": "outside"
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 1,
              "tickcolor": "rgb(36,36,36)",
              "ticks": "outside"
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 1,
              "tickcolor": "rgb(36,36,36)",
              "ticks": "outside"
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 1,
              "tickcolor": "rgb(36,36,36)",
              "ticks": "outside"
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 1,
              "tickcolor": "rgb(36,36,36)",
              "ticks": "outside"
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 1,
              "tickcolor": "rgb(36,36,36)",
              "ticks": "outside"
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 1,
              "tickcolor": "rgb(36,36,36)",
              "ticks": "outside"
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 1,
             "tickcolor": "rgb(36,36,36)",
             "ticks": "outside"
            },
            "colorscale": [
             [
              0.0,
              "#440154"
             ],
             [
              0.1111111111111111,
              "#482878"
             ],
             [
              0.2222222222222222,
              "#3e4989"
             ],
             [
              0.3333333333333333,
              "#31688e"
             ],
             [
              0.4444444444444444,
              "#26828e"
             ],
             [
              0.5555555555555556,
              "#1f9e89"
             ],
             [
              0.6666666666666666,
              "#35b779"
             ],
             [
              0.7777777777777778,
              "#6ece58"
             ],
             [
              0.8888888888888888,
              "#b5de2b"
             ],
             [
              1.0,
              "#fde725"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "rgb(237,237,237)"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "rgb(217,217,217)"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 1,
            "tickcolor": "rgb(36,36,36)",
            "ticks": "outside"
           }
          },
          "colorscale": {
           "diverging": [
            [
             0.0,
             "rgb(103,0,31)"
            ],
            [
             0.1,
             "rgb(178,24,43)"
            ],
            [
             0.2,
             "rgb(214,96,77)"
            ],
            [
             0.3,
             "rgb(244,165,130)"
            ],
            [
             0.4,
             "rgb(253,219,199)"
            ],
            [
             0.5,
             "rgb(247,247,247)"
            ],
            [
             0.6,
             "rgb(209,229,240)"
            ],
            [
             0.7,
             "rgb(146,197,222)"
            ],
            [
             0.8,
             "rgb(67,147,195)"
            ],
            [
             0.9,
             "rgb(33,102,172)"
            ],
            [
             1.0,
             "rgb(5,48,97)"
            ]
           ],
           "sequential": [
            [
             0.0,
             "#440154"
            ],
            [
             0.1111111111111111,
             "#482878"
            ],
            [
             0.2222222222222222,
             "#3e4989"
            ],
            [
             0.3333333333333333,
             "#31688e"
            ],
            [
             0.4444444444444444,
             "#26828e"
            ],
            [
             0.5555555555555556,
             "#1f9e89"
            ],
            [
             0.6666666666666666,
             "#35b779"
            ],
            [
             0.7777777777777778,
             "#6ece58"
            ],
            [
             0.8888888888888888,
             "#b5de2b"
            ],
            [
             1.0,
             "#fde725"
            ]
           ],
           "sequentialminus": [
            [
             0.0,
             "#440154"
            ],
            [
             0.1111111111111111,
             "#482878"
            ],
            [
             0.2222222222222222,
             "#3e4989"
            ],
            [
             0.3333333333333333,
             "#31688e"
            ],
            [
             0.4444444444444444,
             "#26828e"
            ],
            [
             0.5555555555555556,
             "#1f9e89"
            ],
            [
             0.6666666666666666,
             "#35b779"
            ],
            [
             0.7777777777777778,
             "#6ece58"
            ],
            [
             0.8888888888888888,
             "#b5de2b"
            ],
            [
             1.0,
             "#fde725"
            ]
           ]
          },
          "colorway": [
           "#1F77B4",
           "#FF7F0E",
           "#2CA02C",
           "#D62728",
           "#9467BD",
           "#8C564B",
           "#E377C2",
           "#7F7F7F",
           "#BCBD22",
           "#17BECF"
          ],
          "font": {
           "color": "rgb(36,36,36)"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "white",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "white",
          "polar": {
           "angularaxis": {
            "gridcolor": "rgb(232,232,232)",
            "linecolor": "rgb(36,36,36)",
            "showgrid": false,
            "showline": true,
            "ticks": "outside"
           },
           "bgcolor": "white",
           "radialaxis": {
            "gridcolor": "rgb(232,232,232)",
            "linecolor": "rgb(36,36,36)",
            "showgrid": false,
            "showline": true,
            "ticks": "outside"
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "white",
            "gridcolor": "rgb(232,232,232)",
            "gridwidth": 2,
            "linecolor": "rgb(36,36,36)",
            "showbackground": true,
            "showgrid": false,
            "showline": true,
            "ticks": "outside",
            "zeroline": false,
            "zerolinecolor": "rgb(36,36,36)"
           },
           "yaxis": {
            "backgroundcolor": "white",
            "gridcolor": "rgb(232,232,232)",
            "gridwidth": 2,
            "linecolor": "rgb(36,36,36)",
            "showbackground": true,
            "showgrid": false,
            "showline": true,
            "ticks": "outside",
            "zeroline": false,
            "zerolinecolor": "rgb(36,36,36)"
           },
           "zaxis": {
            "backgroundcolor": "white",
            "gridcolor": "rgb(232,232,232)",
            "gridwidth": 2,
            "linecolor": "rgb(36,36,36)",
            "showbackground": true,
            "showgrid": false,
            "showline": true,
            "ticks": "outside",
            "zeroline": false,
            "zerolinecolor": "rgb(36,36,36)"
           }
          },
          "shapedefaults": {
           "fillcolor": "black",
           "line": {
            "width": 0
           },
           "opacity": 0.3
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "rgb(232,232,232)",
            "linecolor": "rgb(36,36,36)",
            "showgrid": false,
            "showline": true,
            "ticks": "outside"
           },
           "baxis": {
            "gridcolor": "rgb(232,232,232)",
            "linecolor": "rgb(36,36,36)",
            "showgrid": false,
            "showline": true,
            "ticks": "outside"
           },
           "bgcolor": "white",
           "caxis": {
            "gridcolor": "rgb(232,232,232)",
            "linecolor": "rgb(36,36,36)",
            "showgrid": false,
            "showline": true,
            "ticks": "outside"
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "rgb(232,232,232)",
           "linecolor": "rgb(36,36,36)",
           "showgrid": false,
           "showline": true,
           "ticks": "outside",
           "title": {
            "standoff": 15
           },
           "zeroline": false,
           "zerolinecolor": "rgb(36,36,36)"
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "rgb(232,232,232)",
           "linecolor": "rgb(36,36,36)",
           "showgrid": false,
           "showline": true,
           "ticks": "outside",
           "title": {
            "standoff": 15
           },
           "zeroline": false,
           "zerolinecolor": "rgb(36,36,36)"
          }
         }
        },
        "title": {
         "text": "Le 33 categorie — passa il mouse su un punto"
        },
        "width": 860,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0.0,
          1.0
         ],
         "title": {
          "text": "numero di app (scala log)  →  più concorrenza"
         },
         "type": "log"
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0.0,
          1.0
         ],
         "title": {
          "text": "% oltre il milione  →  più domanda"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Tutte le categorie in un solo grafico interattivo con PLOTLY ---\n",
    "\n",
    "# il grafico si crea solo se la libreria plotly è installata\n",
    "if PLOTLY:\n",
    "    # 1) preparo i dati: una riga per categoria, con il nome leggibile\n",
    "    dati = per_categoria.reset_index()\n",
    "    dati[\"nome_categoria\"] = dati[\"categoria\"].map(nome_leggibile)\n",
    "\n",
    "    # 2) grafico a bolle, dove ogni bolla è una categoria:\n",
    "    #    - in orizzontale: quante app ci sono (più a destra = più concorrenza)\n",
    "    #    - in verticale: quante app superano il milione (più in alto = più domanda)\n",
    "    #    - grandezza della bolla: quanto gli utenti scrivono recensioni\n",
    "    #    - colore: quanto è forte il leader (rosso = una sola app domina)\n",
    "    figura = px.scatter(\n",
    "        dati, x=\"numero_app\", y=\"quota_oltre_1M\",\n",
    "        size=\"recensioni_per_1000\", color=\"quota_prima_app\",\n",
    "        hover_name=\"nome_categoria\", log_x=True, color_continuous_scale=\"RdYlGn_r\",\n",
    "        hover_data={\"numero_app\": \":,\", \"quota_oltre_1M\": \":.1%\",\n",
    "                    \"quota_prima_app\": \":.1%\", \"categoria\": False},\n",
    "        labels={\"numero_app\": \"numero di app (scala log)  →  più concorrenza\",\n",
    "                \"quota_oltre_1M\": \"% oltre il milione  →  più domanda\",\n",
    "                \"quota_prima_app\": \"quota della<br>prima app\"},\n",
    "        title=\"Le 33 categorie — passa il mouse su un punto\")\n",
    "\n",
    "    # 3) ritocchi grafici e visualizzazione\n",
    "    figura.update_traces(marker=dict(line=dict(width=1, color=\"white\")))\n",
    "    figura.update_layout(width=860, height=500, template=\"simple_white\")\n",
    "    figura.show()\n",
    "else:\n",
    "    print(\"plotly non installato: salto il grafico interattivo.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd914b1c",
   "metadata": {
    "id": "fd914b1c"
   },
   "source": [
    "**Risultato** In tutto lo store il **35,2%** delle app supera il milione di installazioni. Alcune categorie stanno molto sopra questa media: **ENTERTAINMENT** (83,9%, con sole 87 app), **EDUCATION** (57,4%, con 108 app), **SHOPPING** (52,5%) e **FOOD & DRINK** (43,8%).\n",
    "\n",
    "Due categorie così piccole con una probabilità di successo così alta sono però un risultato **sospetto**: se bastasse pubblicare lì per raddoppiare le possibilità, lo farebbero tutti. Lo segno e lo verifico nella sezione 8, e la verifica cambierà la scelta finale.\n",
    "\n",
    "Sul coinvolgimento, il valore tipico dello store è di **17 recensioni ogni 1.000 installazioni**. In testa ci sono i giochi (28,5), poi **EDUCATION** (21,1) e **HEALTH & FITNESS** (20,9): categorie in cui l'utente ha un obiettivo personale e torna a usare l'app. Un'app di sostenibilità somiglia più a queste che a un gioco.\n",
    "\n",
    "**Come leggere il grafico interattivo:** le bolle **verdi in alto a sinistra** sono le categorie candidate (molta domanda, pochi concorrenti, nessun leader dominante). Le bolle **rosse** vanno evitate ovunque si trovino, perché lì una sola app si prende quasi tutto."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d80a99",
   "metadata": {
    "id": "a1d80a99"
   },
   "source": [
    "---\n",
    "\n",
    "## 6. Cinque ipotesi, verificate\n",
    "\n",
    "Da quello che ho visto finora ricavo cinque affermazioni da mettere alla prova:\n",
    "\n",
    "| | Ipotesi |\n",
    "|---|---|\n",
    "| **IPOTESI 1** | Le app a pagamento hanno un voto più alto |\n",
    "| **IPOTESI 2** | Le app gratuite hanno molte più installazioni |\n",
    "| **IPOTESI 3** | Tra le app a pagamento, più costano e meno vengono installate |\n",
    "| **IPOTESI 4** | Le app aggiornate da poco hanno un voto più alto |\n",
    "| **IPOTESI 5** | Il voto cambia a seconda della categoria |\n",
    "\n",
    "**Come le verifico.** Divido le app in gruppi e confronto i valori tipici (mediane) e le percentuali, aiutandomi con un grafico. È un confronto **descrittivo**: guardo i numeri e li metto a confronto, senza test statistici più avanzati, che sarebbero il passo successivo. I gruppi sono grandi e le differenze di solito sono nette; dove una differenza è piccola, lo dico chiaramente invece di ingigantirla."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6f37342a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:14.725571Z",
     "iopub.status.busy": "2026-10-07T04:52:14.725352Z",
     "iopub.status.idle": "2026-10-07T04:52:15.049713Z",
     "shell.execute_reply": "2026-10-07T04:52:15.049022Z"
    },
    "id": "6f37342a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numero_app</th>\n",
       "      <th>voto_medio</th>\n",
       "      <th>voto_mediano</th>\n",
       "      <th>installazioni_mediane</th>\n",
       "      <th>oltre_1_milione</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tipo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A pagamento</th>\n",
       "      <td>604</td>\n",
       "      <td>4.260</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gratuita</th>\n",
       "      <td>7606</td>\n",
       "      <td>4.167</td>\n",
       "      <td>4.3</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>0.379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             numero_app  voto_medio  voto_mediano  installazioni_mediane  oltre_1_milione\n",
       "tipo                                                                                     \n",
       "A pagamento         604       4.260           4.4                 1000.0            0.029\n",
       "Gratuita           7606       4.167           4.3               100000.0            0.379"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1485x352 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- IPOTESI 1 e 2: cosa si guadagna e cosa si perde facendo pagare un'app ---\n",
    "\n",
    "# 1) tengo solo le app che hanno un voto\n",
    "con_voto = app.dropna(subset=[\"voto\"])\n",
    "\n",
    "# 2) IPOTESI 1 — per app gratuite e a pagamento: quante sono e che voto hanno\n",
    "h1 = con_voto.groupby(\"tipo\")[\"voto\"].agg(numero_app=\"count\", voto_medio=\"mean\",\n",
    "                                          voto_mediano=\"median\").round(3)\n",
    "\n",
    "# 3) IPOTESI 2 — per gli stessi due gruppi: installazioni tipiche\n",
    "#    e percentuale di app che supera il milione\n",
    "h2 = app.groupby(\"tipo\").agg(\n",
    "    installazioni_mediane=(\"installazioni\", \"median\"),\n",
    "    oltre_1_milione=(\"installazioni\", lambda s: (s >= 1_000_000).mean()),\n",
    ").round(3)\n",
    "\n",
    "# 4) unisco le due tabelle e le mostro insieme\n",
    "display(h1.join(h2))\n",
    "\n",
    "# 5) preparo tre grafici affiancati\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(13.5, 3.2))\n",
    "\n",
    "# 6) primo grafico (H1) — come si distribuiscono i voti nei due gruppi\n",
    "sns.boxplot(data=con_voto, x=\"tipo\", y=\"voto\", ax=ax1, width=0.45,\n",
    "            palette={\"Gratuita\": BLU, \"A pagamento\": ARANCIONE})\n",
    "ax1.set_title(\"IPOTESI 1 — il voto\")\n",
    "ax1.set_xlabel(\"\")\n",
    "\n",
    "# 7) secondo grafico (IPOTESI 2) — installazioni tipiche, in scala logaritmica\n",
    "ax2.bar(h2.index, h2[\"installazioni_mediane\"], color=[ARANCIONE, BLU], width=0.5)\n",
    "ax2.set_yscale(\"log\")\n",
    "ax2.set_ylabel(\"installazioni mediane (log)\")\n",
    "ax2.set_title(\"IPOTESI 2 — il pubblico\")\n",
    "ax2.set_ylim(top=h2[\"installazioni_mediane\"].max() * 6)\n",
    "for i, v in enumerate(h2[\"installazioni_mediane\"]):\n",
    "    ax2.text(i, v * 1.4, f\"{v:,.0f}\", ha=\"center\", fontweight=\"bold\")\n",
    "\n",
    "# 8) terzo grafico (IPOTESI 2) — percentuale di app che supera il milione\n",
    "ax3.bar(h2.index, h2[\"oltre_1_milione\"] * 100, color=[ARANCIONE, BLU], width=0.5)\n",
    "ax3.set_ylabel(\"% oltre il milione\")\n",
    "ax3.set_title(\"IPOTESI 2 — probabilità di sfondare\")\n",
    "ax3.set_ylim(0, 48)\n",
    "for i, v in enumerate(h2[\"oltre_1_milione\"]):\n",
    "    ax3.text(i, v * 100 + 1.2, f\"{v:.1%}\", ha=\"center\", fontweight=\"bold\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7333428e",
   "metadata": {},
   "source": [
    "**Risultato**\n",
    "\n",
    "**IPOTESI 1 — CONFERMATA, ma la differenza è minima.** Le app a pagamento hanno un voto tipico di **4,4** contro **4,3** delle gratuite: appena un decimo di punto, e nel grafico le due \"scatole\" si sovrappongono quasi del tutto. Una spiegazione possibile è che chi paga abbia già deciso prima che l'app gli serve, e quindi ne resti più soddisfatto; con questi dati non la posso verificare.\n",
    "\n",
    "**IPOTESI 2 — CONFERMATA, e la differenza è enorme.** Un'app gratuita tipica ha **100.000** installazioni, una a pagamento **1.000**: cento volte di meno. La probabilità di superare il milione di installazioni scende dal **37,9%** al **2,9%**.\n",
    "\n",
    "**Cosa posso concluderne, e cosa no.** ⚠ *legame, non causa.* Il confronto è fra **due gruppi di\n",
    "app diverse**, non fra la stessa app gratuita e a pagamento: le app a pagamento sono più spesso\n",
    "app di nicchia (mediche, temi grafici, strumenti professionali), con un pubblico piccolo in\n",
    "partenza. Quindi non posso dire che «far pagare fa perdere il 99% del pubblico». Posso dire una\n",
    "cosa più modesta e comunque utile: **nel 2018 quasi nessuna app a pagamento arrivava a un\n",
    "pubblico grande** (il 2,9%), e un'app nuova che vuole un pubblico grande ha davanti a sé un\n",
    "mercato che è al 92% gratuito."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3f78f25d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:15.051289Z",
     "iopub.status.busy": "2026-10-07T04:52:15.051077Z",
     "iopub.status.idle": "2026-10-07T04:52:15.171673Z",
     "shell.execute_reply": "2026-10-07T04:52:15.170826Z"
    },
    "id": "3f78f25d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numero_app</th>\n",
       "      <th>installazioni_mediane</th>\n",
       "      <th>oltre_10_mila</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fascia_prezzo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fino a 1,49 $</th>\n",
       "      <td>198</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1,50–2,99 $</th>\n",
       "      <td>240</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3,00–5,99 $</th>\n",
       "      <td>190</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6,00–14,99 $</th>\n",
       "      <td>75</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15 $ e oltre</th>\n",
       "      <td>33</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               numero_app  installazioni_mediane  oltre_10_mila\n",
       "fascia_prezzo                                                  \n",
       "fino a 1,49 $         198                  500.0          0.197\n",
       "1,50–2,99 $           240                 5000.0          0.471\n",
       "3,00–5,99 $           190                 5000.0          0.405\n",
       "6,00–14,99 $           75                 1000.0          0.387\n",
       "15 $ e oltre           33                 1000.0          0.212"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA14AAAE4CAYAAABL6iKcAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAQ6wAAEOsBUJTofAAAco1JREFUeJzt3Qm8zNX/x/FDslYqJUohyVKRtNCCUoREKkULJW2KhEqkhTbZlcpSoqKsIYRoR7JkFyJFZcuestz/43X+nfl979yZO9+59869d+68n4/HfXBn5s6dO/Od75zP+XzO5+RKSkpKMiIiIiIiIhIzuWN31yIiIiIiIqLAS0REREREJBMo4yUiIiIiIhJjCrxERERERERiTIGXiIiIiIhIjCnwEhERERERiTEFXiIiIiIiIjGmwEtERERERCTGFHiJiIiIiIjEWJ5Y/wIRyRwHDhwwkyZNMklJSaZs2bLm4osv1lOfYHQMiIiIZF8KvDLJtm3bzKxZs+z/q1evbkqVKmX27NljPv3002S3y507tzn22GNN4cKFzUUXXWROOumksPe5ZcsWs2LFCrN7925z/PHHm4oVK5ozzzwz2W3++ecfM378eF+PsUGDBvbf4McUyqWXXmrKlCmT7LKNGzeaVatWmX379tnHfcEFF5jTTjst4vMQaSA5b94889dff5kSJUrYYOKYY47x9fckmoIFC5pff/3VDB061JQsWdJ89tlnJleuXCYn8R7P7hjk+J86daq9rGbNmub00083iSoRjoH0+v33380XX3xh/3/99deneo6N5rYiIiKRKPDKJL/88ot57rnn7P979uxpAw6CEHdZKPnz5zddunQxTZs2TXb52rVr7c/98MMPKX6GYO2ZZ56xQRj279+f6u/wYiALP7fv0aNHIPD6888/zRNPPGEDpOAgkmDuhRdesAPCcM9DOF9++aW93127dgUuO/fcc81bb71lzjjjDF9/U6Lp0KGDWbNmjfn666/t60Fwm5N4j2d3DHL8ucs4NhI58EqEYyC93PkTF154YarBVDS3FRERiURrvLKxgwcP2iBq+fLlgctWr15tbrvttpBBFxYtWmSaN29uFixYkCmPkbK2hx56KEXQhaNHj5rJkyfb4DFa27dvN4899pgNupi5v+GGG0yBAgXMTz/9ZAeWEhrBbp8+fcwdd9xhdu7cmRBPE9lh3hN8JXrQlajHgIiISDxQxiubqFKlis3mEMhQqkcg4wZNEydONOeff745cuSI6dSpk531dwMsMlxkf8ieEXQRrP3999+mffv2ZubMmTZrxoDU2bp1q5kzZ479/+WXX56sNJEBLGVbwY8plLPPPtv+y++k3BHMBpM1o+yRLMTcuXPN4cOHbRnYk08+aYoVK+b7+ZgyZYotM+QxjRkzxv77wQcf2OzZ4sWL7f0HlzFmBkoep0+fbv9/00032cf4/fff2+ed1+Kss84K+XNk+nie/v33X/ucuoxkeu83GK89r8l5551nihQpYp//PHlSf5t7yz+vvfZac+qpp9r/c/xRqoZrrrnGPt9peZzcz9KlS20wXahQIXssB5fEhsN7geOIY75q1ar254NxjFeoUMH+n2PPi8wPX0wCcPzxGPPmzZviPrh+yZIl9nXid3B/oR4jz+/8+fPNjh077PuO0tdQz6/f3+vnuSHzS1kxrwuvTyIeA5wP+Zfnm5LjcePG2ffSZZddFjgX+X2vBeNcwv3z+lSrVi3VrJaf26bnbxURkZxNgVc2wfqBli1bBr4n08OgAgy6wOCGjA+KFi1qhgwZYsqXLx/4GW7Xpk0bs3LlSjuQIni59dZbbbDifPfdd4HA65Zbbgms63K8gVfwYwqF3+P06tXLXHnllYHvWRsxe/Zs+38GZ9GoU6eOHdQweCLoAgNIsGYl0kAyVv74449A6REDK9bSMOB0gfDjjz9uWrdunez5fPrppwODWm9QS1bCZWiivd9QGBB27Ngx2WtCKefAgQPDBtDB5Z805XCD7s2bNwcuJ+vIoDvax/nxxx+bl156yQYDXnfeeafN5qbmm2++sdlNV2rK2j6yOMFClRpy3Dz88MO23M6LIIjno1KlSoHLNmzYYNq2bRt4b7m/hfcO98v//T6/0fxev8/Nhx9+aN9LBDWRAq+cdgyQuee1cZNQvO95P/Xt29fs3bvXvPzyy4HAy+97zYvHNnbsWPu6gZJoHmu9evXSdNv0/K0iIpLzqdQwmyCLM2rUKDvIGjx4sHnwwQcD15188smBoMlhgOENusDAgnUvTqjyv7Q8puAvMnAOs8lu8T4DzkcffdRmqBic1apVywZ9fAXPSkfC39KoUSM7+P3555/Na6+9Zvr162evY80bM/lZ7fXXX7d/V8OGDW3WhewGA1wXKPA9z4kbCF5xxRWmdu3advDIc9uiRQs7ix/t/YbCAJmSTwbcPHf8LBkZGp7cf//9gSxpZv7969evtwN0BqGXXHKJufnmm20WBu+//7758ccfw943f0e7du3sfdFshkCcNTYjRozw9diGDx9ugx8yE9ddd51p0qSJKV68uD0uH3nkEXPo0CF7Ox7bfffdZ4Mu/gYmG8hQ8Ld89NFHNqiI5vn1+3vT89wkyjFAZo3njKDLHQM07OnevXuKvyWt7zXOtwSkBE8EUkwQsa6UYDza28biNRURkZxFGa9sgtIdV74TrH79+oGZfYcP9lAIhFgLxYc/Hbli8ZhOOeUU07hxY/t/yoruvvtu895779mOczNmzLBfoFyLwIlSx/RkqD7//PPAAJhyM8oWs4MaNWrYIJnA05VB8hzQ2ZGGBgwC3Vq8bt26BbI1ZBwJrDdt2mRGjhxpB8vR3G8oDOwYCBKkf/LJJ+aEE06wA00GiRwHrLW7/fbbM/Xv5/eSVSU7ywCawTGTB61atbI/z4C1cuXKIe+bv8ENlMmkEhCha9euNrCPxJW/EqwRsHP8MYDv37+/LeclSCETRFb4t99+s7d98803bfktj5NBM5ljyg+jeX79/t5onhueZzJNZJxSk9OOAbZGcAEcEy8us9S5c+cUnVrT+l4jmBswYIB9/AsXLrTrY8lokbkKPs9Eum16/lYREUkMCryyMT7gGTQwGIQrcQEzwOF+xrVbZ01YZmAgxGCS4IiBpcPgi4HYtGnTzDvvvBNyjYsfzC7feOONNoPHgIfBI4GeywR68TvdYDk9CGAjDZIo03TZPsrAHFdW6cosTzzxRNOsWbPA9VdffbXNVtIohfU7wYPBSPcbimumQibQux0A37Ouj3VJGT3ojvQ4KTtlxp8ggIwLmQe2UHAYoIfjGsrw+F3Q5bKdfgIvSvqYNKA8l7VJrEXiGHr++eeT3Y7jyf0e9z6jXI4SR0pdXcbC7/Pr9/dG89yEKq9MhGOAdVLgdfCW8zHRExx4pfW9xsSQe/xM6pxzzjlm3bp1yRoa+b1tev5WERFJDAq8sgnXyIKgiZIdZk0pl/Guy/CW1/Fhz/XBKCtymYL0luOFa64R3MCAwQizvwx4GCxRasWaFB4js/wMCCdMmJCsyUc0GHjyRcaPQThlYQR5lPkE43f37t3bpBdrVCIFXgzyHG9Qyd8Mt86G9T1unZBDCRiDQe9aHL/3G64sK7j9tZc3IPaLGfvURHqclIhyXLjHRiaWhgSuBDb4OfFi/Y7Lrnr5bahCaRnBBqWxHDes/eKLATNrhngccBkV798C71rFaJ5fv783Pc9NohwDbr2pW2+W2jGQ1vda8DmS8y7BlDv+orltLF5TERHJWRR4ZRN+GllQvkQ5EVhUTumMt6sWC9y9M+sETrF+TJT4MKA57rjj7DoPghW+KLWhhIt1LgzeCMiiCbwo2yGIJLNHJzM34CKzRgkYA6lQKG9Ma4AX/FxHEmkg5QaldMAL5poFuMYh0dxvKGT/eL7p+BYqIE+to6R3g123BslPQ5RIj/ONN96wg1COUcoFaRbD/bvjMrWfd90Lvc1ekNo6Ny9K/MjE0myGUtWvvvrKln2R4eKYpjyOMjm3v1zw/VKaxjHtsqp+n1+/vzc9z02iHAPuuQ9+bUIdA2l9rwW323cZquDJJT+3jcVrKiIiOYsCrzhCRzMGIwwAmNWuW7eu3d+KVsXMslPi5Dog5suXL7AOK60oleF+QiEAYg0H5TuscQAdv7iMgQiDEq5zM+asN4kG62No4sHvoWyKAS0zy7/++mvITIhz1VVX2a/sgNIr1hARmJKJc4+LWXJXRpVRm9vSiY77ZADNOiiXfWCdDI0IuD4cF3zArXdCuL3i/CJwBuV3LoPkLQP1DvZDlXrSxpymFPxdrhuga20eCeWIdMGkzIxyP74YBFP+x3uF8lcCIJo1cHwxYGd9listZBDNNgisr2SSw+/z6/f3RvPc+G0nn9OOAUr5wPNGYwqXgWatWka913hu3G3JCLIFAIIbF/m5bXr+VhERSQwKvOIIpSt09CKbRCkP2QAWtIfCpsXBJToZ2fCD7BYDGUoAWW/BYJOSmlCdFCmfpENhNAgaWSvhOrIxg89AmOCLAQxrfbI7nqO3337blpxRZsZzRSBLdzTW3zFLT2laRrjrrrvs3kYEpmRW6OhGIOHW+rAmLhyaNpBZJECgTTcZBTKOo0ePTtdjImim3JS28D179rSBACV4jmtBHgrHC80uuA3rHFmbRDbB72PiOGQgTkDB805ZGFlSBvFwrcX5PQRZHFf8HsplCcIYZDNpwAQCmQq/z6/f3xvNc+O3nXxOOwZocEKHRNein+eTxxRqjV9a32u8zqy9opMl98vfz/mK3x3tbdPzt4qISGJQ4BVnGHgNGjTIruHwdjl0GGBQ6pTebJdfzPbScYw9akK1a6ZkjMcaagY5NQR1jz32mM18ffvtt/YLDA75Xa78MDsjy8dgkIEgpWvsu+YQFDPgZ2CeESixpLMaJU6UtbmmEQSp3rVFoRAk0HmNtUgEHdwHGLhTLpdWBDJ0lSPbMmzYMHsZGSX+ZtbpsSYmHFqwszUCxzKPiefKlYDSHS64BDEYne14zsl2eJ93EMDcc8899v9kkOk4yHNEcMT/Hdqkuz3w/D6/fn9vep6bRDkGWFP17LPP2ueU/cLca0MpMa3+veV7aX2v1axZ02ZRXSaV54rnMNT5KtJtY/GaiohIzqLAK5Pw4evWHpUuXTqw5sBdVq5cOd/3RdkSpSzMrDLDyiCU9SjcB2VM/D8c1km530kr+GDex5Qab+MJyrF4PAw6KIFkXQiPgYErj8fbfTDU8xAOAxn2AmMdGdkOHjvdzSitzCqs33CP37shq/d5Y42NdzBMxoGMxbJly+zsPQ1LWD/n1jGl5X5Duffee+3gkIEyQQTlmHR183NstW/f3gYGZGyYxb/00ktt8EuG1dvQIJrHSWkbfzulYQycKR+kC97MmTPt7wluaBEqi8HPcB8E9e7nCcbJULm94by/2z0mLqMElrIznnfKcymBJXDjmPSutyGbymMio0qGlWOXwTSTHN5tEPw8v35/bzTPjd928n4fYzwdA7RnJ3gh8857h/MB5w0XeNGIKNr3Go/ZPU6COjoiLlq0yGaoaBlPRistt03v3yoiIjlfrqTUWqWJiIhkATYkpi2/m+ghkAHrvVypMQGYn0Y4IiIi2YEyXiIiku2QsXNt8cmg0aqdtaQ08QEZJBeMiYiIxANlvEREJNuhuUm7du3MjBkzUlxHqR/79VHuJyIiEi8UeImISLZEJfzcuXPtuirWebJGjrWp7A+Y2r5kIiIi2ZECLxERERERkRj7X2svERERERERiQkFXiIiIiIiIjGmwEtERERERCTG1E4+EyxcuND+6920VURERERE4rsDL6pWrerr9ooEJG4OahEdWxIPdM4SHVsSb3TeyhzKeGUCl+mqUqVKZvy6HGf16tWmfPnyWf0wJAfSsSU6riSe6JwlOrayl8WLF0d1e2W8REREREREYkyBl4iIiIiIiAIvERERERGR+KaMl4iIiIiISIwp8BIREREREYkxBV4iIiIiIiIxpsBLREREREQkxhR4iYiIiIiIxJgCLxERERERkRhT4CUiIiIiIhJjCrxERERERERiLE+sf4GISGZ77bXXzL///mu6dOlivx8/frx58803U9yO23z66afmuOOOM9OmTTPvvPOOOfnkk83TTz9tSpYsaW9z5MgR06pVK/Pqq6+a0047LdP/FhEREckZFHiJSI7yww8/mGHDhpl69eoFLrvuuutM1apVA98TTLVp08aULVvWBl07d+40Xbt2NQMGDDDLly83nTt3Nh9++KG97ZgxY8yZZ56poEtERETSRYGXiOQY//zzj81ynXXWWckuP/744+2X8/rrr5ujR4+ahx56yH6/bt06c/rpp5srrrjClCtXLpAdO3DggA3iPvjgg0z+S0RERCSnUeAlIjlGv379bBarePHiZvv27SFvs3nzZjNkyBAzcOBAc+yxx9rLKCHcunWr2b9/vw3CXEkhQdcNN9xgihYtmql/h4iIiOQ8CrxEJEdYunSpmThxopk0aZIZPHhw2NsRdJHVqlGjhlm9erW9jPVcDRo0MLVq1TK5c+c2L774otm2bZv55JNP7H2KiIiIpJcCLxGJe66RxpNPPmlOPfXUsLfbu3evDaReeeWVFNd169bNtG3b1hQsWNDkzZvXfn/PPfeYQ4cOmccff9xs2LDB1K9f37Ru3TrGf42IiIjkRGonLyJx76233rLlgY0bN071drNnzzbHHHOMueaaa0Jef+KJJ9qga/369WbBggWmadOmdj1Yrly5zPPPP28++ugjM3fu3Bj9FSIiIpKTKeMlInFv1KhRtkSQ7oX466+/bOdCvp8+fboNtvD111+byy+/3AZXkdrRt2vXzq4BW7Vqlbn33ntNpUqVTLVq1cyKFStM9erVM+XvEhERkZxDgZeIxL2PP/7Ydin0ZsBoEc9+XC7owrJly+xarkjt6PnZ66+/3n5frFgxW2bI/f/888/msssui+FfIiIiIjmVAi8RiXvss+XF3lwHDx4MbILsbNmyxZxzzjmp3lfPnj3NE088Efj+gQceMPfdd5/tcEhTjrp162bwoxcREZFEoMBLRHIc9ufyZsCcKVOm2AxWOJQn9u7dO1kgR7A1Z84cs3v3blOkSJGYPWYRERHJ2RR4iUiOc/LJJ4e8PDgDFoyyxODsGfLkyaOgS0RERNJFXQ1FRERERERiLKEyXmyqumfPnhSX33HHHaZ48eKBUiNaTq9Zs8a2lqYrGm2qRURERERE0iphMl4HDhwwffv2NVu3bjUnnHBCsi/aULvbEISxuP7vv/+2AVi9evXMvHnzsvrhi4iIiIhIHEuYjBd78bDYvnXr1qZs2bIhb/PGG2+YX3/91S7AP+mkk+xlnTt3Nk899ZSZMWNGxL1/RCTtNm/ebF588UUzaNAg+/1vv/1mBgwYYJYvX267FNavX9/cddddydrDOz169LDZ6meffTZwGdntXr16me+//95mr+lMeO2112bJS7Rv3z7zyCOP2Db3+fPnz5LHICIiIlkrYTJebHpK4FS6dOmwtxk7dqzd48cFXWDj1N9//918++23mfRIRRLTM888Y2677Tb7fzLO99xzjw1SCL7atGljhg4dat58880UPzd37lzz/vvvm127diW7vH379uaPP/4w/fr1M7fffrvp1KmTWbhwockKBI5suty/f/8s+f0iIiKS9RIm8Fq5cqXdv4fNUVnrxczzkiVLAtdv2rTJDtwqVqyY7OfKlCljjj32WPPjjz9mwaMWSQyU8zLBUbNmzcD3ZKzIYPG+5XJaxE+ePDnZzxGgEbCVKlUq2eWrV6823333nXnppZdM+fLlTePGjc2tt95qhg8fHvYxEMCRlWLyhc2TyXSzkTLYOJn9u2bOnGluuukm07BhQ/Puu+8GfjbS9aCMecyYMWbbtm0Z8pyJiIhIfEmojBfBFwOvf/75x5YeMgverVs3e/327dtDtqFm/VfhwoU1WBKJoREjRthSQueyyy4zn376abKyQjZEDi7T69Onj7ngggvMVVddlexyJkrOOussc8oppwQuq1Klilm8eHHI379hwwZz//3321JEJmUI5gjcXNnj4cOH7eQMv69Lly42e0YG7p133vF1vct6XXzxxWb06NHpfr5EREQk/iTMGi9mvNmfp06dOoHLPv74YzvAYkBWtGhRexnZrWBcRrCWHqwvYxZeokdWQ89dzkXQQikvQU/w6+wmRPbv32+DGJrduNvw7yeffGIGDhxoM0lkyNx1dCUlSPPe3969e82OHTuSXeaOLd7fQ4YMsWvB+F1slHzppZfa6/j65Zdf7Hu4ZcuWNoDiiwwawdXll18e8XqH7N1nn31mu6VKzqVzlujYknij81ba8NnvmvT5kTCBV6tWrVJcxsCod+/eZvr06bbpBg4dOpTidv/++68pWLBgun4/LwolTxI9Br567nKu9evX22xWrVq1Qm58zIcB2ajzzjvPNrshC8Z78vHHH7cZa7Jjs2bNss013HHC/RQqVCjZcbN7926TK1euZJd5jy3u88svvzRr1661j4lgsHLlyvZ63r/8bJMmTQKTM0lJSXbNGdtNRLrerRslkCS7p+M5Z9M5S3RsSbzReSttwlXSmEQPvEJhoMTMNm3k3T5ebk2Hw2COtV+nnnpqFj1KkZyNgAgESqGue/DBB+37jw6FrvTw9ddft6WEN9xwQ8j7ZJsIMlxefH/88ceHvP2ff/5pmjVrZvLly2cDuWrVqtlmPN7zQZ48eZJlxN1jIcDycz3IhJFdC1U2KSIiIjlbQgReDKpGjhxpF7+zHsRhAEQLa0qKTj/9dDu4Y+0XZYnOTz/9ZIMvZr5FJOMRJIESPwIfhz33yFQTCD399NPJUvmUCRPQ1KhRIxBUke7n+zlz5tiMEuV/vMfdffJeLleuXMjHQKkigdakSZMCwRPNNrxBE9lwWtyXKFHCfr9u3To7cUM2i0xWatd728rzexR0iYiIJJ6EaK5B2dG4cePsBsoMzrz7drG+hL2ByH7dfPPNduC1ZcsWez23ffvtt+1AyrtOQ0QyDtlmskUELQ5ZZt6XNM3o2rVrivpp3qes7yIA44vGHLxH+T+ZpqpVq9oSP5pdMHFC18EPP/zQvsdD4WfIfBMYYerUqXYtVvDaTrJulCQSaNF4g3Jlzh1+r2eih7WmIiIikngSIuPFDDYL8Nu1a2ezWZdccomd/V62bJndsJWW8aCkadGiRaZp06bm6quvtgv0mTVn0T0DQxHJeJQYEijx3qtUqZK9bNiwYWbjxo02wGJDc6dAgQI2IHLNcBzWYBI4FStWLBBIsf8X68A++ugjO4nCnnyNGjUK+Rho9c6aLgI9Hg9NMFj3SSYsuFSQAI+AjLbx7C/mFel69hFzWToRERFJLLmSvKOKHI5F+gyumHWmvIlBlrfdNBigcRsW11MmxIL/cOtCol14R/dEiZ4WfOZ8EydONKNGjbJBkst4sQ4qGNkjMlnBKDUks0VXwmCs0+L9HmryJPjYcqWAfFE6SBdEgjzKBtmba8GCBbZMkPOEt1yQiZzUrgfBGOccNdfI+XTOEh1bEm903sqcMX5CpXGYLadldWooaWJwFLwvkIjEDkELHQBZY1mhQoWQAVRqUpscCdUpMRwyVt5MucugeRGUpSbc9Wz+TLZdHQ3j26+//mqDce96RIcy07/++ivF5ZS0uyDeW3pKcP/HH3+o/FREJEEkxBovEcneKA2k7Jc1WTkR2a733nvPrleT+LVkyRK7FyQTBKG89NJL5vnnn092GSWztWvXtusL+aKJjEP2k7WHIiKSGBR4iUi2cPHFF5uXX37ZZEdnn3223eMrXGYt0vVkOeis6ratkPhDNovumt4GTd4y9meeecaWywYbPny43fbgm2++sd1zJ0yYENgqgduztlhERBKDAi8RyTaC11xmF6wPo+zQWyYWzfWUH0ZbPinZC11ww3WkZKsSMlsNGjRIcR0lhqVLl7b/L1WqlNm2bZv9P6W17B1XuHDhGD9yERHJLhR4iUjCYhNmkUgoLRw9erR54YUXQl7fvXt3m9EM1filYsWKZvr06Wbp0qU2K8pekmydwH5zbJkgIiKJI6Gaa4hI7Bz9e585snldXD3FbJV8yMSPY844x+Qu8L8GIBJ7NMbo3Lmz6dixY8jACjVr1gz78y1btrQNNFj7Va9ePdvgqUOHDuahhx6ymVC6btKog20MREQkZ1PgJSIZgqBrd+/WejZjqHCHISb3ORfqOc5E7ONImSibYae1m6634cby5cvtht6vvfaabSjDVgro0qWL3WdSRERyLgVeIiIiIRAgDR482PTr18/u7ehs2bLFlChRIk1rEnv27GkzXqz1Gjt2rC0/3LRpk2nVqpUCLxGRHE6Bl4iISAg0zKC8MLjbZq9evcwtt9xiHn744aiety+++MJunXDllVealStX2j3m2Nyb5ht0OaRzYqR94iRnYdN1Ol4WLFjQNmchoPeaPXu2+fHHH23HVPY79KJMlbWHNPdp3rx5sn0I2Yz+wgsvNOXKlcu0v0VEIlNzDRERkRCuueYa2xjD+wX2m4s26Dpy5Ijp3bu36dSpk/2+ZMmSZt++fWbWrFl2kMwAWUFXYmEPt/bt29s1fmQ9CawIyJ0nn3zSlqDmzp3b/Pnnn3YfuHXr/reOtnXr1nZD77Vr1ybbluCXX36x+wayzYWIZC/KeImIiPhEdip//vwhryODxWbZoXz99dfm0ksvtV0OQTMNArFBgwYF/i+Jg+wm6/zYVqBatWr2sl27dtnumGRYly1bZtf/ffLJJ6Z8+fKBYJ3SV/aFIxAj4BozZoxJSkoylStXNnv27LEZVI6lRx991Bx7LO2DRCQ7UeAlIiLik8t6hUIGAqtXr05xXa1ateyXFyWHfEniIdtJp0yCcbAxN3u+uS0uyICddNJJgaALbEXQo0cPG2CRJeNn6LpJNpXgi5LDxYsX2y6adNAUkRwWeM2bN88uDGYB8t69e+1JgJm7okWL2tpi3vjMvoiIiIjI/7Kjd9xxh/3/gAEDzOeff247YLZp08ZeVqZMGZsBo/TQZUnnzp1rAywas3A9Qfu9995rA6/rr7/erhOjeYsrZxWRHBJ4keJu166dnVnJlSuXPYGw8JNFw1u3bjXff/+9GT9+vD0B0EaXtLiIiIiIJMf6PrJblB0OHz7cPPLIIzbTRVBFcMZaQya3CxcubG9PlgsDBw60ARuZLm4zc+ZMe5tLLrnErh2kKcfll19uqlevrqdcJJ4Dr7Zt25rNmzebvn37mho1aiTrpANmZCi1oHadVHqpUqXM+eefn1GPWURERCRHqFu3rv2XDpo027jzzjvt3nFPPPGEnbhetWqVXd9VpEgRM2nSpEAlEWu4yHS5YIxtD/r3729mzJhh14ndfvvtNvvFWjIFXyJx2tWQvUyWLFliXn/9dVO/fv0UQRfIglWoUMGmz1nwOW7cuIx6vCIiIlEhW0ClRjAaYdAVLhTau7POhjKuUFhnQ5n9oUOHsvTVYE+x/fv3Z+ljkOj99NNPtirIi/VdBFAcrw5lhnQzvPjii83SpUvtmKtYsWIp7o/OmFWqVDHnnHOOmTNnjm0v/8ADD5imTZva70UkTgMvFn8SWLma49Rwu0qVKoX8wBMREckMzPqHCrBeeOGFFHt0EVDRKv6qq66ypV516tSx5fNeZBBq1qxpr6dhxvz5801WWbNmjenWrVuW/X5JG0oLR4wYYb777rvAZXQwPOOMM+xeXjTf6N69u/nrr7/sdX///bd55513zBVXXGHHVl7cluvoZAgab7gJAX6OUkQRyR6ifjeS7gb1w2S8UsMb/quvvrIfUCIiIplt6tSp9l8yBt6BKkEXA93atWsnu/2rr75qM11sXHvKKaeYb7/91g5ouR9KvaZNm2bXME+ePNkOkD/44ANbHuaaI4TD72QSkgE366Kd33//3Q6MKS0jOKTcjCZVfq+/+uqrzbBhw+xnLaX/Eh9OPfVU07VrV7t0g+CdY2PDhg22mojAiswWE91ku1izRZMNSgspQww2dOhQW5LIsYFGjRrZyQP286IBGuvGRCROAy/e2Oyu3rlzZ7N8+XJ7wmANFzXHbPLH3hScQNiD4t1337UfGrfddltsHr2IiEgqaFhAYORFFoty+BtvvDFFmR6BFp3lCLpAhoEsBOtmmjVrZoM1BrYEXaD5Ab+Dfbq432AMfvn9NJ4iYCJ4ItijMQIYaFNaxucpyHCwNtp1vIt0PW655Rbz1ltvKfCKM6zBovnFokWLbIMyWsvzr8OarR9++MEGZARW3Ja9u4KdeeaZgXVioOTw/ffft6WJHMuuRb2IZL005Z8pzWA2hjQ5M23hEJAxE0PbUxERkcxE513WQAXvldWnTx+7aS3ZreDAi6wV2SlvsyjKD1nf7NbmXHfddcl+hs84Sv5CBV6svSF469Chg/2essS7777bDoopxQdBHRvjkrGi9Oy+++4zF110kQ0O/VxPIEf2hMeoz9v4QlCUWmBEptabrQ2FrFgwjgMdCyI5JPDKmzevbRNPy9MFCxbYGRhm5Mh2MaNXvHhx+4FCY43gWmQREZHMwL5H5513nv3M8iLoSi0L8cYbb9jMA5OHdJFjvYwL0Pis82YlwESktyGCF53pvFgfzdYr3oCPwNCVCZLVYKBNaaMLrCJdz+NhkM3fq8G2iEj2lSe9NcqR1nmJiIhkBcr6KBOMRosWLWwg8+mnn9qAi1J5NrJ167coqQ/udMj3rL8Jhc6JY8aMMd98840tGdu5c6c5evSozaQ5ZcuWTfYzBHy//fab7+vB3xmuQ6OIiOSAwGvevHl24SYtdZnt48OEjFfRokXNhRdeaOrVqxfYb0JERCQz0eApLZ9BTZo0sV/O22+/bS644AL7fz7fCJ68+J71OaE8/fTTtqyQDoist2GzXD4fvYJb0pMN8zbqiHQ9yOrx94qISA4LvGie0a5dO1s/TykhHZqYIaR8ggXEtN6l6xN7VFCSyKJQERGRzESDDD6TokFHQyYQ6WAISulZ1+U2oKVxASX2rrkF68FWr15tA6xgZLVmzpxp10XTlAo0niJDxkSlw2epw3V8/9BDD/m+HmTlzj777Kj+VslYvN5sYExHQdbb0eAiFJZpsCSD29AG3ov1Xuy/ldk4rnjsdPAMl70VkSwKvGh/unnzZtO3b19bdx68iTInHz6IBg0aZLsvURZx/vnnZ8DDFRER8YfsEuueokGbeFrGP/bYYyZ//vx2L6Xrr78+8Bl211132WYGdBusWrWqbXpBo4vgLBaYmCQYGjdunN30lknLXr162YGtt4HHihUrzIsvvmibdlCWSPv4hg0b+r4e69ats10XJeuwtcCBAwds9pFMa/AxwYQ0EwFPPfVUoGqIbCjNVhzvVgOZiYlzjkuOZzKzIpJNAi9maJYsWWI7NYX6oHEfNiz6HTBggF2ozIeOAi8REclMbHfy7LPPmu3btwfawwdvjxK8XuvBBx+0A+f33nvPfk+Q1bJly2TrrchS0NGXdVtkwMhwhMPnIF9sukzbbypBCO4IwhzWShPk0T78nHPOsQN4vvd7PeX+u3fvtt0TJWsQSLNFAGsD3Zq7W2+9NXD9jz/+aAPo0aNHB5qz8D2vmfd2qSGoY3+5KVOm2P8zzmJrA45XmptRYcSxOGHCBLv8g26XrgQ20vW455577GWsawz1fhGRLAi82NCPwIrOTJFwO7obkh0TERHJTDSAosU7e2+1atUqxfXegMo789+6dWv7lZ4W397SMbJcwT/vxcDZtZsPJdL1EydONDfddFOK6hPJPExGM8EcKmChrPS5554zzZs3TzZ2YlNkAh0Cc5qwsBl2uOOKdX78fMGCBW3ZKpMJbIewatUq061bN3v92LFjbRaNCQeCO45hfi/HRqTrwfFDx09KJMn4ikg2CLxKlixp/501a1bEjoYs9P3qq69MzZo10/4IRURE0og1K5ROUSIY3FY+p2RayICMHDkyqx9KQmOvNTbkDoXS1Y0bN5p33303cBkZKMpDCbjIMJEBZXKA7Qe8m2N7G6pwefny5QONXpgkYCzmRVMzlni4jC6ZVrJika6nWycI/ig3VOAlEhv//06LAm9UZls6d+5sSyZopEHN8sGDB+2JhA8ByhGZgaPM8Pfff7cnlexm1KhRyU5G3oXVzB7x2FngOn369Cx5fCIikn7sa0Wm4IsvvsiWTydNFvhK6/UM+Gm0EW3bfMk4lKvSNOXcc88NeT2lqZQTnnjiiYHLGC9RBstYhMzrk08+aZ555hkbCFFGGIyf5T7cGnr2caPJSnCprHft37XXXmurlNzm336uJ7Dj+3D70olIFjTXoEMTKekRI0bYOvdwaKoxdOjQbLehIycV/gZmmryYkSJIZLNKZnsoA2BmiP1S7rvvvix7vCIiknbe5gXZDR3u0nO9t+29ZI2//vrLBkChGmOwd9vy5cttcxQvxlDBa7toVkalED/Dxt/BKFll7eFJJ51kSxbJ4Hr3g4PrxonChQvbf72NXCJdz32DUsbgjcJFJIsCL97sLNLkA4G2urTbZXaEGRza8DI7x9quypUr23Ve2Ql1zp06dQp5XZ8+fWznKU5uPG5qnUm/072RD7es6jYkIiIi2ZNrvx683xpohsE6PzJJXkzssi6MrJcr83MBUKi959gzdfjw4XbS2GWtKAkM3jTb20iGiiN4M6aRrmcc5/2bRCSLSw2DFy6zzos9vbp27WpeeOEFmy5ndpGOh9kt6AItgEmtB9dQc7KhFIV2vd7HTT00ZZSc9ERERCJhoC2Jg8wRTS9C7RlHN0O6DwYj80TH588//zxwGYEVDTpKlCiR4vZbtmyxWbLSpUvb7+liSXfC4GCPYM6hSQaT4Ewo+72ewIy1Y2wULiLZJOPl0B2HgIRWtmS86NxDxos3LIEXQUuomZusQj00pY98eTekBKl9Sg+DN6BkTVuBAgVsTbWIiGSufQePmJ/+SF4WHh9SrtPJzs4tls8cl/+YrH4YcYu27OzJRblg8NiCCd1gjC2YsKaZBssb/vjjD5uBYvlGqElr1mO98cYbdvkDZYhUG1FuyDr7w4cPB243f/5820iG+2BZRfBykEjXEygyfsuJjWhE4jbwovsOWS6CF968lOBRC8wsCTM+nAjYKJDmG5Qk3nDDDWHvi2YWdD5khpA1Yfw/Em4bfHKLhMWqZONYZF29evUUgdeePXuS1Tx7McvkrhcRkcxD0HXX27/oKY+xkQ+UNBeVKqjnOY2YaKazZPCaPJp0hdt+hyZeV111lQ3YCHRY3sDkdbgKI7oj0hKe/eAI2Bh3sW8Y4xvGX2DcxUQ4Yxb2CAveYiDS9Uym87eISDYKvNq2bWv35mLtEwFQ8BvXdd0ZNGiQnZ0hoAq3gTJ1zt27dzd169a1ZYv8PxJuG23g9corr5g8efIE2qgGczNGrtbaixNacOegaJENVNYsbVhsrOcue2MyRCsCMgeDLCasEoFK9jJXIh1bGY2Ntbdt22a7DXorZ9jwmqUMqX2GufLB4PVaoZCNogqHhh580VqeMkQ+J8Hrx++nSQaNwZxI14OsG2MymovpMzfxaKyV9vF9qNghwwIvUtNLliyxdcKcAEIhC0ZNM5sCMqNDHXO4wIsUOycqgjdmevh/JNFuEskMDhk4dozPly9fyNu4WSbWcwXjsvR29+FFCV5cK/7wAaDnLvtLuaxcYoG1JIn1foivkr14lnjHVsaiwufjjz82b775ZqZ/HrLPlwvi0nI9KDt86qmnbGM0STwaa6VNcAVdhgdeNKYgsAqXOvfidizcJDuWWhDlDaSiDar8YE0XHXq6dOmS7O8Ae3mR6mfjQjfjEzwDsGvXrpCLXUVERETc5sM0u2C9eLhJ3ljh9/Xo0SPsnm+Rrqeqh8dP5ZGIxE7UgVfJkiXtv+yWHukNStDCmq2aNWtG9TtYYMoCUE5epPBClZ9Qm+wXJ5vgDQnHjBljPvjgA1uCSAMQ0u5sfvjDDz+YZs2aBW7H927hrIiIiEg4derUyZInh6UUwfuCRXM9SyoUdIlkw8CLTjwNGjQwnTt3tpsC1qpVy67hInihnI5aZppvsIv7u+++a4MoNiX2ixT9wIEDU11TxRqvaAIvFyx6uX0svG1eyXrRZYiTT+3atW1bVRaiUg4ZajNDERERkcyidY8iCdhcgw38KAkcMWJEilakXgRklPmVKVPG1/0SsBF00XWQIIhuiaEWrMVqN/XGjRvbUsMOHTqYE0880QZel19+uQ2+REREJOfQVgWxp20KRDIg8KLtKYtIaZvKXhJr16617UnJdtGkghpi1naxQDOaTZTppsPt+/fvH5O1Xl5Nmza19czBHnzwQdOyZUu7Lo0NDgnAREREJGfRVgWxp20KRDJwA2X2lcjImmCCNgKv/Pnzm1ij1NCVGwbj9/vN0omIiIiIiETiv/F8GtDdZ+fOnWbfvn2+bk97ejJMEydOjOXDEhERERERiZ+MVyRz5841rVu3ts0pUlsL5hCgNW/e3Dz33HP2Z9n7K1T2K9quhiIiIiIiIjk28KI5BkFXuM2Tg33//fd2fRemTJlivzKiq6GIiIiIiEiODbwIuPxkuhyCtMmTJ0e8Xay6GoqIiIiIiMRd4BUtOhmyibGIiIiIiEhOkq7Aa968eebLL780P//8s20nf/ToUduZsGjRorZRRr169ezGytFavXq1GTJkiG1Vv2vXLnsfdBm8+eabzY033piehywiIiIiIhIfgRcbHbdr184sXrzYtn9nLRflf8ccc4zZunWrXas1fvx4u/Ew+33dcMMNvu+bQK5Nmzb2/+edd54N4P766y+zZs0a06lTJ7NkyRLTrVu3tDxsERERERGR+Am82rZtazcY7tu3r6lRo0aKzY6TkpJs1mrQoEGmY8eOplSpUr4abNB+vmvXrqZChQqmT58+5swzzwxc988//5jXX3/dDB482AZyF110UVoeuoiIiIiISPbfx2v9+vU260QQxObJwUEXyIIRPA0YMMBUrlzZjBs3ztd9L1261GbMevXqlSzoQr58+UyHDh1sADdjxoxoH7aIiIiIiEj8BF47duywgVXFihUj3pbbVapUyZYm+sF6rrx589p9usJhrde2bduieswiIiIiIiJxFXiVLFnS/jtr1qyIt/3777/NV199ZUqUKOHrvs844wzz77//moULF4a8nnLDRYsW2duJiIiIiIjk2MDrtNNOMw0aNDCdO3e2zTNopEF54MGDB23QtG/fPluOOHHiRHP77beb33//3dx2222+7rt8+fK2oQbrwgjsjhw5Erhu3bp1tunGli1bTMOGDaN92CIiIiIiIvHVXOPll1+2a7tGjBiR6gbJNNUYOnSoLQ/0q3fv3ubee++1QVaePHlMkSJFbKv6AwcO2O+fffZZU7Zs2bQ8bBERERERkfgJvFiHRZv4Rx55xO61tXbtWhsckfFiH6/ixYvbtV001mCdVzRKly5tpkyZYiZMmGDve+fOnaZAgQKmXLlypnHjxlEFcSIiIiIiInG/gfKpp55qOxtmNIK3O++8036JiIiIiIgkdOCVETZt2mQbcNDJkNJE/h8Jt2X/MBERERERkXiQ5YHXypUrTffu3U3dunVt9oz/R8JtFXiJiIiIiEiODbzYQ4tOhtGWJF566aUhr7vyyivN1KlTbbMOSgz5fyShNm0WERERERHJMYEX7dw7deqUrNV7JARX4QIvgihvIBUpqNq4caPZs2ePbWsvIiIiIiKSIwMvOhUOHjzYPPTQQ+aEE04wr7zyiu06mJrChQv7uu+ZM2fa/cG6du1qOxiGQgv77du3mwEDBkT70EVEREREROJnjRcZrG7dutkAafLkyXYj5YxAFo229E8++aRZtGiRvX9a14uIiIiIiMSz3Gn9wVtvvdWWD37yySdm7ty5GfaAihUrZlq0aGE++ugjc/vtt5tff/01w+5bREREREQkrgIvPPfcc/Yrd+503U0yhw8fNk8//bTp27ev2bBhg2nSpImZPXt2ht2/iIiIiIhIZktXxFSmTBnTrFkzc9lll2Vo4AVay48dO9accsop5uGHHza9evWypYh58mR5B3wREREREZGoZFyqKgYI7MaMGWP37RoyZIhp1aqVOXjwYFY/LBERERERkZwTeLn28v379zdPPfWUWbBggV37JSIiIiIiEk+yVeBVvHhxU6dOnZDX3XPPPea9996zmzGnF50Tjx49mupt9u3bFyh7FBERERERSY9stWCKPcL4Cufiiy8233zzTZrum0Br0KBBZuTIkebff/81uXLlMjfddJPp2LFjsn3IaI/fp08fs3PnTvszN954o+nSpYspWLBgmn6viIiIiIhItgq8nHXr1pnly5ebQ4cOBS5LSkqy32/bts3+v3379lHdJ+WKo0ePNkOHDjUXXHCBWbt2rc2i7d692zbuwJdffmn3EHvxxRdNo0aNzPr1680DDzxgAy+6LIqIiIiIiOSIwIsgiEYa4RxzzDGmefPmUd0nAdv06dNtcw6CLpQtW9buRfbWW2+ZHj16mPz585vXX3/d1KhRw2bC3G1obd+mTRvzyCOP2GYfIiIiIiIiWbLGa82aNaZz586mXr16pkOHDvayzZs3mxdeeMGW7Pm1fft2884779iSwsGDB9uAiD3C3n33XZtxKl++vF0D1rVr16ge37HHHms+++wzc//99ye7nMeWN29eG8yx7mvZsmWmevXqyW5TrVo1+29aSxxFRERERETSHXh9++235pZbbjHTpk2zZXu7du2yl+/YscN8+OGHpkWLFuaff/7xdV+rVq2ye3WxxqpmzZqmYcOGgevY14sAjABo4cKFaX683P+mTZvMsGHDzLhx40y7du1sYPbLL7/YEsYzzjgjRVfFE044wWzcuDHNv1NERERERBJbugIvghiyT5dccon5+uuvTZMmTQLXVapUyWavNmzYYCZOnOjr/mh6QROL0047zX5P+R+BEBk1nHzyyebKK680M2fOTPNjJqv12GOP2SDuzDPPDDTzIOOFQoUKpfgZHtP+/fvT/DtFRERERCSxpWuNFwHRli1bbBBz/PHHp7j+8ssvN9dee61ZvHixue222yLeH63iDxw4YP766y9z0kkn2ctKlSplVq9eHbgNwRgNNtLqwgsvNOPHj7et4imFJCNHZo4uhyDrFYzuhpQjpgf34f07xL+///5bz102d9ZZZ5ljs/pBJAjOkWTtE+W4ksyjY0t0XCUujbXSPr5nWVSmBF6UFhKwBJfneVGmx9otPypUqGCKFCliOnXqZNeM0cyCQIn9uwjyCLroPOjNrKVVnjx57KbMY8eOtV+uYQf7dwXjMv6O9OBFYY2aRI+AVc9d9ve/HqQSS2TgE+v9cCCrH0DC0LElOq4Sl8ZaaUNyKdNKDZmNJEP0ww8/hA3MvvrqK9+zlqy1evbZZ838+fPN+++/by9r2rSpzTaxnxaNNfbs2RN14EUJI1kyb3t69yFDMMfjJLNGMBY8k8xaNWYB6XAoIiIiIiKSFukKvMh0seaKzNGcOXMCTTRYLzV79mxz1113ma1bt5rGjRv7vs+6deuaGTNmmNtvv91+X7RoUTNq1CjbwIOAi4As2rbuPB7axLN5shcBI2u3qlatagMwOhh+8cUXyW7D30FAyN8pIiIiIiKSJft4vfrqq7ZN+4MPPhgoqaMdPAhYWEcVbVlM8eLF7ZdTunRpu6lxWlG+yFqugQMH2gYdBFqULvLYKGV0689oukHJ4XPPPWfuvPNO22XxpZdeshstFytWLM2/X0REREQkM1DpNWvWLNuHoUSJEqZ27dp2TB6Mngpst+SSHd5GdAsWLDAXXXSRHSd7l96MGTPGjosliwKvU045xXz88cc2MzR37lxb0sfeWGSlKA+kc2Bq2O+L0sJoM22XXXZZVD/zxBNPmHLlytkW8gMGDLBrtihjvO+++0y+fPnsbdhcmfVkgwYNMq1bt7YB2+OPP26DMBERERGR7IwlOYxvaVJH0DRlyhQ7rqV6zNu5++DBg3aMy5623sCLoIsxMNVqjz76qHn55ZcDVV9DhgwJjJkliwIveyd58tj1V3xFixeYRhrRoBwx2sCLTNxNN91kv1JDdD906NCo7ltEREREJKsNHz7cnHjiiYGO3WS/GjRoYEaPHm1atWplb7N06VLzzDPPmF9//TVFgoQMWKNGjewyIqq9pk6dagOvP//80+7Z+8knn2TRX5aggRdPPJsmR4N9ua644oqQ11WvXt12FYwGB5SIiIiIiPwPFWdVqlQJbJPkqtD++OOPwG0Iuq6//npToEABM2HChGRPHw3t2KcXNNBz99OvXz+bCeNnJBMDL9ZGRZuhIlIOF3gVLlzYlviJiIiIiEjakd0KXsdFMzmW/zgfffSRbSrnOoh7sf8uy3AIuj799FPzyiuvmJ9++sksX77c9OjRQy9NZgdelOJFm6EKtblyZq/xEhERERFJFGyj1LFjR3P++efbDJdD0BUOyZBhw4aZ77//3jalY50Yma727dvbssVJkybZoIxAjm2ZJMaB13HHHZehGarMWuMl2etEsHLlSrsAlIYnbBkQvAv4jz/+aK8nXR7s559/tilzJgG8Jw/KYH///fdkHXhEREREEg1bPNGtm3/ffvtt2+vALwI1vkDjPPazveaaa2wARtDF2IsAjHVkkgXNNdJDa7wSCxtU88al5piOmEuWLDHt2rUzLVu2DLQqZfEnqXGuX7dune1I6bYkYJEnqW7qlbkN2VcXfLENwM0335ylf5+IiIhIVmL/2oceeshuoUT2Kq2dCAmyXnvtNfPss8/afW+//vprs2jRInt/l1xyiZ3s9m7/JHEQeGmNV2JhPzb2UWN/NDeTcu+995qrr77alCxZ0rY8ZVaG9qcEZ3Tn6d+/v908m8vZBLtbt242ZU6b/6+++sp206R+effu3bY2WURERCQRMRZiMrtSpUo2YIom0xWMrBZdDytXrmzbz9Now92ft/GGxFHgFQpZDhbxUZLm8ALzPfuE8X9qTSX+0JGyWbNmyTKeZKx4zQm8pk+fbtq0aWODLnBbZlsoSXVvfNfVkqCd9De4Da1PRURERBIVHQs3btxoatWqZd54443A5SztiGbbJ9ZzMRk+ePBg+z1jtZo1a9q9vch4VahQwbablzgPvHr16mU3aAuHNpfNmzfP1MckGefVV19N9j0BNsHTueeea9/kNFspXbp04Hre3GxkvX79eht4VatWzR4fGzZssFku1gdSfsg6sVDrwUREREQSBWuzypYt6+u2ZMUYV4fCHl9MhDMp7lCBRBaMdvPeLokSp4HX9u3bzTvvvGMuvvhic//995utW7fasjLqU3ft2mUXBzIo79q1a1Y/VMkA1AszM3PLLbfYVDa7pyO4Sw6zLC6zxUJRjhEyYMzksEccXXeYleE2rBljBubss8/WayQiIiIJhfGzXwRefIXCWnq+vJgMv/XWW9P9GBNZhgRe7O3FWhwGvRUrVjS9e/e2mQsCpkceecQu7vNj1apVNoru06ePHVBTVkbghfr169tsB2nShQsX2nVCEr9oovHggw/a15kaZLjyQrdxn3P48OHAwlD+ZcGo895779kOl2wxwDowti5glubxxx+334uIiIiIZAdpX3H3n2+//dZmLKZNm2YX9JGZwo4dO2ybyRYtWthWln5Qaka2g8G4y3QwoCawAwEcmzHPnDkzvQ9bshDHxl133WXOOuss8/rrrwcCLrYq4ItMpxfHlDsmgoM3Ai+Ce5p0kC4fNWqULWd89913M+3vERERERGJaeBFZoKyP1pK0mLSm2EgdUlJGGtxJk6c6Ov+Tj31VFsuRptwp1SpUmb16tWB7wnGaLAh8Yn9t1ijxwJNOhvmyZM86eqOJWfx4sW2qQrru4JResoxR9t5gvtQTTdEREREROK+1JBM1JYtW2x2gRKvYJdffrlt783g+bbbbot4f3RIoZFCp06dbNMEakvZDJesBr+LoOvLL79UCVmcomSQ2mNeR/blooOhw6bcZDcffvhh2yaeLBjrvt566y27NxfBVHAAR1ONyZMn2+85Tlgv9sEHH5hvvvnGXHHFFZn+94mIiIhkNBqM/fzzz+a6664LVIgxBvrtt9/seKp27drJWrtHuj4zsQyJyXBiAklnxovSQl5IBszhnHDCCWbPnj2+7u/YY4+1633mz59v3n//fXtZ06ZNbQkZ3VNY38V9ae1OfCJYIoNJhxxOCN4vTg4uU0q5IOv76HhIi/jbb789xX1xjBCgu0YcdDV88803zYoVK2xHHwJ3ERERkXhGdRljGtddkKCK5Rrjx4+3E9o9e/Y07dq1C9w+0vWZjSRKjx49Ag3UEl26Ml6s0WFPLdp6sx9TqMCMDW7Z7NavunXr2sG3C9YYUDMQHzp0qDl69KgNxIK7rEh8KFGihBkwYEDE25133nn2y/GWmjqNGjVKcdlFF11kv0RERERygrFjx9puzWy7gwkTJtiu0PRRIDFBMoJM2Nq1a20b+UjXh0L12ueff257KLDW/uqrr7YT5Zg3b54Nmugqzm1Yc3/DDTeYAgUK+LqeijiSJ4z/nnvuOZPo0pXxItNFswuyEnPmzAk00di7d6+ZPXu2jbhplNC4cWPf7eRpklC8eHG70ZvDi/niiy+al19+2Xa1475FRERERHIqkhv0S/DumUUQxrjb7b9FooLqM5Zx+Lk+GMmThg0bmp9++sl+T/DF76NDNBYtWmT372IpCCWDH330kV0+RHDn53o0aNDABoQ7lfVKfzt5OsixbofW4MidO7fdh8uVDr7wwgu2vtQPXnxXahjOyJEj7Qt7zTXXpPehi4iIiIhkSytXrrTZqBo1agQuozkZaF5Hj4UZM2bYRnesi/dzfTACpF69etmkB+N1xvOsk2dM7n6GIIw19VScUcrIFk+jR482rVq18nU990OV3Jw5c+y6/USW7sCLjnIff/yxzUKRraLjII0RePKJmMO90A63J/VIVE92jAOAqDkUXkxSmmTSRERERERyKrJJZ599dmDbHS+W37A0hy13yIoRcHnH3JGud1ygtnTpUpv1YnkHGTLWhznsneuW+fBYKF387rvvAoFVpOtdA71FixYp8ErPAcE+SqQz6SZH4wu+okULedqAE7T9/fffdhFhqDU9LoPGAXLvvfem52FLOvH60NSC2QsvZjxIaXvXZ4Fgmjc0DTNoF0/9cHBAzZuRTCaNMVjXl1U4Dnn8NIURERERySokJ+j2HQqBDl/spUtp3+DBg0337t19X+/8+eefpkuXLnZfXtZnEawFd0AMHpeddNJJto+D3+vB37F+/XqT6PKkdwA+a9Ysm6JMD9ZvgfbilBpqDVf2RWBFN0HXddJhRuWhhx4yV111VbLAi4CrTZs29k1IGps392uvvRZo984bngwmtcdc37FjR/P888/beuOswB5yrCXkS0RERCSrsHzHm3kCVWZkrrxN7Qiw3B63ka4PxribHg1kxdzPMJbzCl6bxX15g61I14PESu7c6WotkSOk6xngheVJzKgIltQk9Z+SfbGAkkWS3n212KOB2RT2mAj29NNP2wYsn3zyid3wuE+fPubxxx+32VIMGTLEZj3d9bRMJfAiRZ5a8EftMW3o2SOOMlWHGRsybzyWzz77zPzyyy/JfjbS9fXq1TOrVq2y9y8iIiKSVchA7dixI9llbL/Tu3fvwDiJhnaMbVx/hUjXB2MtGAEXE+Rgv1yW/hw6dChwG8ZEmzdvtv9n/EaixNtrIdL14O847bTTTKJLV8aLVpG0qbz77rttG3haXRYqVCjF7eiwEhw9h0IHFu6TLBoZEAbsDLJZFLhgwQLb2pLSxsqVK6fnYUsaMaMxbty4ZBsf8wa/77777GLMhQsXJrs9pYO0L6XBiktbV6tWzf7LWj021yZTxqyIu56W85QmMvviWpF6EWyxJrBKlSo26KeG+LLLLrMpdLDhMiWp69ats4tEOW4effTRQHlqpOt5HKxNZPuCcCcpERERkVhjeQblgQQzbpkGDe0IjtjjlOUZX3zxhW0T36JFC1/XB6PCiElwqtgYHzGWY7xN8OXwu1u2bGk3QaYBHp3Hb7rpJt/Xuwqodlm4n1iOCLzIdIwZM8b+nwF5OARQfgIvsMkaZWy33nqr/TnK0shMXHjhhbYsjRd22rRpNpiTzMXzzpvJ+9xTs8u+DWTAWKflReDE/g1kldz+WmwZwOyL2zCZ15Ogjdf59NNPNyNGjLDfhwq6wKbKzOS444l94lq3bm3T2mTOwOLQiRMn2sYvzPJwEiKb6haVRrqe/7PhIIHmySefHJPnUkRERCQ155xzjt1SibEKCQ4X5LC3F8HVH3/8YfsruEltP9cHe+SRR+xE89dff20TG0yWEyQxMe59HFQjMWnOHl90WfSWDUa6nnEg47Qanu6MiSpdgReDaQawkQQ3UwiHF4UN3xhIcyCQ9ZgyZYq59NJLA23k2ZSNvQBYTySZi+wQsydeBEjhgiSyR6zvIpgm0CKIIWNFgEUjFTC7QiDHDAlBHNkusl7hPPHEE4H/c1t3PzTocMjCElSBtWQ0AeEEdOedd/q6ngCMQJJZH4IwERERkazAmJgkhwu83NiJqqFwIl0fjMCMRndu+yfv+jCH7op8hZPa9Tx+lqScoMZl6Qu8GOySFXCNEkIhu8CamUht5UGak3LDxx57zOTJk8dmMwi22A8AdNKjrCzUWiKJPWZOaAcajXvuucfOhBDYECS98sorNmh2wTgBNieHJ5980n7PLEvz5s3tQlCynME2btxodz+nnphjz+3k7l3nFXysEdh5U+aRrgflj/y9IiIiIlmF5Q/sifXjjz/G5VIbxmpsyjxq1KisfijxH3hR9kVGY9myZWFvQ3cVUox+Ws2TIeGLoAsM1uEtUyQQ8zZ2kMxDZ53gFqN+8Pq515As5qZNm2yWiVaj/J+GFg5tTJkxIbAKDrxYKMpaLLJugwYNskHX77//nuLY8u6WDn4PMzl+rwcTAMGdhEREREQyEyV7LMfIqqQD2bDUkieRrqfpBp2itXQjjYEXNZ/s20VbSKJYBqfhNjzmOuo9Gzdu7Ou+WfxHFo0Od5Qxfvrpp3Zw7UrP6LxCFoyueJL5WENFu/VosDUAr6t7U44fP95ursdme2S9CHjIchFwgfVfBFPBe4SBy3kDk/FyJY8sGgXHo8MxwtoxUOK4Zs2awJYFfq533XfcmjERERGRrMIYyk/lWCwwHnfr9NNy/QUXXBCjR5YggRcDZdpBMqBmQ1yyEKlteEyHE5ol+MH9NmvWLLCOh+wK+wu4Rh6swaHdpd9ALhjlaGTnGLzzd4TbKJeOd5RIchsWHIbaMTwRERyxFisatHt/9dVXTdOmTe3xQst4AndXasieYCzkJIhnjRcLQmngUbt27RT3RUlgyZIl7cwJxwABE+lrZoO8G/XR5KNDhw42Y0ajlkaNGiUrkYx0PUEXpYfxmNIXERERkRxUakh7d77IHFBqmJEbHtMqnGwIe4MRtNFYwwVltKYkiKNTXrS4P9qGs86I6JtMB/s58Xcw8HZZEzb4pZ09LTwJwAgsaVXOmqNEx1osAifKPVlvF4zXyjWtcNq3b2+zlpQOskXA8OHDk82MsKM6GTECKJ7vO+64wwZVlPoF4zICJeqECaBZOzZp0iTbIt7tC4ZWrVrZlDaBGevJqI/2inT93Llz7WPOqtklERERkcwUqtJIstkaL5pqRJsB8YM9BYIVL17c7mWQVuwdQOkY+zORiQOb+bIvGBkWOrkQFNC6ntb4rDOi7JGBOc0+KH9M9B23CZDIANKZkIApmCvf8+I54/UM9Zo6ZJb8ZpdoehG8D4RrzOEN0Ajgwol0Pa+/29dLRERExK99B4+Yn/74J06fsAMmXpxbLJ85Ln/KSfocF3ixlxb7CUSDbFVqnQ9DreUhoKMRg9t5Ozgqj+b+WDfEmiAyKS7oci06yeCQ4SLwIptCu3rXDpMSw7Zt29oyOVqp01Ex0XXs2NGWgpJ9DJWVindk0ljHllqgKCIiIhIKQdddb/+iJyfGRj5Q0lxUKmX1VY4LvCjP6ty5c1Q/w0bIfgOlN9980wwcODBZs4Rg7GUQTeBFaSJtzIO5PaD4XbQO37JlS6DJg8M6MLI27OmkwMvYAPWuu+6y6/p4brIbylPJzKX1ehp9sCbNddYUEREREckIUY8uWZ9DA4Ro+F2TRTaNoIuN29w6nFDlfWlZ4xXKRx99ZP9lJ21+tytl82IATpMNsnDy/2699dZs+1RE2lg70vWplSCKiIiIiGRa4EU3uli1hly5cqXtZNi/f/9A17tYYSM6mmZQMlelShXz3Xff2cvz5cuX4rZc5rJjIiIiIiIi0cqQeiq60RHEfP/993Z9DEET66RYU9WkSRPfm+4WKlTI3pbud7FEq/gHHnjAduGjlTnc2q9Qm+YeOnTIFChQIF2/M7W2+5mFtXahAsvsjn3c6KQYb1ij6DKpOR3rLv+3elJiifcCG48nAnXZylw6tkTHVfronJW5ssM5i/F9NM330h14EWzRpILghDU/ZMP27Nljg7Gnn37aNqUItb4qFPZVoqxv4sSJIbvmZYQVK1bYMkaCrl69egX26HJt0NkgOnjvL/aIYv+w9OBFYX1UVovPbjvx102SbjsnnVQw3cdNPDmU1Q8gQbCVQ3Y4l2Se+Jt0iVc6tkTHVUbQOSuRzlmLFy+O6vZ50hvlEVyxqe2AAQNMqVKlAtfRin3IkCH2cjoF0mAjEvZiat68ud3Li72U6EQYKvsVbVdDhyCQfcB4PGzM7I1QuU8ydWvXrk32M7/88osNKnksOYG67WSOeO22IyIiIiKxkSe9JXtsQkyGyht0gUwSmyvPmzfPzJgxw1fgRfaM9V2YMmWK/cqIroZgs2c2UL777rtNhw4dUlxPa/QGDRrY30kGz60xY7NemnxcddVVUf0+ERERERGRDAm8WM/FmqwyZcqEvQ3X+V3nQnA2efLkiLeLtqshwSH7cZ1wwgmmdOnSZvz48SkeIxv4sjEvmTY627E+bdWqVTYQI2uX3jVeIiIiIiKSuNIVeJ1xxhl2DRRZLVqyB6NE74cffjDVqlXzdX9kmc4991yT0dif6/rrrw9k1UJluwi8ihQpYiZMmGDGjRtn1q9fb/8+AsHUAksREREREZGYBl6si7rkkkvshspdunQxderUCWw8u3HjRvPaa6/ZJhsvvfRS2PugGwllgNwX5Yr838/vDRXohcPGx343Pyb4a9Gihe/7FhERERERiXlXQzoW3nPPPaZ9+/Y2c0R3wL1799oWjzSveOqpp0ylSpVS3bure/fudt1W/fr17f8j4bbRBF4iIiIiIiJxHXixx9KkSZNsgw3K+Hbs2GH3ijrnnHPsOqly5cpFXNc1depUm2liHy/+H0msN1cWERERERHJdhso03iiWbNm9itaBFHeQEpBlYiIiIiI5DTxtzOtiIiIiIhInFHgJSIiIiIiEmMKvERERERERGJMgZeIiIiIiEiMKfASERERERGJh66GXsuWLTOLFi0y+/fvtxsiX3vttSZv3rwZ/WtEREREREQSM/Dq1auXGTp0qN1EmWBr27ZtplixYmb06NGmSJEiGfmrREREREREEq/UcPPmzWbkyJFmzJgx5ptvvjGzZ8828+bNM+eee64ZMmRIRv0aERERERGRnB94rVixwkyaNMkkJSUlu3z79u3mhBNOMBUrVgxcVqhQIVOlShWb+RIREREREUlUUZca5s+f3/Tu3du88847pmPHjubKK6+0l5crV84cc8wx5q677jK1atWyt9u0aZMZN26ceeWVV2Lx2EVERERERHJmxqtMmTJmxowZpn79+qZ9+/amZcuWZvny5TbQGj58uClatKgZMWKEGThwoFm8eLF5/vnnTd26dWPz6EVERERERHJqc418+fKZ+++/3zRt2tS89dZbpnnz5rZ7IYFYv379Mv5RioiIiIiIJGpzjRNPPNE89dRTZtq0aSZPnjymQYMGpkePHmbnzp0Z9whFREREREQSNfCiucaff/5p1q9fb9vH9+zZ03z88cdmw4YNNvv1xhtvmAMHDmTsoxUREREREUmUwGvu3LnmuuuuMzVq1LBrvapWrWpeffVV22Bj2LBhNuj6/PPPTZ06dcyoUaPM4cOHM/6Ri4iIiIiI5NQ1Xvv27TNt27Y1t9xyi7n66qvtei+yXAMGDDAlSpQwd9xxh6levbrtZjh16lTTt29fM3/+fK39EhERERGRhBV14PXTTz+Z448/3jz55JOByypXrmx27dplvv/+ext4IVeuXHbNF5mxRYsWZeyjFhERERERycmlhmeddZbZunWree+998xvv/1mduzYYRYuXGjGjx9vypYtm+L2efPmNdWqVcuoxysiIiIiIpLzM1400njhhRfMSy+9ZL+ca665xtx3330Z/fhEREREREQScx+vJk2a2MYZa9asMf/++68588wz7fouERERERERyaDAC8cdd5ztZigiIiIiIiIx3EBZREREREREIlPgJSIiIiIiEmMKvERERERERLLrGq9417NnT7vHWJUqVVJsED127FjbOOTEE080N954o6lQoUKWPU4REREREYl/CZnxGjBggBk2bJjZsmVLssvZk4yOjdOnTzcXXHCB2bt3r7nlllvM1KlTs+yxioiIiIhI/EuojNfu3btN9+7dzeTJk0Ne379/f3PkyBG7OXS+fPnsZWS9nn/+eVOrVi1TsGDBTH7EIiIiIiKSEyRMxmvbtm3m2muvNXPnzjVdunRJcf3Ro0fNlClTTP369QNBF5o2bWp27dplvvzyy0x+xCIiIiIiklMkTOBFYHX//febzz77zNSsWTPF9Rs2bDD79+835557brLL2Rw6b968ZsWKFZn4aEVEREREJCdJmMDrtNNOM61bt7YbP4dCVgsnnXRSsstz5cplChcubNd/iYiIiIiIpEVCrfFKzT///GP/zZMn5VPCZYcOHUp3xm316tUmK5111llZ+vsTzYEDB8ymTZtMIuDYOjarH0SCSLTjSjKPji3RcZU+Omcl3jnr6NGjJndu/3ksBV7/KVCgQLIAzOvgwYNhM2V+8aKUL1/eZL0DWf0AEgbNWLLHa5450jc1IX4l2nGlc1bm0bElOq4ygsZZiXTOWrx4cVS3T5hSw0hOP/30QBMOLzJddEMsVqxYFj0yERERERGJdwq8PGvAzjjjDLN06dJkT9CyZctsGvGiiy7KitdHRERERERyAAVeHnfccYfd42vVqlWBskP29qpQoYK55JJLsuo1EhERERGROKc1Xh4tWrQwa9asMc2aNbMZLlrM00p+yJAhtruhiIiIiIhIWiRk4HXKKaeYPn36mCpVqqToXtizZ0/zwAMPmJ9//tkUKVLEVK5c2RxzzDFZ9lhFRERERCT+JWTgVahQIdOgQYOw15cpU8Z+iYiIiIiIZASt8RIREREREYkxBV4iIiIiIiIxpsBLREREREQkxhR4iYiIiIiIxJgCLxERERERkRhT4CUiIiIiIhJjCrxERERERERiTIGXiIiIiIhIjCnwEhERERERiTEFXiIiIiIiIgq8RERERERE4psyXiIiIiIiIjGmwEtERERERCTGFHiJiIiIiIjEmAIvERERERGRGFPgJSIiIiIiEmMKvERERERERGJMgZeIiIiIiEiMKfASERERERGJMQVeIiIiIiIiMabAS0REREREJMYUeImIiIiIiMSYAi8REREREZEYU+AlIiIiIiISYwq8REREREREYkyBl4iIiIiISIwp8BIREREREYkxBV5BFi9ebFq1amWuvPJKc8MNN5hhw4aZI0eOxPp1EBERERGRHEyBl8eyZctMy5YtTcWKFc2oUaPMo48+at58803Tq1evrHuFREREREQk7uXJ6geQnfTt29ecd955pkOHDvb7M8880+zfv98888wzpkWLFqZYsWJZ/RBFRERERCQOKeP1n4MHD5r58+ebWrVqJXuC+P7w4cPmiy++yIrXR0REREREcgAFXv/ZsGGDDbBKlSqV7Ak6+eSTTaFChcy6deuy4vUREREREZEcQKWG/9mzZ4/997jjjkvxJBF47d27N81P8tGjRwONO7KDEbckZfVDyPn+WmMW/2USS1KSMfe9ldWPIkf7dc9RTiQm0eiclQkS8ZylYyv2dFxJDj+2jv43xvdLgVfQE5crV64UTxKXhbo8HvFX5MqdM/4WyWZ4j+SQ94lkHzpniY4tiSc6Z0lqFHj9x2W6Dhw4kOJJ4rJQmTC/qlatmuafFRERERGR+Kc1Xv8pXbq0zWpt2bIl2RO0b98+W2bI9SIiIiIiImmhwOs/ZLQqV65svvvuu2RP0LfffmsDsmrVqqXpCRYREREREVHg5fHwww/btvEjR440//77r1mxYoV56aWXTMOGDU2ZMmV0tIiIiIiISJrkSkqiFZk4kyZNMv369TN//PGHyZ8/v2nUqJF56qmnTL58+fQkiYiIiIhImijwSmVDZQIvERERERGR9FLgJSIiIiIiEmNa4yUiIiIiIhJjCrxERERERERiTIGXiIiIiIhIjCnwEhERERERiTEFXiIiIiIiIjGmwEtERERERCTGFHiJZICFCxfaL5GMsGrVKvPNN9/oyZQM9/7775u///5bz6zE3JgxY8xff/2lZzqTbN++3UyYMEHPdzaXJ6sfgGS+P//800ybNs3s2bPHXHLJJebff/81P/74o2nbtm3cvBzz588327ZtMzfccENUP7dv3z4zePBgc+ONN5pzzjkn2XUMdJcsWWKOO+44U7NmTVO6dOmI93f48GHz5Zdfmo8//tjkypXLbrx9xRVXRP335GRLly41a9asMbfeemvE286dO9d8++23KS6vVKmSqVOnTuD7n376yd6O5/viiy+2x3Fa8PsIcg4dOmTKli1ratWqZXLnjjwftWDBAvP999+bAgUKmMsvv9yUL18+TbcJlpSUZL7++mv74cmHKI+LY9HPY0oUvIdnzJhhtmzZYooVK2auu+46U7hwYV/v1ZkzZ5r169ebk046yVx//fWmSJEiUd/Gj9dff90em8FatmxpTjnllLA/t3fvXjN9+nTzxx9/mBIlSthjvlChQlHfJhTOl1988YV56623zP79++3fVrJkSZPIfvjhB/uVJ08eew6pXLlyxJ85evSomT17tj2nnXDCCfb5P+2006K+TbQ+/PBDU6VKFVOhQoWwt1m2bJn57LPPzOOPPx7xnHHgwAF7299++82+jzgejj/++KhvE8rOnTsDxxo/y+dtmTJlovhrczbeu+PGjTNt2rRJdjnnnVCBE8/9nXfemep9MnbhvPj555/b45nzYv78+U1WWLdunZk4cWKy892OHTvMr7/+ai688EKT6PRpnmCY6bz55pvtm54TM8FCvnz5fJ1MswtOTo899pg9wUTrxRdfNG+//bbZuHFj4DICzwceeMA89NBDNijl5NCkSRM7eIr04XLTTTeZ559/3g5kGBB16NDBPPLII/aDV4wdHLdr1858+umnvp4OjkteVwYr3i+OUee9996zrw/HwdatW839999vnn322ajfB82bNzedOnWyr/nu3bvNc889Z4NDAp5weF07duxoWrRoYY8TPkDvvvtu06NHDxs0+b1NuIDi9ttvN0888YR9PAzc+bv48OIYlf8PuBnEfvLJJzZI4t+6deva4D41vDebNm1qBgwYYJ9XAhcGkQxWormNH5wXBg4caHbt2pXiOE5tMLx69WpTr149M2TIEPv7p06dagdPTIpFc5tQeE9xu0mTJgUG6A0aNDCTJ082iYhjh88QBr681zZt2mTuuOMO06dPn4jnDQbAr7zyig1KCC44Rr777ruobhOt8ePH288Zjv9w+Azib+LYiPT5s2HDBjtpybHOY2Ty8JprrrETUdHcJhSu51j76KOP7Plu7dq1plGjRmbUqFFp+MtzHp7L9u3bmxEjRqS4jmNl9OjRKc4bkSZW+My466677OcNzzmTyw0bNkz1syyWGF9xHHIuBMcj52kmIuX/Z1glgaxZsybp3HPPTZo2bVpSPJozZ07SZZddZv+Gxx57LKqfnTFjhv05vmbOnBm4fODAgfayb7/9NnDZggUL7GWzZ88Oe3/du3dPuvDCC5O2bNmS9O6779qvpUuX2p8bP358UqKbP39+0lVXXWWfjxYtWvj6meuvvz7pxRdfDHv9unXrksqXL580bty4ZK9VuXLlkj7//HPfj61fv35JVapUSfrzzz8Dl23fvt0eWx06dAj7c++//779ez799NNk76mKFSsGHpOf24TCccht1q5dmzRx4sSkPn36JG3cuDGpQoUKScOGDfP9t+VkN954Y1Lr1q2TXXbHHXckNWzYMNWfe+6555Jq1aqVtGfPHvv90aNHk9q2bZtUp06dpMOHD/u+jR9fffWVff1/++033z9z5MiRpLp169qvvXv3Bi7v1q2bfQ/t27fP121COXToUFL16tWT7r//fvs33XTTTUk7duxI6tGjR1KlSpWS3VeiGDJkiH3/b9iwIXDZhAkT7Ou2cuXKsD/He5JzBM+f06VLl6Qrr7wy6eDBg75v4xevXf/+/e05j8fGeSGczp07Bz7f+LnUNGnSJKlGjRpJO3fuDFz22muvJV166aWBy/zcJpTatWsnNW/e3L5nOO9zDhswYIA9t23bti0pkXG88f7jNeJ5DNa+ffukO++8M6r7nDVrlr2/qVOn2s8Z3uf79++354SOHTsmZQXGVzwmHg84Hvn+7bffzpLHk90o45VAKKV799137f+ZKe3Vq5ediWAmi1kth+9ZB8DMDGlvrqOUjtLEYJRpcJ9vvPGGvf/UZvQdbkOZGJkn7pssR6j7DvbUU0/ZzNSVV15pzjrrrKj+dmZ+unXrZho3bpziOmZ9L730UlsO5lC+RlkHz0M4zLKTNi9evHjgsgsuuMB06dLFnHzyySaRMdvLDNx5551nKlas6OtnmLFlpiy1kjxmfpn9YwbV+1pRIjR27Fjfj4+yQo6HokWLBi6jpIwy0dRmpjlW+Nn69esHLjv33HPNVVddFThW/NwmFLIQ/Jy3BJZSMDJxlJQlOjIJZGmYLfbitScTcOTIkbDZDcpeOGZcZp9M/7333muPN85hfm7j14oVK+ws9RlnnOH7Z5YvX24zDK1atbKlzs59991nM7KUP/q5TSibN2+2ZT5kXfibHLKwZHz8nHtzmg8++MBml0uVKhW4jEwiFQupfYZxjuEY9J7fOUbIvFMi7Pc2fpAx5X6GDh1qfz5SRpNsJrePhOOZY4nXn3Ja73HE75wyZYqv24RChoOsC1npY445JnA5mWQqH8jqJyqOCzKIHF9kDsOdO/yUpAePQ3iuvaX4BQsWtNUSjGEiIdtLFQljMT67/vnnH1+/95dffrHZObL7HHvh1o3ymvft29f+n+O/73//p2KFMSjnLn4/x/nPP/+c7scVDxR4JRDqfV3KmjcmgwM+iOfNmxcIyMD377zzji3nYhBKcMabgpO6Sx3zJnvwwQdteR4nWi7nQ4vyKgbQ4XAdJV7du3e3/+fNRFqcQSqDg9Tw2Hkj8mb1lp750bVrV1tjfs8996S4jg/EUOu5qKvmwyecU0891a6bCMaHFetyEhk15gTWb775ZrJBYmpYa8WxxnHJwIgTOqVk3hMuHzLlypVL9qEOLotUbubF8RYqCOdYSK0uPtyxQvBNGRgDeD+3CXc88R4ILhNi0OL9UE1UrJWjrNS7xoXz0FdffWUnPIKPCe96AyaRgicAOGZAmZ6f2/i1cuVKG0C7ySW+CKpTwzGD4OOGc5AbkPm5TSgMnHk/Bp+rzjzzTPt8nn766SaR8HlFCfRll11m/+8mDnndeD7CTRQRwDKBF7zGitcjb9689hjxcxu/OO/xGJkkbdasWdjbcc545pln7BptP4P2cMfRiSeeaN9jqR1r3tuEwrme64OPNda38dx6A91Ew7mKYIiGI6Em0ghQCGb4nGCCkc8/yjPdmCscJg+ZdAq+Xe3ate1YKzVMBDLhQHkoASHjPIJD3hepYRkG40HW2nOcspaP+wlXCusmsxi3HfffeIDAinJEjm1+H4+BNc3peVzxQoFXAiEzwPouUG/LidA7A+rFBwhB1WuvvWbrxkeOHGk/UDghgFp4BhbUcZM54MRPVowPlldffTXsYyBwouabmRIWALPGhp/jpOHuOxx+R7Vq1aL+u7l/aovJwoRaY8HiT2ZdgrE2h9m9cPXyzDxzonzppZfsAuJwA+pExBonGlVEw32Y8zr9/vvvdiae4897wmWQEarZAYNLjk8/GddwGBzTEIPMQDjhjhUeLx9+PGY/twnFTVowScDMn46n8DgeXnjhBRtAcw7r169f2Nu6dQ7BWWgCbCagGCT6uY1fTNYsWrTIDppYm8eEwC233GKP63DcAvTg44ZzEDg/+rlNKExksOZo2LBhNqvH2jA3wElEnKvBZwIZHF5bzuOs8Urts8tNDAaffzj+OP9wP35u4xfBCsd4pGw35wsy4/wtfrjHFnwc8VlHcMBx5Oc2oRBc8jhoBMLnLuezRD7WvKgAYQ0xkyChEPjz+cVEDf8noGHcxViNz6VwWMvFJApjKdaj8rnh53OQ8xJr3hnHEDiRkSQoZIKbNWPhzJkzxwaFBJFkr/isZ+zGufPRRx9NUXlAoOWOTSqLWrdunex6sn8cw5yf3ARqWh5XPFHgJaEPjNy57YyD90OAD35mKXhTk1pm0HP22WcHbsPJn+5FZCnCNQNgxosPE2bOuA0zzZRJHHvssXYWMqPxeF9++WVb/hfuA4y/gxS4N2NCaRHf84EZ7iR20UUX2YGM6/xEJo4ZSk6c4cqeJDxK7J588kn7nHIy5zXjWGImkMvBhz7HSjAu43VKaxMKWh7zO5nhffjhh8PejmOFyQVvq3c6l3k7Mfq5TShkSfh7GexTWsEHEZ3WmORQc43keH9R5stEEgNpMhbh3nOuBIZBYajjhgGOn9v4wSQNJWxkepkVZiDCY+vcubPNrHCeCIWMHYMnKg28JTvM9LrJIj+3CYffT8kqJWIErUyKkPFNxMXuriKDzzAmDhnM9ezZ0zav4LklgxoK53mEO/9wjPi5TUZiQEqFCo/fb+dTKj/IjNHcwVv65z2O/NwmHJpL8VlIV0cmWQkMyI6Ee17lfxMkVA0RxBCI8HnEZyFjJoKPcKV8/BzLNRiPEKjxOcO5keM6tWwZv4fxDcs3HM5/t912m504oqw5FB4TWXYmkxw+swiuoi3LdsFYRjyueKJ28hISs7zBgxA+OBhYMOvFV6hyKk7YzKhSqhAq0Ln22mtteplZHAYA/B7WVvFGy+hghfvj5MP9u0xfKAy0maVmRorHBVLmlFrSdj9cCZP7e+lWN3z4cPvc8GHFYItZPj6AxD/WVwW34mfmlc6RBCHMFlOqEGoGlct4nfiwD1cG4zCBQKdAhwwUHxrMFFLikFppJMfI4sWLbZkt5X88Ho4dPhT40ONn/dwmHAbWZJBZt0QNPCWrlBAxWCTjK/+PwYgrW+I9x1olMvq8Z4O5suRQwSuXMZMa6TZcz2AyEl5vKglCHTcE0HRKdOcYL45dZo/5WQIizlkMWpnY4phgcOXnNqlhIo0vniMyhKw54bhn0EbQnyhcxoHngQlAh88IqhdoyV2jRo0UP+dKkEOdfzhG+Czzcxs/xxGBMcdzavj8ZFLx6aeftq9/NHr37m3XjTFRytpT7ouMHCWSrizMz23CIYvBF+dZJs0IujgfkgX207I/EXEeCy4VZQzG0gWCMLJeoY5LsJ0GnxNUa/C68ZwTxDEW4TMtVGUTVRUcr0wSBVc7gQAn1BiP7DCT7MH36bYLYLLbuy4wkuKeNfLpeVzxRIGXhBSuBNGb/QmVCXIfOKFmjsGsKzONnEg4ifAG4ncxW5PRyB4wAOYD1X3Yuc0cuY7BMGWUDKqYyWM9GwMZTgQEUzzWcAvkCeoYvDD4qV69ur2MmUC+Z/DFrI0Cr4zB2icwm8xrE2otIJcRULG+INIA1NualxlBShoIuhgUeJtthMLgt3///nZWj+OHx0ZARFaDn3XHvZ/bBKN8g2YkbuE174uqVavahg+U5jILGu59mcgYpDIpxAbmoQIv98EePPvL8cQsMseNn9v4ee7DvbYcN5TiuIxIKGS03D48TAYw4cBl7GHnzkN+bhOMY5CAjxIft98ZwT+z6EwYkQULbliSCOeT4Pc6528mesKVArtjJPj8w+cgnyve4yi12/jZcy3cceRFGTbHJANh9/nm1pARoFO2RdAUrrqASUUmqjjmyUoR6PH5RZDl9zbB+Pzks5WJBrdvGcEm2QuWGXCdAq/ouBLjcOcOsrZgYs9hMoagmXEWgVCo/fo4Jt1afy++J7hObY+/UGM/VxofKtubmjxBpZfpeVzxQoGXRI3ZDAYRnGSDUTrIbFiodTjM+LnOYd6MA9mz1AYkacUHB7M+wY8BDNDdrB0DD/bwYZGnN+NCWpsOiuEGUpSDMQvoAi+HzJfq2qNHNojnNXhBsJsBI4PKAJP1AwS+3kwkNfGUVzAI97u2jDVXNFvhg23QoEERAzYw4OWDjJ/zzkgz6HclE35uEwoDHIK14I5XHE/8vfybWvY1p2PGk+CYdRLeD1+3jiTcLDyTO1xH8xZvkxKOGTAQjHQbAuBIGQhwPmAQzCDVNeYA5VpUAXg7XXrx+hKssyDe2/SFcmfOWRw3fm4TCoEjVQZMboU6thJtLSHZPQZ2wY0AeA5Z0xRujSeBBF80yPHic5Djj/OPn9tcffXVGfJ3sFfW+eefn+wyl7nlWOZvDIdOcayX5jh1COAIGN1x5Oc2wThGOdb47A1uXsR1iXasRYPJEY5JMldertOft9utF+u62P+L86KXqyAK95xzzmNShs8pb7BExQgBd7jzKUEdJcrBn8FuPEglQvD+YdFMGJZO4+OKJ1rjJWlCFomZVzcwAR82bJTLCTfUAJFZPGb7gmeVWRPFLEdGBysM0inL8X65umQGV2Q6XODHwne3SB101WPtCA0PwmF2nUG2NwBlndqsWbMSvqthWvDcUTpDiV3wDCrlFhxTHFsE6QRfDmUsnJS9M36R8NoSaDNIoozRT9AFymhZT+F9jHQd48PPtXz2c5twxxMfaGRpHd4rTAwwAZDIQRcIkFnT4t36wmUKeW68WwwEz6hy3FBS55obMBhhgMggnKDKz238YMKJ7CQBkhfZcX4HDRxC4fGzVoesqMN6IMqGWOfHuczPbULhsTMYYv2St5SSx8k5N1z5Uk7F5xCfA1ResCbFoeqBQDRUt1OHn2PCzZU9uWYIZBvdJJ2f22QEAqLgzzcXEFFCSll/OGTGyIq5zAXHOp+BZBTctip+bhOMUjmy9qwN865J4rnmczbRjrVo8F7mve3dFoLzP+9bMozhAi8+N7gdzUwc3ue8v9l2J1wnSX6O14iu0g6vMY0teAwuMxyM45tzJBOlDhPXvH94jKEympy7OP/5CbybpPFxxRNlvCRNmJXhQ4vsBDNvzGjwpuCDJTjL5EVpH2V8fDC4mQ1OGpwg0ttcw52kGIRE086dATvZBv7lw4pZT8oOWZORWu08Net0wmMwxe0oVeFkwYdPTum+EyucWPmQ4UPaNXGh9IkZP9qnsw6GGTWCez50XNkmxwxrGgjQGISSueQ2lFEFrw9LDR01yXjxMwzcvfiA4DgN9RjJWPD76BLHzPju3bttlyeOaW7n9zahcBwRdBGckS0hgCNDTEkUHaQSHbP5vFa8NrxXKbHj/EHjEgJd18KbpiYcGwTrrpyMY4vMEBNGZET5OSZaaHjhZmP93CYSsgwEXRxfDCDYR4fJKSaleIyuHDDUY6S5A7O8lGlxDuF6fi8DGsfPbYJxXuJcxsCcWXEGTeyJyDmO55KGQImG14fsJOca3qNkI3lNKC93A1yyz2QSONbc5wDPIZfzPJI9pMKDjAQBupud93ObzBTq76BsmWOPySfeR2TjyRxzHLlj3c9twpVA8vnO+4jbs3UMx2mkYDDRMXHE+Ydjk+OGsmA+N5hwYu1hOIx3OHfxPJOV57jmuScYcpNSoZB9pQTefZbymcPEH6W2qb3GBN2UMPI6855hOwr+ZUKDz8twP8f5mXWl69atsw3Wwknr44onudhFOasfhGQeygSY1eXDxm1CzMFNiY3b44rvGSh4ywFBloGBgzeo4UThuv8x0xFc+hAKg2vK+PgZ0tbMyLrHwKDTzxuLmW/KHSm7cZhR4+RDnT0nhnDBGW9+Ml7BM0HsGcFJgRMeM3PehdepYWaQrIkbPDPrmBNODhmF2V8Gf97NPQkqCEQ4/TCTFfx8cvwxa8csfqgZNEpe6N7E88zgNppNJwno3OsVCh9UZENTe4wMZhjwk8EleAs1C+fnNqHwPmA2kQkA3g+U+yR6tsuLjCevPRkFyp4JzL1t4Hne+eAmAGLjau/rTvdSJox4LQiugtfb+LmNH5QWEtgwmcR5hPOJn8fIYIkMLudpJhk4boK7yPm5TShMJDBA4phmooLnLSesl0gPPocIsJnA4flwe6KBzzUmPOiu6i0z5XzA8cdnBa8px0hwxtzPbaLBa06ZLWWKqTVCIavOIJXX130Ghfs7eB99+eWXNhBnLMCkafB6Gz+3CYVzN8ca7fkJAvicds0X5P/HGlRzENgGI0jndSTQYBKApQx+zv8EXGQWqa5gzSY/5+fcxecMxyq/j3J+zlV+1hhy/uUcR3DNOI6AzB0bnD+ZfCQ75s57rHGkq+s///xjJ6X4nvJKjo9QS1PS+rjigQIvyVGYcWHheGolgrHAInUEB6sSHoE8M83MumdXWfUYKa/kAziRmh5kFAa9ZB0pFw7elyu7yMrHyKCHmePs+txkJ2StKOV0k5TxKqv+Dj4PydImeoCfWZjU5nWmtFWyL5UaSo7BDAmtU8loZTa/a0DkfxkB9jmiNDS7ysrHWLFixbBd6iRysEz5S3YOLLLyMVKmmFrjBfl/ZA8Q70FXVv4dBHzRtBaX9KEskU6nkr0p4yU5CmVabq2HZG/x8FrFw2OUlI1TKOtKT2lXrMXDY0x0TOSxJis7B/CJ9HeI5BQKvERERERERGJM7eRFRERERERiTIGXiIiIiIiIAi8REREREZH4poyXiIiIiIhIjCnwEhERERERiTEFXiIiIiIiIjGmwEtERERERCTGFHiJiIiIiIjEmAIvERERERGRGFPgJSIiIiIiYmLr/wBdQmBaNUHpNgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 880x330 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indice di correlazione tra prezzo e installazioni: +0.142\n"
     ]
    }
   ],
   "source": [
    "# --- IPOTESI 3: un prezzo più alto scoraggia le installazioni? ---\n",
    "\n",
    "# 1) tengo solo le app a pagamento, escludendo le 20 app-scherzo sopra i 100 $\n",
    "a_pagamento = app[(app[\"tipo\"] == \"A pagamento\") & (app[\"prezzo\"] < 100)].copy()\n",
    "\n",
    "# 2) divido le app in 5 fasce di prezzo\n",
    "a_pagamento[\"fascia_prezzo\"] = pd.cut(\n",
    "    a_pagamento[\"prezzo\"], bins=[0, 1.49, 2.99, 5.99, 14.99, 100],\n",
    "    labels=[\"fino a 1,49 $\", \"1,50–2,99 $\", \"3,00–5,99 $\", \"6,00–14,99 $\", \"15 $ e oltre\"])\n",
    "\n",
    "# 3) per ogni fascia calcolo: quante app ci sono, le installazioni tipiche\n",
    "#    e la percentuale di app che supera le 10.000 installazioni\n",
    "h3 = a_pagamento.groupby(\"fascia_prezzo\", observed=True).agg(\n",
    "    numero_app=(\"nome\", \"count\"),\n",
    "    installazioni_mediane=(\"installazioni\", \"median\"),\n",
    "    oltre_10_mila=(\"installazioni\", lambda s: (s >= 10_000).mean()),\n",
    ")\n",
    "display(h3.round(3))\n",
    "\n",
    "# 4) grafico a barre: percentuale di app oltre le 10.000 installazioni, per fascia\n",
    "fig, ax = plt.subplots(figsize=(8, 3))\n",
    "ax.bar(range(len(h3)), h3[\"oltre_10_mila\"] * 100, width=0.6,\n",
    "       color=[ARANCIONE if f == \"1,50–2,99 $\" else BLU for f in h3.index])\n",
    "ax.set_xticks(range(len(h3)), h3.index)\n",
    "ax.set_ylabel(\"% oltre le 10.000\\ninstallazioni\")\n",
    "ax.set_title(\"IPOTESI 3 — non è una discesa: è una gobba\")\n",
    "ax.set_ylim(0, 58)\n",
    "for i, (v, n) in enumerate(zip(h3[\"oltre_10_mila\"], h3[\"numero_app\"])):\n",
    "    ax.text(i, v * 100 + 1.5, f\"{v:.0%}\\n({n} app)\", ha=\"center\", fontsize=9)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 5) un indice che riassume se prezzo e installazioni crescono insieme oppure no\n",
    "indice = a_pagamento[[\"prezzo\", \"installazioni\"]].corr(method=\"spearman\").iloc[0, 1]\n",
    "print(f\"Indice di correlazione tra prezzo e installazioni: {indice:+.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ad41a69e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:15.173289Z",
     "iopub.status.busy": "2026-10-07T04:52:15.173081Z",
     "iopub.status.idle": "2026-10-07T04:52:15.187705Z",
     "shell.execute_reply": "2026-10-07T04:52:15.186840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1ª categoria</th>\n",
       "      <th>2ª categoria</th>\n",
       "      <th>3ª categoria</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fino a 1,49 $</th>\n",
       "      <td>Personalization 27%</td>\n",
       "      <td>Family 20%</td>\n",
       "      <td>Game 13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1,50–2,99 $</th>\n",
       "      <td>Family 32%</td>\n",
       "      <td>Tools 12%</td>\n",
       "      <td>Game 11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3,00–5,99 $</th>\n",
       "      <td>Family 21%</td>\n",
       "      <td>Medical 12%</td>\n",
       "      <td>Tools 12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6,00–14,99 $</th>\n",
       "      <td>Medical 25%</td>\n",
       "      <td>Family 24%</td>\n",
       "      <td>Game 9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15 $ e oltre</th>\n",
       "      <td>Medical 39%</td>\n",
       "      <td>Family 18%</td>\n",
       "      <td>Business 9%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      1ª categoria 2ª categoria 3ª categoria\n",
       "fino a 1,49 $  Personalization 27%   Family 20%     Game 13%\n",
       "1,50–2,99 $             Family 32%    Tools 12%     Game 11%\n",
       "3,00–5,99 $             Family 21%  Medical 12%    Tools 12%\n",
       "6,00–14,99 $           Medical 25%   Family 24%      Game 9%\n",
       "15 $ e oltre           Medical 39%   Family 18%  Business 9%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temi grafici (PERSONALIZATION) nella fascia fino a 1,49 $: 27% | app mediche (MEDICAL) sopra i 15 $: 39%\n"
     ]
    }
   ],
   "source": [
    "# --- Controprova per l'IPOTESI 3: dentro ogni fascia di prezzo ci sono le stesse app? ---\n",
    "\n",
    "# per ogni fascia, che percentuale delle app viene da ciascuna categoria\n",
    "composizione = pd.crosstab(a_pagamento[\"fascia_prezzo\"], a_pagamento[\"categoria\"],\n",
    "                           normalize=\"index\")\n",
    "\n",
    "# le tre categorie più presenti in ogni fascia\n",
    "prime_tre = pd.DataFrame({\n",
    "    fascia: [f\"{nome_leggibile(c) if 'nome_leggibile' in globals() else c} {q:.0%}\"\n",
    "             for c, q in riga.nlargest(3).items()]\n",
    "    for fascia, riga in composizione.iterrows()\n",
    "}, index=[\"1ª categoria\", \"2ª categoria\", \"3ª categoria\"]).T\n",
    "display(prime_tre)\n",
    "\n",
    "print(f\"Temi grafici (PERSONALIZATION) nella fascia fino a 1,49 $: \"\n",
    "      f\"{composizione.loc['fino a 1,49 $', 'PERSONALIZATION']:.0%} | \"\n",
    "      f\"app mediche (MEDICAL) sopra i 15 $: {composizione.loc['15 $ e oltre', 'MEDICAL']:.0%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7391e6",
   "metadata": {},
   "source": [
    "**Risultato**\n",
    "\n",
    "**IPOTESI 3 : RESPINTA, ed è la più interessante delle cinque.** Mi aspettavo che un prezzo più alto facesse scendere le installazioni. Invece l'indice è **positivo** (+0,14): in media prezzo e installazioni salgono un po' insieme. Ma il grafico mostra che non è nemmeno una crescita regolare: è una **gobba**.\n",
    "\n",
    "- la fascia più economica (fino a 1,49 $) è la peggiore: solo il **19,7%** delle app supera le 10.000 installazioni;\n",
    "- il punto più alto è nella fascia **1,50–2,99 $**, con il **47,1%**;\n",
    "- poi si torna a scendere, fino al **21,2%** sopra i 15 $.\n",
    "\n",
    "**Perché c'è la gobba? Non lo so, e la tabella qui sopra spiega perché.** ⚠ *legame, non\n",
    "causa.* Nella prima versione scrivevo che «il prezzo funziona come segnale di qualità». È una\n",
    "spiegazione causale, e i dati non la sostengono: **in ogni fascia stanno app di tipo diverso.**\n",
    "Fino a 1,49 $ più di un'app su quattro (27%) è un tema grafico o un pacchetto di icone; sopra\n",
    "i 15 $ il 39% sono app mediche per professionisti. Sono mercati diversi, con pubblici diversi:\n",
    "la gobba può dipendere da **cosa c'è** in ogni fascia, non dal prezzo in sé. Per sapere se il\n",
    "prezzo da solo sposta le installazioni servirebbe confrontare app dello stesso tipo a prezzi\n",
    "diversi, o meglio un esperimento sul prezzo della propria app.\n",
    "\n",
    "**La lezione di metodo:** l'indice di correlazione riassume tutto in un solo numero e presume che la relazione vada sempre nella stessa direzione. Qui non è così: un valore piccolo e positivo nascondeva due tendenze opposte che si compensavano. Quando un'ipotesi non torna, conviene guardare **la forma** della relazione in un grafico, e **cosa c'è dentro ogni gruppo**, prima di trarre conclusioni dal solo numero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b9ca3f69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:15.189513Z",
     "iopub.status.busy": "2026-10-07T04:52:15.189317Z",
     "iopub.status.idle": "2026-10-07T04:52:15.635533Z",
     "shell.execute_reply": "2026-10-07T04:52:15.634504Z"
    },
    "id": "b9ca3f69"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1485x462 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPOTESI 4: voto medio con aggiornamento < 1 mese 4.27 | oltre 1 anno 4.06\n",
      "IPOTESI 5: escursione delle mediane fra 33 categorie: 0.4 punti\n"
     ]
    }
   ],
   "source": [
    "# --- IPOTESI 4 e 5: contano gli aggiornamenti? E conta la categoria? ---\n",
    "\n",
    "# 1) tengo solo le app con un voto e le divido in 5 fasce,\n",
    "#    secondo il tempo passato dall'ultimo aggiornamento\n",
    "con_voto = app.dropna(subset=[\"voto\"]).copy()\n",
    "con_voto[\"eta_aggiornamento\"] = pd.cut(\n",
    "    con_voto[\"giorni_da_aggiornamento\"], bins=[-1, 30, 90, 180, 365, 10_000],\n",
    "    labels=[\"< 1 mese\", \"1–3 mesi\", \"3–6 mesi\", \"6–12 mesi\", \"oltre 1 anno\"])\n",
    "\n",
    "# 2) IPOTESI 4 — per ogni fascia: quante app ci sono e qual è il voto medio\n",
    "h4 = con_voto.groupby(\"eta_aggiornamento\", observed=True).agg(\n",
    "    numero_app=(\"nome\", \"count\"), voto_medio=(\"voto\", \"mean\"))\n",
    "\n",
    "# 3) IPOTESI 5 — per ogni categoria con almeno 30 app votate: il voto tipico,\n",
    "#    con le categorie ordinate dalla migliore alla peggiore\n",
    "categorie_grandi = (con_voto.groupby(\"categoria\")[\"voto\"].agg([\"count\", \"median\"])\n",
    "                    .query(\"count >= 30\").sort_values(\"median\", ascending=False))\n",
    "\n",
    "# 4) preparo due grafici affiancati (il secondo un po' più largo)\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13.5, 4.2),\n",
    "                               gridspec_kw={\"width_ratios\": [1, 1.25]})\n",
    "\n",
    "# 5) primo grafico (IPOTESI 4) — voto medio per fascia di aggiornamento\n",
    "ax1.bar(range(len(h4)), h4[\"voto_medio\"], color=sns.color_palette(\"Blues_r\", len(h4)),\n",
    "        width=0.6)\n",
    "ax1.set_xticks(range(len(h4)), h4.index, rotation=20, ha=\"right\")\n",
    "ax1.set_ylim(3.9, 4.35)\n",
    "ax1.set_ylabel(\"voto medio\")\n",
    "ax1.set_title(\"H4 — più tempo passa, più il voto scende\")\n",
    "for i, v in enumerate(h4[\"voto_medio\"]):\n",
    "    ax1.text(i, v + 0.008, f\"{v:.2f}\", ha=\"center\", fontsize=9, fontweight=\"bold\")\n",
    "\n",
    "# 6) secondo grafico (IPOTESI 5) — i voti di ogni categoria, con una linea arancione\n",
    "#    sul voto tipico di tutto lo store\n",
    "sns.boxplot(data=con_voto[con_voto[\"categoria\"].isin(categorie_grandi.index)],\n",
    "            y=\"categoria\", x=\"voto\", order=categorie_grandi.index, ax=ax2,\n",
    "            color=BLU, width=0.6, fliersize=1, linewidth=0.7)\n",
    "ax2.axvline(app[\"voto\"].median(), color=ARANCIONE, linewidth=1.5)\n",
    "ax2.set_yticks(range(len(categorie_grandi)),\n",
    "               [nome_leggibile(c) for c in categorie_grandi.index], fontsize=7)\n",
    "ax2.set_ylabel(\"\")\n",
    "ax2.set_xlim(1, 5.1)\n",
    "ax2.set_title(\"IPOTESI 5 — le mediane vanno da 4,1 a 4,5\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 7) riepilogo numerico delle due ipotesi\n",
    "print(f\"IPOTESI 4: voto medio con aggiornamento < 1 mese {h4['voto_medio'].iloc[0]:.2f} | \"\n",
    "      f\"oltre 1 anno {h4['voto_medio'].iloc[-1]:.2f}\")\n",
    "print(f\"IPOTESI 5: escursione delle mediane fra {len(categorie_grandi)} categorie: \"\n",
    "      f\"{categorie_grandi['median'].max() - categorie_grandi['median'].min():.1f} punti\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de934a46",
   "metadata": {},
   "source": [
    "**Risultato**\n",
    "\n",
    "**IPOTESI 4 è CONFERMATA, come legame.** L'andamento è **regolare**: passando dalle app aggiornate nell'ultimo mese a quelle ferme da più di un anno, il voto medio scende un gradino alla volta, da **4,27** a **4,06**. ⚠ *legame, non causa.* Da questi dati non posso dire se aggiornare spesso faccia salire il voto, o se invece le app di successo vengano aggiornate più spesso perché hanno più utenti, più segnalazioni e più risorse. Nella prima versione chiamavo gli aggiornamenti «l'unica leva nelle mani di chi sviluppa»: se è il successo a generarli, non sono una leva. Quello che resta vero è più piccolo: la frequenza degli aggiornamenti **è una scelta di chi sviluppa**, e se funzioni lo si può scoprire solo misurandolo sulla propria app.\n",
    "\n",
    "**IPOTESI 5 è CONFERMATA, ma conta pochissimo.** Il voto tipico cambia davvero da una categoria all'altra, ma tra la migliore e la peggiore ci sono solo **0,4 punti su una scala da 1 a 5**, e nel grafico le \"scatole\" si sovrappongono quasi tutte. Quindi scegliere una categoria **non serve** ad avere un voto più alto: serve a farsi trovare e ad avere meno concorrenti. È un buon promemoria: una differenza può essere reale e, allo stesso tempo, troppo piccola per cambiare le decisioni."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "917dcdac",
   "metadata": {
    "id": "917dcdac"
   },
   "source": [
    "---\n",
    "\n",
    "## 7. Il segmento sostenibilità: si può misurare?\n",
    "\n",
    "Nel dataset non c'è una colonna che dica quali app siano dedicate alla sostenibilità, provo a costruirmi questa informazione da solo. Procedo in tre passi:\n",
    "\n",
    "1. cerco delle **parole chiave** nel nome e nei generi delle app;\n",
    "2. **controllo a mano** i risultati, uno per uno;\n",
    "3. misuro **quanto sbaglia** il metodo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c14f07a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:15.637696Z",
     "iopub.status.busy": "2026-10-07T04:52:15.637413Z",
     "iopub.status.idle": "2026-10-07T04:52:15.891329Z",
     "shell.execute_reply": "2026-10-07T04:52:15.890600Z"
    },
    "id": "c14f07a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77 parole chiave -> 66 candidati su 9,674 app\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nome</th>\n",
       "      <th>categoria</th>\n",
       "      <th>installazioni</th>\n",
       "      <th>voto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Download free book with green book</td>\n",
       "      <td>BOOKS_AND_REFERENCE</td>\n",
       "      <td>100000</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>FamilySearch Tree</td>\n",
       "      <td>BOOKS_AND_REFERENCE</td>\n",
       "      <td>1000000</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>The green alien dance</td>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>Adult Glitter Color by Number Book - Sandbox P...</td>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>1000000</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>Frigo Magic: Easy recipe idea and anti-waste</td>\n",
       "      <td>FOOD_AND_DRINK</td>\n",
       "      <td>500000</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>Uber Eats: Local Food Delivery</td>\n",
       "      <td>FOOD_AND_DRINK</td>\n",
       "      <td>10000000</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>foodpanda - Local Food Delivery</td>\n",
       "      <td>FOOD_AND_DRINK</td>\n",
       "      <td>10000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>Sportractive GPS Running Cycling Distance Tracker</td>\n",
       "      <td>HEALTH_AND_FITNESS</td>\n",
       "      <td>1000000</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   nome            categoria  installazioni  voto\n",
       "141                  Download free book with green book  BOOKS_AND_REFERENCE         100000   4.6\n",
       "158                                   FamilySearch Tree  BOOKS_AND_REFERENCE        1000000   4.3\n",
       "868                               The green alien dance        ENTERTAINMENT        1000000   3.8\n",
       "887   Adult Glitter Color by Number Book - Sandbox P...        ENTERTAINMENT        1000000   4.3\n",
       "1185       Frigo Magic: Easy recipe idea and anti-waste       FOOD_AND_DRINK         500000   4.1\n",
       "1250                     Uber Eats: Local Food Delivery       FOOD_AND_DRINK       10000000   4.2\n",
       "1254                    foodpanda - Local Food Delivery       FOOD_AND_DRINK       10000000   4.0\n",
       "1266  Sportractive GPS Running Cycling Distance Tracker   HEALTH_AND_FITNESS        1000000   4.8"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Cerco le app sostenibili con 77 parole chiave ---\n",
    "\n",
    "# 1) l'elenco delle parole chiave, in inglese come i nomi delle app, raggruppate per tema.\n",
    "#    \\b indica l'inizio o la fine di una parola: così \"tree\" (albero)\n",
    "#    non trova anche \"street\" (strada)\n",
    "TERMINI = [\n",
    "    # energia\n",
    "    r\"energy sav\", r\"save energy\", r\"energy efficien\", r\"energy monitor\", r\"power consum\",\n",
    "    r\"electricity\", r\"\\bkwh\\b\", r\"smart meter\", r\"solar\", r\"wind power\", r\"renewab\",\n",
    "    # mobilità\n",
    "    r\"\\bev charg\", r\"charging station\", r\"electric car\", r\"electric vehicle\",\n",
    "    r\"public transport\", r\"\\btransit\\b\", r\"carpool\", r\"car pool\", r\"rideshar\",\n",
    "    r\"bicycl\", r\"\\bcycling\\b\", r\"\\bbike shar\",\n",
    "    # rifiuti e riuso\n",
    "    r\"recycl\", r\"compost\", r\"upcycl\", r\"\\bwaste\\b\", r\"zero waste\", r\"\\btrash\\b\", r\"garbage\",\n",
    "    r\"thrift\", r\"second[- ]?hand\", r\"preloved\", r\"\\bswap\\b\", r\"\\breuse\\b\", r\"refill\",\n",
    "    r\"plastic\", r\"litter\", r\"clean ?up\",\n",
    "    # alimentazione\n",
    "    r\"\\bvegan\\b\", r\"vegetarian\", r\"plant[- ]based\", r\"\\borganic\\b\", r\"food waste\",\n",
    "    r\"leftover\", r\"shelf life\", r\"local food\", r\"farmers market\",\n",
    "    # natura, clima, acqua\n",
    "    r\"eco[- ]?friendly\", r\"ecolog\", r\"sustainab\", r\"\\bgreen\\b\", r\"carbon\", r\"\\bco2\\b\",\n",
    "    r\"climate\", r\"climat\", r\"environment\", r\"footprint\", r\"emission\", r\"pollut\",\n",
    "    r\"air quality\", r\"smog\", r\"\\btree\\b\", r\"\\bforest\\b\", r\"wildlife\", r\"conservation\",\n",
    "    r\"biodivers\", r\"\\bocean\\b\", r\"\\breef\\b\", r\"water sav\", r\"water consum\", r\"rainwater\",\n",
    "    # etica\n",
    "    r\"\\bsdg\\b\", r\"\\besg\\b\", r\"fair ?trade\", r\"ethical\", r\"sharing econom\",\n",
    "]\n",
    "\n",
    "# 2) unisco nome e generi di ogni app in un unico testo in cui cercare\n",
    "testo = app[\"nome\"].fillna(\"\") + \" | \" + app[\"generi\"].fillna(\"\")\n",
    "\n",
    "# 3) tengo le app che contengono almeno una parola chiave\n",
    "#    (\"|\" significa \"oppure\"; case=False ignora maiuscole e minuscole)\n",
    "candidati = app[testo.str.contains(\"|\".join(TERMINI), case=False, regex=True, na=False)]\n",
    "\n",
    "# 4) quante app ho trovato, e le prime 8 come esempio\n",
    "print(f\"{len(TERMINI)} parole chiave -> {len(candidati)} candidati su {len(app):,} app\")\n",
    "candidati[[\"nome\", \"categoria\", \"installazioni\", \"voto\"]].head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e202e16",
   "metadata": {},
   "source": [
    "**Risultato** La ricerca trova **66 candidati su 9.674 app**. Ma già dai primi nomi si vede un problema: *«The green alien dance»*, *«Solar System AR»*, *«Bike Race Free»* contengono le parole giuste, ma non c'entrano nulla con la sostenibilità. La ricerca per parole chiave raccoglie molto \"rumore\", quindi ho letto tutti i 66 nomi e li ho classificati a mano.\n",
    "\n",
    "**Le regole che ho usato**, decise prima di guardare i risultati:\n",
    "\n",
    "- un **gioco o un simulatore** non è un'app sostenibile, anche se parla di biciclette o di foreste;\n",
    "- **temi grafici, sfondi e tastiere** nemmeno;\n",
    "- conta lo **scopo principale** dell'app, non un dettaglio (*WardenCam – reuse old phones* riusa vecchi telefoni, ma serve per la videosorveglianza);\n",
    "- la **parola può avere un altro significato** (*Green* usato come cognome, *Cleanup* nel senso di pulire la memoria del telefono, *Ethical Hacking*);\n",
    "- la **consegna di cibo a domicilio** non è alimentazione sostenibile;\n",
    "- le app per **allenarsi in bicicletta** sono un caso dubbio e le **escludo** (Strava serve per lo sport, non per spostarsi).\n",
    "\n",
    "La cella qui sotto contiene **entrambi gli elenchi**, le 34 app tenute e le 32 scartate con il\n",
    "motivo, e controlla che insieme diano esattamente i 66 candidati: il conteggio dei motivi non è\n",
    "più una tabella scritta a mano, è un output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dfe52d1a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:15.892977Z",
     "iopub.status.busy": "2026-10-07T04:52:15.892788Z",
     "iopub.status.idle": "2026-10-07T04:52:15.904356Z",
     "shell.execute_reply": "2026-10-07T04:52:15.903650Z"
    },
    "id": "dfe52d1a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Controllo: ogni scartata ha un motivo -> OK\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>app scartate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>motivo</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gioco o simulatore</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>la parola ha un altro significato</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>allenamento in bicicletta (caso dubbio, escluso)</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tema grafico, sfondo o tastiera</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meteo, suoni per rilassarsi, videosorveglianza</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>consegna di cibo a domicilio</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pulizia della memoria del telefono</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  app scartate\n",
       "motivo                                                        \n",
       "gioco o simulatore                                           8\n",
       "la parola ha un altro significato                            6\n",
       "allenamento in bicicletta (caso dubbio, escluso)             6\n",
       "tema grafico, sfondo o tastiera                              5\n",
       "meteo, suoni per rilassarsi, videosorveglianza               3\n",
       "consegna di cibo a domicilio                                 2\n",
       "pulizia della memoria del telefono                           2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sostenibili: 34 | scartate: 32 | totale: 66\n",
      "Precisione del metodo: 34/66 = 51.5%\n",
      "\n",
      "Il segmento è il 0.35% del catalogo ma solo il 0.03% delle installazioni.\n",
      "Installazioni mediane: 10,000 contro 100,000 del negozio.\n"
     ]
    }
   ],
   "source": [
    "# --- Le 34 app che, dopo il controllo a mano, sono davvero sostenibili ---\n",
    "\n",
    "# 1) l'elenco delle app sostenibili, ognuna con il suo tema\n",
    "SOSTENIBILI = {\n",
    "    'Frigo Magic: Easy recipe idea and anti-waste': 'alimentazione',\n",
    "    'Yahoo! transit guide free timetable, operation information, transfer search': 'mobilita_pubblica',\n",
    "    'Transit: Real-Time Transit App': 'mobilita_pubblica',\n",
    "    'Mapy.cz - Cycling & Hiking offline maps': 'mobilita_attiva',\n",
    "    'Czech Public Transport IDOS': 'mobilita_pubblica',\n",
    "    'NAVITIME Bus Transit JAPAN': 'mobilita_pubblica',\n",
    "    'British Columbia Transit Info': 'mobilita_pubblica',\n",
    "    'Electricity Bill Calculator BD': 'energia',\n",
    "    'Remix Second Hand': 'rifiuti_riuso',\n",
    "    'sustainability@BU': 'informazione',\n",
    "    'DIY Recycled CD Wall Art': 'rifiuti_riuso',\n",
    "    'Offline Jízdní řády CG Transit': 'mobilita_pubblica',\n",
    "    'Solar CT PV System Power': 'energia',\n",
    "    'Recycling Cy': 'rifiuti_riuso',\n",
    "    'Hamburg Transit - Offline HVV DB times and plans': 'mobilita_pubblica',\n",
    "    'DC Metro Transit - Free': 'mobilita_pubblica',\n",
    "    'DC Metro Transit': 'mobilita_pubblica',\n",
    "    'Citymapper - Transit Navigation': 'mobilita_pubblica',\n",
    "    'Tamilnadu Electricity Info': 'energia',\n",
    "    'TN Electricity (TNEB)': 'energia',\n",
    "    'ES Solar': 'energia',\n",
    "    '¿Es Vegan?': 'alimentazione',\n",
    "    'CALIOPE EU: Air Quality': 'natura_clima',\n",
    "    'EU Brazil Green Business Forum': 'informazione',\n",
    "    'ChargeHub - Find EV & Tesla Charging Stations': 'mobilita_elettrica',\n",
    "    'NEXTCHARGE - Charging Stations': 'mobilita_elettrica',\n",
    "    'JuiceNet - Smart EV Charging': 'mobilita_elettrica',\n",
    "    'Zap-Map: EV charging points UK': 'mobilita_elettrica',\n",
    "    'EV Charging': 'mobilita_elettrica',\n",
    "    'Electric Car Charging Points: Ev charger Stations': 'mobilita_elettrica',\n",
    "    'ELMO EV Charging': 'mobilita_elettrica',\n",
    "    'Bolt - EV Charging Service': 'mobilita_elettrica',\n",
    "    'Power Plug EV charger': 'mobilita_elettrica',\n",
    "    'Carpooling FH Hagenberg': 'mobilita_pubblica',\n",
    "}\n",
    "\n",
    "# 2) le 32 app scartate, raggruppate per motivo\n",
    "SCARTATE = {\n",
    "    \"gioco o simulatore\": [\n",
    "        \"The green alien dance\", \"Fuzzy Seasons: Animal Forest\", \"Solar System AR ( ARCore )\",\n",
    "        \"Skill Tree - BL Pre Sequel\", \"Water Surfer Floating BMX Bicycle Rider Racing\",\n",
    "        \"Plastic Surgery Surgeon Simulator Er Doctor Games\",\n",
    "        \"Electric Car Taxi Driver: NY City Cab Taxi Games\",\n",
    "        \"Survival Forest : Survivor Home Builder\"],\n",
    "    \"la parola ha un altro significato\": [\n",
    "        \"Download free book with green book\", \"FamilySearch Tree\", \"Forest Crossing AH\",\n",
    "        \"BV Forest\", \"Green Build - An unofficial Travis CI client\", \"Ethical Hacking\"],\n",
    "    \"allenamento in bicicletta (caso dubbio, escluso)\": [\n",
    "        \"Sportractive GPS Running Cycling Distance Tracker\", \"Cycling - Bike Tracker\",\n",
    "        \"Bike Computer - GPS Cycling Tracker\",\n",
    "        \"Strava Training: Track Running, Cycling & Swimming\",\n",
    "        \"Map My Ride GPS Cycling Riding\", \"Sports Tracker Running Cycling\"],\n",
    "    \"tema grafico, sfondo o tastiera\": [\n",
    "        \"Adult Glitter Color by Number Book - Sandbox Pages\",\n",
    "        \"Colorful Glitter Neon Butterfly Keyboard Theme\", \"M Theme - Dark Green Icon Pack\",\n",
    "        \"A.J. Green Wallpapers 4 Fans\", \"Glitter Color By Number - Glitter Number Coloring\"],\n",
    "    \"pulizia della memoria del telefono\": [\n",
    "        \"RAM Cleanup Ad-Free Option\", \"Avast Cleanup & Boost, Phone Cleaner, Optimizer\"],\n",
    "    \"consegna di cibo a domicilio\": [\n",
    "        \"Uber Eats: Local Food Delivery\", \"foodpanda - Local Food Delivery\"],\n",
    "    \"meteo, suoni per rilassarsi, videosorveglianza\": [\n",
    "        \"Climatempo Lite - 15 day weather forecast\", \"Relax Ocean ~ Nature Sounds\",\n",
    "        \"Home Security Camera WardenCam - reuse old phones\"],\n",
    "}\n",
    "MOTIVO = {nome: motivo for motivo, nomi in SCARTATE.items() for nome in nomi}\n",
    "\n",
    "# 3) dal dataset prendo solo le 34 app sostenibili e aggiungo la colonna con il tema\n",
    "segmento = app[app[\"nome\"].isin(SOSTENIBILI)].copy()\n",
    "segmento[\"tema\"] = segmento[\"nome\"].map(SOSTENIBILI)\n",
    "\n",
    "# 4) le altre app trovate con le parole chiave sono quelle scartate:\n",
    "#    controllo che siano ESATTAMENTE quelle dell'elenco, e conto i motivi\n",
    "scartate = candidati[~candidati[\"nome\"].isin(SOSTENIBILI)].copy()\n",
    "scartate[\"motivo\"] = scartate[\"nome\"].map(MOTIVO)\n",
    "print(\"Controllo: ogni scartata ha un motivo ->\",\n",
    "      \"OK\" if scartate[\"motivo\"].notna().all() and len(scartate) == len(MOTIVO) else \"NO\")\n",
    "display(scartate[\"motivo\"].value_counts().rename(\"app scartate\").to_frame())\n",
    "\n",
    "# 5) quanto è preciso il metodo, e quanto pesa il segmento su tutto lo store\n",
    "print(f\"Sostenibili: {len(segmento)} | scartate: {len(scartate)} | totale: {len(candidati)}\")\n",
    "print(f\"Precisione del metodo: {len(segmento)}/{len(candidati)} = {len(segmento) / len(candidati):.1%}\")\n",
    "print(f\"\\nIl segmento è il {len(segmento) / len(app):.2%} del catalogo \"\n",
    "      f\"ma solo il {segmento['installazioni'].sum() / app['installazioni'].sum():.2%} \"\n",
    "      \"delle installazioni.\")\n",
    "print(f\"Installazioni mediane: {segmento['installazioni'].median():,.0f} \"\n",
    "      f\"contro {app['installazioni'].median():,.0f} del negozio.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8e567c7",
   "metadata": {},
   "source": [
    "### 7.1 La precisione non basta: quante app sostenibili sfuggono alle parole chiave?\n",
    "\n",
    "La precisione (51,5%) dice quante delle app **trovate** erano davvero sostenibili. Non dice\n",
    "quante ne ho **perse**: un'app chiamata *OfferUp*, per comprare e vendere oggetti usati, non\n",
    "contiene nessuna delle 77 parole chiave e non entra nemmeno tra i candidati, e lo stesso vale\n",
    "per *OLX*. È la **copertura**\n",
    "del metodo, e senza misurarla «il segmento è lo 0,35% del catalogo» è solo un minimo.\n",
    "\n",
    "Il modo più semplice per stimarla: **estraggo a caso 1.000 app fra le 9.608 che le parole\n",
    "chiave non hanno trovato, e le leggo una per una** con le stesse regole usate per i 66\n",
    "candidati. Se nel campione trovo *k* app sostenibili, fuori dai candidati ce ne sono circa\n",
    "*k*/1.000 × 9.608. Con un campione di 1.000 la stima è grezza, quindi la accompagno con\n",
    "l'intervallo di confidenza al 95% (metodo di Wilson, che funziona bene anche quando *k* è\n",
    "piccolo)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4d360b6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:15.905778Z",
     "iopub.status.busy": "2026-10-07T04:52:15.905587Z",
     "iopub.status.idle": "2026-10-07T04:52:15.917319Z",
     "shell.execute_reply": "2026-10-07T04:52:15.916705Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Campione: 1000 app su 9608 fuori dai candidati | estrazione verificata\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nome</th>\n",
       "      <th>categoria</th>\n",
       "      <th>installazioni</th>\n",
       "      <th>esito</th>\n",
       "      <th>tema o motivo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2666</th>\n",
       "      <td>OLX - Buy and Sell</td>\n",
       "      <td>SHOPPING</td>\n",
       "      <td>50000000</td>\n",
       "      <td>sostenibile</td>\n",
       "      <td>rifiuti_riuso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3161</th>\n",
       "      <td>NTES</td>\n",
       "      <td>TRAVEL_AND_LOCAL</td>\n",
       "      <td>10000000</td>\n",
       "      <td>sostenibile</td>\n",
       "      <td>mobilita_pubblica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3830</th>\n",
       "      <td>Yandex.Transport</td>\n",
       "      <td>MAPS_AND_NAVIGATION</td>\n",
       "      <td>10000000</td>\n",
       "      <td>sostenibile</td>\n",
       "      <td>mobilita_pubblica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9182</th>\n",
       "      <td>TANGEDCO Mobile App (Official)</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>500000</td>\n",
       "      <td>sostenibile</td>\n",
       "      <td>energia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8204</th>\n",
       "      <td>DB Streckenagent</td>\n",
       "      <td>MAPS_AND_NAVIGATION</td>\n",
       "      <td>500000</td>\n",
       "      <td>sostenibile</td>\n",
       "      <td>mobilita_pubblica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9202</th>\n",
       "      <td>TNEB Quick Pay Easy</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>10000</td>\n",
       "      <td>sostenibile</td>\n",
       "      <td>energia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9949</th>\n",
       "      <td>EV Stations Hawaii</td>\n",
       "      <td>MAPS_AND_NAVIGATION</td>\n",
       "      <td>1000</td>\n",
       "      <td>sostenibile</td>\n",
       "      <td>mobilita_elettrica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7519</th>\n",
       "      <td>cPro Marketplace: Buy. Sell. Rent. Date. Jobs.</td>\n",
       "      <td>SHOPPING</td>\n",
       "      <td>10000000</td>\n",
       "      <td>dubbia</td>\n",
       "      <td>annunci di ogni tipo, l'usato è solo una parte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2688</th>\n",
       "      <td>Real Estate, Car, Shopping and Others</td>\n",
       "      <td>SHOPPING</td>\n",
       "      <td>10000000</td>\n",
       "      <td>dubbia</td>\n",
       "      <td>annunci di ogni tipo, l'usato è solo una parte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2671</th>\n",
       "      <td>Horn, free country requirements</td>\n",
       "      <td>SHOPPING</td>\n",
       "      <td>1000000</td>\n",
       "      <td>dubbia</td>\n",
       "      <td>nome tradotto male: sembra un mercato di scamb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9963</th>\n",
       "      <td>EV Finder</td>\n",
       "      <td>TOOLS</td>\n",
       "      <td>1000</td>\n",
       "      <td>dubbia</td>\n",
       "      <td>il nome fa pensare alla ricarica elettrica, ma...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                nome            categoria  installazioni        esito                                      tema o motivo\n",
       "2666                              OLX - Buy and Sell             SHOPPING       50000000  sostenibile                                      rifiuti_riuso\n",
       "3161                                            NTES     TRAVEL_AND_LOCAL       10000000  sostenibile                                  mobilita_pubblica\n",
       "3830                                Yandex.Transport  MAPS_AND_NAVIGATION       10000000  sostenibile                                  mobilita_pubblica\n",
       "9182                  TANGEDCO Mobile App (Official)             BUSINESS         500000  sostenibile                                            energia\n",
       "8204                                DB Streckenagent  MAPS_AND_NAVIGATION         500000  sostenibile                                  mobilita_pubblica\n",
       "9202                             TNEB Quick Pay Easy              FINANCE          10000  sostenibile                                            energia\n",
       "9949                              EV Stations Hawaii  MAPS_AND_NAVIGATION           1000  sostenibile                                 mobilita_elettrica\n",
       "7519  cPro Marketplace: Buy. Sell. Rent. Date. Jobs.             SHOPPING       10000000       dubbia     annunci di ogni tipo, l'usato è solo una parte\n",
       "2688           Real Estate, Car, Shopping and Others             SHOPPING       10000000       dubbia     annunci di ogni tipo, l'usato è solo una parte\n",
       "2671                 Horn, free country requirements             SHOPPING        1000000       dubbia  nome tradotto male: sembra un mercato di scamb...\n",
       "9963                                       EV Finder                TOOLS           1000       dubbia  il nome fa pensare alla ricarica elettrica, ma..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- 7.1 Un campione casuale FUORI dai candidati, letto a mano ---\n",
    "\n",
    "# 1) le app che le parole chiave NON hanno trovato\n",
    "fuori = app[~app.index.isin(candidati.index)]\n",
    "\n",
    "# 2) ne estraggo 1.000 a caso; il seme fisso (2026) rende l'estrazione ripetibile\n",
    "campione = fuori.sample(n=1000, random_state=2026)\n",
    "\n",
    "# 3) il risultato della lettura: le 1.000 righe le ho lette tutte, con le stesse regole\n",
    "#    dei 66 candidati. Queste sono le sostenibili, divise fra sicure e dubbie.\n",
    "CAMPIONE_SOSTENIBILI = {\n",
    "    \"NTES\": \"mobilita_pubblica\",                      # orari dei treni delle ferrovie indiane\n",
    "    \"Yandex.Transport\": \"mobilita_pubblica\",\n",
    "    \"DB Streckenagent\": \"mobilita_pubblica\",\n",
    "    \"EV Stations Hawaii\": \"mobilita_elettrica\",\n",
    "    \"OLX - Buy and Sell\": \"rifiuti_riuso\",            # compravendita dell'usato\n",
    "    \"TANGEDCO Mobile App (Official)\": \"energia\",      # come 'TN Electricity (TNEB)' fra i 34\n",
    "    \"TNEB Quick Pay Easy\": \"energia\",                 # idem\n",
    "}\n",
    "CAMPIONE_DUBBI = {\n",
    "    \"EV Finder\": \"il nome fa pensare alla ricarica elettrica, ma categoria e dati non lo confermano\",\n",
    "    \"cPro Marketplace: Buy. Sell. Rent. Date. Jobs.\": \"annunci di ogni tipo, l'usato è solo una parte\",\n",
    "    \"Real Estate, Car, Shopping and Others\": \"annunci di ogni tipo, l'usato è solo una parte\",\n",
    "    \"Horn, free country requirements\": \"nome tradotto male: sembra un mercato di scambio, ma non si capisce\",\n",
    "}\n",
    "\n",
    "# 4) controllo che l'estrazione sia quella che ho letto (stessi dati, stessa pulizia)\n",
    "mancanti = (set(CAMPIONE_SOSTENIBILI) | set(CAMPIONE_DUBBI)) - set(campione[\"nome\"])\n",
    "print(\"Campione:\", len(campione), \"app su\", len(fuori), \"fuori dai candidati |\",\n",
    "      \"estrazione verificata\" if not mancanti else f\"ATTENZIONE, campione diverso: {mancanti}\")\n",
    "\n",
    "trovate = campione[campione[\"nome\"].isin(CAMPIONE_SOSTENIBILI) | campione[\"nome\"].isin(CAMPIONE_DUBBI)].copy()\n",
    "trovate[\"esito\"] = np.where(trovate[\"nome\"].isin(CAMPIONE_SOSTENIBILI), \"sostenibile\", \"dubbia\")\n",
    "trovate[\"tema o motivo\"] = trovate[\"nome\"].map({**CAMPIONE_SOSTENIBILI, **CAMPIONE_DUBBI})\n",
    "display(trovate.sort_values([\"esito\", \"installazioni\"], ascending=[False, False])[\n",
    "    [\"nome\", \"categoria\", \"installazioni\", \"esito\", \"tema o motivo\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "77587d35",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:15.918823Z",
     "iopub.status.busy": "2026-10-07T04:52:15.918629Z",
     "iopub.status.idle": "2026-10-07T04:52:15.968466Z",
     "shell.execute_reply": "2026-10-07T04:52:15.967755Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_ae1df\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ae1df_level0_col0\" class=\"col_heading level0 col0\" >app sostenibili perse</th>\n",
       "      <th id=\"T_ae1df_level0_col1\" class=\"col_heading level0 col1\" >app sostenibili in tutto</th>\n",
       "      <th id=\"T_ae1df_level0_col2\" class=\"col_heading level0 col2\" >quota del catalogo</th>\n",
       "      <th id=\"T_ae1df_level0_col3\" class=\"col_heading level0 col3\" >copertura delle parole chiave</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ae1df_level0_row0\" class=\"row_heading level0 row0\" >minimo certo (solo le 34 trovate)</th>\n",
       "      <td id=\"T_ae1df_row0_col0\" class=\"data row0 col0\" >0</td>\n",
       "      <td id=\"T_ae1df_row0_col1\" class=\"data row0 col1\" >34</td>\n",
       "      <td id=\"T_ae1df_row0_col2\" class=\"data row0 col2\" >0.35%</td>\n",
       "      <td id=\"T_ae1df_row0_col3\" class=\"data row0 col3\" >100%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ae1df_level0_row1\" class=\"row_heading level0 row1\" >stima bassa (7 su 1000, limite basso al 95%)</th>\n",
       "      <td id=\"T_ae1df_row1_col0\" class=\"data row1 col0\" >33</td>\n",
       "      <td id=\"T_ae1df_row1_col1\" class=\"data row1 col1\" >67</td>\n",
       "      <td id=\"T_ae1df_row1_col2\" class=\"data row1 col2\" >0.69%</td>\n",
       "      <td id=\"T_ae1df_row1_col3\" class=\"data row1 col3\" >51%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ae1df_level0_row2\" class=\"row_heading level0 row2\" >stima centrale (7 su 1000)</th>\n",
       "      <td id=\"T_ae1df_row2_col0\" class=\"data row2 col0\" >67</td>\n",
       "      <td id=\"T_ae1df_row2_col1\" class=\"data row2 col1\" >101</td>\n",
       "      <td id=\"T_ae1df_row2_col2\" class=\"data row2 col2\" >1.05%</td>\n",
       "      <td id=\"T_ae1df_row2_col3\" class=\"data row2 col3\" >34%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ae1df_level0_row3\" class=\"row_heading level0 row3\" >stima alta (11 su 1000, limite alto al 95%)</th>\n",
       "      <td id=\"T_ae1df_row3_col0\" class=\"data row3 col0\" >188</td>\n",
       "      <td id=\"T_ae1df_row3_col1\" class=\"data row3 col1\" >222</td>\n",
       "      <td id=\"T_ae1df_row3_col2\" class=\"data row3 col2\" >2.30%</td>\n",
       "      <td id=\"T_ae1df_row3_col3\" class=\"data row3 col3\" >15%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2cbb8badbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quota delle installazioni: 0.03% con le sole 34, circa 0.9% contando le perse (stima grezza)\n"
     ]
    }
   ],
   "source": [
    "# --- Dal campione a una stima: quante ne perdo, e quanto vale il segmento ---\n",
    "\n",
    "def wilson(k, n, z=1.96):\n",
    "    \"\"\"Intervallo di confidenza al 95% per una proporzione k/n (metodo di Wilson).\"\"\"\n",
    "    p = k / n\n",
    "    centro = (p + z**2 / (2 * n)) / (1 + z**2 / n)\n",
    "    margine = z * np.sqrt(p * (1 - p) / n + z**2 / (4 * n**2)) / (1 + z**2 / n)\n",
    "    return centro - margine, centro + margine\n",
    "\n",
    "n, N_fuori, N_store = len(campione), len(fuori), len(app)\n",
    "k_sicure = int(campione[\"nome\"].isin(CAMPIONE_SOSTENIBILI).sum())\n",
    "k_con_dubbi = k_sicure + int(campione[\"nome\"].isin(CAMPIONE_DUBBI).sum())\n",
    "trovate_kw = len(segmento)\n",
    "\n",
    "# tre scenari: il limite basso usa solo le sicure, il limite alto conta anche le dubbie\n",
    "scenari = {\n",
    "    \"minimo certo (solo le 34 trovate)\": 0.0,\n",
    "    f\"stima bassa ({k_sicure} su {n}, limite basso al 95%)\": wilson(k_sicure, n)[0],\n",
    "    f\"stima centrale ({k_sicure} su {n})\": k_sicure / n,\n",
    "    f\"stima alta ({k_con_dubbi} su {n}, limite alto al 95%)\": wilson(k_con_dubbi, n)[1],\n",
    "}\n",
    "stima = pd.DataFrame({\n",
    "    nome: {\"app sostenibili perse\": p * N_fuori,\n",
    "           \"app sostenibili in tutto\": trovate_kw + p * N_fuori,\n",
    "           \"quota del catalogo\": (trovate_kw + p * N_fuori) / N_store,\n",
    "           \"copertura delle parole chiave\": trovate_kw / (trovate_kw + p * N_fuori)}\n",
    "    for nome, p in scenari.items()}).T\n",
    "display(stima.style.format({\"app sostenibili perse\": \"{:,.0f}\", \"app sostenibili in tutto\": \"{:,.0f}\",\n",
    "                            \"quota del catalogo\": \"{:.2%}\", \"copertura delle parole chiave\": \"{:.0%}\"}))\n",
    "\n",
    "# la quota delle installazioni è ancora più incerta: due app del campione da 10 milioni\n",
    "# pesano più di tutte le altre insieme. La riporto solo come ordine di grandezza.\n",
    "inst_campione = campione.loc[campione[\"nome\"].isin(CAMPIONE_SOSTENIBILI), \"installazioni\"].sum()\n",
    "quota_inst_kw = segmento[\"installazioni\"].sum() / app[\"installazioni\"].sum()\n",
    "quota_inst_stimata = quota_inst_kw + inst_campione * N_fuori / n / app[\"installazioni\"].sum()\n",
    "print(f\"Quota delle installazioni: {quota_inst_kw:.2%} con le sole 34, \"\n",
    "      f\"circa {quota_inst_stimata:.1%} contando le perse (stima grezza)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a72c43f",
   "metadata": {},
   "source": [
    "**Risultato** Nel campione ci sono **7 app sostenibili su 1.000**, più 4 dubbie: le parole\n",
    "chiave **vedono circa un'app sostenibile su tre.**\n",
    "\n",
    "| | app sostenibili | quota del catalogo | copertura delle parole chiave |\n",
    "|---|---|---|---|\n",
    "| minimo certo (le 34 trovate) | 34 | 0,35% | — |\n",
    "| stima bassa | 67 | 0,69% | 51% |\n",
    "| **stima centrale** | **101** | **1,05%** | **34%** |\n",
    "| stima alta (con le dubbie) | 222 | 2,30% | 15% |\n",
    "\n",
    "Quindi la frase giusta non è «il segmento è lo 0,35% del catalogo», ma: **il segmento vale fra\n",
    "lo 0,35% e il 2,3% del catalogo, con una stima centrale intorno all'1%.** Resta un segmento\n",
    "piccolo, ma fino a sei volte più grande di quanto dicessi.\n",
    "\n",
    "Tre cose che il campione insegna sul metodo:\n",
    "\n",
    "1. **Le app perse sono quelle che non si dichiarano «verdi».** OLX compra e vende l'usato senza\n",
    "   la parola *second hand*; NTES, Yandex.Transport e DB Streckenagent sono app di treni e mezzi\n",
    "   pubblici che non scrivono *transit*. È lo stesso errore di Olio, che citavo fra i limiti: ora\n",
    "   è misurato.\n",
    "2. **Anche le installazioni erano sottostimate, e di molto.** Tre app del campione hanno da 10 a\n",
    "   50 milioni di installazioni (OLX da sola 50 milioni): contando le perse, la quota del segmento\n",
    "   sulle installazioni passa dallo 0,03% a un ordine di grandezza vicino all'1%. Con tre sole app\n",
    "   grandi nel campione, è una stima molto incerta.\n",
    "3. **Il quadro dei temi regge.** Nel campione le app di treni e mezzi pubblici hanno da 500.000 a\n",
    "   10 milioni di installazioni, l'unica app di ricarica elettrica ne ha 1.000: lo stesso divario\n",
    "   che si vede fra i 34."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "020a0262",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:15.969871Z",
     "iopub.status.busy": "2026-10-07T04:52:15.969723Z",
     "iopub.status.idle": "2026-10-07T04:52:16.248269Z",
     "shell.execute_reply": "2026-10-07T04:52:16.247325Z"
    },
    "id": "020a0262"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numero_app</th>\n",
       "      <th>installazioni_mediane</th>\n",
       "      <th>voto_mediano</th>\n",
       "      <th>giorni_da_aggiornamento</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tema</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mobilita_attiva</th>\n",
       "      <td>1</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mobilita_pubblica</th>\n",
       "      <td>11</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alimentazione</th>\n",
       "      <td>2</td>\n",
       "      <td>255000.0</td>\n",
       "      <td>4.35</td>\n",
       "      <td>191.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>energia</th>\n",
       "      <td>5</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>4.30</td>\n",
       "      <td>595.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>natura_clima</th>\n",
       "      <td>1</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.90</td>\n",
       "      <td>1013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mobilita_elettrica</th>\n",
       "      <td>9</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.70</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rifiuti_riuso</th>\n",
       "      <td>3</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.10</td>\n",
       "      <td>371.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>informazione</th>\n",
       "      <td>2</td>\n",
       "      <td>505.0</td>\n",
       "      <td>4.80</td>\n",
       "      <td>339.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    numero_app  installazioni_mediane  voto_mediano  giorni_da_aggiornamento\n",
       "tema                                                                                        \n",
       "mobilita_attiva              1              1000000.0          4.50                     43.0\n",
       "mobilita_pubblica           11               500000.0          4.40                     85.0\n",
       "alimentazione                2               255000.0          4.35                    191.5\n",
       "energia                      5                10000.0          4.30                    595.0\n",
       "natura_clima                 1                 1000.0          3.90                   1013.0\n",
       "mobilita_elettrica           9                 1000.0          3.70                     82.0\n",
       "rifiuti_riuso                3                 1000.0          4.10                    371.0\n",
       "informazione                 2                  505.0          4.80                    339.5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1320x462 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- I temi sostenibili: dove c'è davvero un pubblico ---\n",
    "\n",
    "# 1) raggruppo le 34 app per tema e per ognuno calcolo: quante app ci sono,\n",
    "#    le installazioni tipiche, il voto tipico e da quanto non vengono aggiornate\n",
    "per_tema = segmento.groupby(\"tema\").agg(\n",
    "    numero_app=(\"nome\", \"count\"),\n",
    "    installazioni_mediane=(\"installazioni\", \"median\"),\n",
    "    voto_mediano=(\"voto\", \"median\"),\n",
    "    giorni_da_aggiornamento=(\"giorni_da_aggiornamento\", \"median\"),\n",
    ").sort_values(\"installazioni_mediane\", ascending=False)\n",
    "display(per_tema)\n",
    "\n",
    "# 2) nomi leggibili per i grafici, con la ricarica elettrica evidenziata in arancione\n",
    "etichette = [t.replace(\"_\", \" \").replace(\"mobilita\", \"mobilità\") for t in per_tema.index]\n",
    "colori = [ARANCIONE if t == \"mobilita_elettrica\" else BLU for t in per_tema.index]\n",
    "righe = range(len(etichette))\n",
    "giorni_tipici_store = app[\"giorni_da_aggiornamento\"].median()\n",
    "\n",
    "# 3) preparo due grafici affiancati e ravvicinati, con gli stessi temi in verticale\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4.2), sharey=True,\n",
    "                               gridspec_kw={\"wspace\": 0.06})\n",
    "\n",
    "# 4) una riga sì e una no con lo sfondo grigio chiaro, uguale nei due grafici,\n",
    "#    per seguire un tema con lo sguardo da sinistra a destra\n",
    "for ax in (ax1, ax2):\n",
    "    for r in righe:\n",
    "        if r % 2 == 0:\n",
    "            ax.axhspan(r - 0.5, r + 0.5, color=\"#eeeeec\", zorder=0)\n",
    "    ax.grid(axis=\"y\", visible=False)\n",
    "    ax.set_ylim(len(etichette) - 0.5, -0.5)   # stesso ordine dall'alto in basso\n",
    "\n",
    "# 5) primo grafico — installazioni tipiche per tema (in scala logaritmica),\n",
    "#    con accanto il valore e il numero di app del tema\n",
    "ax1.barh(righe, per_tema[\"installazioni_mediane\"], height=0.65, color=colori, zorder=2)\n",
    "ax1.set_xscale(\"log\")\n",
    "ax1.set_yticks(righe, etichette, fontsize=9.5)\n",
    "ax1.set_xlim(right=per_tema[\"installazioni_mediane\"].max() * 9)\n",
    "ax1.set_xticks([1_000, 10_000, 100_000, 1_000_000], [\"1.000\", \"10.000\", \"100.000\", \"1 milione\"])\n",
    "ax1.set_xlabel(\"installazioni mediane (scala logaritmica)\")\n",
    "ax1.set_title(\"Il pubblico c'è solo dove l'app\\nrisolve un problema pratico\")\n",
    "for r, (v, n) in zip(righe, zip(per_tema[\"installazioni_mediane\"], per_tema[\"numero_app\"])):\n",
    "    ax1.text(v * 1.3, r, f\"{v:,.0f}\".replace(\",\", \".\") + f\"  ({n} app)\",\n",
    "             va=\"center\", fontsize=8)\n",
    "\n",
    "# 6) secondo grafico — giorni passati dall'ultimo aggiornamento, per tema\n",
    "#    (la linea nera è il valore tipico di tutto lo store)\n",
    "ax2.barh(righe, per_tema[\"giorni_da_aggiornamento\"], height=0.65, color=colori, zorder=2)\n",
    "ax2.axvline(giorni_tipici_store, color=\"black\", linewidth=1.1, zorder=3)\n",
    "ax2.set_xlim(0, per_tema[\"giorni_da_aggiornamento\"].max() * 1.15)\n",
    "ax2.set_xlabel(\"giorni dall'ultimo aggiornamento\")\n",
    "ax2.set_title(\"Ma non per abbandono: le app di ricarica\\n\"\n",
    "              f\"sono aggiornate di recente (linea = store, {giorni_tipici_store:.0f} gg)\")\n",
    "for r, v in zip(righe, per_tema[\"giorni_da_aggiornamento\"]):\n",
    "    # il numero va scritto dopo la barra, o dopo la linea nera se la barra è più corta,\n",
    "    # così non finisce sopra la linea\n",
    "    ax2.text(max(v, giorni_tipici_store) + 12, r, f\"{v:.0f} gg\", va=\"center\", fontsize=8)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "430e0a97",
   "metadata": {},
   "source": [
    "**Risultato** Qui la domanda di partenza cambia.\n",
    "\n",
    "Il metodo a parole chiave ha una precisione del **51,5%** (un candidato su due era un falso\n",
    "positivo) e, come visto in 7.1, una copertura intorno al **34%**. Le 34 app trovate sono lo\n",
    "**0,35%** del catalogo e raccolgono lo **0,03%** delle installazioni; contando quelle perse il\n",
    "segmento sale all'**1%** circa del catalogo, con un margine di incertezza ampio. Resta un\n",
    "segmento piccolo, come numero di app e come pubblico.\n",
    "\n",
    "Il dato più importante, però, è il confronto tra due temi:\n",
    "\n",
    "| Tema | App | Installazioni tipiche | Voto tipico |\n",
    "|---|---|---|---|\n",
    "| **trasporto pubblico** | 11 | **500.000** | **4,40** |\n",
    "| ricarica auto elettriche | 9 | **1.000** | **3,70** |\n",
    "\n",
    "Quasi lo stesso numero di app, ma un pubblico **cinquecento volte diverso**. E il secondo grafico esclude la spiegazione più comoda: le app di ricarica elettrica **non sono abbandonate**. Il loro ultimo aggiornamento risale in genere a 82 giorni prima, meno del valore tipico dello store (96 giorni), ed è il più recente tra tutti i temi con più di un'app. Sono prodotti curati e aggiornati, ma senza utenti.\n",
    "\n",
    "**La mia lettura**, che i dati non possono confermare perché non contengono il numero di auto\n",
    "elettriche in circolazione: nel 2018 quelle app servivano a pochissime persone, perché le auto\n",
    "elettriche erano poche. Il trasporto pubblico invece lo usano tutti, e **nessuno scarica\n",
    "Citymapper per salvare il pianeta**: lo scarica per sapere quando passa il tram. Entrambi i\n",
    "temi sono pratici; a fare la differenza sembra essere **quante persone hanno il problema** che\n",
    "l'app risolve, non il fatto che l'app sia «verde».\n",
    "\n",
    "**Quindi la domanda cambia.** Non più «in quale nicchia verde entro?», ma:\n",
    "\n",
    "> **In quale categoria conviene pubblicare un'app che risolve un problema concreto e diffuso e che, come effetto, riduce l'impatto ambientale di chi la usa?**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d78d7dad",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 8. Quale categoria scegliere\n",
    "\n",
    "Prendo undici categorie in cui un'app di sostenibilità potrebbe stare, e le confronto con tre misure già calcolate:\n",
    "\n",
    "- **domanda** — la percentuale di app che supera il milione di installazioni (più alta è, meglio è);\n",
    "- **concorrenti** — il numero di app nella categoria (meno sono, meglio è);\n",
    "- **leader** — la quota di pubblico presa dalla prima app (più bassa è, meglio è).\n",
    "\n",
    "Per metterle insieme parto dal metodo più semplice: **per ogni misura faccio una classifica delle categorie, poi sommo le tre posizioni.** Vince chi ha il totale più basso, come in una gara a punti.\n",
    "\n",
    "Il metodo ha due scelte nascoste, che nella prima versione non dichiaravo:\n",
    "\n",
    "1. **dà lo stesso peso alle tre misure.** Sommare le posizioni non vuol dire «non decidere i\n",
    "   pesi»: vuol dire sceglierli uguali;\n",
    "2. **butta via l'ampiezza delle differenze.** Una categoria avanti di un decimo di punto\n",
    "   guadagna una posizione intera, esattamente come una avanti di trenta punti.\n",
    "\n",
    "Per questo la tabella mostra le **misure grezze accanto alle posizioni**, e in 8.1 metto alla\n",
    "prova la classifica cambiando metodo e pesi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7723bf97",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:16.249890Z",
     "iopub.status.busy": "2026-10-07T04:52:16.249688Z",
     "iopub.status.idle": "2026-10-07T04:52:16.259022Z",
     "shell.execute_reply": "2026-10-07T04:52:16.258326Z"
    },
    "id": "7723bf97"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_962d0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_962d0_level0_col0\" class=\"col_heading level0 col0\" >quota_oltre_1M</th>\n",
       "      <th id=\"T_962d0_level0_col1\" class=\"col_heading level0 col1\" >pos_domanda</th>\n",
       "      <th id=\"T_962d0_level0_col2\" class=\"col_heading level0 col2\" >numero_app</th>\n",
       "      <th id=\"T_962d0_level0_col3\" class=\"col_heading level0 col3\" >pos_concorrenti</th>\n",
       "      <th id=\"T_962d0_level0_col4\" class=\"col_heading level0 col4\" >quota_prima_app</th>\n",
       "      <th id=\"T_962d0_level0_col5\" class=\"col_heading level0 col5\" >pos_leader</th>\n",
       "      <th id=\"T_962d0_level0_col6\" class=\"col_heading level0 col6\" >totale</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >categoria</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_962d0_level0_row0\" class=\"row_heading level0 row0\" >EDUCATION</th>\n",
       "      <td id=\"T_962d0_row0_col0\" class=\"data row0 col0\" >57.4%</td>\n",
       "      <td id=\"T_962d0_row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "      <td id=\"T_962d0_row0_col2\" class=\"data row0 col2\" >108</td>\n",
       "      <td id=\"T_962d0_row0_col3\" class=\"data row0 col3\" >3</td>\n",
       "      <td id=\"T_962d0_row0_col4\" class=\"data row0 col4\" >5.3%</td>\n",
       "      <td id=\"T_962d0_row0_col5\" class=\"data row0 col5\" >2</td>\n",
       "      <td id=\"T_962d0_row0_col6\" class=\"data row0 col6\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_962d0_level0_row1\" class=\"row_heading level0 row1\" >FOOD_AND_DRINK</th>\n",
       "      <td id=\"T_962d0_row1_col0\" class=\"data row1 col0\" >43.8%</td>\n",
       "      <td id=\"T_962d0_row1_col1\" class=\"data row1 col1\" >3</td>\n",
       "      <td id=\"T_962d0_row1_col2\" class=\"data row1 col2\" >112</td>\n",
       "      <td id=\"T_962d0_row1_col3\" class=\"data row1 col3\" >4</td>\n",
       "      <td id=\"T_962d0_row1_col4\" class=\"data row1 col4\" >4.7%</td>\n",
       "      <td id=\"T_962d0_row1_col5\" class=\"data row1 col5\" >1</td>\n",
       "      <td id=\"T_962d0_row1_col6\" class=\"data row1 col6\" >8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_962d0_level0_row2\" class=\"row_heading level0 row2\" >HOUSE_AND_HOME</th>\n",
       "      <td id=\"T_962d0_row2_col0\" class=\"data row2 col0\" >40.5%</td>\n",
       "      <td id=\"T_962d0_row2_col1\" class=\"data row2 col1\" >6</td>\n",
       "      <td id=\"T_962d0_row2_col2\" class=\"data row2 col2\" >74</td>\n",
       "      <td id=\"T_962d0_row2_col3\" class=\"data row2 col3\" >1</td>\n",
       "      <td id=\"T_962d0_row2_col4\" class=\"data row2 col4\" >10.3%</td>\n",
       "      <td id=\"T_962d0_row2_col5\" class=\"data row2 col5\" >4</td>\n",
       "      <td id=\"T_962d0_row2_col6\" class=\"data row2 col6\" >11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_962d0_level0_row3\" class=\"row_heading level0 row3\" >SHOPPING</th>\n",
       "      <td id=\"T_962d0_row3_col0\" class=\"data row3 col0\" >52.5%</td>\n",
       "      <td id=\"T_962d0_row3_col1\" class=\"data row3 col1\" >2</td>\n",
       "      <td id=\"T_962d0_row3_col2\" class=\"data row3 col2\" >202</td>\n",
       "      <td id=\"T_962d0_row3_col3\" class=\"data row3 col3\" >6</td>\n",
       "      <td id=\"T_962d0_row3_col4\" class=\"data row3 col4\" >7.1%</td>\n",
       "      <td id=\"T_962d0_row3_col5\" class=\"data row3 col5\" >3</td>\n",
       "      <td id=\"T_962d0_row3_col6\" class=\"data row3 col6\" >11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_962d0_level0_row4\" class=\"row_heading level0 row4\" >MAPS_AND_NAVIGATION</th>\n",
       "      <td id=\"T_962d0_row4_col0\" class=\"data row4 col0\" >41.2%</td>\n",
       "      <td id=\"T_962d0_row4_col1\" class=\"data row4 col1\" >5</td>\n",
       "      <td id=\"T_962d0_row4_col2\" class=\"data row4 col2\" >131</td>\n",
       "      <td id=\"T_962d0_row4_col3\" class=\"data row4 col3\" >5</td>\n",
       "      <td id=\"T_962d0_row4_col4\" class=\"data row4 col4\" >19.9%</td>\n",
       "      <td id=\"T_962d0_row4_col5\" class=\"data row4 col5\" >9</td>\n",
       "      <td id=\"T_962d0_row4_col6\" class=\"data row4 col6\" >19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_962d0_level0_row5\" class=\"row_heading level0 row5\" >AUTO_AND_VEHICLES</th>\n",
       "      <td id=\"T_962d0_row5_col0\" class=\"data row5 col0\" >21.2%</td>\n",
       "      <td id=\"T_962d0_row5_col1\" class=\"data row5 col1\" >11</td>\n",
       "      <td id=\"T_962d0_row5_col2\" class=\"data row5 col2\" >85</td>\n",
       "      <td id=\"T_962d0_row5_col3\" class=\"data row5 col3\" >2</td>\n",
       "      <td id=\"T_962d0_row5_col4\" class=\"data row5 col4\" >18.8%</td>\n",
       "      <td id=\"T_962d0_row5_col5\" class=\"data row5 col5\" >7</td>\n",
       "      <td id=\"T_962d0_row5_col6\" class=\"data row5 col6\" >20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_962d0_level0_row6\" class=\"row_heading level0 row6\" >TRAVEL_AND_LOCAL</th>\n",
       "      <td id=\"T_962d0_row6_col0\" class=\"data row6 col0\" >42.5%</td>\n",
       "      <td id=\"T_962d0_row6_col1\" class=\"data row6 col1\" >4</td>\n",
       "      <td id=\"T_962d0_row6_col2\" class=\"data row6 col2\" >219</td>\n",
       "      <td id=\"T_962d0_row6_col3\" class=\"data row6 col3\" >7</td>\n",
       "      <td id=\"T_962d0_row6_col4\" class=\"data row6 col4\" >34.5%</td>\n",
       "      <td id=\"T_962d0_row6_col5\" class=\"data row6 col5\" >10</td>\n",
       "      <td id=\"T_962d0_row6_col6\" class=\"data row6 col6\" >21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_962d0_level0_row7\" class=\"row_heading level0 row7\" >PRODUCTIVITY</th>\n",
       "      <td id=\"T_962d0_row7_col0\" class=\"data row7 col0\" >39.5%</td>\n",
       "      <td id=\"T_962d0_row7_col1\" class=\"data row7 col1\" >7</td>\n",
       "      <td id=\"T_962d0_row7_col2\" class=\"data row7 col2\" >375</td>\n",
       "      <td id=\"T_962d0_row7_col3\" class=\"data row7 col3\" >10</td>\n",
       "      <td id=\"T_962d0_row7_col4\" class=\"data row7 col4\" >17.2%</td>\n",
       "      <td id=\"T_962d0_row7_col5\" class=\"data row7 col5\" >6</td>\n",
       "      <td id=\"T_962d0_row7_col6\" class=\"data row7 col6\" >23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_962d0_level0_row8\" class=\"row_heading level0 row8\" >TOOLS</th>\n",
       "      <td id=\"T_962d0_row8_col0\" class=\"data row8 col0\" >33.2%</td>\n",
       "      <td id=\"T_962d0_row8_col1\" class=\"data row8 col1\" >9</td>\n",
       "      <td id=\"T_962d0_row8_col2\" class=\"data row8 col2\" >829</td>\n",
       "      <td id=\"T_962d0_row8_col3\" class=\"data row8 col3\" >11</td>\n",
       "      <td id=\"T_962d0_row8_col4\" class=\"data row8 col4\" >12.3%</td>\n",
       "      <td id=\"T_962d0_row8_col5\" class=\"data row8 col5\" >5</td>\n",
       "      <td id=\"T_962d0_row8_col6\" class=\"data row8 col6\" >25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_962d0_level0_row9\" class=\"row_heading level0 row9\" >HEALTH_AND_FITNESS</th>\n",
       "      <td id=\"T_962d0_row9_col0\" class=\"data row9 col0\" >38.9%</td>\n",
       "      <td id=\"T_962d0_row9_col1\" class=\"data row9 col1\" >8</td>\n",
       "      <td id=\"T_962d0_row9_col2\" class=\"data row9 col2\" >288</td>\n",
       "      <td id=\"T_962d0_row9_col3\" class=\"data row9 col3\" >8</td>\n",
       "      <td id=\"T_962d0_row9_col4\" class=\"data row9 col4\" >43.7%</td>\n",
       "      <td id=\"T_962d0_row9_col5\" class=\"data row9 col5\" >11</td>\n",
       "      <td id=\"T_962d0_row9_col6\" class=\"data row9 col6\" >27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_962d0_level0_row10\" class=\"row_heading level0 row10\" >LIFESTYLE</th>\n",
       "      <td id=\"T_962d0_row10_col0\" class=\"data row10 col0\" >22.8%</td>\n",
       "      <td id=\"T_962d0_row10_col1\" class=\"data row10 col1\" >10</td>\n",
       "      <td id=\"T_962d0_row10_col2\" class=\"data row10 col2\" >369</td>\n",
       "      <td id=\"T_962d0_row10_col3\" class=\"data row10 col3\" >9</td>\n",
       "      <td id=\"T_962d0_row10_col4\" class=\"data row10 col4\" >19.8%</td>\n",
       "      <td id=\"T_962d0_row10_col5\" class=\"data row10 col5\" >8</td>\n",
       "      <td id=\"T_962d0_row10_col6\" class=\"data row10 col6\" >27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2cbbaa5c050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Classifica delle categorie candidate, sommando le posizioni ---\n",
    "\n",
    "# 1) le 11 categorie in cui un'app di sostenibilità potrebbe stare\n",
    "CANDIDATE = [\"FOOD_AND_DRINK\", \"HOUSE_AND_HOME\", \"EDUCATION\", \"SHOPPING\",\n",
    "             \"HEALTH_AND_FITNESS\", \"MAPS_AND_NAVIGATION\", \"LIFESTYLE\", \"TOOLS\",\n",
    "             \"AUTO_AND_VEHICLES\", \"TRAVEL_AND_LOCAL\", \"PRODUCTIVITY\"]\n",
    "candidate = per_categoria.loc[CANDIDATE].copy()\n",
    "\n",
    "# 2) per ogni misura do una posizione in classifica (1 = la migliore):\n",
    "#    - domanda: più app oltre il milione = posizione migliore\n",
    "#    - concorrenti: meno app = posizione migliore\n",
    "#    - leader: prima app più debole = posizione migliore\n",
    "candidate[\"pos_domanda\"] = candidate[\"quota_oltre_1M\"].rank(ascending=False)\n",
    "candidate[\"pos_concorrenti\"] = candidate[\"numero_app\"].rank(ascending=True)\n",
    "candidate[\"pos_leader\"] = candidate[\"quota_prima_app\"].rank(ascending=True)\n",
    "\n",
    "# 3) sommo le tre posizioni: vince il totale più basso\n",
    "candidate[\"totale\"] = candidate[[\"pos_domanda\", \"pos_concorrenti\", \"pos_leader\"]].sum(axis=1)\n",
    "\n",
    "# 4) ordino dalla migliore alla peggiore e mostro, per ogni misura,\n",
    "#    il valore grezzo accanto alla posizione che gli corrisponde\n",
    "classifica = candidate.sort_values(\"totale\")\n",
    "display(classifica[[\"quota_oltre_1M\", \"pos_domanda\", \"numero_app\", \"pos_concorrenti\",\n",
    "                    \"quota_prima_app\", \"pos_leader\", \"totale\"]]\n",
    "        .style.format({\"quota_oltre_1M\": \"{:.1%}\", \"quota_prima_app\": \"{:.1%}\",\n",
    "                       \"pos_domanda\": \"{:.0f}\", \"pos_concorrenti\": \"{:.0f}\",\n",
    "                       \"pos_leader\": \"{:.0f}\", \"totale\": \"{:.0f}\"}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5aa4bf1",
   "metadata": {},
   "source": [
    "**Risultato** Ai primi posti della classifica c'è **EDUCATION** con 6 punti, seguita da **FOOD & DRINK** con 8. Più indietro, a pari merito con 11 punti, ci sono **HOUSE & HOME** e **SHOPPING**.\n",
    "\n",
    "Leggendo le misure grezze accanto alle posizioni si vede quanto la somma appiattisce. Sulla\n",
    "domanda SHOPPING (52,5%) è seconda e FOOD & DRINK (43,8%) è terza: una posizione di\n",
    "differenza per quasi nove punti percentuali. Sul leader FOOD & DRINK (4,7%) è prima ed\n",
    "EDUCATION (5,3%) seconda: una posizione intera per mezzo punto. E il margine fra le prime due,\n",
    "6 contro 8, sono **due posizioni su undici**: poco, per un metodo così grossolano.\n",
    "\n",
    "### 8.1 Quanto è solida questa classifica?\n",
    "\n",
    "La metto alla prova in due modi.\n",
    "\n",
    "1. **Un punteggio che conserva le distanze.** Per ogni misura porto la peggiore delle undici\n",
    "   categorie a 0 e la migliore a 1, in proporzione: una categoria avanti di un decimo prende\n",
    "   un decimo, non una posizione intera. Poi faccio la media dei tre punteggi.\n",
    "2. **Pesi diversi.** Provo tutte le 36 combinazioni di pesi in cui ogni misura pesa almeno il\n",
    "   10%, a passi del 10% (per esempio 60% domanda, 20% concorrenti, 20% leader), con entrambi i\n",
    "   metodi, e conto quante volte vince ciascuna categoria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ce5fbf21",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:16.260458Z",
     "iopub.status.busy": "2026-10-07T04:52:16.260263Z",
     "iopub.status.idle": "2026-10-07T04:52:16.308889Z",
     "shell.execute_reply": "2026-10-07T04:52:16.308247Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_eeb3e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_eeb3e_level0_col0\" class=\"col_heading level0 col0\" >quota_oltre_1M</th>\n",
       "      <th id=\"T_eeb3e_level0_col1\" class=\"col_heading level0 col1\" >numero_app</th>\n",
       "      <th id=\"T_eeb3e_level0_col2\" class=\"col_heading level0 col2\" >quota_prima_app</th>\n",
       "      <th id=\"T_eeb3e_level0_col3\" class=\"col_heading level0 col3\" >totale</th>\n",
       "      <th id=\"T_eeb3e_level0_col4\" class=\"col_heading level0 col4\" >punteggio 0-1 (pesi uguali)</th>\n",
       "      <th id=\"T_eeb3e_level0_col5\" class=\"col_heading level0 col5\" >vittorie su 36 con le posizioni</th>\n",
       "      <th id=\"T_eeb3e_level0_col6\" class=\"col_heading level0 col6\" >vittorie su 36 con il punteggio 0-1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >categoria</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_eeb3e_level0_row0\" class=\"row_heading level0 row0\" >EDUCATION</th>\n",
       "      <td id=\"T_eeb3e_row0_col0\" class=\"data row0 col0\" >57.4%</td>\n",
       "      <td id=\"T_eeb3e_row0_col1\" class=\"data row0 col1\" >108</td>\n",
       "      <td id=\"T_eeb3e_row0_col2\" class=\"data row0 col2\" >5.3%</td>\n",
       "      <td id=\"T_eeb3e_row0_col3\" class=\"data row0 col3\" >6</td>\n",
       "      <td id=\"T_eeb3e_row0_col4\" class=\"data row0 col4\" >0.980</td>\n",
       "      <td id=\"T_eeb3e_row0_col5\" class=\"data row0 col5\" >27.5</td>\n",
       "      <td id=\"T_eeb3e_row0_col6\" class=\"data row0 col6\" >36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eeb3e_level0_row1\" class=\"row_heading level0 row1\" >FOOD_AND_DRINK</th>\n",
       "      <td id=\"T_eeb3e_row1_col0\" class=\"data row1 col0\" >43.8%</td>\n",
       "      <td id=\"T_eeb3e_row1_col1\" class=\"data row1 col1\" >112</td>\n",
       "      <td id=\"T_eeb3e_row1_col2\" class=\"data row1 col2\" >4.7%</td>\n",
       "      <td id=\"T_eeb3e_row1_col3\" class=\"data row1 col3\" >8</td>\n",
       "      <td id=\"T_eeb3e_row1_col4\" class=\"data row1 col4\" >0.858</td>\n",
       "      <td id=\"T_eeb3e_row1_col5\" class=\"data row1 col5\" >4.5</td>\n",
       "      <td id=\"T_eeb3e_row1_col6\" class=\"data row1 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eeb3e_level0_row2\" class=\"row_heading level0 row2\" >HOUSE_AND_HOME</th>\n",
       "      <td id=\"T_eeb3e_row2_col0\" class=\"data row2 col0\" >40.5%</td>\n",
       "      <td id=\"T_eeb3e_row2_col1\" class=\"data row2 col1\" >74</td>\n",
       "      <td id=\"T_eeb3e_row2_col2\" class=\"data row2 col2\" >10.3%</td>\n",
       "      <td id=\"T_eeb3e_row2_col3\" class=\"data row2 col3\" >11</td>\n",
       "      <td id=\"T_eeb3e_row2_col4\" class=\"data row2 col4\" >0.797</td>\n",
       "      <td id=\"T_eeb3e_row2_col5\" class=\"data row2 col5\" >4.0</td>\n",
       "      <td id=\"T_eeb3e_row2_col6\" class=\"data row2 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eeb3e_level0_row3\" class=\"row_heading level0 row3\" >SHOPPING</th>\n",
       "      <td id=\"T_eeb3e_row3_col0\" class=\"data row3 col0\" >52.5%</td>\n",
       "      <td id=\"T_eeb3e_row3_col1\" class=\"data row3 col1\" >202</td>\n",
       "      <td id=\"T_eeb3e_row3_col2\" class=\"data row3 col2\" >7.1%</td>\n",
       "      <td id=\"T_eeb3e_row3_col3\" class=\"data row3 col3\" >11</td>\n",
       "      <td id=\"T_eeb3e_row3_col4\" class=\"data row3 col4\" >0.877</td>\n",
       "      <td id=\"T_eeb3e_row3_col5\" class=\"data row3 col5\" >0.0</td>\n",
       "      <td id=\"T_eeb3e_row3_col6\" class=\"data row3 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eeb3e_level0_row4\" class=\"row_heading level0 row4\" >MAPS_AND_NAVIGATION</th>\n",
       "      <td id=\"T_eeb3e_row4_col0\" class=\"data row4 col0\" >41.2%</td>\n",
       "      <td id=\"T_eeb3e_row4_col1\" class=\"data row4 col1\" >131</td>\n",
       "      <td id=\"T_eeb3e_row4_col2\" class=\"data row4 col2\" >19.9%</td>\n",
       "      <td id=\"T_eeb3e_row4_col3\" class=\"data row4 col3\" >19</td>\n",
       "      <td id=\"T_eeb3e_row4_col4\" class=\"data row4 col4\" >0.696</td>\n",
       "      <td id=\"T_eeb3e_row4_col5\" class=\"data row4 col5\" >0.0</td>\n",
       "      <td id=\"T_eeb3e_row4_col6\" class=\"data row4 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eeb3e_level0_row5\" class=\"row_heading level0 row5\" >AUTO_AND_VEHICLES</th>\n",
       "      <td id=\"T_eeb3e_row5_col0\" class=\"data row5 col0\" >21.2%</td>\n",
       "      <td id=\"T_eeb3e_row5_col1\" class=\"data row5 col1\" >85</td>\n",
       "      <td id=\"T_eeb3e_row5_col2\" class=\"data row5 col2\" >18.8%</td>\n",
       "      <td id=\"T_eeb3e_row5_col3\" class=\"data row5 col3\" >20</td>\n",
       "      <td id=\"T_eeb3e_row5_col4\" class=\"data row5 col4\" >0.541</td>\n",
       "      <td id=\"T_eeb3e_row5_col5\" class=\"data row5 col5\" >0.0</td>\n",
       "      <td id=\"T_eeb3e_row5_col6\" class=\"data row5 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eeb3e_level0_row6\" class=\"row_heading level0 row6\" >TRAVEL_AND_LOCAL</th>\n",
       "      <td id=\"T_eeb3e_row6_col0\" class=\"data row6 col0\" >42.5%</td>\n",
       "      <td id=\"T_eeb3e_row6_col1\" class=\"data row6 col1\" >219</td>\n",
       "      <td id=\"T_eeb3e_row6_col2\" class=\"data row6 col2\" >34.5%</td>\n",
       "      <td id=\"T_eeb3e_row6_col3\" class=\"data row6 col3\" >21</td>\n",
       "      <td id=\"T_eeb3e_row6_col4\" class=\"data row6 col4\" >0.544</td>\n",
       "      <td id=\"T_eeb3e_row6_col5\" class=\"data row6 col5\" >0.0</td>\n",
       "      <td id=\"T_eeb3e_row6_col6\" class=\"data row6 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eeb3e_level0_row7\" class=\"row_heading level0 row7\" >PRODUCTIVITY</th>\n",
       "      <td id=\"T_eeb3e_row7_col0\" class=\"data row7 col0\" >39.5%</td>\n",
       "      <td id=\"T_eeb3e_row7_col1\" class=\"data row7 col1\" >375</td>\n",
       "      <td id=\"T_eeb3e_row7_col2\" class=\"data row7 col2\" >17.2%</td>\n",
       "      <td id=\"T_eeb3e_row7_col3\" class=\"data row7 col3\" >23</td>\n",
       "      <td id=\"T_eeb3e_row7_col4\" class=\"data row7 col4\" >0.595</td>\n",
       "      <td id=\"T_eeb3e_row7_col5\" class=\"data row7 col5\" >0.0</td>\n",
       "      <td id=\"T_eeb3e_row7_col6\" class=\"data row7 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eeb3e_level0_row8\" class=\"row_heading level0 row8\" >TOOLS</th>\n",
       "      <td id=\"T_eeb3e_row8_col0\" class=\"data row8 col0\" >33.2%</td>\n",
       "      <td id=\"T_eeb3e_row8_col1\" class=\"data row8 col1\" >829</td>\n",
       "      <td id=\"T_eeb3e_row8_col2\" class=\"data row8 col2\" >12.3%</td>\n",
       "      <td id=\"T_eeb3e_row8_col3\" class=\"data row8 col3\" >25</td>\n",
       "      <td id=\"T_eeb3e_row8_col4\" class=\"data row8 col4\" >0.379</td>\n",
       "      <td id=\"T_eeb3e_row8_col5\" class=\"data row8 col5\" >0.0</td>\n",
       "      <td id=\"T_eeb3e_row8_col6\" class=\"data row8 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eeb3e_level0_row9\" class=\"row_heading level0 row9\" >HEALTH_AND_FITNESS</th>\n",
       "      <td id=\"T_eeb3e_row9_col0\" class=\"data row9 col0\" >38.9%</td>\n",
       "      <td id=\"T_eeb3e_row9_col1\" class=\"data row9 col1\" >288</td>\n",
       "      <td id=\"T_eeb3e_row9_col2\" class=\"data row9 col2\" >43.7%</td>\n",
       "      <td id=\"T_eeb3e_row9_col3\" class=\"data row9 col3\" >27</td>\n",
       "      <td id=\"T_eeb3e_row9_col4\" class=\"data row9 col4\" >0.402</td>\n",
       "      <td id=\"T_eeb3e_row9_col5\" class=\"data row9 col5\" >0.0</td>\n",
       "      <td id=\"T_eeb3e_row9_col6\" class=\"data row9 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eeb3e_level0_row10\" class=\"row_heading level0 row10\" >LIFESTYLE</th>\n",
       "      <td id=\"T_eeb3e_row10_col0\" class=\"data row10 col0\" >22.8%</td>\n",
       "      <td id=\"T_eeb3e_row10_col1\" class=\"data row10 col1\" >369</td>\n",
       "      <td id=\"T_eeb3e_row10_col2\" class=\"data row10 col2\" >19.8%</td>\n",
       "      <td id=\"T_eeb3e_row10_col3\" class=\"data row10 col3\" >27</td>\n",
       "      <td id=\"T_eeb3e_row10_col4\" class=\"data row10 col4\" >0.422</td>\n",
       "      <td id=\"T_eeb3e_row10_col5\" class=\"data row10 col5\" >0.0</td>\n",
       "      <td id=\"T_eeb3e_row10_col6\" class=\"data row10 col6\" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2cbbaa5c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- 8.1 La classifica cambiando metodo e pesi ---\n",
    "\n",
    "def scala_0_1(serie, meglio_alto):\n",
    "    \"\"\"Porta una misura fra 0 (la peggiore) e 1 (la migliore), conservando le distanze.\"\"\"\n",
    "    s = (serie - serie.min()) / (serie.max() - serie.min())\n",
    "    return s if meglio_alto else 1 - s\n",
    "\n",
    "def misure(tabella):\n",
    "    \"\"\"Per un gruppo di categorie: posizioni e punteggi 0-1 delle tre misure.\"\"\"\n",
    "    posizioni = pd.DataFrame({\"domanda\": tabella[\"quota_oltre_1M\"].rank(ascending=False),\n",
    "                              \"concorrenti\": tabella[\"numero_app\"].rank(),\n",
    "                              \"leader\": tabella[\"quota_prima_app\"].rank()})\n",
    "    punteggi = pd.DataFrame({\"domanda\": scala_0_1(tabella[\"quota_oltre_1M\"], True),\n",
    "                             \"concorrenti\": scala_0_1(tabella[\"numero_app\"], False),\n",
    "                             \"leader\": scala_0_1(tabella[\"quota_prima_app\"], False)})\n",
    "    return posizioni, punteggi\n",
    "\n",
    "# 1) tutte le combinazioni di pesi: ogni misura almeno 10%, a passi del 10%\n",
    "PESI = [(a / 10, b / 10, (10 - a - b) / 10) for a in range(1, 9) for b in range(1, 10 - a)]\n",
    "\n",
    "def vittorie(tabella):\n",
    "    \"\"\"Quante volte, su 36 combinazioni di pesi, ogni categoria arriva prima.\"\"\"\n",
    "    posizioni, punteggi = misure(tabella)\n",
    "    conto = {\"con le posizioni\": pd.Series(0.0, index=tabella.index),\n",
    "             \"con il punteggio 0-1\": pd.Series(0.0, index=tabella.index)}\n",
    "    for w in PESI:\n",
    "        totale = (posizioni * w).sum(axis=1)                  # più basso = meglio\n",
    "        primi = totale[totale == totale.min()].index\n",
    "        conto[\"con le posizioni\"][primi] += 1 / len(primi)    # i pari merito si dividono la vittoria\n",
    "        conto[\"con il punteggio 0-1\"][(punteggi * w).sum(axis=1).idxmax()] += 1\n",
    "    return pd.DataFrame(conto)\n",
    "\n",
    "# 2) la tabella completa: misure grezze, totale delle posizioni, punteggio 0-1, vittorie\n",
    "posizioni, punteggi = misure(candidate)\n",
    "solidita = candidate[[\"quota_oltre_1M\", \"numero_app\", \"quota_prima_app\", \"totale\"]].copy()\n",
    "solidita[\"punteggio 0-1 (pesi uguali)\"] = punteggi.mean(axis=1)\n",
    "solidita = solidita.join(vittorie(candidate).add_prefix(\"vittorie su 36 \"))\n",
    "solidita = solidita.sort_values(\"totale\")\n",
    "display(solidita.style.format({\"quota_oltre_1M\": \"{:.1%}\", \"quota_prima_app\": \"{:.1%}\",\n",
    "                               \"totale\": \"{:.0f}\", \"punteggio 0-1 (pesi uguali)\": \"{:.3f}\",\n",
    "                               \"vittorie su 36 con le posizioni\": \"{:.1f}\",\n",
    "                               \"vittorie su 36 con il punteggio 0-1\": \"{:.0f}\"}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7faefa33",
   "metadata": {},
   "source": [
    "**Risultato** Finché EDUCATION è in gara, **vince quasi sempre**: con le posizioni in 27,5\n",
    "combinazioni di pesi su 36, con il punteggio 0-1 in tutte e 36. Il punteggio, però, cambia\n",
    "l'ordine dietro di lei: **SHOPPING (0,877) passa davanti a FOOD & DRINK (0,858)**, perché la\n",
    "somma di posizioni nascondeva i nove punti di domanda in più di SHOPPING.\n",
    "\n",
    "Nella sezione 5, però, avevo notato che i numeri di EDUCATION erano un po' troppo belli per\n",
    "essere veri. Prima di sceglierla voglio capire se c'è qualcosa che mi sfugge.\n",
    "\n",
    "### 8.2 Un controllo che cambia tutto\n",
    "\n",
    "Nel dataset ci sono due colonne che a prima vista sembrano uguali:\n",
    "\n",
    "- `categoria` indica lo \"scaffale\" dello store su cui l'app viene mostrata;\n",
    "- `generi` indica di che tipo di app si tratta.\n",
    "\n",
    "Mi aspetterei che coincidano: un'app educativa dovrebbe stare nello scaffale EDUCATION. Per verificarlo conto quante app hanno un certo **genere** e quante di queste si trovano davvero nella **categoria** con lo stesso nome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "23c9b8c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:16.310602Z",
     "iopub.status.busy": "2026-10-07T04:52:16.310388Z",
     "iopub.status.idle": "2026-10-07T04:52:16.484728Z",
     "shell.execute_reply": "2026-10-07T04:52:16.483662Z"
    },
    "id": "23c9b8c2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ambito</th>\n",
       "      <th>app nella CATEGORIA</th>\n",
       "      <th>app con quel GENERE</th>\n",
       "      <th>rapporto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Education</td>\n",
       "      <td>108</td>\n",
       "      <td>642</td>\n",
       "      <td>5.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Entertainment</td>\n",
       "      <td>87</td>\n",
       "      <td>592</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Food &amp; Drink</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>House &amp; Home</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shopping</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Maps &amp; Navigation</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ambito  app nella CATEGORIA  app con quel GENERE  rapporto\n",
       "0          Education                  108                  642      5.94\n",
       "1      Entertainment                   87                  592      6.80\n",
       "2       Food & Drink                  112                  112      1.00\n",
       "3       House & Home                   74                   74      1.00\n",
       "4           Shopping                  202                  202      1.00\n",
       "5  Maps & Navigation                  131                  131      1.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numero_app</th>\n",
       "      <th>installazioni_mediane</th>\n",
       "      <th>quota_oltre_1M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>categoria</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FAMILY</th>\n",
       "      <td>526</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUCATION</th>\n",
       "      <td>108</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>0.574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARENTING</th>\n",
       "      <td>7</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>0.429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAME</th>\n",
       "      <td>1</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOOLS</th>\n",
       "      <td>1</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           numero_app  installazioni_mediane  quota_oltre_1M\n",
       "categoria                                                   \n",
       "FAMILY            526                 5000.0           0.126\n",
       "EDUCATION         108              1000000.0           0.574\n",
       "PARENTING           7               100000.0           0.429\n",
       "GAME                1               100000.0           0.000\n",
       "TOOLS               1             10000000.0           1.000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 825x330 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rapporto fra i due collocamenti: 200 volte\n"
     ]
    }
   ],
   "source": [
    "# --- Categoria e genere: dicono davvero la stessa cosa? ---\n",
    "\n",
    "# 1) un'app può avere più generi separati da \";\" (es. \"Education;Pretend Play\"):\n",
    "#    explode() crea una riga per ogni genere, così posso contarli uno per uno\n",
    "per_genere = app.assign(genere=app[\"generi\"].str.split(\";\")).explode(\"genere\")\n",
    "per_genere[\"genere\"] = per_genere[\"genere\"].str.strip()\n",
    "\n",
    "# 2) per alcune categorie confronto quante app stanno nella CATEGORIA\n",
    "#    e quante hanno quel GENERE: se il rapporto è 1, le due colonne coincidono\n",
    "confronti = [(\"Education\", \"EDUCATION\"), (\"Entertainment\", \"ENTERTAINMENT\"),\n",
    "             (\"Food & Drink\", \"FOOD_AND_DRINK\"), (\"House & Home\", \"HOUSE_AND_HOME\"),\n",
    "             (\"Shopping\", \"SHOPPING\"), (\"Maps & Navigation\", \"MAPS_AND_NAVIGATION\")]\n",
    "display(pd.DataFrame([\n",
    "    {\"ambito\": genere,\n",
    "     \"app nella CATEGORIA\": (app[\"categoria\"] == categoria).sum(),\n",
    "     \"app con quel GENERE\": per_genere.loc[per_genere[\"genere\"] == genere, \"nome\"].nunique(),\n",
    "     \"rapporto\": per_genere.loc[per_genere[\"genere\"] == genere, \"nome\"].nunique()\n",
    "                 / (app[\"categoria\"] == categoria).sum()}\n",
    "    for genere, categoria in confronti]).round(2))\n",
    "\n",
    "# 3) le app con genere \"Education\": in quale categoria sono state messe?\n",
    "educative = per_genere[per_genere[\"genere\"] == \"Education\"]\n",
    "dove = educative.groupby(\"categoria\").agg(\n",
    "    numero_app=(\"nome\", \"nunique\"),\n",
    "    installazioni_mediane=(\"installazioni\", \"median\"),\n",
    "    quota_oltre_1M=(\"installazioni\", lambda s: (s >= 1_000_000).mean()),\n",
    ").sort_values(\"numero_app\", ascending=False)\n",
    "display(dove.round(3))\n",
    "\n",
    "# 4) grafico: installazioni tipiche delle app educative,\n",
    "#    a seconda dello \"scaffale\" (categoria) su cui sono esposte\n",
    "fig, ax = plt.subplots(figsize=(7.5, 3))\n",
    "valori = [app.loc[app[\"categoria\"] == \"EDUCATION\", \"installazioni\"].median(),\n",
    "          educative.loc[educative[\"categoria\"] == \"FAMILY\", \"installazioni\"].median()]\n",
    "n_cat_edu = (app[\"categoria\"] == \"EDUCATION\").sum()\n",
    "n_fam_edu = educative.loc[educative[\"categoria\"] == \"FAMILY\", \"nome\"].nunique()\n",
    "ax.bar([f\"Categoria EDUCATION\\n({n_cat_edu} app)\", f\"Genere Education,\\nscaffale FAMILY ({n_fam_edu} app)\"],\n",
    "       valori, color=[ARANCIONE, BLU], width=0.45)\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_ylabel(\"installazioni mediane (log)\")\n",
    "ax.set_title(\"Lo stesso genere, due collocamenti:\\ndipende dallo scaffale su cui il negozio lo mette\")\n",
    "for i, v in enumerate(valori):\n",
    "    ax.text(i, v * 1.4, f\"{v:,.0f}\", ha=\"center\", fontweight=\"bold\", fontsize=11)\n",
    "ax.set_ylim(top=max(valori) * 4)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(f\"Rapporto fra i due collocamenti: {valori[0] / valori[1]:.0f} volte\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d496b9dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:16.486426Z",
     "iopub.status.busy": "2026-10-07T04:52:16.486206Z",
     "iopub.status.idle": "2026-10-07T04:52:16.643367Z",
     "shell.execute_reply": "2026-10-07T04:52:16.642576Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_4bb1d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4bb1d_level0_col0\" class=\"col_heading level0 col0\" >posizione</th>\n",
       "      <th id=\"T_4bb1d_level0_col1\" class=\"col_heading level0 col1\" >numero_app</th>\n",
       "      <th id=\"T_4bb1d_level0_col2\" class=\"col_heading level0 col2\" >quota_oltre_1M</th>\n",
       "      <th id=\"T_4bb1d_level0_col3\" class=\"col_heading level0 col3\" >quota_prima_app</th>\n",
       "      <th id=\"T_4bb1d_level0_col4\" class=\"col_heading level0 col4\" >recensioni_per_1000</th>\n",
       "      <th id=\"T_4bb1d_level0_col5\" class=\"col_heading level0 col5\" >quota_a_pagamento</th>\n",
       "      <th id=\"T_4bb1d_level0_col6\" class=\"col_heading level0 col6\" >giorni_da_aggiornamento</th>\n",
       "      <th id=\"T_4bb1d_level0_col7\" class=\"col_heading level0 col7\" >totale</th>\n",
       "      <th id=\"T_4bb1d_level0_col8\" class=\"col_heading level0 col8\" >punteggio 0-1</th>\n",
       "      <th id=\"T_4bb1d_level0_col9\" class=\"col_heading level0 col9\" >vittorie su 36 con le posizioni</th>\n",
       "      <th id=\"T_4bb1d_level0_col10\" class=\"col_heading level0 col10\" >vittorie su 36 con il punteggio 0-1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >categoria</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4bb1d_level0_row0\" class=\"row_heading level0 row0\" >FOOD_AND_DRINK</th>\n",
       "      <td id=\"T_4bb1d_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_4bb1d_row0_col1\" class=\"data row0 col1\" >112</td>\n",
       "      <td id=\"T_4bb1d_row0_col2\" class=\"data row0 col2\" >43.8%</td>\n",
       "      <td id=\"T_4bb1d_row0_col3\" class=\"data row0 col3\" >4.7%</td>\n",
       "      <td id=\"T_4bb1d_row0_col4\" class=\"data row0 col4\" >13.6</td>\n",
       "      <td id=\"T_4bb1d_row0_col5\" class=\"data row0 col5\" >1.8%</td>\n",
       "      <td id=\"T_4bb1d_row0_col6\" class=\"data row0 col6\" >36</td>\n",
       "      <td id=\"T_4bb1d_row0_col7\" class=\"data row0 col7\" >6</td>\n",
       "      <td id=\"T_4bb1d_row0_col8\" class=\"data row0 col8\" >0.890</td>\n",
       "      <td id=\"T_4bb1d_row0_col9\" class=\"data row0 col9\" >25.5</td>\n",
       "      <td id=\"T_4bb1d_row0_col10\" class=\"data row0 col10\" >12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bb1d_level0_row1\" class=\"row_heading level0 row1\" >SHOPPING</th>\n",
       "      <td id=\"T_4bb1d_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_4bb1d_row1_col1\" class=\"data row1 col1\" >202</td>\n",
       "      <td id=\"T_4bb1d_row1_col2\" class=\"data row1 col2\" >52.5%</td>\n",
       "      <td id=\"T_4bb1d_row1_col3\" class=\"data row1 col3\" >7.1%</td>\n",
       "      <td id=\"T_4bb1d_row1_col4\" class=\"data row1 col4\" >17.2</td>\n",
       "      <td id=\"T_4bb1d_row1_col5\" class=\"data row1 col5\" >1.0%</td>\n",
       "      <td id=\"T_4bb1d_row1_col6\" class=\"data row1 col6\" >22</td>\n",
       "      <td id=\"T_4bb1d_row1_col7\" class=\"data row1 col7\" >8</td>\n",
       "      <td id=\"T_4bb1d_row1_col8\" class=\"data row1 col8\" >0.923</td>\n",
       "      <td id=\"T_4bb1d_row1_col9\" class=\"data row1 col9\" >4.5</td>\n",
       "      <td id=\"T_4bb1d_row1_col10\" class=\"data row1 col10\" >22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bb1d_level0_row2\" class=\"row_heading level0 row2\" >HOUSE_AND_HOME</th>\n",
       "      <td id=\"T_4bb1d_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_4bb1d_row2_col1\" class=\"data row2 col1\" >74</td>\n",
       "      <td id=\"T_4bb1d_row2_col2\" class=\"data row2 col2\" >40.5%</td>\n",
       "      <td id=\"T_4bb1d_row2_col3\" class=\"data row2 col3\" >10.3%</td>\n",
       "      <td id=\"T_4bb1d_row2_col4\" class=\"data row2 col4\" >10.2</td>\n",
       "      <td id=\"T_4bb1d_row2_col5\" class=\"data row2 col5\" >0.0%</td>\n",
       "      <td id=\"T_4bb1d_row2_col6\" class=\"data row2 col6\" >27</td>\n",
       "      <td id=\"T_4bb1d_row2_col7\" class=\"data row2 col7\" >9</td>\n",
       "      <td id=\"T_4bb1d_row2_col8\" class=\"data row2 col8\" >0.825</td>\n",
       "      <td id=\"T_4bb1d_row2_col9\" class=\"data row2 col9\" >6.0</td>\n",
       "      <td id=\"T_4bb1d_row2_col10\" class=\"data row2 col10\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bb1d_level0_row3\" class=\"row_heading level0 row3\" >MAPS_AND_NAVIGATION</th>\n",
       "      <td id=\"T_4bb1d_row3_col0\" class=\"data row3 col0\" >4</td>\n",
       "      <td id=\"T_4bb1d_row3_col1\" class=\"data row3 col1\" >131</td>\n",
       "      <td id=\"T_4bb1d_row3_col2\" class=\"data row3 col2\" >41.2%</td>\n",
       "      <td id=\"T_4bb1d_row3_col3\" class=\"data row3 col3\" >19.9%</td>\n",
       "      <td id=\"T_4bb1d_row3_col4\" class=\"data row3 col4\" >15.0</td>\n",
       "      <td id=\"T_4bb1d_row3_col5\" class=\"data row3 col5\" >3.8%</td>\n",
       "      <td id=\"T_4bb1d_row3_col6\" class=\"data row3 col6\" >54</td>\n",
       "      <td id=\"T_4bb1d_row3_col7\" class=\"data row3 col7\" >16</td>\n",
       "      <td id=\"T_4bb1d_row3_col8\" class=\"data row3 col8\" >0.725</td>\n",
       "      <td id=\"T_4bb1d_row3_col9\" class=\"data row3 col9\" >0.0</td>\n",
       "      <td id=\"T_4bb1d_row3_col10\" class=\"data row3 col10\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bb1d_level0_row4\" class=\"row_heading level0 row4\" >AUTO_AND_VEHICLES</th>\n",
       "      <td id=\"T_4bb1d_row4_col0\" class=\"data row4 col0\" >5</td>\n",
       "      <td id=\"T_4bb1d_row4_col1\" class=\"data row4 col1\" >85</td>\n",
       "      <td id=\"T_4bb1d_row4_col2\" class=\"data row4 col2\" >21.2%</td>\n",
       "      <td id=\"T_4bb1d_row4_col3\" class=\"data row4 col3\" >18.8%</td>\n",
       "      <td id=\"T_4bb1d_row4_col4\" class=\"data row4 col4\" >14.0</td>\n",
       "      <td id=\"T_4bb1d_row4_col5\" class=\"data row4 col5\" >3.5%</td>\n",
       "      <td id=\"T_4bb1d_row4_col6\" class=\"data row4 col6\" >36</td>\n",
       "      <td id=\"T_4bb1d_row4_col7\" class=\"data row4 col7\" >18</td>\n",
       "      <td id=\"T_4bb1d_row4_col8\" class=\"data row4 col8\" >0.541</td>\n",
       "      <td id=\"T_4bb1d_row4_col9\" class=\"data row4 col9\" >0.0</td>\n",
       "      <td id=\"T_4bb1d_row4_col10\" class=\"data row4 col10\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bb1d_level0_row5\" class=\"row_heading level0 row5\" >TRAVEL_AND_LOCAL</th>\n",
       "      <td id=\"T_4bb1d_row5_col0\" class=\"data row5 col0\" >6</td>\n",
       "      <td id=\"T_4bb1d_row5_col1\" class=\"data row5 col1\" >219</td>\n",
       "      <td id=\"T_4bb1d_row5_col2\" class=\"data row5 col2\" >42.5%</td>\n",
       "      <td id=\"T_4bb1d_row5_col3\" class=\"data row5 col3\" >34.5%</td>\n",
       "      <td id=\"T_4bb1d_row5_col4\" class=\"data row5 col4\" >11.6</td>\n",
       "      <td id=\"T_4bb1d_row5_col5\" class=\"data row5 col5\" >5.5%</td>\n",
       "      <td id=\"T_4bb1d_row5_col6\" class=\"data row5 col6\" >56</td>\n",
       "      <td id=\"T_4bb1d_row5_col7\" class=\"data row5 col7\" >18</td>\n",
       "      <td id=\"T_4bb1d_row5_col8\" class=\"data row5 col8\" >0.574</td>\n",
       "      <td id=\"T_4bb1d_row5_col9\" class=\"data row5 col9\" >0.0</td>\n",
       "      <td id=\"T_4bb1d_row5_col10\" class=\"data row5 col10\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bb1d_level0_row6\" class=\"row_heading level0 row6\" >PRODUCTIVITY</th>\n",
       "      <td id=\"T_4bb1d_row6_col0\" class=\"data row6 col0\" >7</td>\n",
       "      <td id=\"T_4bb1d_row6_col1\" class=\"data row6 col1\" >375</td>\n",
       "      <td id=\"T_4bb1d_row6_col2\" class=\"data row6 col2\" >39.5%</td>\n",
       "      <td id=\"T_4bb1d_row6_col3\" class=\"data row6 col3\" >17.2%</td>\n",
       "      <td id=\"T_4bb1d_row6_col4\" class=\"data row6 col4\" >15.2</td>\n",
       "      <td id=\"T_4bb1d_row6_col5\" class=\"data row6 col5\" >7.5%</td>\n",
       "      <td id=\"T_4bb1d_row6_col6\" class=\"data row6 col6\" >101</td>\n",
       "      <td id=\"T_4bb1d_row6_col7\" class=\"data row6 col7\" >20</td>\n",
       "      <td id=\"T_4bb1d_row6_col8\" class=\"data row6 col8\" >0.621</td>\n",
       "      <td id=\"T_4bb1d_row6_col9\" class=\"data row6 col9\" >0.0</td>\n",
       "      <td id=\"T_4bb1d_row6_col10\" class=\"data row6 col10\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bb1d_level0_row7\" class=\"row_heading level0 row7\" >TOOLS</th>\n",
       "      <td id=\"T_4bb1d_row7_col0\" class=\"data row7 col0\" >8</td>\n",
       "      <td id=\"T_4bb1d_row7_col1\" class=\"data row7 col1\" >829</td>\n",
       "      <td id=\"T_4bb1d_row7_col2\" class=\"data row7 col2\" >33.2%</td>\n",
       "      <td id=\"T_4bb1d_row7_col3\" class=\"data row7 col3\" >12.3%</td>\n",
       "      <td id=\"T_4bb1d_row7_col4\" class=\"data row7 col4\" >13.4</td>\n",
       "      <td id=\"T_4bb1d_row7_col5\" class=\"data row7 col5\" >9.4%</td>\n",
       "      <td id=\"T_4bb1d_row7_col6\" class=\"data row7 col6\" >151</td>\n",
       "      <td id=\"T_4bb1d_row7_col7\" class=\"data row7 col7\" >22</td>\n",
       "      <td id=\"T_4bb1d_row7_col8\" class=\"data row7 col8\" >0.396</td>\n",
       "      <td id=\"T_4bb1d_row7_col9\" class=\"data row7 col9\" >0.0</td>\n",
       "      <td id=\"T_4bb1d_row7_col10\" class=\"data row7 col10\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bb1d_level0_row8\" class=\"row_heading level0 row8\" >LIFESTYLE</th>\n",
       "      <td id=\"T_4bb1d_row8_col0\" class=\"data row8 col0\" >9</td>\n",
       "      <td id=\"T_4bb1d_row8_col1\" class=\"data row8 col1\" >369</td>\n",
       "      <td id=\"T_4bb1d_row8_col2\" class=\"data row8 col2\" >22.8%</td>\n",
       "      <td id=\"T_4bb1d_row8_col3\" class=\"data row8 col3\" >19.8%</td>\n",
       "      <td id=\"T_4bb1d_row8_col4\" class=\"data row8 col4\" >12.8</td>\n",
       "      <td id=\"T_4bb1d_row8_col5\" class=\"data row8 col5\" >5.1%</td>\n",
       "      <td id=\"T_4bb1d_row8_col6\" class=\"data row8 col6\" >139</td>\n",
       "      <td id=\"T_4bb1d_row8_col7\" class=\"data row8 col7\" >24</td>\n",
       "      <td id=\"T_4bb1d_row8_col8\" class=\"data row8 col8\" >0.424</td>\n",
       "      <td id=\"T_4bb1d_row8_col9\" class=\"data row8 col9\" >0.0</td>\n",
       "      <td id=\"T_4bb1d_row8_col10\" class=\"data row8 col10\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bb1d_level0_row9\" class=\"row_heading level0 row9\" >HEALTH_AND_FITNESS</th>\n",
       "      <td id=\"T_4bb1d_row9_col0\" class=\"data row9 col0\" >10</td>\n",
       "      <td id=\"T_4bb1d_row9_col1\" class=\"data row9 col1\" >288</td>\n",
       "      <td id=\"T_4bb1d_row9_col2\" class=\"data row9 col2\" >38.9%</td>\n",
       "      <td id=\"T_4bb1d_row9_col3\" class=\"data row9 col3\" >43.7%</td>\n",
       "      <td id=\"T_4bb1d_row9_col4\" class=\"data row9 col4\" >20.9</td>\n",
       "      <td id=\"T_4bb1d_row9_col5\" class=\"data row9 col5\" >5.2%</td>\n",
       "      <td id=\"T_4bb1d_row9_col6\" class=\"data row9 col6\" >38</td>\n",
       "      <td id=\"T_4bb1d_row9_col7\" class=\"data row9 col7\" >24</td>\n",
       "      <td id=\"T_4bb1d_row9_col8\" class=\"data row9 col8\" >0.427</td>\n",
       "      <td id=\"T_4bb1d_row9_col9\" class=\"data row9 col9\" >0.0</td>\n",
       "      <td id=\"T_4bb1d_row9_col10\" class=\"data row9 col10\" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2cbb5c916d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 880x330 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Classifica finale, senza la categoria distorta (EDUCATION) ---\n",
    "\n",
    "# 1) tolgo EDUCATION e ricalcolo TUTTO sulle dieci rimaste: posizioni, punteggi 0-1, vittorie\n",
    "dieci = candidate.drop(index=\"EDUCATION\")\n",
    "posizioni, punteggi = misure(dieci)\n",
    "classifica_finale = dieci.copy()\n",
    "classifica_finale[\"totale\"] = posizioni.sum(axis=1)\n",
    "classifica_finale[\"punteggio 0-1\"] = punteggi.mean(axis=1)\n",
    "classifica_finale = classifica_finale.join(vittorie(dieci).add_prefix(\"vittorie su 36 \"))\n",
    "classifica_finale = classifica_finale.sort_values(\"totale\")\n",
    "classifica_finale.insert(0, \"posizione\", range(1, len(classifica_finale) + 1))\n",
    "\n",
    "# 2) mostro la classifica finale con gli indicatori principali\n",
    "display(classifica_finale[[\"posizione\", \"numero_app\", \"quota_oltre_1M\", \"quota_prima_app\",\n",
    "                           \"recensioni_per_1000\", \"quota_a_pagamento\", \"giorni_da_aggiornamento\",\n",
    "                           \"totale\", \"punteggio 0-1\", \"vittorie su 36 con le posizioni\",\n",
    "                           \"vittorie su 36 con il punteggio 0-1\"]]\n",
    "        .style.format({\"quota_oltre_1M\": \"{:.1%}\", \"quota_prima_app\": \"{:.1%}\",\n",
    "                       \"recensioni_per_1000\": \"{:.1f}\", \"quota_a_pagamento\": \"{:.1%}\",\n",
    "                       \"giorni_da_aggiornamento\": \"{:.0f}\", \"totale\": \"{:.0f}\",\n",
    "                       \"punteggio 0-1\": \"{:.3f}\", \"vittorie su 36 con le posizioni\": \"{:.1f}\",\n",
    "                       \"vittorie su 36 con il punteggio 0-1\": \"{:.0f}\"}))\n",
    "\n",
    "# 3) le vittorie delle prime categorie, in un grafico\n",
    "prime = classifica_finale.head(4)\n",
    "fig, ax = plt.subplots(figsize=(8, 3))\n",
    "y = np.arange(len(prime))\n",
    "ax.barh(y - 0.18, prime[\"vittorie su 36 con le posizioni\"], height=0.34, color=BLU,\n",
    "        label=\"sommando le posizioni\")\n",
    "ax.barh(y + 0.18, prime[\"vittorie su 36 con il punteggio 0-1\"], height=0.34, color=ARANCIONE,\n",
    "        label=\"con il punteggio 0-1\")\n",
    "ax.set_yticks(y, [nome_leggibile(c) for c in prime.index])\n",
    "ax.invert_yaxis()\n",
    "ax.set_xlim(0, 36)\n",
    "ax.set_xlabel(\"combinazioni di pesi vinte, su 36\")\n",
    "ax.set_title(\"Senza EDUCATION, il vincitore dipende dal metodo\")\n",
    "ax.legend(loc=\"lower right\", fontsize=8, frameon=False)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f18ccf0",
   "metadata": {},
   "source": [
    "**Risultato** Questo controllo cambia la scelta finale.\n",
    "\n",
    "Ci sono 642 app di tipo educativo (genere «Education»), ma solo 108 stanno nella categoria EDUCATION. Le altre 526 sono nella categoria FAMILY. E i due gruppi vanno in modo molto diverso:\n",
    "\n",
    "| | app | installazioni tipiche | oltre il milione |\n",
    "|---|---|---|---|\n",
    "| nella categoria EDUCATION | 108 | 1.000.000 | 57,4% |\n",
    "| educative, ma nella categoria FAMILY | 526 | 5.000 | 12,6% |\n",
    "\n",
    "Sono app dello stesso tipo, ma le prime hanno duecento volte le installazioni delle seconde.\n",
    "\n",
    "Il motivo non è il mercato, è il modo in cui Google organizza lo store: le app educative per bambini vanno in FAMILY. Nella categoria EDUCATION restano poche app, quasi tutte già famose. Quindi non è una nicchia libera dove è facile emergere: è uno scaffale dove arrivano solo le app che ce l'hanno già fatta. Io stavo guardando quei numeri e li scambiavo per un'opportunità.\n",
    "\n",
    "Lo stesso succede con ENTERTAINMENT: 592 app hanno quel genere, ma solo 87 stanno nella categoria. Le due categorie che nella sezione 5 sembravano le più promettenti sono proprio queste due. Il dubbio che avevo era giusto, ed escludo EDUCATION.\n",
    "\n",
    "**Senza EDUCATION, però, i dati non danno un vincitore netto.** Ricalcolo tutto sulle dieci\n",
    "categorie rimaste. Sommando le posizioni vince FOOD & DRINK (6 punti contro gli 8 di SHOPPING e\n",
    "i 9 di HOUSE & HOME, prima in 25,5 combinazioni di pesi su 36). Con il punteggio che conserva le\n",
    "distanze vince SHOPPING (0,923 contro 0,890, prima in 22 combinazioni su 36 contro le 12 di\n",
    "FOOD & DRINK). Le due categorie si scambiano il primo posto a seconda di come si misura, e\n",
    "HOUSE & HOME resta terza in entrambi i casi. Per scegliere fra loro serve un'informazione\n",
    "che il dataset non contiene: la sezione 9 la aggiunge, e lo dichiara."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9a68c5",
   "metadata": {},
   "source": [
    "---\n",
    "## 9. La strategia: dove conviene lanciare l'app?\n",
    "\n",
    "Torno alla domanda da cui sono partito: **un'app per la sostenibilità, dove conviene lanciarla?**\n",
    "\n",
    "I numeri della sezione 8 dicono com'è fatta una categoria, ma non quanto è difficile costruirci\n",
    "sopra un'app. Aggiungo quindi una mia valutazione della **difficoltà di ingresso**. Nel dataset\n",
    "questa informazione non c'è: è un mio giudizio, e la tabella qui sotto la tiene **in una colonna\n",
    "separata** da quello che dicono i dati.\n",
    "\n",
    "**La regola, scritta prima di guardare l'esito: quando le due colonne non concordano, prevale la\n",
    "difficoltà stimata.** Un'app che non so costruire, o che richiede un'organizzazione che non ho,\n",
    "non diventa una buona scelta perché la sua categoria ha numeri migliori."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "da71ff32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:16.645077Z",
     "iopub.status.busy": "2026-10-07T04:52:16.644866Z",
     "iopub.status.idle": "2026-10-07T04:52:16.654502Z",
     "shell.execute_reply": "2026-10-07T04:52:16.653714Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_18348\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_18348_level0_col0\" class=\"col_heading level0 col0\" >posizione secondo i dati (somma)</th>\n",
       "      <th id=\"T_18348_level0_col1\" class=\"col_heading level0 col1\" >punteggio 0-1</th>\n",
       "      <th id=\"T_18348_level0_col2\" class=\"col_heading level0 col2\" >vittorie (posizioni)</th>\n",
       "      <th id=\"T_18348_level0_col3\" class=\"col_heading level0 col3\" >vittorie (punteggio)</th>\n",
       "      <th id=\"T_18348_level0_col4\" class=\"col_heading level0 col4\" >difficoltà stimata (mio giudizio)</th>\n",
       "      <th id=\"T_18348_level0_col5\" class=\"col_heading level0 col5\" >perché</th>\n",
       "      <th id=\"T_18348_level0_col6\" class=\"col_heading level0 col6\" >esito</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_18348_level0_row0\" class=\"row_heading level0 row0\" >Food & Drink</th>\n",
       "      <td id=\"T_18348_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_18348_row0_col1\" class=\"data row0 col1\" >0.890</td>\n",
       "      <td id=\"T_18348_row0_col2\" class=\"data row0 col2\" >25.5</td>\n",
       "      <td id=\"T_18348_row0_col3\" class=\"data row0 col3\" >12</td>\n",
       "      <td id=\"T_18348_row0_col4\" class=\"data row0 col4\" >bassa</td>\n",
       "      <td id=\"T_18348_row0_col5\" class=\"data row0 col5\" >un'app contro lo spreco di cibo funziona con il solo telefono: scadenze, dispensa, ricette con quello che c'è in frigo</td>\n",
       "      <td id=\"T_18348_row0_col6\" class=\"data row0 col6\" >scelta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_18348_level0_row1\" class=\"row_heading level0 row1\" >Shopping</th>\n",
       "      <td id=\"T_18348_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_18348_row1_col1\" class=\"data row1 col1\" >0.923</td>\n",
       "      <td id=\"T_18348_row1_col2\" class=\"data row1 col2\" >4.5</td>\n",
       "      <td id=\"T_18348_row1_col3\" class=\"data row1 col3\" >22</td>\n",
       "      <td id=\"T_18348_row1_col4\" class=\"data row1 col4\" >alta</td>\n",
       "      <td id=\"T_18348_row1_col5\" class=\"data row1 col5\" >un mercato dell'usato funziona solo con tanti venditori e compratori insieme; i concorrenti sono piattaforme enormi (nel campione di 7.1 OLX ha 50 milioni di installazioni)</td>\n",
       "      <td id=\"T_18348_row1_col6\" class=\"data row1 col6\" >scartata</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_18348_level0_row2\" class=\"row_heading level0 row2\" >House & Home</th>\n",
       "      <td id=\"T_18348_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_18348_row2_col1\" class=\"data row2 col1\" >0.825</td>\n",
       "      <td id=\"T_18348_row2_col2\" class=\"data row2 col2\" >6.0</td>\n",
       "      <td id=\"T_18348_row2_col3\" class=\"data row2 col3\" >2</td>\n",
       "      <td id=\"T_18348_row2_col4\" class=\"data row2 col4\" >alta</td>\n",
       "      <td id=\"T_18348_row2_col5\" class=\"data row2 col5\" >le app più scaricate sono annunci immobiliari; misurare i consumi di casa richiede dati o apparecchi che non ho</td>\n",
       "      <td id=\"T_18348_row2_col6\" class=\"data row2 col6\" >scartata</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_18348_level0_row3\" class=\"row_heading level0 row3\" >Maps & Navigation</th>\n",
       "      <td id=\"T_18348_row3_col0\" class=\"data row3 col0\" >4</td>\n",
       "      <td id=\"T_18348_row3_col1\" class=\"data row3 col1\" >0.725</td>\n",
       "      <td id=\"T_18348_row3_col2\" class=\"data row3 col2\" >0.0</td>\n",
       "      <td id=\"T_18348_row3_col3\" class=\"data row3 col3\" >0</td>\n",
       "      <td id=\"T_18348_row3_col4\" class=\"data row3 col4\" >alta</td>\n",
       "      <td id=\"T_18348_row3_col5\" class=\"data row3 col5\" >un'app di mobilità sostenibile richiede gli orari e le reti dei trasporti, città per città</td>\n",
       "      <td id=\"T_18348_row3_col6\" class=\"data row3 col6\" >scartata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2cbb5d2b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Dati e giudizio, nella stessa tabella ---\n",
    "\n",
    "# 1) il mio giudizio sulla difficoltà di ingresso: NON viene dal dataset\n",
    "GIUDIZIO = {\n",
    "    \"FOOD_AND_DRINK\": (\"bassa\",\n",
    "        \"un'app contro lo spreco di cibo funziona con il solo telefono: scadenze, dispensa, \"\n",
    "        \"ricette con quello che c'è in frigo\"),\n",
    "    \"SHOPPING\": (\"alta\",\n",
    "        \"un mercato dell'usato funziona solo con tanti venditori e compratori insieme; \"\n",
    "        \"i concorrenti sono piattaforme enormi (nel campione di 7.1 OLX ha 50 milioni di installazioni)\"),\n",
    "    \"HOUSE_AND_HOME\": (\"alta\",\n",
    "        \"le app più scaricate sono annunci immobiliari; misurare i consumi di casa \"\n",
    "        \"richiede dati o apparecchi che non ho\"),\n",
    "    \"MAPS_AND_NAVIGATION\": (\"alta\",\n",
    "        \"un'app di mobilità sostenibile richiede gli orari e le reti dei trasporti, \"\n",
    "        \"città per città\"),\n",
    "}\n",
    "\n",
    "# 2) le prime quattro della classifica finale, con le due colonne affiancate\n",
    "decisione = classifica_finale.loc[list(GIUDIZIO), [\"posizione\", \"punteggio 0-1\",\n",
    "                                  \"vittorie su 36 con le posizioni\",\n",
    "                                  \"vittorie su 36 con il punteggio 0-1\"]].copy()\n",
    "decisione.columns = [\"posizione secondo i dati (somma)\", \"punteggio 0-1\",\n",
    "                     \"vittorie (posizioni)\", \"vittorie (punteggio)\"]\n",
    "decisione[\"difficoltà stimata (mio giudizio)\"] = [GIUDIZIO[c][0] for c in decisione.index]\n",
    "decisione[\"perché\"] = [GIUDIZIO[c][1] for c in decisione.index]\n",
    "decisione[\"esito\"] = np.where(decisione[\"difficoltà stimata (mio giudizio)\"] == \"bassa\",\n",
    "                              \"scelta\", \"scartata\")\n",
    "decisione.index = [nome_leggibile(c) for c in decisione.index]\n",
    "\n",
    "with pd.option_context(\"display.max_colwidth\", 110):\n",
    "    display(decisione.style.format({\"punteggio 0-1\": \"{:.3f}\", \"vittorie (posizioni)\": \"{:.1f}\",\n",
    "                                    \"vittorie (punteggio)\": \"{:.0f}\"}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc7833aa",
   "metadata": {},
   "source": [
    "**Risultato** Nelle prime quattro colonne SHOPPING e FOOD & DRINK si contendono il primo posto\n",
    "(sezione 8.2); la quinta le separa. **La scelta di FOOD & DRINK viene dal giudizio sulla\n",
    "difficoltà, non dalla classifica**, ed è giusto dirlo così: se un giorno avessi la struttura\n",
    "per un mercato dell'usato, i dati non mi impedirebbero di scegliere SHOPPING.\n",
    "\n",
    "**La risposta è quindi Food & Drink**, con un'app che aiuta a non sprecare il cibo in casa.\n",
    "\n",
    "E c'è un ultimo argomento: ho letto i nomi di tutte le 112 app della categoria, ed **esiste una\n",
    "sola app contro lo spreco di cibo**, *Frigo Magic*, con mezzo milione di installazioni. Le più\n",
    "vicine sono app di ricette con lista della spesa, che non nascono per questo scopo. Le persone\n",
    "installano questo tipo di app, ma quasi nessuno la offre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cbfa12da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:16.656374Z",
     "iopub.status.busy": "2026-10-07T04:52:16.656128Z",
     "iopub.status.idle": "2026-10-07T04:52:16.670472Z",
     "shell.execute_reply": "2026-10-07T04:52:16.670051Z"
    },
    "id": "cbfa12da"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nome</th>\n",
       "      <th>installazioni</th>\n",
       "      <th>voto</th>\n",
       "      <th>recensioni_per_1000</th>\n",
       "      <th>dimensione_mb</th>\n",
       "      <th>giorni_da_aggiornamento</th>\n",
       "      <th>tipo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>Frigo Magic: Easy recipe idea and anti-waste</td>\n",
       "      <td>500000</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.946</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11</td>\n",
       "      <td>Gratuita</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              nome  installazioni  voto  recensioni_per_1000  dimensione_mb  giorni_da_aggiornamento      tipo\n",
       "1185  Frigo Magic: Easy recipe idea and anti-waste         500000   4.1                4.946           14.0                       11  Gratuita"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frigo Magic</th>\n",
       "      <th>mediana Food &amp; Drink</th>\n",
       "      <th>quartile migliore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>installazioni</th>\n",
       "      <td>500000.00</td>\n",
       "      <td>300000.00</td>\n",
       "      <td>1000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voto</th>\n",
       "      <td>4.10</td>\n",
       "      <td>4.30</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recensioni per 1.000 inst.</th>\n",
       "      <td>4.95</td>\n",
       "      <td>13.57</td>\n",
       "      <td>30.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dimensione (MB)</th>\n",
       "      <td>14.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>27.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Frigo Magic  mediana Food & Drink  quartile migliore\n",
       "installazioni                 500000.00             300000.00         1000000.00\n",
       "voto                               4.10                  4.30               4.50\n",
       "recensioni per 1.000 inst.         4.95                 13.57              30.22\n",
       "dimensione (MB)                   14.00                 17.00              27.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "App di Food & Drink con voto >= 4,5: 37 su 112 (33%) | app a pagamento: 1.8%\n",
      "App votate con un voto più alto di Frigo Magic: 58 su 94 (62%)\n"
     ]
    }
   ],
   "source": [
    "# --- Il concorrente diretto: com'è messo rispetto alla sua categoria ---\n",
    "\n",
    "# 1) prendo le app di Food & Drink e cerco Frigo Magic, l'unica app anti-spreco\n",
    "food = app[app[\"categoria\"] == \"FOOD_AND_DRINK\"]\n",
    "frigo = food[food[\"nome\"].str.contains(\"Frigo\", case=False)]\n",
    "display(frigo[[\"nome\", \"installazioni\", \"voto\", \"recensioni_per_1000\",\n",
    "               \"dimensione_mb\", \"giorni_da_aggiornamento\", \"tipo\"]])\n",
    "\n",
    "# 2) confronto Frigo Magic con il valore tipico (mediana) della categoria\n",
    "#    e con il quartile migliore (il livello raggiunto dal 25% delle app più forti)\n",
    "riferimenti = pd.DataFrame({\n",
    "    \"Frigo Magic\": [frigo[\"installazioni\"].iloc[0], frigo[\"voto\"].iloc[0],\n",
    "                    frigo[\"recensioni_per_1000\"].iloc[0], frigo[\"dimensione_mb\"].iloc[0]],\n",
    "    \"mediana Food & Drink\": [food[\"installazioni\"].median(), food[\"voto\"].median(),\n",
    "                             food[\"recensioni_per_1000\"].median(),\n",
    "                             food[\"dimensione_mb\"].median()],\n",
    "    \"quartile migliore\": [food[\"installazioni\"].quantile(0.75), food[\"voto\"].quantile(0.75),\n",
    "                          food[\"recensioni_per_1000\"].quantile(0.75),\n",
    "                          food[\"dimensione_mb\"].quantile(0.75)],\n",
    "}, index=[\"installazioni\", \"voto\", \"recensioni per 1.000 inst.\", \"dimensione (MB)\"])\n",
    "display(riferimenti.round(2))\n",
    "\n",
    "# 3) quante app della categoria hanno un voto alto, e quante sono a pagamento\n",
    "print(f\"App di Food & Drink con voto >= 4,5: {(food['voto'] >= 4.5).sum()} su {len(food)} \"\n",
    "      f\"({(food['voto'] >= 4.5).mean():.0%}) | app a pagamento: \"\n",
    "      f\"{(food['tipo'] == 'A pagamento').mean():.1%}\")\n",
    "votate = food[\"voto\"].dropna()\n",
    "print(f\"App votate con un voto più alto di Frigo Magic: {(votate > frigo['voto'].iloc[0]).sum()} \"\n",
    "      f\"su {len(votate)} ({(votate > frigo['voto'].iloc[0]).mean():.0%})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fdc962a",
   "metadata": {},
   "source": [
    "**Risultato** *Frigo Magic* è l'unica app contro lo spreco di cibo della categoria, e il suo profilo descrive un mercato **che esiste, ma che nessuno sta servendo bene**:\n",
    "\n",
    "- **500.000 installazioni**: la domanda c'è, non bisogna inventarla. È il rischio più grande, ed è già escluso.\n",
    "- **voto 4,1**, sotto il valore tipico della categoria (4,3): quasi due terzi delle app votate di Food & Drink (58 su 94) hanno un voto più alto, e 37 su 112 arrivano almeno a 4,5.\n",
    "- **4,95 recensioni ogni 1.000 installazioni**, contro le 13,6 della categoria: **un terzo** del coinvolgimento normale. Mezzo milione di persone l'ha installata, ma quasi nessuno ha avuto voglia di scriverne.\n",
    "\n",
    "**Cosa ne deduco, e con quale cautela.** ⚠ *legame, non causa.* Nella prima versione scrivevo\n",
    "che, siccome «il voto segue il coinvolgimento», questi due numeri erano il segno di un'app che si\n",
    "installa e si dimentica, e che la debolezza «si batte con un prodotto fatto meglio». È proprio il\n",
    "salto che la sezione 4.2 vieta: in 4.1 ho visto che voto e coinvolgimento salgono insieme, non\n",
    "che uno causi l'altro. Quello che i dati dicono di Frigo Magic è più modesto: **è sotto la\n",
    "categoria su entrambe le misure.** Che sia un'app dimenticata dopo l'installazione è\n",
    "un'**ipotesi**: compatibile con i numeri, ma ce ne sono altre (un pubblico che scrive poco per\n",
    "abitudine, recensioni chieste di rado). Per questo il piano non la dà per vera: la mette alla\n",
    "prova nei primi sei mesi (9.2).\n",
    "\n",
    "### 9.1 La proposta\n",
    "\n",
    "**Nome di lavoro:** *Dal frigo non si butta niente* · **categoria:** `FOOD_AND_DRINK` · **età minima:** Everyone (adatta a tutti)\n",
    "\n",
    "**Il messaggio principale:** *«Smetti di buttare la spesa»*, cioè soldi e fatica risparmiati. **Non** *«Riduci la tua impronta di carbonio»*. È una mia scelta, e la dichiaro come tale: il dataset non contiene i messaggi delle app e non può dire quale funzioni meglio. È coerente con la lettura della sezione 7, dove le app con più pubblico sono quelle che risolvono un problema che hanno in tanti: lo spreco di cibo riguarda chiunque faccia la spesa, non solo chi è sensibile all'ambiente.\n",
    "\n",
    "Il beneficio ambientale resta dentro l'app e diventa **qualcosa da mostrare all'utente**, per esempio «questo mese non hai buttato 4,2 kg di cibo», ma come *premio*, non come argomento di vendita. È anche un modo per **provare** ad aumentare il coinvolgimento, che è proprio la misura su cui Frigo Magic è più debole.\n",
    "\n",
    "Ogni scelta dice da quale dato viene e **che tipo di prova** ha dietro:\n",
    "\n",
    "| Scelta | Valore | Da quale dato viene | Tipo di prova |\n",
    "|---|---|---|---|\n",
    "| categoria | `FOOD_AND_DRINK` | leader al 4,7% (il più debole dello store), 112 app, 43,8% oltre il milione | dati + **giudizio** sulla difficoltà (sez. 9) |\n",
    "| modello | **gratuita** + abbonamento facoltativo | H2: le app gratuite hanno 100 volte le installazioni; in questa categoria solo l'1,8% è a pagamento | ⚠ confronto fra app diverse, non effetto del prezzo |\n",
    "| prezzo abbonamento | 2,99 $ al mese | H3: la fascia 1,50–2,99 $ ha più app oltre le 10.000 installazioni | ⚠ legame; le fasce contengono app diverse: è un **punto di partenza da provare**, non un prezzo dimostrato |\n",
    "| voto da raggiungere | almeno 4,5 | è il livello del 25% delle app migliori della categoria; *Frigo Magic* è a 4,1 | descrittivo: un obiettivo, non una previsione |\n",
    "| coinvolgimento | almeno 20 recensioni ogni 1.000 installazioni | è il livello di Education e Health; *Frigo Magic* è a 4,95 | descrittivo: un obiettivo |\n",
    "| dimensione | massimo 17 MB | valore tipico della categoria | descrittivo; non è dimostrato che un'app più leggera vada meglio |\n",
    "| aggiornamenti | almeno uno al mese | nella categoria l'ultimo aggiornamento risale in genere a 36 giorni prima | ⚠ H4 è un legame: lo tengo come buona pratica e ne **misuro** l'effetto |\n",
    "| obiettivo a 18 mesi | 500.000 installazioni | come *Frigo Magic*; il valore tipico della categoria è 300.000 | descrittivo; la scadenza è un mio giudizio (il dataset non ha date di lancio) |\n",
    "\n",
    "### 9.2 Quando fermarsi\n",
    "\n",
    "Un piano serio dice anche quando ammettere che non sta funzionando. Siccome diverse scelte\n",
    "poggiano su legami e non su cause, gli indicatori servono anche a **verificare quei legami\n",
    "sulla mia app**.\n",
    "\n",
    "| Indicatore | Obiettivo | Se dopo 6 mesi non ci siamo |\n",
    "|---|---|---|\n",
    "| voto | almeno 4,5 | sotto 4,2 l'app non piace: va riprogettata, non pubblicizzata |\n",
    "| recensioni ogni 1.000 installazioni | almeno 20 | sotto 10 l'ipotesi «app installata e dimenticata» vale anche per me |\n",
    "| installazioni | 500.000 in 18 mesi | sotto 50.000 dopo 6 mesi, la categoria o il messaggio sono sbagliati |\n",
    "| utenti che si abbonano | almeno il 2% | sotto l'1% serve un altro modo per guadagnare, o un altro prezzo |\n",
    "\n",
    "**In che ordine lavorare:** prima il voto e il coinvolgimento, poi le installazioni, l'abbonamento per ultimo. È un ordine che scelgo io, non una legge dei dati: punta sulla misura dove l'unico concorrente è più debole. Se l'ipotesi su Frigo Magic fosse sbagliata, me ne accorgerei dal secondo indicatore, e cambierei ordine."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40045e24",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 10. Limiti\n",
    "\n",
    "Ogni analisi ha dei limiti. Ecco quelli di questa, cioè le cose che i dati non mi permettono di dire.\n",
    "\n",
    "1. **I dati sono del 2018.** È il limite più importante, perché il mercato nel frattempo è cambiato. Per esempio, nella sezione 7 ho visto che le app per ricaricare le auto elettriche non avevano utenti. Oggi quasi certamente non è più così, perché le auto elettriche sono molte di più. Quel risultato vale quindi solo per il 2018. Quello che resta valido è il modo in cui ho ragionato.\n",
    "\n",
    "2. **Le installazioni non sono numeri precisi.** Il dataset non dice \"12.345 installazioni\", ma solo fasce come \"oltre 10.000\". Le fasce sono venti in tutto. Per questo, se due categorie differiscono di meno di 5 volte, non posso dire con sicurezza quale sia davvero più scaricata.\n",
    "\n",
    "3. **Il segmento sostenibilità è stimato, non contato.** Le parole chiave ne trovano circa una su tre (sezione 7.1). La stima viene da 1.000 app lette a mano: l'intervallo è ampio (dallo 0,35% al 2,3% del catalogo) e la classificazione resta un giudizio, con alcuni casi dubbi dichiarati.\n",
    "\n",
    "4. **La classifica delle categorie dipende dal metodo** (sezione 8.1): senza EDUCATION, FOOD & DRINK e SHOPPING si scambiano il primo posto. La scelta finale poggia su un giudizio dichiarato.\n",
    "\n",
    "5. **Correlazioni, non cause** (sezione 4.2). Voto e coinvolgimento, voto e aggiornamenti, prezzo e installazioni si muovono insieme, ma questi dati non dicono perché. Le scelte della strategia che ci poggiano sono segnate con ⚠ e diventano cose da misurare sulla propria app.\n",
    "\n",
    "6. **Non ho dati economici.** Non so quanto guadagna un'app, quanto costa farla conoscere, quanto tempo le persone la usano o se la riaprono dopo una settimana. Le mie conclusioni sul modello di business si basano solo su installazioni e voto, perché sono gli unici dati disponibili.\n",
    "\n",
    "7. **Vedo solo le app che ce l'hanno fatta.** Le app tolte dallo store perché non funzionavano non sono nel dataset. Per questo le possibilità di successo che ho calcolato sono più alte di quelle reali. Inoltre il voto esiste solo per le app che qualcuno ha usato, quindi le medie dei voti valgono solo per quelle.\n",
    "\n",
    "8. **Non ho fatto test statistici sulle differenze fra gruppi.** Ho confrontato gruppi di app guardando i numeri; l'unico intervallo di confidenza è quello della stima del segmento. Per sapere se una differenza piccola potrebbe essere dovuta al caso servirebbe un test statistico, che sarebbe il passo successivo di questo lavoro.\n",
    "\n",
    "---\n",
    "\n",
    "### La lezione più utile di questa analisi\n",
    "\n",
    "Nel 2018 nove sviluppatori avevano già avuto la mia stessa idea: un'app per ricaricare le auto elettriche. Le loro app avevano in genere mille installazioni e i voti più bassi tra le app sostenibili, anche se venivano aggiornate spesso. La mia lettura, che il dataset non può confermare, è che fosse il momento sbagliato: le auto elettriche erano ancora poche.\n",
    "\n",
    "Un'analisi di mercato serve a scegliere il settore giusto, ma può anche far nascere la domanda giusta sul momento. Tante app e pochi utenti **è un segnale da indagare**: può voler dire che il mercato non è pronto, o che le app non sono buone, e i dati da soli non distinguono le due cose. Con lo spreco di cibo il segnale è opposto: c'è una sola app, e mezzo milione di persone l'ha già installata.\n",
    "\n",
    "E una lezione di metodo, che devo alla correzione: un numero vale quanto la prova che lo\n",
    "accompagna. Per questo la sezione 11 ricalcola ogni numero citato nel testo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772eae75",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 11. Registro dei numeri citati\n",
    "\n",
    "Ogni numero che il testo usa per argomentare è ricalcolato qui dagli stessi dati, e confrontato\n",
    "con il valore scritto. Se un giorno cambiassero i dati o il codice, la colonna «coincide» lo\n",
    "direbbe subito."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "86811751",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:16.672572Z",
     "iopub.status.busy": "2026-10-07T04:52:16.672409Z",
     "iopub.status.idle": "2026-10-07T04:52:16.749417Z",
     "shell.execute_reply": "2026-10-07T04:52:16.748626Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sezione</th>\n",
       "      <th>numero citato</th>\n",
       "      <th>scritto nel testo</th>\n",
       "      <th>ricalcolato</th>\n",
       "      <th>coincide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>righe del file grezzo</td>\n",
       "      <td>10.841</td>\n",
       "      <td>10.841</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>voti validi nel file grezzo</td>\n",
       "      <td>9.367</td>\n",
       "      <td>9.367</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.1</td>\n",
       "      <td>righe identiche da eliminare</td>\n",
       "      <td>483</td>\n",
       "      <td>483</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.1</td>\n",
       "      <td>righe diverse ripetute (conteggio di ydata)</td>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.3</td>\n",
       "      <td>nomi ripetuti</td>\n",
       "      <td>523</td>\n",
       "      <td>523</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.3</td>\n",
       "      <td>righe con un nome già visto</td>\n",
       "      <td>698</td>\n",
       "      <td>698</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.3</td>\n",
       "      <td>nomi con stessa categoria e installazioni</td>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.3</td>\n",
       "      <td>nomi con categoria diversa</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.3</td>\n",
       "      <td>nomi con installazioni diverse</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.3</td>\n",
       "      <td>voto mancante nelle righe grezze</td>\n",
       "      <td>13,6%</td>\n",
       "      <td>13,6%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.3</td>\n",
       "      <td>app analizzate</td>\n",
       "      <td>9.674</td>\n",
       "      <td>9.674</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.3</td>\n",
       "      <td>recensioni mediane: senza voto / con voto</td>\n",
       "      <td>1 / 3.020</td>\n",
       "      <td>1 / 3.020</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.3</td>\n",
       "      <td>installazioni mediane: senza voto / con voto</td>\n",
       "      <td>100 / 100.000</td>\n",
       "      <td>100 / 100.000</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.3</td>\n",
       "      <td>app senza voto con meno di 10 recensioni</td>\n",
       "      <td>83,5%</td>\n",
       "      <td>83,5%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>media / mediana: recensioni</td>\n",
       "      <td>223</td>\n",
       "      <td>223</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>media / mediana: installazioni</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>app con voto sopra 4,0</td>\n",
       "      <td>76,7%</td>\n",
       "      <td>76,7%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>app con voto sotto 3,0</td>\n",
       "      <td>3,4%</td>\n",
       "      <td>3,4%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>voto mediano</td>\n",
       "      <td>4,3</td>\n",
       "      <td>4,3</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>inizio del quartile superiore del voto</td>\n",
       "      <td>4,5</td>\n",
       "      <td>4,5</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>installazioni del top 1% delle app</td>\n",
       "      <td>49,4%</td>\n",
       "      <td>49,4%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>installazioni del top 10% delle app</td>\n",
       "      <td>88,1%</td>\n",
       "      <td>88,1%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>app gratuite</td>\n",
       "      <td>92,2%</td>\n",
       "      <td>92,2%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>prezzo mediano delle app a pagamento</td>\n",
       "      <td>2,99</td>\n",
       "      <td>2,99</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>dimensione mediana (MB)</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2</td>\n",
       "      <td>giorni dall'ultimo aggiornamento (mediana)</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>outlier IQR sulle recensioni</td>\n",
       "      <td>1.661</td>\n",
       "      <td>1.661</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>outlier IQR sulle recensioni, in %</td>\n",
       "      <td>17,2%</td>\n",
       "      <td>17,2%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>outlier IQR sul voto</td>\n",
       "      <td>492</td>\n",
       "      <td>492</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>app a pagamento</td>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "      <td>media dei prezzi con / senza le 20 app da 100 $</td>\n",
       "      <td>14,05 / 4,74</td>\n",
       "      <td>14,05 / 4,74</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4.1</td>\n",
       "      <td>voto medio: quintile meno / più recensito</td>\n",
       "      <td>3,96 / 4,44</td>\n",
       "      <td>3,96 / 4,44</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4.1</td>\n",
       "      <td>app coperte dal file delle recensioni</td>\n",
       "      <td>819</td>\n",
       "      <td>819</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5</td>\n",
       "      <td>app in FAMILY</td>\n",
       "      <td>1.877</td>\n",
       "      <td>1.877</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>5</td>\n",
       "      <td>quota installazioni di FAMILY</td>\n",
       "      <td>8,3%</td>\n",
       "      <td>8,3%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5</td>\n",
       "      <td>indice domanda/offerta di FAMILY</td>\n",
       "      <td>0,43</td>\n",
       "      <td>0,43</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5</td>\n",
       "      <td>indice domanda/offerta di COMMUNICATION</td>\n",
       "      <td>4,49</td>\n",
       "      <td>4,49</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>5</td>\n",
       "      <td>quota della prima app in BOOKS (Google Play Books)</td>\n",
       "      <td>60,0%</td>\n",
       "      <td>60,0%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>5</td>\n",
       "      <td>quota della prima app in HEALTH (Samsung Health)</td>\n",
       "      <td>43,7%</td>\n",
       "      <td>43,7%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5</td>\n",
       "      <td>quota della prima app in FOOD &amp; DRINK</td>\n",
       "      <td>4,7%</td>\n",
       "      <td>4,7%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5</td>\n",
       "      <td>app oltre il milione, tutto lo store</td>\n",
       "      <td>35,2%</td>\n",
       "      <td>35,2%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>5</td>\n",
       "      <td>app oltre il milione: ENTERTAINMENT / EDUCATION</td>\n",
       "      <td>83,9% / 57,4%</td>\n",
       "      <td>83,9% / 57,4%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>5</td>\n",
       "      <td>app oltre il milione: SHOPPING / FOOD &amp; DRINK</td>\n",
       "      <td>52,5% / 43,8%</td>\n",
       "      <td>52,5% / 43,8%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5</td>\n",
       "      <td>coinvolgimento tipico: store / GAME / EDUCATION / HEALTH</td>\n",
       "      <td>17 / 28,5 / 21,1 / 20,9</td>\n",
       "      <td>17 / 28,5 / 21,1 / 20,9</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>6</td>\n",
       "      <td>voto mediano: a pagamento / gratuite</td>\n",
       "      <td>4,4 / 4,3</td>\n",
       "      <td>4,4 / 4,3</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>6</td>\n",
       "      <td>installazioni mediane: gratuite / a pagamento</td>\n",
       "      <td>100.000 / 1.000</td>\n",
       "      <td>100.000 / 1.000</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>6</td>\n",
       "      <td>oltre il milione: gratuite / a pagamento</td>\n",
       "      <td>37,9% / 2,9%</td>\n",
       "      <td>37,9% / 2,9%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>6</td>\n",
       "      <td>indice prezzo ~ installazioni</td>\n",
       "      <td>+0,14</td>\n",
       "      <td>+0,14</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>6</td>\n",
       "      <td>oltre 10.000: fino a 1,49 $ / 1,50-2,99 $ / 15 $ e oltre</td>\n",
       "      <td>19,7% / 47,1% / 21,2%</td>\n",
       "      <td>19,7% / 47,1% / 21,2%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>6</td>\n",
       "      <td>temi grafici fino a 1,49 $ / app mediche sopra 15 $</td>\n",
       "      <td>27% / 39%</td>\n",
       "      <td>27% / 39%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>6</td>\n",
       "      <td>voto medio: aggiornata da meno di un mese / oltre un anno</td>\n",
       "      <td>4,27 / 4,06</td>\n",
       "      <td>4,27 / 4,06</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6</td>\n",
       "      <td>escursione delle mediane del voto fra categorie</td>\n",
       "      <td>0,4</td>\n",
       "      <td>0,4</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>7</td>\n",
       "      <td>candidati trovati dalle parole chiave</td>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>7</td>\n",
       "      <td>app sostenibili fra i candidati / scartate</td>\n",
       "      <td>34 / 32</td>\n",
       "      <td>34 / 32</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>7</td>\n",
       "      <td>precisione delle parole chiave</td>\n",
       "      <td>51,5%</td>\n",
       "      <td>51,5%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>7</td>\n",
       "      <td>segmento: quota del catalogo (solo le 34)</td>\n",
       "      <td>0,35%</td>\n",
       "      <td>0,35%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>7</td>\n",
       "      <td>segmento: quota delle installazioni (solo le 34)</td>\n",
       "      <td>0,03%</td>\n",
       "      <td>0,03%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>7</td>\n",
       "      <td>trasporto pubblico: app / installazioni / voto</td>\n",
       "      <td>11 / 500.000 / 4,40</td>\n",
       "      <td>11 / 500.000 / 4,40</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>7</td>\n",
       "      <td>ricarica elettrica: app / installazioni / voto / giorni</td>\n",
       "      <td>9 / 1.000 / 3,70 / 82</td>\n",
       "      <td>9 / 1.000 / 3,70 / 82</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>7.1</td>\n",
       "      <td>app fuori dai candidati</td>\n",
       "      <td>9.608</td>\n",
       "      <td>9.608</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>7.1</td>\n",
       "      <td>sostenibili nel campione: sicure / con le dubbie</td>\n",
       "      <td>7 / 11</td>\n",
       "      <td>7 / 11</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>7.1</td>\n",
       "      <td>app sostenibili in tutto: bassa / centrale / alta</td>\n",
       "      <td>67 / 101 / 222</td>\n",
       "      <td>67 / 101 / 222</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>7.1</td>\n",
       "      <td>quota del catalogo: bassa / centrale / alta</td>\n",
       "      <td>0,69% / 1,05% / 2,30%</td>\n",
       "      <td>0,69% / 1,05% / 2,30%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>7.1</td>\n",
       "      <td>copertura: bassa / centrale / alta</td>\n",
       "      <td>51% / 34% / 15%</td>\n",
       "      <td>51% / 34% / 15%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>7.1</td>\n",
       "      <td>quota installazioni contando le perse (ordine di grandezza)</td>\n",
       "      <td>0,9%</td>\n",
       "      <td>0,9%</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>8</td>\n",
       "      <td>totale EDUCATION / FOOD &amp; DRINK / HOUSE / SHOPPING</td>\n",
       "      <td>6 / 8 / 11 / 11</td>\n",
       "      <td>6 / 8 / 11 / 11</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>8.1</td>\n",
       "      <td>vittorie di EDUCATION: posizioni / punteggio</td>\n",
       "      <td>27,5 / 36</td>\n",
       "      <td>27,5 / 36</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>8.1</td>\n",
       "      <td>punteggio 0-1: SHOPPING / FOOD &amp; DRINK (con EDUCATION)</td>\n",
       "      <td>0,877 / 0,858</td>\n",
       "      <td>0,877 / 0,858</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>8.2</td>\n",
       "      <td>app di genere Education / nella categoria EDUCATION / in FAMILY</td>\n",
       "      <td>642 / 108 / 526</td>\n",
       "      <td>642 / 108 / 526</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>8.2</td>\n",
       "      <td>app di genere Entertainment / nella categoria</td>\n",
       "      <td>592 / 87</td>\n",
       "      <td>592 / 87</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>8.2</td>\n",
       "      <td>senza EDUCATION, vittorie FOOD &amp; DRINK: posizioni / punteggio</td>\n",
       "      <td>25,5 / 12</td>\n",
       "      <td>25,5 / 12</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>8.2</td>\n",
       "      <td>senza EDUCATION, vittorie SHOPPING col punteggio</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>8.2</td>\n",
       "      <td>senza EDUCATION, totale FOOD &amp; DRINK / SHOPPING / HOUSE</td>\n",
       "      <td>6 / 8 / 9</td>\n",
       "      <td>6 / 8 / 9</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>8.2</td>\n",
       "      <td>senza EDUCATION, punteggio 0-1 SHOPPING / FOOD &amp; DRINK</td>\n",
       "      <td>0,923 / 0,890</td>\n",
       "      <td>0,923 / 0,890</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>9</td>\n",
       "      <td>app in FOOD &amp; DRINK / in SHOPPING / in HOUSE &amp; HOME</td>\n",
       "      <td>112 / 202 / 74</td>\n",
       "      <td>112 / 202 / 74</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>9</td>\n",
       "      <td>FOOD &amp; DRINK: a pagamento / giorni dall'aggiornamento</td>\n",
       "      <td>1,8% / 36</td>\n",
       "      <td>1,8% / 36</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>9</td>\n",
       "      <td>Frigo Magic: installazioni / voto / recensioni per 1.000</td>\n",
       "      <td>500.000 / 4,1 / 4,95</td>\n",
       "      <td>500.000 / 4,1 / 4,95</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>9</td>\n",
       "      <td>FOOD &amp; DRINK: voto / coinvolgimento / installazioni / MB tipici</td>\n",
       "      <td>4,3 / 13,6 / 300.000 / 17</td>\n",
       "      <td>4,3 / 13,6 / 300.000 / 17</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>9</td>\n",
       "      <td>FOOD &amp; DRINK: quartile migliore del voto</td>\n",
       "      <td>4,5</td>\n",
       "      <td>4,5</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>9</td>\n",
       "      <td>app con voto più alto di Frigo Magic / app votate</td>\n",
       "      <td>58 / 94</td>\n",
       "      <td>58 / 94</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>9</td>\n",
       "      <td>app di FOOD &amp; DRINK con voto almeno 4,5</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>sì</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sezione                                                    numero citato          scritto nel testo                ricalcolato coincide\n",
       "0        1                                            righe del file grezzo                     10.841                     10.841       sì\n",
       "1        1                                      voti validi nel file grezzo                      9.367                      9.367       sì\n",
       "2      1.1                                     righe identiche da eliminare                        483                        483       sì\n",
       "3      1.1                      righe diverse ripetute (conteggio di ydata)                        410                        410       sì\n",
       "4      1.3                                                    nomi ripetuti                        523                        523       sì\n",
       "5      1.3                                      righe con un nome già visto                        698                        698       sì\n",
       "6      1.3                        nomi con stessa categoria e installazioni                        432                        432       sì\n",
       "7      1.3                                       nomi con categoria diversa                         76                         76       sì\n",
       "8      1.3                                   nomi con installazioni diverse                         15                         15       sì\n",
       "9      1.3                                 voto mancante nelle righe grezze                      13,6%                      13,6%       sì\n",
       "10     1.3                                                   app analizzate                      9.674                      9.674       sì\n",
       "11     1.3                        recensioni mediane: senza voto / con voto                  1 / 3.020                  1 / 3.020       sì\n",
       "12     1.3                     installazioni mediane: senza voto / con voto              100 / 100.000              100 / 100.000       sì\n",
       "13     1.3                         app senza voto con meno di 10 recensioni                      83,5%                      83,5%       sì\n",
       "14       2                                      media / mediana: recensioni                        223                        223       sì\n",
       "15       2                                   media / mediana: installazioni                         78                         78       sì\n",
       "16       2                                           app con voto sopra 4,0                      76,7%                      76,7%       sì\n",
       "17       2                                           app con voto sotto 3,0                       3,4%                       3,4%       sì\n",
       "18       2                                                     voto mediano                        4,3                        4,3       sì\n",
       "19       2                           inizio del quartile superiore del voto                        4,5                        4,5       sì\n",
       "20       2                               installazioni del top 1% delle app                      49,4%                      49,4%       sì\n",
       "21       2                              installazioni del top 10% delle app                      88,1%                      88,1%       sì\n",
       "22       2                                                     app gratuite                      92,2%                      92,2%       sì\n",
       "23       2                             prezzo mediano delle app a pagamento                       2,99                       2,99       sì\n",
       "24       2                                          dimensione mediana (MB)                         12                         12       sì\n",
       "25       2                       giorni dall'ultimo aggiornamento (mediana)                         96                         96       sì\n",
       "26       3                                     outlier IQR sulle recensioni                      1.661                      1.661       sì\n",
       "27       3                               outlier IQR sulle recensioni, in %                      17,2%                      17,2%       sì\n",
       "28       3                                             outlier IQR sul voto                        492                        492       sì\n",
       "29       3                                                  app a pagamento                        756                        756       sì\n",
       "30       3                  media dei prezzi con / senza le 20 app da 100 $               14,05 / 4,74               14,05 / 4,74       sì\n",
       "31     4.1                        voto medio: quintile meno / più recensito                3,96 / 4,44                3,96 / 4,44       sì\n",
       "32     4.1                            app coperte dal file delle recensioni                        819                        819       sì\n",
       "33       5                                                    app in FAMILY                      1.877                      1.877       sì\n",
       "34       5                                    quota installazioni di FAMILY                       8,3%                       8,3%       sì\n",
       "35       5                                 indice domanda/offerta di FAMILY                       0,43                       0,43       sì\n",
       "36       5                          indice domanda/offerta di COMMUNICATION                       4,49                       4,49       sì\n",
       "37       5               quota della prima app in BOOKS (Google Play Books)                      60,0%                      60,0%       sì\n",
       "38       5                 quota della prima app in HEALTH (Samsung Health)                      43,7%                      43,7%       sì\n",
       "39       5                            quota della prima app in FOOD & DRINK                       4,7%                       4,7%       sì\n",
       "40       5                             app oltre il milione, tutto lo store                      35,2%                      35,2%       sì\n",
       "41       5                  app oltre il milione: ENTERTAINMENT / EDUCATION              83,9% / 57,4%              83,9% / 57,4%       sì\n",
       "42       5                    app oltre il milione: SHOPPING / FOOD & DRINK              52,5% / 43,8%              52,5% / 43,8%       sì\n",
       "43       5         coinvolgimento tipico: store / GAME / EDUCATION / HEALTH    17 / 28,5 / 21,1 / 20,9    17 / 28,5 / 21,1 / 20,9       sì\n",
       "44       6                             voto mediano: a pagamento / gratuite                  4,4 / 4,3                  4,4 / 4,3       sì\n",
       "45       6                    installazioni mediane: gratuite / a pagamento            100.000 / 1.000            100.000 / 1.000       sì\n",
       "46       6                         oltre il milione: gratuite / a pagamento               37,9% / 2,9%               37,9% / 2,9%       sì\n",
       "47       6                                    indice prezzo ~ installazioni                      +0,14                      +0,14       sì\n",
       "48       6         oltre 10.000: fino a 1,49 $ / 1,50-2,99 $ / 15 $ e oltre      19,7% / 47,1% / 21,2%      19,7% / 47,1% / 21,2%       sì\n",
       "49       6              temi grafici fino a 1,49 $ / app mediche sopra 15 $                  27% / 39%                  27% / 39%       sì\n",
       "50       6        voto medio: aggiornata da meno di un mese / oltre un anno                4,27 / 4,06                4,27 / 4,06       sì\n",
       "51       6                  escursione delle mediane del voto fra categorie                        0,4                        0,4       sì\n",
       "52       7                            candidati trovati dalle parole chiave                         66                         66       sì\n",
       "53       7                       app sostenibili fra i candidati / scartate                    34 / 32                    34 / 32       sì\n",
       "54       7                                   precisione delle parole chiave                      51,5%                      51,5%       sì\n",
       "55       7                        segmento: quota del catalogo (solo le 34)                      0,35%                      0,35%       sì\n",
       "56       7                 segmento: quota delle installazioni (solo le 34)                      0,03%                      0,03%       sì\n",
       "57       7                   trasporto pubblico: app / installazioni / voto        11 / 500.000 / 4,40        11 / 500.000 / 4,40       sì\n",
       "58       7          ricarica elettrica: app / installazioni / voto / giorni      9 / 1.000 / 3,70 / 82      9 / 1.000 / 3,70 / 82       sì\n",
       "59     7.1                                          app fuori dai candidati                      9.608                      9.608       sì\n",
       "60     7.1                 sostenibili nel campione: sicure / con le dubbie                     7 / 11                     7 / 11       sì\n",
       "61     7.1                app sostenibili in tutto: bassa / centrale / alta             67 / 101 / 222             67 / 101 / 222       sì\n",
       "62     7.1                      quota del catalogo: bassa / centrale / alta      0,69% / 1,05% / 2,30%      0,69% / 1,05% / 2,30%       sì\n",
       "63     7.1                               copertura: bassa / centrale / alta            51% / 34% / 15%            51% / 34% / 15%       sì\n",
       "64     7.1      quota installazioni contando le perse (ordine di grandezza)                       0,9%                       0,9%       sì\n",
       "65       8               totale EDUCATION / FOOD & DRINK / HOUSE / SHOPPING            6 / 8 / 11 / 11            6 / 8 / 11 / 11       sì\n",
       "66     8.1                     vittorie di EDUCATION: posizioni / punteggio                  27,5 / 36                  27,5 / 36       sì\n",
       "67     8.1           punteggio 0-1: SHOPPING / FOOD & DRINK (con EDUCATION)              0,877 / 0,858              0,877 / 0,858       sì\n",
       "68     8.2  app di genere Education / nella categoria EDUCATION / in FAMILY            642 / 108 / 526            642 / 108 / 526       sì\n",
       "69     8.2                    app di genere Entertainment / nella categoria                   592 / 87                   592 / 87       sì\n",
       "70     8.2    senza EDUCATION, vittorie FOOD & DRINK: posizioni / punteggio                  25,5 / 12                  25,5 / 12       sì\n",
       "71     8.2                 senza EDUCATION, vittorie SHOPPING col punteggio                         22                         22       sì\n",
       "72     8.2          senza EDUCATION, totale FOOD & DRINK / SHOPPING / HOUSE                  6 / 8 / 9                  6 / 8 / 9       sì\n",
       "73     8.2           senza EDUCATION, punteggio 0-1 SHOPPING / FOOD & DRINK              0,923 / 0,890              0,923 / 0,890       sì\n",
       "74       9              app in FOOD & DRINK / in SHOPPING / in HOUSE & HOME             112 / 202 / 74             112 / 202 / 74       sì\n",
       "75       9            FOOD & DRINK: a pagamento / giorni dall'aggiornamento                  1,8% / 36                  1,8% / 36       sì\n",
       "76       9         Frigo Magic: installazioni / voto / recensioni per 1.000       500.000 / 4,1 / 4,95       500.000 / 4,1 / 4,95       sì\n",
       "77       9  FOOD & DRINK: voto / coinvolgimento / installazioni / MB tipici  4,3 / 13,6 / 300.000 / 17  4,3 / 13,6 / 300.000 / 17       sì\n",
       "78       9                         FOOD & DRINK: quartile migliore del voto                        4,5                        4,5       sì\n",
       "79       9                app con voto più alto di Frigo Magic / app votate                    58 / 94                    58 / 94       sì\n",
       "80       9                          app di FOOD & DRINK con voto almeno 4,5                         37                         37       sì"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81 numeri su 81 coincidono con il testo\n"
     ]
    }
   ],
   "source": [
    "# --- 11. Ogni numero del testo, ricalcolato e confrontato con quello scritto ---\n",
    "\n",
    "def it(x, decimali=0, percentuale=False):\n",
    "    \"\"\"Formatta come nel testo: punto per le migliaia, virgola per i decimali.\"\"\"\n",
    "    if percentuale:\n",
    "        x = x * 100\n",
    "    s = f\"{x:,.{decimali}f}\".replace(\",\", \"§\").replace(\".\", \",\").replace(\"§\", \".\")\n",
    "    return s + (\"%\" if percentuale else \"\")\n",
    "\n",
    "v = app[\"voto\"].dropna()\n",
    "gratis, pagam = app[app[\"tipo\"] == \"Gratuita\"], app[app[\"tipo\"] == \"A pagamento\"]\n",
    "ap_no_scherzi = pagam[pagam[\"prezzo\"] < 100]\n",
    "fasce_prezzo = pd.cut(ap_no_scherzi[\"prezzo\"], bins=[0, 1.49, 2.99, 5.99, 14.99, 100])\n",
    "oltre_10k = ap_no_scherzi.groupby(fasce_prezzo, observed=True)[\"installazioni\"].apply(lambda s: (s >= 1e4).mean())\n",
    "pc = per_categoria\n",
    "sv = app[app[\"voto\"].isna()]\n",
    "tema = segmento.groupby(\"tema\").agg(n=(\"nome\", \"count\"), inst=(\"installazioni\", \"median\"),\n",
    "                                    voto=(\"voto\", \"median\"), giorni=(\"giorni_da_aggiornamento\", \"median\"))\n",
    "coinv = app.dropna(subset=[\"voto\", \"recensioni_per_1000\"])\n",
    "quintili = coinv.groupby(pd.qcut(coinv[\"recensioni_per_1000\"], 5, labels=False))[\"voto\"].mean()\n",
    "aggiorn = app.dropna(subset=[\"voto\"]).groupby(pd.cut(app[\"giorni_da_aggiornamento\"],\n",
    "                                                     bins=[-1, 30, 90, 180, 365, 10_000]),\n",
    "                                              observed=True)[\"voto\"].mean()\n",
    "votate_food = food[\"voto\"].dropna()\n",
    "cf = classifica_finale\n",
    "\n",
    "NUMERI = [\n",
    "    (\"1\",   \"righe del file grezzo\", \"10.841\", it(len(grezzo))),\n",
    "    (\"1\",   \"voti validi nel file grezzo\", \"9.367\", it(grezzo[\"Rating\"].notna().sum())),\n",
    "    (\"1.1\", \"righe identiche da eliminare\", \"483\", it(grezzo.duplicated().sum())),\n",
    "    (\"1.1\", \"righe diverse ripetute (conteggio di ydata)\", \"410\", it(len(grezzo[grezzo.duplicated(keep=False)].drop_duplicates()))),\n",
    "    (\"1.3\", \"nomi ripetuti\", \"523\", it(ripetute[\"App\"].nunique())),\n",
    "    (\"1.3\", \"righe con un nome già visto\", \"698\", it(len(ripetute) - ripetute[\"App\"].nunique())),\n",
    "    (\"1.3\", \"nomi con stessa categoria e installazioni\", \"432\", it((esito == \"stessa categoria, stesse installazioni\").sum())),\n",
    "    (\"1.3\", \"nomi con categoria diversa\", \"76\", it((esito == \"categoria diversa, stesse installazioni\").sum())),\n",
    "    (\"1.3\", \"nomi con installazioni diverse\", \"15\", it((esito == \"installazioni diverse\").sum())),\n",
    "    (\"1.3\", \"voto mancante nelle righe grezze\", \"13,6%\", it(grezzo[\"Rating\"].isna().mean(), 1, True)),\n",
    "    (\"1.3\", \"app analizzate\", \"9.674\", it(len(app))),\n",
    "    (\"1.3\", \"recensioni mediane: senza voto / con voto\", \"1 / 3.020\", f\"{it(sv['recensioni'].median())} / {it(app.loc[app['voto'].notna(), 'recensioni'].median())}\"),\n",
    "    (\"1.3\", \"installazioni mediane: senza voto / con voto\", \"100 / 100.000\", f\"{it(sv['installazioni'].median())} / {it(app.loc[app['voto'].notna(), 'installazioni'].median())}\"),\n",
    "    (\"1.3\", \"app senza voto con meno di 10 recensioni\", \"83,5%\", it((sv[\"recensioni\"] < 10).mean(), 1, True)),\n",
    "    (\"2\",   \"media / mediana: recensioni\", \"223\", it(app[\"recensioni\"].mean() / app[\"recensioni\"].median())),\n",
    "    (\"2\",   \"media / mediana: installazioni\", \"78\", it(app[\"installazioni\"].mean() / app[\"installazioni\"].median())),\n",
    "    (\"2\",   \"app con voto sopra 4,0\", \"76,7%\", it((v >= 4).mean(), 1, True)),\n",
    "    (\"2\",   \"app con voto sotto 3,0\", \"3,4%\", it((v < 3).mean(), 1, True)),\n",
    "    (\"2\",   \"voto mediano\", \"4,3\", it(v.median(), 1)),\n",
    "    (\"2\",   \"inizio del quartile superiore del voto\", \"4,5\", it(v.quantile(0.75), 1)),\n",
    "    (\"2\",   \"installazioni del top 1% delle app\", \"49,4%\", it(app[\"installazioni\"].nlargest(int(len(app) * 0.01)).sum() / app[\"installazioni\"].sum(), 1, True)),\n",
    "    (\"2\",   \"installazioni del top 10% delle app\", \"88,1%\", it(app[\"installazioni\"].nlargest(int(len(app) * 0.10)).sum() / app[\"installazioni\"].sum(), 1, True)),\n",
    "    (\"2\",   \"app gratuite\", \"92,2%\", it(len(gratis) / len(app), 1, True)),\n",
    "    (\"2\",   \"prezzo mediano delle app a pagamento\", \"2,99\", it(pagam[\"prezzo\"].median(), 2)),\n",
    "    (\"2\",   \"dimensione mediana (MB)\", \"12\", it(app[\"dimensione_mb\"].median())),\n",
    "    (\"2\",   \"giorni dall'ultimo aggiornamento (mediana)\", \"96\", it(app[\"giorni_da_aggiornamento\"].median())),\n",
    "    (\"3\",   \"outlier IQR sulle recensioni\", \"1.661\", it(conta_outlier(app[\"recensioni\"])[\"n outlier\"])),\n",
    "    (\"3\",   \"outlier IQR sulle recensioni, in %\", \"17,2%\", it(conta_outlier(app[\"recensioni\"])[\"% outlier\"] / 100, 1, True)),\n",
    "    (\"3\",   \"outlier IQR sul voto\", \"492\", it(conta_outlier(app[\"voto\"])[\"n outlier\"])),\n",
    "    (\"3\",   \"app a pagamento\", \"756\", it(len(pagam))),\n",
    "    (\"3\",   \"media dei prezzi con / senza le 20 app da 100 $\", \"14,05 / 4,74\", f\"{it(pagam['prezzo'].mean(), 2)} / {it(ap_no_scherzi['prezzo'].mean(), 2)}\"),\n",
    "    (\"4.1\", \"voto medio: quintile meno / più recensito\", \"3,96 / 4,44\", f\"{it(quintili.iloc[0], 2)} / {it(quintili.iloc[-1], 2)}\"),\n",
    "    (\"4.1\", \"app coperte dal file delle recensioni\", \"819\", it(len(unite))),\n",
    "    (\"5\",   \"app in FAMILY\", \"1.877\", it(pc.loc[\"FAMILY\", \"numero_app\"])),\n",
    "    (\"5\",   \"quota installazioni di FAMILY\", \"8,3%\", it(pc.loc[\"FAMILY\", \"quota_installazioni\"], 1, True)),\n",
    "    (\"5\",   \"indice domanda/offerta di FAMILY\", \"0,43\", it(pc.loc[\"FAMILY\", \"domanda_su_offerta\"], 2)),\n",
    "    (\"5\",   \"indice domanda/offerta di COMMUNICATION\", \"4,49\", it(pc.loc[\"COMMUNICATION\", \"domanda_su_offerta\"], 2)),\n",
    "    (\"5\",   \"quota della prima app in BOOKS (Google Play Books)\", \"60,0%\", it(pc.loc[\"BOOKS_AND_REFERENCE\", \"quota_prima_app\"], 1, True)),\n",
    "    (\"5\",   \"quota della prima app in HEALTH (Samsung Health)\", \"43,7%\", it(pc.loc[\"HEALTH_AND_FITNESS\", \"quota_prima_app\"], 1, True)),\n",
    "    (\"5\",   \"quota della prima app in FOOD & DRINK\", \"4,7%\", it(pc.loc[\"FOOD_AND_DRINK\", \"quota_prima_app\"], 1, True)),\n",
    "    (\"5\",   \"app oltre il milione, tutto lo store\", \"35,2%\", it((app[\"installazioni\"] >= 1e6).mean(), 1, True)),\n",
    "    (\"5\",   \"app oltre il milione: ENTERTAINMENT / EDUCATION\", \"83,9% / 57,4%\", f\"{it(pc.loc['ENTERTAINMENT', 'quota_oltre_1M'], 1, True)} / {it(pc.loc['EDUCATION', 'quota_oltre_1M'], 1, True)}\"),\n",
    "    (\"5\",   \"app oltre il milione: SHOPPING / FOOD & DRINK\", \"52,5% / 43,8%\", f\"{it(pc.loc['SHOPPING', 'quota_oltre_1M'], 1, True)} / {it(pc.loc['FOOD_AND_DRINK', 'quota_oltre_1M'], 1, True)}\"),\n",
    "    (\"5\",   \"coinvolgimento tipico: store / GAME / EDUCATION / HEALTH\", \"17 / 28,5 / 21,1 / 20,9\", f\"{it(app['recensioni_per_1000'].median())} / {it(pc.loc['GAME', 'recensioni_per_1000'], 1)} / {it(pc.loc['EDUCATION', 'recensioni_per_1000'], 1)} / {it(pc.loc['HEALTH_AND_FITNESS', 'recensioni_per_1000'], 1)}\"),\n",
    "    (\"6\",   \"voto mediano: a pagamento / gratuite\", \"4,4 / 4,3\", f\"{it(pagam['voto'].median(), 1)} / {it(gratis['voto'].median(), 1)}\"),\n",
    "    (\"6\",   \"installazioni mediane: gratuite / a pagamento\", \"100.000 / 1.000\", f\"{it(gratis['installazioni'].median())} / {it(pagam['installazioni'].median())}\"),\n",
    "    (\"6\",   \"oltre il milione: gratuite / a pagamento\", \"37,9% / 2,9%\", f\"{it((gratis['installazioni'] >= 1e6).mean(), 1, True)} / {it((pagam['installazioni'] >= 1e6).mean(), 1, True)}\"),\n",
    "    (\"6\",   \"indice prezzo ~ installazioni\", \"+0,14\", \"+\" + it(ap_no_scherzi[[\"prezzo\", \"installazioni\"]].corr(method=\"spearman\").iloc[0, 1], 2)),\n",
    "    (\"6\",   \"oltre 10.000: fino a 1,49 $ / 1,50-2,99 $ / 15 $ e oltre\", \"19,7% / 47,1% / 21,2%\", \" / \".join(it(oltre_10k.iloc[i], 1, True) for i in (0, 1, -1))),\n",
    "    (\"6\",   \"temi grafici fino a 1,49 $ / app mediche sopra 15 $\", \"27% / 39%\", f\"{it(composizione.loc['fino a 1,49 $', 'PERSONALIZATION'], 0, True)} / {it(composizione.loc['15 $ e oltre', 'MEDICAL'], 0, True)}\"),\n",
    "    (\"6\",   \"voto medio: aggiornata da meno di un mese / oltre un anno\", \"4,27 / 4,06\", f\"{it(aggiorn.iloc[0], 2)} / {it(aggiorn.iloc[-1], 2)}\"),\n",
    "    (\"6\",   \"escursione delle mediane del voto fra categorie\", \"0,4\", it(categorie_grandi[\"median\"].max() - categorie_grandi[\"median\"].min(), 1)),\n",
    "    (\"7\",   \"candidati trovati dalle parole chiave\", \"66\", it(len(candidati))),\n",
    "    (\"7\",   \"app sostenibili fra i candidati / scartate\", \"34 / 32\", f\"{it(len(segmento))} / {it(len(scartate))}\"),\n",
    "    (\"7\",   \"precisione delle parole chiave\", \"51,5%\", it(len(segmento) / len(candidati), 1, True)),\n",
    "    (\"7\",   \"segmento: quota del catalogo (solo le 34)\", \"0,35%\", it(len(segmento) / len(app), 2, True)),\n",
    "    (\"7\",   \"segmento: quota delle installazioni (solo le 34)\", \"0,03%\", it(segmento[\"installazioni\"].sum() / app[\"installazioni\"].sum(), 2, True)),\n",
    "    (\"7\",   \"trasporto pubblico: app / installazioni / voto\", \"11 / 500.000 / 4,40\", f\"{it(tema.loc['mobilita_pubblica', 'n'])} / {it(tema.loc['mobilita_pubblica', 'inst'])} / {it(tema.loc['mobilita_pubblica', 'voto'], 2)}\"),\n",
    "    (\"7\",   \"ricarica elettrica: app / installazioni / voto / giorni\", \"9 / 1.000 / 3,70 / 82\", f\"{it(tema.loc['mobilita_elettrica', 'n'])} / {it(tema.loc['mobilita_elettrica', 'inst'])} / {it(tema.loc['mobilita_elettrica', 'voto'], 2)} / {it(tema.loc['mobilita_elettrica', 'giorni'])}\"),\n",
    "    (\"7.1\", \"app fuori dai candidati\", \"9.608\", it(len(fuori))),\n",
    "    (\"7.1\", \"sostenibili nel campione: sicure / con le dubbie\", \"7 / 11\", f\"{k_sicure} / {k_con_dubbi}\"),\n",
    "    (\"7.1\", \"app sostenibili in tutto: bassa / centrale / alta\", \"67 / 101 / 222\", \" / \".join(it(x) for x in stima[\"app sostenibili in tutto\"].iloc[1:])),\n",
    "    (\"7.1\", \"quota del catalogo: bassa / centrale / alta\", \"0,69% / 1,05% / 2,30%\", \" / \".join(it(x, 2, True) for x in stima[\"quota del catalogo\"].iloc[1:])),\n",
    "    (\"7.1\", \"copertura: bassa / centrale / alta\", \"51% / 34% / 15%\", \" / \".join(it(x, 0, True) for x in stima[\"copertura delle parole chiave\"].iloc[1:])),\n",
    "    (\"7.1\", \"quota installazioni contando le perse (ordine di grandezza)\", \"0,9%\", it(quota_inst_stimata, 1, True)),\n",
    "    (\"8\",   \"totale EDUCATION / FOOD & DRINK / HOUSE / SHOPPING\", \"6 / 8 / 11 / 11\", \" / \".join(it(classifica.loc[c, \"totale\"]) for c in [\"EDUCATION\", \"FOOD_AND_DRINK\", \"HOUSE_AND_HOME\", \"SHOPPING\"])),\n",
    "    (\"8.1\", \"vittorie di EDUCATION: posizioni / punteggio\", \"27,5 / 36\", f\"{it(solidita.loc['EDUCATION', 'vittorie su 36 con le posizioni'], 1)} / {it(solidita.loc['EDUCATION', 'vittorie su 36 con il punteggio 0-1'])}\"),\n",
    "    (\"8.1\", \"punteggio 0-1: SHOPPING / FOOD & DRINK (con EDUCATION)\", \"0,877 / 0,858\", f\"{it(solidita.loc['SHOPPING', 'punteggio 0-1 (pesi uguali)'], 3)} / {it(solidita.loc['FOOD_AND_DRINK', 'punteggio 0-1 (pesi uguali)'], 3)}\"),\n",
    "    (\"8.2\", \"app di genere Education / nella categoria EDUCATION / in FAMILY\", \"642 / 108 / 526\", f\"{it(per_genere.loc[per_genere['genere'] == 'Education', 'nome'].nunique())} / {it(n_cat_edu)} / {it(n_fam_edu)}\"),\n",
    "    (\"8.2\", \"app di genere Entertainment / nella categoria\", \"592 / 87\", f\"{it(per_genere.loc[per_genere['genere'] == 'Entertainment', 'nome'].nunique())} / {it((app['categoria'] == 'ENTERTAINMENT').sum())}\"),\n",
    "    (\"8.2\", \"senza EDUCATION, vittorie FOOD & DRINK: posizioni / punteggio\", \"25,5 / 12\", f\"{it(cf.loc['FOOD_AND_DRINK', 'vittorie su 36 con le posizioni'], 1)} / {it(cf.loc['FOOD_AND_DRINK', 'vittorie su 36 con il punteggio 0-1'])}\"),\n",
    "    (\"8.2\", \"senza EDUCATION, vittorie SHOPPING col punteggio\", \"22\", it(cf.loc[\"SHOPPING\", \"vittorie su 36 con il punteggio 0-1\"])),\n",
    "    (\"8.2\", \"senza EDUCATION, totale FOOD & DRINK / SHOPPING / HOUSE\", \"6 / 8 / 9\", \" / \".join(it(cf.loc[c, \"totale\"]) for c in [\"FOOD_AND_DRINK\", \"SHOPPING\", \"HOUSE_AND_HOME\"])),\n",
    "    (\"8.2\", \"senza EDUCATION, punteggio 0-1 SHOPPING / FOOD & DRINK\", \"0,923 / 0,890\", f\"{it(cf.loc['SHOPPING', 'punteggio 0-1'], 3)} / {it(cf.loc['FOOD_AND_DRINK', 'punteggio 0-1'], 3)}\"),\n",
    "    (\"9\",   \"app in FOOD & DRINK / in SHOPPING / in HOUSE & HOME\", \"112 / 202 / 74\", \" / \".join(it(pc.loc[c, \"numero_app\"]) for c in [\"FOOD_AND_DRINK\", \"SHOPPING\", \"HOUSE_AND_HOME\"])),\n",
    "    (\"9\",   \"FOOD & DRINK: a pagamento / giorni dall'aggiornamento\", \"1,8% / 36\", f\"{it(pc.loc['FOOD_AND_DRINK', 'quota_a_pagamento'], 1, True)} / {it(pc.loc['FOOD_AND_DRINK', 'giorni_da_aggiornamento'])}\"),\n",
    "    (\"9\",   \"Frigo Magic: installazioni / voto / recensioni per 1.000\", \"500.000 / 4,1 / 4,95\", f\"{it(frigo['installazioni'].iloc[0])} / {it(frigo['voto'].iloc[0], 1)} / {it(frigo['recensioni_per_1000'].iloc[0], 2)}\"),\n",
    "    (\"9\",   \"FOOD & DRINK: voto / coinvolgimento / installazioni / MB tipici\", \"4,3 / 13,6 / 300.000 / 17\", f\"{it(food['voto'].median(), 1)} / {it(food['recensioni_per_1000'].median(), 1)} / {it(food['installazioni'].median())} / {it(food['dimensione_mb'].median())}\"),\n",
    "    (\"9\",   \"FOOD & DRINK: quartile migliore del voto\", \"4,5\", it(food[\"voto\"].quantile(0.75), 1)),\n",
    "    (\"9\",   \"app con voto più alto di Frigo Magic / app votate\", \"58 / 94\", f\"{it((votate_food > frigo['voto'].iloc[0]).sum())} / {it(len(votate_food))}\"),\n",
    "    (\"9\",   \"app di FOOD & DRINK con voto almeno 4,5\", \"37\", it((food[\"voto\"] >= 4.5).sum())),\n",
    "]\n",
    "\n",
    "registro_numeri = pd.DataFrame(NUMERI, columns=[\"sezione\", \"numero citato\", \"scritto nel testo\", \"ricalcolato\"])\n",
    "registro_numeri[\"coincide\"] = np.where(registro_numeri[\"scritto nel testo\"] == registro_numeri[\"ricalcolato\"], \"sì\", \"NO\")\n",
    "with pd.option_context(\"display.max_rows\", 200, \"display.max_colwidth\", 70):\n",
    "    display(registro_numeri)\n",
    "print(f\"{(registro_numeri['coincide'] == 'sì').sum()} numeri su {len(registro_numeri)} coincidono con il testo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76e673c0",
   "metadata": {
    "id": "76e673c0"
   },
   "source": [
    "---\n",
    "\n",
    "## Appendice come ripetere l'analisi\n",
    "\n",
    "Un riepilogo di tutto quello che serve per rifare l'analisi e ottenere gli stessi risultati.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fb7aaf8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T04:52:16.751022Z",
     "iopub.status.busy": "2026-10-07T04:52:16.750829Z",
     "iopub.status.idle": "2026-10-07T04:52:16.756860Z",
     "shell.execute_reply": "2026-10-07T04:52:16.755897Z"
    },
    "id": "fb7aaf8c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AMBIENTE\n",
      "  pandas 3.0.6 | numpy 2.5.3 | seaborn 0.13.2\n",
      "  plotly: presente | ydata-profiling: assente\n",
      "\n",
      "DATI\n",
      "  origine  kaggle.com/datasets/lava18/google-play-store-apps (licenza CC BY 3.0)\n",
      "  i dati arrivano al 08/08/2018\n",
      "\n",
      "PULIZIA\n",
      "  dataset grezzo                             10,841\n",
      "  meno la riga sfalsata                      10,840\n",
      "  meno le righe duplicate                    10,357\n",
      "  meno le letture ripetute della stessa app   9,674\n",
      "  dataset pulito                              9,674\n",
      "\n",
      "SCELTE CHE INFLUENZANO I RISULTATI\n",
      "  - app ripetute: stessa app = stesso nome e stessa fascia di installazioni;\n",
      "    della stessa app tengo la lettura con più recensioni (la più recente)\n",
      "  - Installs: convertite al MINIMO della fascia ('10,000+' -> 10000)\n",
      "  - voto mancante: NON riempito\n",
      "  - Size 'Varies with device': lasciato vuoto\n",
      "  - 20 app sopra 100 $ ('I am rich'): escluse dalle analisi sul prezzo\n",
      "  - segmento sostenibilità: 77 parole chiave + controllo a mano dei 66 candidati\n",
      "  - copertura: 1.000 app estratte a caso fuori dai candidati, lette a mano\n",
      "  - classifica delle categorie: pesi uguali, verificata con 36 combinazioni di pesi\n",
      "\n",
      "UN SOLO PASSAGGIO CASUALE: l'estrazione del campione in 7.1, con seme fisso 2026.\n",
      "Ogni esecuzione sugli stessi file produce gli stessi risultati.\n"
     ]
    }
   ],
   "source": [
    "# --- Riepilogo per chi vuole rifare l'analisi: ambiente, dati e scelte fatte ---\n",
    "\n",
    "# 1) versioni delle librerie usate\n",
    "print(\"AMBIENTE\")\n",
    "print(f\"  pandas {pd.__version__} | numpy {np.__version__} | seaborn {sns.__version__}\")\n",
    "print(f\"  plotly: {'presente' if PLOTLY else 'assente'} | \"\n",
    "      f\"ydata-profiling: {'presente' if YDATA else 'assente'}\")\n",
    "\n",
    "# 2) da dove vengono i dati\n",
    "print(\"\\nDATI\")\n",
    "print(\"  origine  kaggle.com/datasets/lava18/google-play-store-apps (licenza CC BY 3.0)\")\n",
    "print(f\"  i dati arrivano al {app['data_aggiornamento'].max():%d/%m/%Y}\")\n",
    "\n",
    "# 3) quante righe sono rimaste dopo ogni passaggio di pulizia\n",
    "print(\"\\nPULIZIA\")\n",
    "for _, riga in registro.iterrows():\n",
    "    print(f\"  {riga['passaggio']:42s} {riga['righe']:>6,}\")\n",
    "\n",
    "# 4) le scelte fatte durante l'analisi che influenzano i risultati\n",
    "print(\"\\nSCELTE CHE INFLUENZANO I RISULTATI\")\n",
    "print(\"  - app ripetute: stessa app = stesso nome e stessa fascia di installazioni;\")\n",
    "print(\"    della stessa app tengo la lettura con più recensioni (la più recente)\")\n",
    "print(\"  - Installs: convertite al MINIMO della fascia ('10,000+' -> 10000)\")\n",
    "print(\"  - voto mancante: NON riempito\")\n",
    "print(\"  - Size 'Varies with device': lasciato vuoto\")\n",
    "print(\"  - 20 app sopra 100 $ ('I am rich'): escluse dalle analisi sul prezzo\")\n",
    "print(\"  - segmento sostenibilità: 77 parole chiave + controllo a mano dei 66 candidati\")\n",
    "print(\"  - copertura: 1.000 app estratte a caso fuori dai candidati, lette a mano\")\n",
    "print(\"  - classifica delle categorie: pesi uguali, verificata con 36 combinazioni di pesi\")\n",
    "\n",
    "# 5) un solo passaggio casuale, con il seme fisso: i risultati non cambiano fra un'esecuzione e l'altra\n",
    "print(\"\\nUN SOLO PASSAGGIO CASUALE: l'estrazione del campione in 7.1, con seme fisso 2026.\")\n",
    "print(\"Ogni esecuzione sugli stessi file produce gli stessi risultati.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36560527",
   "metadata": {},
   "source": [
    "### Note finali: strumenti di intelligenza artificiale utilizzati\n",
    "\n",
    "Per l'impostazione delle analisi e la revisione del codice mi sono avvalso di **Claude**\n",
    "(Anthropic), modelli Opus 5, Sonnet 5 e Opus 5.5, utilizzato all'interno di **Google\n",
    "Antigravity** e, per la revisione di ottobre 2026, di **Claude Code**. Le 1.000 app del campione\n",
    "della sezione 7.1 sono state lette una per una con Claude, applicando le regole scritte in\n",
    "sezione 7; un secondo passaggio con un modello locale (Qwen 3.8 via Ollama) non ha aggiunto casi\n",
    "nuovi, ed è risultato molto più prudente (ne ha riconosciuti solo due).\n",
    "\n",
    "Le scelte di analisi, la verifica dei risultati e le conclusioni sono mie. Ogni numero riportato\n",
    "nel testo è ricalcolato nella **sezione 11**, che lo confronta con il valore scritto."
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "Fabio Mencio"
   }
  ],
  "colab": {
   "provenance": [
    {
     "file_id": "1nY9BAdPR4bHBGgb85KKjWWM8k0Bk__m9",
     "timestamp": 1790952642752
    },
    {
     "file_id": "1d0KnlaCAoDLVk_LIKrpXRPyOvim-MZff",
     "timestamp": 1790916040305
    },
    {
     "file_id": "1IJUwLRhmGoS9s6RC2ZVhz1jS7P09hP-e",
     "timestamp": 1790743249004
    }
   ]
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.15"
  },
  "title": "Un'app per la sostenibilità - analisi del Google Play Store (rivista)"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}