{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc909829",
   "metadata": {},
   "outputs": [],
   "source": [
    "# App Recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9897f34c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn import datasets,linear_model\n",
    "from re import sub\n",
    "from decimal import Decimal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a1b49608",
   "metadata": {},
   "outputs": [],
   "source": [
    "store = {}\n",
    "df = pd.read_csv('UserReview.csv')\n",
    "\n",
    "df = df[df['Translated_Review'].notna()]\n",
    "index_names = df[ df['Translated_Review'] == \"nan\" ].index\n",
    "df.drop(index_names, inplace = True)\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    if row['App'] in store:\n",
    "        store[row['App']]+=row['Translated_Review']\n",
    "    else:\n",
    "        store[row['App']]=row['Translated_Review']\n",
    "\n",
    "list_reviews = []\n",
    "for key, value in store.items():\n",
    "    list_reviews.append([key,value])\n",
    "\n",
    "data_reviews = pd.DataFrame(list_reviews,columns=['App','Translated_Review'])\n",
    "data_reviews.set_index('App', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "54a41e5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Category  Rating  Reviews   Size     Installs  Type  Price  \\\n",
      "App                                                                         \n",
      "Telegram      Messaging     3.7       20  70.6M      10,000+     0      0   \n",
      "WhatsApp      Messaging     3.2       12    19M     500,000+     0      0   \n",
      "Instagram  Social Media     3.3       12    14M   5,000,000+     0      0   \n",
      "Facebook   Social Media     3.5       15   8.7M  50,000,000+     0      0   \n",
      "Twitter    Social Media     3.5       15    25M     100,000+     0      0   \n",
      "\n",
      "          Content Rating                                             Genres  \\\n",
      "App                                                                           \n",
      "Telegram        Everyone  Messanger; Messaging; Chats; Video Call; Call;...   \n",
      "WhatsApp        Everyone  Messanger; Messaging; Chats; Video Call; Call;...   \n",
      "Instagram       Everyone                            Business; Social Media;   \n",
      "Facebook        Everyone                            Business; Social Media;   \n",
      "Twitter         Everyone        Business; News and Magazines; Social Media;   \n",
      "\n",
      "           Last Updated Current Ver   Android Ver   App Name  \n",
      "App                                                           \n",
      "Telegram           2023       1.0.1  4.0.3 and up   Telegram  \n",
      "WhatsApp           2023       1.0.0  4.0.3 and up   WhatsApp  \n",
      "Instagram          2023       2.0.0  4.0.3 and up  Instagram  \n",
      "Facebook           2023       1.2.4  4.0.3 and up   Facebook  \n",
      "Twitter            2023       2.2.4  4.0.3 and up    Twitter  \n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('Review.csv',index_col = \"App\")\n",
    "data[\"App Name\"]=data.index\n",
    "\n",
    "#removing duplicate entries\n",
    "data.drop_duplicates(subset=['App Name'], keep='first',inplace = True)\n",
    "\n",
    "data.Type = pd.Categorical(data.Type)\n",
    "data.Type=data.Type.astype('category').cat.codes\n",
    "\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "bae912a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "app_name = \"YouTube\"\n",
    "num_rec = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e957cf83",
   "metadata": {},
   "outputs": [],
   "source": [
    "matching_apps_index=data[\"App Name\"].str.contains(app_name, case= False)\n",
    "matching_apps = data[matching_apps_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "7188f711",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommending for app:  YouTube\n",
      "Category                                  Entertainment\n",
      "Rating                                              3.2\n",
      "Reviews                                              12\n",
      "Size                                                19M\n",
      "Installs                                     1,000,000+\n",
      "Type                                                  0\n",
      "Price                                                 0\n",
      "Content Rating                                 Everyone\n",
      "Genres            Entertainment; Editors; Video Player;\n",
      "Last Updated                                       2023\n",
      "Current Ver                                       1.1.8\n",
      "Android Ver                                4.0.3 and up\n",
      "App Name                                        YouTube\n",
      "Name: YouTube, dtype: object\n"
     ]
    }
   ],
   "source": [
    "if len(matching_apps)==0:\n",
    "    print(\"NO SUCH APP\")\n",
    "else:\n",
    "    given_app=matching_apps.iloc[0,:]\n",
    "    print(\"Recommending for app: \",given_app[12])\n",
    "    print(given_app)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "1c53300e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.loc[data['Category'] == given_app[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8904099c",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf = TfidfVectorizer(analyzer='word', stop_words='english')\n",
    "tfidf_matrix = pd.DataFrame((tf.fit_transform(data.index)).toarray(),index=data.index)\n",
    "matrix = cosine_similarity(tfidf_matrix,[tfidf_matrix.loc[given_app[12]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "9955c9d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf = TfidfVectorizer(analyzer='word', stop_words='english')\n",
    "tfidf_matrix = pd.DataFrame((tf.fit_transform(data_reviews['Translated_Review'])).toarray(),index=data_reviews.index)\n",
    "\n",
    "reviews_similarity = {}\n",
    "for index, row in data.iterrows():\n",
    "    reviews_similarity[index]=0\n",
    "\n",
    "if given_app[12] in data_reviews.index:\n",
    "    matrix_reviews = cosine_similarity(tfidf_matrix,[tfidf_matrix.loc[given_app[12]]])\n",
    "    for i in range(0,len(matrix_reviews)):\n",
    "        reviews_similarity[data_reviews.index[i]]=matrix_reviews[i][0]\n",
    "else:\n",
    "    print(\"No reviews found\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "8c33e440",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_similarities = []\n",
    "\n",
    "for i in range(0,len(matrix)):\n",
    "    list_similarities.append([matrix[i][0]+reviews_similarity[data.index[i]],data.index[i],matrix[i][0],reviews_similarity[data.index[i]]])\n",
    "list_similarities.sort(reverse = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "fead5ab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_simi = {}\n",
    "for entry in list_similarities:\n",
    "    if entry[0] !=0 and entry[1] != given_app[12]:\n",
    "        final_simi[entry[1]]=entry[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "c6b22fa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "buckets = []\n",
    "\n",
    "ranges = []\n",
    "ranges.append([1.5,2.0])\n",
    "ranges.append([1.0,1.5])\n",
    "ranges.append([0.6,1.0])\n",
    "ranges.append([0.4,0.6])\n",
    "ranges.append([0.3,0.4])\n",
    "ranges.append([0.25,0.3])\n",
    "ranges.append([0.2,0.25])\n",
    "ranges.append([0.15,0.2])\n",
    "ranges.append([0.1,0.15])\n",
    "ranges.append([0.075,0.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "26c131e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "low = 1.5\n",
    "high = 2.0\n",
    "temp= []\n",
    "for entry in list_similarities:\n",
    "    if entry[0] <= high and entry[0] > low:\n",
    "        temp.append(entry[1])\n",
    "buckets.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6e9ceb2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for ran in ranges:\n",
    "    low = ran[0]\n",
    "    high = ran[1]\n",
    "    temp= []\n",
    "    for entry in list_similarities:\n",
    "        if entry[0] <= high and entry[0] > low:\n",
    "            temp.append(entry[1])\n",
    "    buckets.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1b9cb3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_ans=[]\n",
    "final_simi[given_app[12]]=2.0\n",
    "for entry in buckets:\n",
    "    if len(entry) > 0:\n",
    "        index_names=entry\n",
    "        data_temp=data.loc[index_names]\n",
    "        final_data = data_temp.sort_values(by=['Rating'], ascending=False)\n",
    "        for index,row in final_data.iterrows():\n",
    "            if index!=given_app[12]:\n",
    "                final_ans.append([index,row['Rating'],final_simi[index]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "a4c56217",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "App,\t Rating,\t Similarity_measure\n",
      "Amazon Prime Video \t 3.5 \t 0.191\n",
      "Netflix \t 3.5 \t 0.162\n",
      "Disney+ \t 3.6 \t 0.114\n"
     ]
    }
   ],
   "source": [
    "if len(final_ans)==0:\n",
    "    print(\"No Good recommendations\")\n",
    "else:\n",
    "    print(\"App,\\t Rating,\\t Similarity_measure\")\n",
    "    for i in range(0,min(num_rec,len(final_ans))):\n",
    "        print(final_ans[i][0],'\\t',final_ans[i][1],'\\t',round(final_ans[i][2],3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c0df9e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "fa353776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# App is True or False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "33685368",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('Review.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "58d97ce1",
   "metadata": {},
   "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>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>Telegram</td>\n",
       "      <td>Messaging</td>\n",
       "      <td>3.7</td>\n",
       "      <td>20</td>\n",
       "      <td>70.6M</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Messanger; Messaging; Chats; Video Call; Call;...</td>\n",
       "      <td>2023</td>\n",
       "      <td>1.0.1</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WhatsApp</td>\n",
       "      <td>Messaging</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12</td>\n",
       "      <td>19M</td>\n",
       "      <td>500,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Messanger; Messaging; Chats; Video Call; Call;...</td>\n",
       "      <td>2023</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Instagram</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>3.3</td>\n",
       "      <td>12</td>\n",
       "      <td>14M</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business; Social Media;</td>\n",
       "      <td>2023</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Facebook</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>3.5</td>\n",
       "      <td>15</td>\n",
       "      <td>8.7M</td>\n",
       "      <td>50,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business; Social Media;</td>\n",
       "      <td>2023</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Twitter</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>3.5</td>\n",
       "      <td>15</td>\n",
       "      <td>25M</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business; News and Magazines; Social Media;</td>\n",
       "      <td>2023</td>\n",
       "      <td>2.2.4</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  \\\n",
       "0   Telegram     Messaging     3.7       20  70.6M      10,000+  Free      0   \n",
       "1   WhatsApp     Messaging     3.2       12    19M     500,000+  Free      0   \n",
       "2  Instagram  Social Media     3.3       12    14M   5,000,000+  Free      0   \n",
       "3   Facebook  Social Media     3.5       15   8.7M  50,000,000+  Free      0   \n",
       "4    Twitter  Social Media     3.5       15    25M     100,000+  Free      0   \n",
       "\n",
       "  Content Rating                                             Genres  \\\n",
       "0       Everyone  Messanger; Messaging; Chats; Video Call; Call;...   \n",
       "1       Everyone  Messanger; Messaging; Chats; Video Call; Call;...   \n",
       "2       Everyone                            Business; Social Media;   \n",
       "3       Everyone                            Business; Social Media;   \n",
       "4       Everyone        Business; News and Magazines; Social Media;   \n",
       "\n",
       "   Last Updated Current Ver   Android Ver  \n",
       "0          2023       1.0.1  4.0.3 and up  \n",
       "1          2023       1.0.0  4.0.3 and up  \n",
       "2          2023       2.0.0  4.0.3 and up  \n",
       "3          2023       1.2.4  4.0.3 and up  \n",
       "4          2023       2.2.4  4.0.3 and up  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "fe6c2c7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating'>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.groupby([df1['Rating']]).count().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "87a0b09a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Type'>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.groupby([df1['Type']]).count().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c93e151",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "bf0d377d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "b7494f41",
   "metadata": {},
   "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>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>Telegram</td>\n",
       "      <td>Messaging</td>\n",
       "      <td>3.7</td>\n",
       "      <td>20</td>\n",
       "      <td>70.6M</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Messanger; Messaging; Chats; Video Call; Call;...</td>\n",
       "      <td>2023</td>\n",
       "      <td>1.0.1</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WhatsApp</td>\n",
       "      <td>Messaging</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12</td>\n",
       "      <td>19M</td>\n",
       "      <td>500,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Messanger; Messaging; Chats; Video Call; Call;...</td>\n",
       "      <td>2023</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Instagram</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>3.3</td>\n",
       "      <td>12</td>\n",
       "      <td>14M</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business; Social Media;</td>\n",
       "      <td>2023</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Facebook</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>3.5</td>\n",
       "      <td>15</td>\n",
       "      <td>8.7M</td>\n",
       "      <td>50,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business; Social Media;</td>\n",
       "      <td>2023</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Twitter</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>3.5</td>\n",
       "      <td>15</td>\n",
       "      <td>25M</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business; News and Magazines; Social Media;</td>\n",
       "      <td>2023</td>\n",
       "      <td>2.2.4</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  \\\n",
       "0   Telegram     Messaging     3.7       20  70.6M      10,000+  Free      0   \n",
       "1   WhatsApp     Messaging     3.2       12    19M     500,000+  Free      0   \n",
       "2  Instagram  Social Media     3.3       12    14M   5,000,000+  Free      0   \n",
       "3   Facebook  Social Media     3.5       15   8.7M  50,000,000+  Free      0   \n",
       "4    Twitter  Social Media     3.5       15    25M     100,000+  Free      0   \n",
       "\n",
       "  Content Rating                                             Genres  \\\n",
       "0       Everyone  Messanger; Messaging; Chats; Video Call; Call;...   \n",
       "1       Everyone  Messanger; Messaging; Chats; Video Call; Call;...   \n",
       "2       Everyone                            Business; Social Media;   \n",
       "3       Everyone                            Business; Social Media;   \n",
       "4       Everyone        Business; News and Magazines; Social Media;   \n",
       "\n",
       "   Last Updated Current Ver   Android Ver  \n",
       "0          2023       1.0.1  4.0.3 and up  \n",
       "1          2023       1.0.0  4.0.3 and up  \n",
       "2          2023       2.0.0  4.0.3 and up  \n",
       "3          2023       1.2.4  4.0.3 and up  \n",
       "4          2023       2.2.4  4.0.3 and up  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.read_csv(\"Review.csv\")\n",
    "df2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "b61541f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.columns=df2.columns.str.replace(' ','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "60842ec4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=df2.Type.unique()\n",
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "69170c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Free    43\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.Type.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "c781b152",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type  Rating\n",
       "Free  3.50      31\n",
       "      3.60       4\n",
       "      3.20       3\n",
       "      3.70       3\n",
       "      3.30       1\n",
       "      3.75       1\n",
       "Name: Rating, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('Type')['Rating'].value_counts().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "43ff762a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43, 13)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "90b64cc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Business; Social Media;                                       4\n",
       "Travel and Navigation; Tourism;                               3\n",
       "Entertainment; Movies;                                        3\n",
       "Entertainment; Songs;                                         3\n",
       "E-commerce; Shopping; Delivery;                               3\n",
       "Education; Courses; Students;                                 3\n",
       "News and Magazines; Daily News;                               3\n",
       "Money; Transcation; QR;                                       3\n",
       "Game; Run;                                                    2\n",
       "E-commerce; Food Delivery;                                    2\n",
       "Messanger; Messaging; Chats; Video Call; Call; Audio Call;    2\n",
       "Health and Fitness; Action;                                   2\n",
       "Travel and Navigation; Season;                                1\n",
       "Travel and Navigation;                                        1\n",
       "Health and Fitness; Action; Health;                           1\n",
       "Health and Fitness; Action; Food; Health;                     1\n",
       "E-commerce; Shopping; Delivery;                               1\n",
       "Entertainment; Editors; Video Player;                         1\n",
       "Game; Fight;                                                  1\n",
       "Game;                                                         1\n",
       "Business; News and Magazines; Social Media;                   1\n",
       "Game; Candy;                                                  1\n",
       "Name: Genres, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.Genres.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "db303374",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df2.drop_duplicates(subset=None, keep='first', inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "18f6acc2",
   "metadata": {},
   "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>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>ContentRating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>LastUpdated</th>\n",
       "      <th>CurrentVer</th>\n",
       "      <th>AndroidVer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [App, Category, Rating, Reviews, Size, Installs, Type, Price, ContentRating, Genres, LastUpdated, CurrentVer, AndroidVer]\n",
       "Index: []"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.Rating>4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b25bb1cd",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'[400] not found in axis'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_11820\\4278036919.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf2\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m400\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRating\u001b[0m\u001b[1;33m<\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    330\u001b[0m                 )\n\u001b[1;32m--> 331\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    332\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    333\u001b[0m         \u001b[1;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   5397\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[1;36m1.0\u001b[0m     \u001b[1;36m0.8\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5398\u001b[0m         \"\"\"\n\u001b[1;32m-> 5399\u001b[1;33m         return super().drop(\n\u001b[0m\u001b[0;32m   5400\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5401\u001b[0m             \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    330\u001b[0m                 )\n\u001b[1;32m--> 331\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    332\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    333\u001b[0m         \u001b[1;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4503\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4504\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4505\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4506\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4507\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[0;32m   4544\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4545\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4546\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4547\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4548\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   6932\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6933\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6934\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{list(labels[mask])} not found in axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6935\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6936\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '[400] not found in axis'"
     ]
    }
   ],
   "source": [
    "df2=df2.drop(400)\n",
    "df2[df2.Rating<5]   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "994f2f58",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Installs2']=df2['Installs']\n",
    "df2['Installs2']=df2.Installs2.apply(lambda x: x.replace('+',''))\n",
    "df2['Installs2']=df2.Installs2.apply(lambda x: x.replace(',',''))\n",
    "df2['Installs2']=(df2['Installs2']).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "b239c63a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Size'] = df2.Size.apply(lambda x: x.replace('M', ''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "2f8f8d10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "App              0\n",
      "Category         0\n",
      "Rating           0\n",
      "Reviews          0\n",
      "Size             0\n",
      "Installs         0\n",
      "Type             0\n",
      "Price            0\n",
      "ContentRating    0\n",
      "Genres           0\n",
      "LastUpdated      0\n",
      "CurrentVer       0\n",
      "AndroidVer       0\n",
      "Installs2        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df2.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "0332fadc",
   "metadata": {},
   "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>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>ContentRating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>LastUpdated</th>\n",
       "      <th>CurrentVer</th>\n",
       "      <th>AndroidVer</th>\n",
       "      <th>Installs2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [App, Category, Rating, Reviews, Size, Installs, Type, Price, ContentRating, Genres, LastUpdated, CurrentVer, AndroidVer, Installs2]\n",
       "Index: []"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df2[~df2['Size'].str.contains(\"Varies with device\")]\n",
    "df2[df2['Size'].str.contains(\"Varies with device\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "3cc92241",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     70.6\n",
      "1     19.0\n",
      "2     14.0\n",
      "3      8.7\n",
      "4     25.0\n",
      "5      2.8\n",
      "6      5.6\n",
      "7     19.0\n",
      "8     29.0\n",
      "9     33.0\n",
      "10     3.1\n",
      "11    28.0\n",
      "12    12.0\n",
      "13    20.0\n",
      "14    21.0\n",
      "15    37.0\n",
      "16     2.7\n",
      "17     5.5\n",
      "18    17.0\n",
      "19    39.0\n",
      "20    31.0\n",
      "21    14.0\n",
      "22    12.0\n",
      "23     4.2\n",
      "24     7.0\n",
      "25    23.0\n",
      "26     6.0\n",
      "27    25.0\n",
      "28     6.1\n",
      "29     4.6\n",
      "30     4.2\n",
      "31     9.2\n",
      "32     5.2\n",
      "33    11.0\n",
      "34    11.0\n",
      "35     4.2\n",
      "36     9.2\n",
      "37    24.0\n",
      "39    11.0\n",
      "40     9.4\n",
      "41    15.0\n",
      "42    10.0\n",
      "Name: Size, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_11820\\2600207047.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2['Size'] = pd.to_numeric(df2['Size'], errors='coerce')\n"
     ]
    }
   ],
   "source": [
    "df2['Size'] = pd.to_numeric(df2['Size'], errors='coerce')\n",
    "print(df2.Size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "fa46fd8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "App              0\n",
      "Category         0\n",
      "Rating           0\n",
      "Reviews          0\n",
      "Size             0\n",
      "Installs         0\n",
      "Type             0\n",
      "Price            0\n",
      "ContentRating    0\n",
      "Genres           0\n",
      "LastUpdated      0\n",
      "CurrentVer       0\n",
      "AndroidVer       0\n",
      "Installs2        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df2 = df2[~df2['Size'].isin(['Nan'])]\n",
    "print(df2.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "9603b94a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "App              0\n",
      "Category         0\n",
      "Rating           0\n",
      "Reviews          0\n",
      "Size             0\n",
      "Installs         0\n",
      "Type             0\n",
      "Price            0\n",
      "ContentRating    0\n",
      "Genres           0\n",
      "LastUpdated      0\n",
      "CurrentVer       0\n",
      "AndroidVer       0\n",
      "Installs2        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print (df2[ pd.to_numeric(df2['Size'], errors='coerce').isnull()].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "31c63d01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "App                       0\n",
      "Translated_Review         0\n",
      "Sentiment                 0\n",
      "Sentiment_Polarity        0\n",
      "Sentiment_Subjectivity    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df2['Rating'].fillna(df2['Rating'].mean(), inplace=True)\n",
    "#fill missing values of Size with mean column values\n",
    "df2['Size'].fillna(df2['Size'].mean(), inplace=True)\n",
    "# count the number of NaN values in each column\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "06f89f28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       10000\n",
       "1      500000\n",
       "2     5000000\n",
       "3    50000000\n",
       "4      100000\n",
       "Name: Installs, dtype: int32"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Installs']=df2.Installs.apply(lambda x: x.replace('+',''))\n",
    "df2['Installs']=df2.Installs.apply(lambda x: x.replace(',',''))\n",
    "df2['Installs']=(df2['Installs']).astype(int)\n",
    "df2.Installs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "aaf15df3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Installs']=np.log(df2.Installs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "5b38532c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "App              0\n",
      "Category         0\n",
      "Rating           0\n",
      "Reviews          0\n",
      "Size             0\n",
      "Installs         0\n",
      "Type             0\n",
      "Price            0\n",
      "ContentRating    0\n",
      "Genres           0\n",
      "LastUpdated      0\n",
      "CurrentVer       0\n",
      "AndroidVer       0\n",
      "Installs2        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print (df2[ pd.to_numeric(df2['Size'], errors='coerce').isnull()].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "8a39d740",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Size'] = pd.to_numeric(df2['Size'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "92562ac2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.20\n",
       "1     0.12\n",
       "2     0.12\n",
       "3     0.15\n",
       "4     0.15\n",
       "5     0.15\n",
       "6     0.15\n",
       "7     0.12\n",
       "8     0.15\n",
       "9     0.15\n",
       "10    0.15\n",
       "11    0.15\n",
       "12    0.15\n",
       "13    0.15\n",
       "14    0.15\n",
       "15    0.15\n",
       "16    0.15\n",
       "17    0.15\n",
       "18    0.12\n",
       "19    0.12\n",
       "20    0.15\n",
       "21    0.15\n",
       "22    0.15\n",
       "23    0.15\n",
       "24    0.15\n",
       "25    0.15\n",
       "26    0.15\n",
       "27    0.15\n",
       "28    0.15\n",
       "29    0.15\n",
       "30    0.15\n",
       "31    0.15\n",
       "32    0.15\n",
       "33    0.15\n",
       "34    0.15\n",
       "35    0.15\n",
       "36    0.15\n",
       "37    0.15\n",
       "39    0.15\n",
       "40    0.15\n",
       "41    0.02\n",
       "42    0.02\n",
       "Name: Reviews, dtype: float64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Reviews']=(df2['Reviews']).astype(int)\n",
    "df2.Reviews=((df2.Reviews)/100)\n",
    "df2.Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "29d7c0e3",
   "metadata": {},
   "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>Category</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E-commerce</td>\n",
       "      <td>10.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Education</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Entertainment</td>\n",
       "      <td>20.585714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finance and Banking</td>\n",
       "      <td>11.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gaming</td>\n",
       "      <td>11.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Health and Fitness</td>\n",
       "      <td>16.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Messaging</td>\n",
       "      <td>44.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>News and Information</td>\n",
       "      <td>12.366667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Social Media</td>\n",
       "      <td>11.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Travel and Navigation</td>\n",
       "      <td>22.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Category       Size\n",
       "0             E-commerce  10.766667\n",
       "1              Education   6.000000\n",
       "2          Entertainment  20.585714\n",
       "3    Finance and Banking  11.400000\n",
       "4                 Gaming  11.350000\n",
       "5     Health and Fitness  16.550000\n",
       "6              Messaging  44.800000\n",
       "7   News and Information  12.366667\n",
       "8           Social Media  11.220000\n",
       "9  Travel and Navigation  22.600000"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbb=df2[df2.Rating>1].groupby('Category')['Size'].mean().reset_index()\n",
    "dbb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "2681d2bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Apps per genre')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "genre= df2['Genres'].value_counts()\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(x=genre.index,y=genre.values)\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('Number of Apps')\n",
    "plt.xlabel('Genres')\n",
    "plt.title(\"Number of Apps per genre\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "fd064660",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max:  Category\n",
      "Travel and Navigation    13.538252\n",
      "Name: Installs, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       " [Text(0, 0, 'E-commerce'),\n",
       "  Text(1, 0, 'Education'),\n",
       "  Text(2, 0, 'Entertainment'),\n",
       "  Text(3, 0, 'Finance and Banking'),\n",
       "  Text(4, 0, 'Gaming'),\n",
       "  Text(5, 0, 'Health and Fitness'),\n",
       "  Text(6, 0, 'Messaging'),\n",
       "  Text(7, 0, 'News and Information'),\n",
       "  Text(8, 0, 'Social Media'),\n",
       "  Text(9, 0, 'Travel and Navigation')])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d2=df2[df2.Rating>1].groupby('Category')['Installs'].mean()\n",
    "print(\"Max: \", d2[d2==d2.max()])\n",
    "# a1=d2.plot(kind='bar',rot=90)\n",
    "# a1.set_ylabel(\"Reviews\")\n",
    "plt.figure(figsize=(20,7))\n",
    "ax = sns.barplot(x=d2.index, y=d2.values)\n",
    "plt.xlabel(\"Category\")\n",
    "plt.ylabel(\"Installs\")\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "ef19af05",
   "metadata": {},
   "outputs": [],
   "source": [
    "d4=df2.groupby('Category')['Rating'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "37651e90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "Travel and Navigation    3.56\n",
       "Name: Rating, dtype: float64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d4[d4==d4.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "baeae526",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 3.5)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = d4.plot.bar(x='Category', y='Rating', rot=90, figsize=(10,5))\n",
    "a.set_ylabel(\"Rating\")\n",
    "a.set_ylim(1,3.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "836caf23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df5=df2.groupby('Category')['ContentRating'].count()\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Category\")\n",
    "plt.ylabel(\"Content Rating\")\n",
    "sns.lineplot(x=df5.index , y =df5.values, palette=\"Set1\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "9a827a36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ContentRating\n",
       "Everyone    3.503571\n",
       "Name: Rating, dtype: float64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d7=df2.groupby('ContentRating')['Rating'].mean()\n",
    "d7[d7==d7.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "7a5ba658",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_11820\\3064633039.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(df.corr(), annot=True, fmt='.2f',ax=ax)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,6)) \n",
    "sns.heatmap(df.corr(), annot=True, fmt='.2f',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "80b17bc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.App = df2.App.astype('category')\n",
    "df2.Genres = df2.Genres.astype('category')\n",
    "df2.Reviews = df2.Reviews.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "05fb384b",
   "metadata": {},
   "outputs": [],
   "source": [
    "App_genre1 = df2.loc[(df2.Genres == 'Business') | (df2.Genres == 'Social Media') | \\\n",
    "                       (df2.Genres == 'Health and Fitness') | (df2.Genres == 'Action') | \\\n",
    "                       (df2.Genres == 'Travel and Navigation') | (df2.Genres == 'Tourism')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "61d8d760",
   "metadata": {},
   "outputs": [],
   "source": [
    "App_genre2 = df2.loc[(df2.Genres == 'Money') | (df2.Genres == 'Transcation') | \\\n",
    "                       (df2.Genres == 'News and Magazines') | (df2.Genres == 'Daily News') | \\\n",
    "                       (df2.Genres == 'Education') | (df2.Genres == 'Courses')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "b62e5b4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "App_genre3 = df2.loc[(df2.Genres == 'Business') | (df2.Genres == 'E-commerce') | \\\n",
    "                       (df2.Genres == 'Shopping') | (df2.Genres == 'Food') | \\\n",
    "                       (df2.Genres == 'Game') | (df2.Genres == 'Delivery')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "bbf8940d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, sharey=True, figsize=(18, 6))\n",
    "sns.scatterplot(x=\"Installs\", y=\"Reviews\", hue=\"Genres\", data=App_genre2, ax=axes[0])\n",
    "axes[0].set_title('App genre 2')\n",
    "sns.scatterplot(x=\"Installs\", y=\"Reviews\", hue=\"Genres\", data=App_genre3, ax=axes[1])\n",
    "axes[1].set_title('App genre 3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "d862fbdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The App with the least rating is Telegram and its rating is 3.2 .\n"
     ]
    }
   ],
   "source": [
    "min_rating=df2['Rating'].idxmin()\n",
    "print('The App with the least rating is',df2.at[min_rating,'App'],\\\n",
    "      \"and its rating is\",df2['Rating'].min(),'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "e94af715",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The App with the highest rating is Instagram and its rating is 3.75 .\n"
     ]
    }
   ],
   "source": [
    "max_rating=df2['Rating'].idxmax()\n",
    "print('The App with the highest rating is',df2.at[max_rating,'App'],\\\n",
    "      \"and its rating is\",df2['Rating'].max(),'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "197425e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The App with the highest Installs in play store is Telegram .\n"
     ]
    }
   ],
   "source": [
    "max_install=df2['Installs'].idxmax()\n",
    "print('The App with the highest Installs in play store is',df2.at[max_install,'App'],'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "944bd01b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The App with the least no of Install in play store is Telegram .\n"
     ]
    }
   ],
   "source": [
    "min_install=df2['Installs'].idxmin()\n",
    "print('The App with the least no of Install in play store is',df2.at[min_install,'App'],'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "2e8932b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The App with the least consumption of Memory in play store is Telegram and its Size is 2.7 .\n"
     ]
    }
   ],
   "source": [
    "min_Size=df2['Size'].idxmin()\n",
    "print('The App with the least consumption of Memory in play store is',df2.at[min_Size,'App'],\\\n",
    "      \"and its Size is\",df2['Size'].min(),'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "41232150",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The App with the highest consumption of Memory in play store is Telegram and its Size is 70.6 .\n"
     ]
    }
   ],
   "source": [
    "max_Size=df2['Size'].idxmax()\n",
    "print('The App with the highest consumption of Memory in play store is',df2.at[max_Size,'App'],\\\n",
    "      \"and its Size is\",df2['Size'].max(),'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "fe8be9ec",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'ImageDraw' object has no attribute 'textsize'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_11820\\2543700957.py\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mwordcloud\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mWordCloud\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m25\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m wordcloud = WordCloud(\n\u001b[0m\u001b[0;32m      5\u001b[0m                           \u001b[0mbackground_color\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'black'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m                           \u001b[0mwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m920\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\wordcloud\\wordcloud.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m(self, text)\u001b[0m\n\u001b[0;32m    630\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    631\u001b[0m         \"\"\"\n\u001b[1;32m--> 632\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate_from_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    633\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    634\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_check_generated\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\wordcloud\\wordcloud.py\u001b[0m in \u001b[0;36mgenerate_from_text\u001b[1;34m(self, text)\u001b[0m\n\u001b[0;32m    612\u001b[0m         \"\"\"\n\u001b[0;32m    613\u001b[0m         \u001b[0mwords\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 614\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate_from_frequencies\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    615\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    616\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\wordcloud\\wordcloud.py\u001b[0m in \u001b[0;36mgenerate_from_frequencies\u001b[1;34m(self, frequencies, max_font_size)\u001b[0m\n\u001b[0;32m    444\u001b[0m                 \u001b[0mfont_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    445\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 446\u001b[1;33m                 self.generate_from_frequencies(dict(frequencies[:2]),\n\u001b[0m\u001b[0;32m    447\u001b[0m                                                max_font_size=self.height)\n\u001b[0;32m    448\u001b[0m                 \u001b[1;31m# find font sizes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\wordcloud\\wordcloud.py\u001b[0m in \u001b[0;36mgenerate_from_frequencies\u001b[1;34m(self, frequencies, max_font_size)\u001b[0m\n\u001b[0;32m    499\u001b[0m                     font, orientation=orientation)\n\u001b[0;32m    500\u001b[0m                 \u001b[1;31m# get size of resulting text\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 501\u001b[1;33m                 \u001b[0mbox_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtextsize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfont\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtransposed_font\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    502\u001b[0m                 \u001b[1;31m# find possible places using integral image:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    503\u001b[0m                 result = occupancy.sample_position(box_size[1] + self.margin,\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'ImageDraw' object has no attribute 'textsize'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ins_asc=df2.sort_values(by=['Size'],axis=0,ascending=False,inplace=False)\n",
    "from wordcloud import WordCloud \n",
    "plt.subplots(figsize=(25,15))\n",
    "wordcloud = WordCloud(\n",
    "                          background_color='black',\n",
    "                          width=920,\n",
    "                          height=180\n",
    "                         ).generate(\" \".join(df_ins_asc.head(20)['App']))\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fe1e360",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}