Team_EDA_Capstone_Project.ipynb

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        "<a href=\"https://colab.research.google.com/github/sonika-07/Play_Store_App_Review_Analysis/blob/main/Team_EDA_Capstone_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#**Project Topic: Play Store App Review Analysis**\n",
        " \n",
        " Team Members:\n",
        "\n",
        " Aanchal Kankrecha\n",
        "\n",
        " Sonika Baheti"
      ],
      "metadata": {
        "id": "7TajYKUZfHYu"
      }
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCVy21aOckxL"
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      "source": [
        "**The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market.\n",
        "Each app (row) has values for catergory, rating, size, and more. Another dataset contains customer reviews of the android apps.\n",
        "Explore and analyze the data to discover key factors responsible for app engagement and success.**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Area of Interest** \n",
        "1.   Is different variables related?\n",
        "2.   How many reviews are there for apps having 1 Billion installs?\n",
        "3.   How many installs are made for each application?\n",
        "4.   How many apps are uninstalled?\n",
        "5.   Which category has highest installs?\n",
        "6.   What are the top 3 gaming Apps?\n",
        "7.   What are the top 3 communication Apps?\n",
        "8.   What are the top 3 tools Apps?\n",
        "9.   How many apps are there for each content rating?\n",
        "10.  How many installs are made for each type of content rating?\n",
        "11.  What is the revenue generated by paid apps?\n",
        "12.  How apps are distributed on the basis of category?\n",
        "13.  Percentage of free vs paid apps?\n",
        "14.  How many apps are free and paid for each category?\n",
        "15.  Apps with their type having rating 5?\n",
        "16.  Distribution of Rating?\n",
        "17.  Distribution of size of apps?\n",
        "18.  Average rating of each category?\n",
        "19.  No. of apps in each generes?\n",
        "20.  Rating vs price?\n",
        "21.  Rating vs installs?\n",
        "22.  When apps were last updated?\n",
        "23.  No. of translated reviews of different apps?\n",
        "24.  Which app has maximum translated reviews?\n",
        "25.  Distribution plot of sentiment polarity?\n",
        "26.  Distribution plot of sentiment subjectivity?\n",
        "27.  Percentage of sentiment count?\n",
        "28.  Sentimental analysis of free and paid apps?\n",
        "\n",
        "\n"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WQmWrhaFffXj"
      },
      "source": [
        "# **1. Importing libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "opGLzXAncsOI"
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      "outputs": [],
      "source": [
        "#Importing the required libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Handshaking with google drive**"
      ],
      "metadata": {
        "id": "lTymDCDy_dqN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
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        "id": "6zmkBLHVc79J",
        "outputId": "db890c28-b2aa-49f6-9b63-999c82ad1959"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4e_E8tzvm52"
      },
      "source": [
        "## **2. Loading the Play store dataset**"
      ]
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      "execution_count": 6,
      "metadata": {
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      "source": [
        "# first Dataset(Play store App Review Analysis\n",
        "df=pd.read_csv('/content/drive/MyDrive/Capston Project/EDA/Play Store Review Analysis /Play Store Data.csv')"
      ]
    },
    {
      "cell_type": "markdown",
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      },
      "source": [
        "## **3. Having overview and understanding of the dataset**"
      ]
    },
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              "                                                 App        Category  Rating  \\\n",
              "0     Photo Editor & Candy Camera & Grid & ScrapBook  ART_AND_DESIGN     4.1   \n",
              "1                                Coloring book moana  ART_AND_DESIGN     3.9   \n",
              "2  U Launcher Lite – FREE Live Cool Themes, Hide ...  ART_AND_DESIGN     4.7   \n",
              "3                              Sketch - Draw & Paint  ART_AND_DESIGN     4.5   \n",
              "4              Pixel Draw - Number Art Coloring Book  ART_AND_DESIGN     4.3   \n",
              "5                         Paper flowers instructions  ART_AND_DESIGN     4.4   \n",
              "6            Smoke Effect Photo Maker - Smoke Editor  ART_AND_DESIGN     3.8   \n",
              "7                                   Infinite Painter  ART_AND_DESIGN     4.1   \n",
              "8                               Garden Coloring Book  ART_AND_DESIGN     4.4   \n",
              "9                      Kids Paint Free - Drawing Fun  ART_AND_DESIGN     4.7   \n",
              "\n",
              "  Reviews  Size     Installs  Type Price Content Rating  \\\n",
              "0     159   19M      10,000+  Free     0       Everyone   \n",
              "1     967   14M     500,000+  Free     0       Everyone   \n",
              "2   87510  8.7M   5,000,000+  Free     0       Everyone   \n",
              "3  215644   25M  50,000,000+  Free     0           Teen   \n",
              "4     967  2.8M     100,000+  Free     0       Everyone   \n",
              "5     167  5.6M      50,000+  Free     0       Everyone   \n",
              "6     178   19M      50,000+  Free     0       Everyone   \n",
              "7   36815   29M   1,000,000+  Free     0       Everyone   \n",
              "8   13791   33M   1,000,000+  Free     0       Everyone   \n",
              "9     121  3.1M      10,000+  Free     0       Everyone   \n",
              "\n",
              "                      Genres        Last Updated         Current Ver  \\\n",
              "0               Art & Design     January 7, 2018               1.0.0   \n",
              "1  Art & Design;Pretend Play    January 15, 2018               2.0.0   \n",
              "2               Art & Design      August 1, 2018               1.2.4   \n",
              "3               Art & Design        June 8, 2018  Varies with device   \n",
              "4    Art & Design;Creativity       June 20, 2018                 1.1   \n",
              "5               Art & Design      March 26, 2017                 1.0   \n",
              "6               Art & Design      April 26, 2018                 1.1   \n",
              "7               Art & Design       June 14, 2018            6.1.61.1   \n",
              "8               Art & Design  September 20, 2017               2.9.2   \n",
              "9    Art & Design;Creativity        July 3, 2018                 2.8   \n",
              "\n",
              "    Android Ver  \n",
              "0  4.0.3 and up  \n",
              "1  4.0.3 and up  \n",
              "2  4.0.3 and up  \n",
              "3    4.2 and up  \n",
              "4    4.4 and up  \n",
              "5    2.3 and up  \n",
              "6  4.0.3 and up  \n",
              "7    4.2 and up  \n",
              "8    3.0 and up  \n",
              "9  4.0.3 and up  "
            ],
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              "    <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",
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              "      <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",
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              "      <td>4.0.3 and up</td>\n",
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              "      <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",
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              "    <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",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sketch - Draw &amp; Paint</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.5</td>\n",
              "      <td>215644</td>\n",
              "      <td>25M</td>\n",
              "      <td>50,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>Art &amp; Design</td>\n",
              "      <td>June 8, 2018</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>4.2 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.3</td>\n",
              "      <td>967</td>\n",
              "      <td>2.8M</td>\n",
              "      <td>100,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Creativity</td>\n",
              "      <td>June 20, 2018</td>\n",
              "      <td>1.1</td>\n",
              "      <td>4.4 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Paper flowers instructions</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.4</td>\n",
              "      <td>167</td>\n",
              "      <td>5.6M</td>\n",
              "      <td>50,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
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              "      <td>March 26, 2017</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Smoke Effect Photo Maker - Smoke Editor</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.8</td>\n",
              "      <td>178</td>\n",
              "      <td>19M</td>\n",
              "      <td>50,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design</td>\n",
              "      <td>April 26, 2018</td>\n",
              "      <td>1.1</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Infinite Painter</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.1</td>\n",
              "      <td>36815</td>\n",
              "      <td>29M</td>\n",
              "      <td>1,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design</td>\n",
              "      <td>June 14, 2018</td>\n",
              "      <td>6.1.61.1</td>\n",
              "      <td>4.2 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Garden Coloring Book</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.4</td>\n",
              "      <td>13791</td>\n",
              "      <td>33M</td>\n",
              "      <td>1,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design</td>\n",
              "      <td>September 20, 2017</td>\n",
              "      <td>2.9.2</td>\n",
              "      <td>3.0 and up</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Kids Paint Free - Drawing Fun</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.7</td>\n",
              "      <td>121</td>\n",
              "      <td>3.1M</td>\n",
              "      <td>10,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Creativity</td>\n",
              "      <td>July 3, 2018</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.0.3 and up</td>\n",
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      ],
      "source": [
        "#Top 10 Rows\n",
        "df.head(10)"
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              "                                                 App             Category  \\\n",
              "10831                       payermonstationnement.fr  MAPS_AND_NAVIGATION   \n",
              "10832                                       FR Tides              WEATHER   \n",
              "10833                                    Chemin (fr)  BOOKS_AND_REFERENCE   \n",
              "10834                                  FR Calculator               FAMILY   \n",
              "10835                                       FR Forms             BUSINESS   \n",
              "10836                               Sya9a Maroc - FR               FAMILY   \n",
              "10837               Fr. Mike Schmitz Audio Teachings               FAMILY   \n",
              "10838                         Parkinson Exercices FR              MEDICAL   \n",
              "10839                  The SCP Foundation DB fr nn5n  BOOKS_AND_REFERENCE   \n",
              "10840  iHoroscope - 2018 Daily Horoscope & Astrology            LIFESTYLE   \n",
              "\n",
              "       Rating Reviews                Size     Installs  Type Price  \\\n",
              "10831     NaN      38                9.8M       5,000+  Free     0   \n",
              "10832     3.8    1195                582k     100,000+  Free     0   \n",
              "10833     4.8      44                619k       1,000+  Free     0   \n",
              "10834     4.0       7                2.6M         500+  Free     0   \n",
              "10835     NaN       0                9.6M          10+  Free     0   \n",
              "10836     4.5      38                 53M       5,000+  Free     0   \n",
              "10837     5.0       4                3.6M         100+  Free     0   \n",
              "10838     NaN       3                9.5M       1,000+  Free     0   \n",
              "10839     4.5     114  Varies with device       1,000+  Free     0   \n",
              "10840     4.5  398307                 19M  10,000,000+  Free     0   \n",
              "\n",
              "      Content Rating             Genres        Last Updated  \\\n",
              "10831       Everyone  Maps & Navigation       June 13, 2018   \n",
              "10832       Everyone            Weather   February 16, 2014   \n",
              "10833       Everyone  Books & Reference      March 23, 2014   \n",
              "10834       Everyone          Education       June 18, 2017   \n",
              "10835       Everyone           Business  September 29, 2016   \n",
              "10836       Everyone          Education       July 25, 2017   \n",
              "10837       Everyone          Education        July 6, 2018   \n",
              "10838       Everyone            Medical    January 20, 2017   \n",
              "10839     Mature 17+  Books & Reference    January 19, 2015   \n",
              "10840       Everyone          Lifestyle       July 25, 2018   \n",
              "\n",
              "              Current Ver         Android Ver  \n",
              "10831           2.0.148.0          4.0 and up  \n",
              "10832                 6.0          2.1 and up  \n",
              "10833                 0.8          2.2 and up  \n",
              "10834               1.0.0          4.1 and up  \n",
              "10835               1.1.5          4.0 and up  \n",
              "10836                1.48          4.1 and up  \n",
              "10837                 1.0          4.1 and up  \n",
              "10838                 1.0          2.2 and up  \n",
              "10839  Varies with device  Varies with device  \n",
              "10840  Varies with device  Varies with device  "
            ],
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              "\n",
              "  <div id=\"df-4a4e1e29-7755-472c-8e6e-0c44b1b8a7f2\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App</th>\n",
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              "      <th>Current Ver</th>\n",
              "      <th>Android Ver</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10831</th>\n",
              "      <td>payermonstationnement.fr</td>\n",
              "      <td>MAPS_AND_NAVIGATION</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38</td>\n",
              "      <td>9.8M</td>\n",
              "      <td>5,000+</td>\n",
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              "      <td>Everyone</td>\n",
              "      <td>Maps &amp; Navigation</td>\n",
              "      <td>June 13, 2018</td>\n",
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              "      <td>4.0 and up</td>\n",
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              "    <tr>\n",
              "      <th>10832</th>\n",
              "      <td>FR Tides</td>\n",
              "      <td>WEATHER</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1195</td>\n",
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              "      <td>Everyone</td>\n",
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              "    <tr>\n",
              "      <th>10833</th>\n",
              "      <td>Chemin (fr)</td>\n",
              "      <td>BOOKS_AND_REFERENCE</td>\n",
              "      <td>4.8</td>\n",
              "      <td>44</td>\n",
              "      <td>619k</td>\n",
              "      <td>1,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Books &amp; Reference</td>\n",
              "      <td>March 23, 2014</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2.2 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10834</th>\n",
              "      <td>FR Calculator</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7</td>\n",
              "      <td>2.6M</td>\n",
              "      <td>500+</td>\n",
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              "      <td>1.0.0</td>\n",
              "      <td>4.1 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10835</th>\n",
              "      <td>FR Forms</td>\n",
              "      <td>BUSINESS</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6M</td>\n",
              "      <td>10+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Business</td>\n",
              "      <td>September 29, 2016</td>\n",
              "      <td>1.1.5</td>\n",
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              "      <th>10836</th>\n",
              "      <td>Sya9a Maroc - FR</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>4.5</td>\n",
              "      <td>38</td>\n",
              "      <td>53M</td>\n",
              "      <td>5,000+</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1.48</td>\n",
              "      <td>4.1 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10837</th>\n",
              "      <td>Fr. Mike Schmitz Audio Teachings</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4</td>\n",
              "      <td>3.6M</td>\n",
              "      <td>100+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Education</td>\n",
              "      <td>July 6, 2018</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.1 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10838</th>\n",
              "      <td>Parkinson Exercices FR</td>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>9.5M</td>\n",
              "      <td>1,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Medical</td>\n",
              "      <td>January 20, 2017</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.2 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10839</th>\n",
              "      <td>The SCP Foundation DB fr nn5n</td>\n",
              "      <td>BOOKS_AND_REFERENCE</td>\n",
              "      <td>4.5</td>\n",
              "      <td>114</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>1,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Mature 17+</td>\n",
              "      <td>Books &amp; Reference</td>\n",
              "      <td>January 19, 2015</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10840</th>\n",
              "      <td>iHoroscope - 2018 Daily Horoscope &amp; Astrology</td>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>4.5</td>\n",
              "      <td>398307</td>\n",
              "      <td>19M</td>\n",
              "      <td>10,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Lifestyle</td>\n",
              "      <td>July 25, 2018</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
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              "  </tbody>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "#Bottom 10 Rows\n",
        "df.tail(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDuWf55ghB4U",
        "outputId": "36580e6d-dc67-46cd-f481-a5b47b8d3cc7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10841, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "#To check how many rows and columns are there in dataset 1\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3dKfTiEwrkVI",
        "outputId": "25552889-7603-45cd-94d5-120e2d7306ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.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  object \n",
            " 1   Category        10841 non-null  object \n",
            " 2   Rating          9367 non-null   float64\n",
            " 3   Reviews         10841 non-null  object \n",
            " 4   Size            10841 non-null  object \n",
            " 5   Installs        10841 non-null  object \n",
            " 6   Type            10840 non-null  object \n",
            " 7   Price           10841 non-null  object \n",
            " 8   Content Rating  10840 non-null  object \n",
            " 9   Genres          10841 non-null  object \n",
            " 10  Last Updated    10841 non-null  object \n",
            " 11  Current Ver     10833 non-null  object \n",
            " 12  Android Ver     10838 non-null  object \n",
            "dtypes: float64(1), object(12)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ],
      "source": [
        "#See the datatype and Basic information\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RYN9iFOEro8I",
        "outputId": "7925ce24-7602-45b6-8c49-577822073a4d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "App                  0\n",
              "Category             0\n",
              "Rating            1474\n",
              "Reviews              0\n",
              "Size                 0\n",
              "Installs             0\n",
              "Type                 1\n",
              "Price                0\n",
              "Content Rating       1\n",
              "Genres               0\n",
              "Last Updated         0\n",
              "Current Ver          8\n",
              "Android Ver          3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "#Check if there are null values\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yN_HSAd6vzjZ"
      },
      "source": [
        "*There are 5 columns : Rating, Type, Content Rating, Current Version and Android Version which are null.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zuKqOIw-w4Q-"
      },
      "source": [
        "## **4. Data Preprocessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We go through column by column while data preprocessing"
      ],
      "metadata": {
        "id": "VnU8XKzkAikS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "a. **App**"
      ],
      "metadata": {
        "id": "ZWuSUqU_A06t"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "47dqo_hB-sno",
        "outputId": "29329837-350f-453f-e01c-1a4a1365244a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "#Checking for duplicate values in app column\n",
        "df['App'].duplicated().any()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "R7DY30LTle98",
        "outputId": "f49ba45b-ea1c-4863-8fa6-798f01bc2ce8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         App Category  Rating  Reviews Size      Installs  Type Price  \\\n",
              "1653  ROBLOX     GAME     4.5  4447388  67M  100,000,000+  Free     0   \n",
              "1701  ROBLOX     GAME     4.5  4447346  67M  100,000,000+  Free     0   \n",
              "1748  ROBLOX     GAME     4.5  4448791  67M  100,000,000+  Free     0   \n",
              "1841  ROBLOX     GAME     4.5  4449882  67M  100,000,000+  Free     0   \n",
              "1870  ROBLOX     GAME     4.5  4449910  67M  100,000,000+  Free     0   \n",
              "2016  ROBLOX   FAMILY     4.5  4449910  67M  100,000,000+  Free     0   \n",
              "2088  ROBLOX   FAMILY     4.5  4450855  67M  100,000,000+  Free     0   \n",
              "2206  ROBLOX   FAMILY     4.5  4450890  67M  100,000,000+  Free     0   \n",
              "4527  ROBLOX   FAMILY     4.5  4443407  67M  100,000,000+  Free     0   \n",
              "\n",
              "     Content Rating                        Genres   Last Updated  \\\n",
              "1653   Everyone 10+  Adventure;Action & Adventure  July 31, 2018   \n",
              "1701   Everyone 10+  Adventure;Action & Adventure  July 31, 2018   \n",
              "1748   Everyone 10+  Adventure;Action & Adventure  July 31, 2018   \n",
              "1841   Everyone 10+  Adventure;Action & Adventure  July 31, 2018   \n",
              "1870   Everyone 10+  Adventure;Action & Adventure  July 31, 2018   \n",
              "2016   Everyone 10+  Adventure;Action & Adventure  July 31, 2018   \n",
              "2088   Everyone 10+  Adventure;Action & Adventure  July 31, 2018   \n",
              "2206   Everyone 10+  Adventure;Action & Adventure  July 31, 2018   \n",
              "4527   Everyone 10+  Adventure;Action & Adventure  July 31, 2018   \n",
              "\n",
              "       Current Ver Android Ver  \n",
              "1653  2.347.225742  4.1 and up  \n",
              "1701  2.347.225742  4.1 and up  \n",
              "1748  2.347.225742  4.1 and up  \n",
              "1841  2.347.225742  4.1 and up  \n",
              "1870  2.347.225742  4.1 and up  \n",
              "2016  2.347.225742  4.1 and up  \n",
              "2088  2.347.225742  4.1 and up  \n",
              "2206  2.347.225742  4.1 and up  \n",
              "4527  2.347.225742  4.1 and up  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>App</th>\n",
              "      <th>Category</th>\n",
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              "      <td>ROBLOX</td>\n",
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              "      <td>2.347.225742</td>\n",
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              "      <th>1748</th>\n",
              "      <td>ROBLOX</td>\n",
              "      <td>GAME</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4448791</td>\n",
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              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>Adventure;Action &amp; Adventure</td>\n",
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              "      <td>ROBLOX</td>\n",
              "      <td>GAME</td>\n",
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              "      <td>4449882</td>\n",
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              "      <td>100,000,000+</td>\n",
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              "      <td>ROBLOX</td>\n",
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              "      <td>4.5</td>\n",
              "      <td>4449910</td>\n",
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              "      <td>Free</td>\n",
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              "      <td>Everyone 10+</td>\n",
              "      <td>Adventure;Action &amp; Adventure</td>\n",
              "      <td>July 31, 2018</td>\n",
              "      <td>2.347.225742</td>\n",
              "      <td>4.1 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>ROBLOX</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4449910</td>\n",
              "      <td>67M</td>\n",
              "      <td>100,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>Adventure;Action &amp; Adventure</td>\n",
              "      <td>July 31, 2018</td>\n",
              "      <td>2.347.225742</td>\n",
              "      <td>4.1 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2088</th>\n",
              "      <td>ROBLOX</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4450855</td>\n",
              "      <td>67M</td>\n",
              "      <td>100,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>Adventure;Action &amp; Adventure</td>\n",
              "      <td>July 31, 2018</td>\n",
              "      <td>2.347.225742</td>\n",
              "      <td>4.1 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2206</th>\n",
              "      <td>ROBLOX</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4450890</td>\n",
              "      <td>67M</td>\n",
              "      <td>100,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>Adventure;Action &amp; Adventure</td>\n",
              "      <td>July 31, 2018</td>\n",
              "      <td>2.347.225742</td>\n",
              "      <td>4.1 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4527</th>\n",
              "      <td>ROBLOX</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4443407</td>\n",
              "      <td>67M</td>\n",
              "      <td>100,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>Adventure;Action &amp; Adventure</td>\n",
              "      <td>July 31, 2018</td>\n",
              "      <td>2.347.225742</td>\n",
              "      <td>4.1 and up</td>\n",
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              "  </tbody>\n",
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          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "df[df['App']=='ROBLOX']"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Droping the duplicate values for different apps."
      ],
      "metadata": {
        "id": "8-WyzpzbA-MD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EjL2mCJ7_Ixd"
      },
      "outputs": [],
      "source": [
        "df.drop_duplicates(subset='App',keep='first',inplace=True) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wX0lfXOC_ZN5",
        "outputId": "4d7f288f-0641-40fc-be44-35da2e9e0521"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Photo Editor & Candy Camera & Grid & ScrapBook    1\n",
              "CK Pharmacies                                     1\n",
              "CK Call NEW                                       1\n",
              "Ck Coif                                           1\n",
              "CONTRACT KILLER: ZOMBIES                          1\n",
              "                                                 ..\n",
              "Bike Race Free - Top Motorcycle Racing Games      1\n",
              "Dance School Stories - Dance Dreams Come True     1\n",
              "3D Bowling                                        1\n",
              "Mind Games Pro                                    1\n",
              "iHoroscope - 2018 Daily Horoscope & Astrology     1\n",
              "Name: App, Length: 9660, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "df.App.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "v-V3ECDOiHNk",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       App Category  Rating Reviews                Size     Installs  Type  \\\n",
              "2959  ESPN   SPORTS     4.2  521138  Varies with device  10,000,000+  Free   \n",
              "\n",
              "     Price Content Rating  Genres   Last Updated         Current Ver  \\\n",
              "2959     0   Everyone 10+  Sports  July 19, 2018  Varies with device   \n",
              "\n",
              "     Android Ver  \n",
              "2959  5.0 and up  "
            ],
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              "\n",
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              "    </tr>\n",
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              "    <tr>\n",
              "      <th>2959</th>\n",
              "      <td>ESPN</td>\n",
              "      <td>SPORTS</td>\n",
              "      <td>4.2</td>\n",
              "      <td>521138</td>\n",
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              "      <td>Free</td>\n",
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              "      <td>Everyone 10+</td>\n",
              "      <td>Sports</td>\n",
              "      <td>July 19, 2018</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>5.0 and up</td>\n",
              "    </tr>\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "df[df['App']=='ESPN']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9RuWLtzvERIk",
        "outputId": "84cda275-6552-4664-b998-db269f029b20"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10841, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "#cheching the shape of the dataset after removing duplicates\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jBmjZgvOxWDK"
      },
      "source": [
        "*Now we have total 9660 records and 13 columns*"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "b. **Category**"
      ],
      "metadata": {
        "id": "2YGXMVMNBgc0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
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          "height": 1000
        },
        "id": "TVsp1hsnEcrv",
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      "outputs": [
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            "text/plain": [
              "               category\n",
              "0        ART_AND_DESIGN\n",
              "1     AUTO_AND_VEHICLES\n",
              "2                BEAUTY\n",
              "3   BOOKS_AND_REFERENCE\n",
              "4              BUSINESS\n",
              "5                COMICS\n",
              "6         COMMUNICATION\n",
              "7                DATING\n",
              "8             EDUCATION\n",
              "9         ENTERTAINMENT\n",
              "10               EVENTS\n",
              "11              FINANCE\n",
              "12       FOOD_AND_DRINK\n",
              "13   HEALTH_AND_FITNESS\n",
              "14       HOUSE_AND_HOME\n",
              "15   LIBRARIES_AND_DEMO\n",
              "16            LIFESTYLE\n",
              "17                 GAME\n",
              "18               FAMILY\n",
              "19              MEDICAL\n",
              "20               SOCIAL\n",
              "21             SHOPPING\n",
              "22          PHOTOGRAPHY\n",
              "23               SPORTS\n",
              "24     TRAVEL_AND_LOCAL\n",
              "25                TOOLS\n",
              "26      PERSONALIZATION\n",
              "27         PRODUCTIVITY\n",
              "28            PARENTING\n",
              "29              WEATHER\n",
              "30        VIDEO_PLAYERS\n",
              "31   NEWS_AND_MAGAZINES\n",
              "32  MAPS_AND_NAVIGATION\n",
              "33                  1.9"
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              "      <th>22</th>\n",
              "      <td>PHOTOGRAPHY</td>\n",
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              "      <th>23</th>\n",
              "      <td>SPORTS</td>\n",
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              "      <th>24</th>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
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              "      <td>TOOLS</td>\n",
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              "      <td>PERSONALIZATION</td>\n",
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              "    <tr>\n",
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              "      <td>MAPS_AND_NAVIGATION</td>\n",
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              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1.9</td>\n",
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              "                                                     [key], {});\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "#Find unique categories\n",
        "pd.DataFrame(df.Category.unique(),columns=['category'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KxmZvvbxV4mm"
      },
      "source": [
        "*Category 1.9 seems to be doubtful so exploring that record*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "A0ZAuENvEoZZ",
        "outputId": "225c27e5-cd1f-4d14-83f2-60e04b82a27e"
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        {
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          "data": {
            "text/plain": [
              "                                           App Category  Rating Reviews  \\\n",
              "10472  Life Made WI-Fi Touchscreen Photo Frame      1.9    19.0    3.0M   \n",
              "\n",
              "         Size Installs Type     Price Content Rating             Genres  \\\n",
              "10472  1,000+     Free    0  Everyone            NaN  February 11, 2018   \n",
              "\n",
              "      Last Updated Current Ver Android Ver  \n",
              "10472       1.0.19  4.0 and up         NaN  "
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              "      <th>10472</th>\n",
              "      <td>Life Made WI-Fi Touchscreen Photo Frame</td>\n",
              "      <td>1.9</td>\n",
              "      <td>19.0</td>\n",
              "      <td>3.0M</td>\n",
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              "      <td>Free</td>\n",
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              "      <td>February 11, 2018</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "df.loc[df['Category']=='1.9']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AD39yKpgEwlq"
      },
      "source": [
        "*We can clearly see that in Row number 10472, Rating is 19.0(which may be an outlier), Installs is Free(we want some number not 'FREE'),Price is 'Everyone'(Again not useful). Hence it's better to drop the row.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "xCGC-Ud6F3n9"
      },
      "outputs": [],
      "source": [
        "#Dropping the row\n",
        "df=df.drop(10472)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hk_hB9CmGDYT",
        "outputId": "a68c3f00-92fd-4668-d8ee-ba918781b970"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9659, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "c. **Rating**"
      ],
      "metadata": {
        "id": "nmGKJNwqD_CS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Null values of rating are to be filled with 0 as it seems that users are not interested in that app"
      ],
      "metadata": {
        "id": "5IHcC9G-O3kv"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "-QAGy73GSlIj"
      },
      "outputs": [],
      "source": [
        "#Fill the null values with 0\n",
        "df['Rating']=df['Rating'].fillna(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "d. **Reviews**"
      ],
      "metadata": {
        "id": "dUHWIg0FED5p"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exZuuMLaJ-3G",
        "outputId": "ef4506cf-6024-40e5-e401-db8920455230"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['159', '967', '87510', ..., '603', '1195', '398307'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "df['Reviews'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "QGDHoItyJ--N"
      },
      "outputs": [],
      "source": [
        "#Change the datatype of reviews.\n",
        "df['Reviews']=df['Reviews'].astype(float)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " **e. Installs**"
      ],
      "metadata": {
        "id": "7u0pWOAIh447"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66dc010a-1a68-4c34-9c87-04dd8939cc36",
        "id": "OU_0i3mTh1CT"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['10,000+', '500,000+', '5,000,000+', '50,000,000+', '100,000+',\n",
              "       '50,000+', '1,000,000+', '10,000,000+', '5,000+', '100,000,000+',\n",
              "       '1,000,000,000+', '1,000+', '500,000,000+', '50+', '100+', '500+',\n",
              "       '10+', '1+', '5+', '0+', '0'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "#Checking for unique values in installs column\n",
        "df.Installs.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Ze5LNY8th1CV"
      },
      "outputs": [],
      "source": [
        "#Removing the special characters '+' , ',' and changing the datatype\n",
        "\n",
        "char_to_remove=['+',',']\n",
        "for char in char_to_remove:\n",
        "  df['Installs']=df['Installs'].apply(lambda x:x.replace(char,\"\"))\n",
        "df['Installs']=df['Installs'].astype(int)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ei2ZRhT19gjR"
      },
      "source": [
        "*Check if there are fake reviews*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "4EmMAqpzkcyC",
        "outputId": "c8e42ceb-2ce7-449b-8541-6377c08fd26e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     App         Category  Rating  Reviews  \\\n",
              "2454                 KBA-EZ Health Guide          MEDICAL     5.0      4.0   \n",
              "4550   RMEduS - 음성인식을 활용한 R 프로그래밍 실습 시스템           FAMILY     0.0      4.0   \n",
              "4663       Alarmy (Sleep If U Can) - Pro        LIFESTYLE     4.8  10249.0   \n",
              "5812             AX Watch for WatchMaker  PERSONALIZATION     0.0      2.0   \n",
              "5917                            Ra Ga Ba             GAME     5.0      2.0   \n",
              "6508                          Sam.BN Pro            TOOLS     0.0     11.0   \n",
              "6700                    Brick Breaker BR             GAME     5.0      7.0   \n",
              "7402                Trovami se ci riesci             GAME     5.0     11.0   \n",
              "8591                             DN Blog           SOCIAL     5.0     20.0   \n",
              "9096                           DZ Puzzle           FAMILY     0.0     14.0   \n",
              "10697                            Mu.F.O.             GAME     5.0      2.0   \n",
              "\n",
              "                     Size  Installs  Type  Price Content Rating  \\\n",
              "2454                  25M         1  Free      0       Everyone   \n",
              "4550                  64M         1  Free      0       Everyone   \n",
              "4663   Varies with device     10000  Paid  $2.49       Everyone   \n",
              "5812                 238k         1  Paid  $0.99       Everyone   \n",
              "5917                  20M         1  Paid  $1.49       Everyone   \n",
              "6508                 2.0M        10  Paid  $0.99       Everyone   \n",
              "6700                  19M         5  Free      0       Everyone   \n",
              "7402                 6.1M        10  Free      0       Everyone   \n",
              "8591                 4.2M        10  Free      0           Teen   \n",
              "9096                  47M        10  Paid  $0.99       Everyone   \n",
              "10697                 16M         1  Paid  $0.99       Everyone   \n",
              "\n",
              "                Genres      Last Updated         Current Ver  \\\n",
              "2454           Medical    August 2, 2018              1.0.72   \n",
              "4550         Education     July 17, 2018               1.0.1   \n",
              "4663         Lifestyle     July 30, 2018  Varies with device   \n",
              "5812   Personalization   August 18, 2017                 1.0   \n",
              "5917            Arcade  February 8, 2017               1.0.4   \n",
              "6508             Tools    March 27, 2015               1.0.0   \n",
              "6700            Arcade     July 23, 2018                 1.0   \n",
              "7402            Arcade    March 11, 2017                 0.1   \n",
              "8591            Social     July 23, 2018                 1.0   \n",
              "9096            Puzzle    April 22, 2017                 1.2   \n",
              "10697           Arcade     March 3, 2017                 1.0   \n",
              "\n",
              "              Android Ver  \n",
              "2454         4.0.3 and up  \n",
              "4550           4.4 and up  \n",
              "4663   Varies with device  \n",
              "5812           2.3 and up  \n",
              "5917           2.3 and up  \n",
              "6508         4.0.3 and up  \n",
              "6700           4.1 and up  \n",
              "7402           2.3 and up  \n",
              "8591           4.0 and up  \n",
              "9096           2.3 and up  \n",
              "10697          2.3 and up  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c3c46119-24c5-40dc-b8c8-740edb58f087\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "\n",
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              "</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>2454</th>\n",
              "      <td>KBA-EZ Health Guide</td>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>25M</td>\n",
              "      <td>1</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Medical</td>\n",
              "      <td>August 2, 2018</td>\n",
              "      <td>1.0.72</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4550</th>\n",
              "      <td>RMEduS - 음성인식을 활용한 R 프로그래밍 실습 시스템</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>64M</td>\n",
              "      <td>1</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Education</td>\n",
              "      <td>July 17, 2018</td>\n",
              "      <td>1.0.1</td>\n",
              "      <td>4.4 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4663</th>\n",
              "      <td>Alarmy (Sleep If U Can) - Pro</td>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>4.8</td>\n",
              "      <td>10249.0</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>10000</td>\n",
              "      <td>Paid</td>\n",
              "      <td>$2.49</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Lifestyle</td>\n",
              "      <td>July 30, 2018</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5812</th>\n",
              "      <td>AX Watch for WatchMaker</td>\n",
              "      <td>PERSONALIZATION</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>238k</td>\n",
              "      <td>1</td>\n",
              "      <td>Paid</td>\n",
              "      <td>$0.99</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Personalization</td>\n",
              "      <td>August 18, 2017</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5917</th>\n",
              "      <td>Ra Ga Ba</td>\n",
              "      <td>GAME</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>20M</td>\n",
              "      <td>1</td>\n",
              "      <td>Paid</td>\n",
              "      <td>$1.49</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Arcade</td>\n",
              "      <td>February 8, 2017</td>\n",
              "      <td>1.0.4</td>\n",
              "      <td>2.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6508</th>\n",
              "      <td>Sam.BN Pro</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2.0M</td>\n",
              "      <td>10</td>\n",
              "      <td>Paid</td>\n",
              "      <td>$0.99</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Tools</td>\n",
              "      <td>March 27, 2015</td>\n",
              "      <td>1.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6700</th>\n",
              "      <td>Brick Breaker BR</td>\n",
              "      <td>GAME</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>19M</td>\n",
              "      <td>5</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Arcade</td>\n",
              "      <td>July 23, 2018</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.1 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7402</th>\n",
              "      <td>Trovami se ci riesci</td>\n",
              "      <td>GAME</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.1M</td>\n",
              "      <td>10</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Arcade</td>\n",
              "      <td>March 11, 2017</td>\n",
              "      <td>0.1</td>\n",
              "      <td>2.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8591</th>\n",
              "      <td>DN Blog</td>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>5.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>4.2M</td>\n",
              "      <td>10</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>Social</td>\n",
              "      <td>July 23, 2018</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9096</th>\n",
              "      <td>DZ Puzzle</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>47M</td>\n",
              "      <td>10</td>\n",
              "      <td>Paid</td>\n",
              "      <td>$0.99</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Puzzle</td>\n",
              "      <td>April 22, 2017</td>\n",
              "      <td>1.2</td>\n",
              "      <td>2.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10697</th>\n",
              "      <td>Mu.F.O.</td>\n",
              "      <td>GAME</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16M</td>\n",
              "      <td>1</td>\n",
              "      <td>Paid</td>\n",
              "      <td>$0.99</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Arcade</td>\n",
              "      <td>March 3, 2017</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.3 and up</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "df[df.Reviews>df.Installs]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3nBUnHNolA80",
        "outputId": "f038af52-562f-4f3d-e934-31d1e44cccf4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "df[df.Reviews>df.Installs].shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_7l4f0192L6"
      },
      "source": [
        "*Maximum possible no. of reviews cannot exceed the total no. of installs of particular app, therefore replacing it with no. of installs*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "bEqh_R24lybw"
      },
      "outputs": [],
      "source": [
        "#Replacing those values of reviews which are greater than intalls with Number of installs\n",
        "df.loc[df['Reviews']>df['Installs'],'Reviews']=df['Installs']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpxeyKheHCA7",
        "outputId": "ea732a9a-deda-4b11-9796-7ca8ed792371"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "df[df.Reviews>df.Installs].shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9zRqiu_-scC"
      },
      "source": [
        "*Now there is no fake review in the table*"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "f. **Size**"
      ],
      "metadata": {
        "id": "XIVm0EIXEPG_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EY4e-0NWftZb",
        "outputId": "13efdb5b-2a8d-48f4-bf5e-a62379681032"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['19M', '14M', '8.7M', '25M', '2.8M', '5.6M', '29M', '33M', '3.1M',\n",
              "       '28M', '12M', '20M', '21M', '37M', '2.7M', '5.5M', '17M', '39M',\n",
              "       '31M', '4.2M', '7.0M', '23M', '6.0M', '6.1M', '4.6M', '9.2M',\n",
              "       '5.2M', '11M', '24M', 'Varies with device', '9.4M', '15M', '10M',\n",
              "       '1.2M', '26M', '8.0M', '7.9M', '56M', '57M', '35M', '54M', '201k',\n",
              "       '3.6M', '5.7M', '8.6M', '2.4M', '27M', '2.5M', '16M', '3.4M',\n",
              "       '8.9M', '3.9M', '2.9M', '38M', '32M', '5.4M', '18M', '1.1M',\n",
              "       '2.2M', '4.5M', '9.8M', '52M', '9.0M', '6.7M', '30M', '2.6M',\n",
              "       '7.1M', '3.7M', '22M', '7.4M', '6.4M', '3.2M', '8.2M', '9.9M',\n",
              "       '4.9M', '9.5M', '5.0M', '5.9M', '13M', '73M', '6.8M', '3.5M',\n",
              "       '4.0M', '2.3M', '7.2M', '2.1M', '42M', '7.3M', '9.1M', '55M',\n",
              "       '23k', '6.5M', '1.5M', '7.5M', '51M', '41M', '48M', '8.5M', '46M',\n",
              "       '8.3M', '4.3M', '4.7M', '3.3M', '40M', '7.8M', '8.8M', '6.6M',\n",
              "       '5.1M', '61M', '66M', '79k', '8.4M', '118k', '44M', '695k', '1.6M',\n",
              "       '6.2M', '18k', '53M', '1.4M', '3.0M', '5.8M', '3.8M', '9.6M',\n",
              "       '45M', '63M', '49M', '77M', '4.4M', '4.8M', '70M', '6.9M', '9.3M',\n",
              "       '10.0M', '8.1M', '36M', '84M', '97M', '2.0M', '1.9M', '1.8M',\n",
              "       '5.3M', '47M', '556k', '526k', '76M', '7.6M', '59M', '9.7M', '78M',\n",
              "       '72M', '43M', '7.7M', '6.3M', '334k', '34M', '93M', '65M', '79M',\n",
              "       '100M', '58M', '50M', '68M', '64M', '67M', '60M', '94M', '232k',\n",
              "       '99M', '624k', '95M', '8.5k', '41k', '292k', '11k', '80M', '1.7M',\n",
              "       '74M', '62M', '69M', '75M', '98M', '85M', '82M', '96M', '87M',\n",
              "       '71M', '86M', '91M', '81M', '92M', '83M', '88M', '704k', '862k',\n",
              "       '899k', '378k', '266k', '375k', '1.3M', '975k', '980k', '4.1M',\n",
              "       '89M', '696k', '544k', '525k', '920k', '779k', '853k', '720k',\n",
              "       '713k', '772k', '318k', '58k', '241k', '196k', '857k', '51k',\n",
              "       '953k', '865k', '251k', '930k', '540k', '313k', '746k', '203k',\n",
              "       '26k', '314k', '239k', '371k', '220k', '730k', '756k', '91k',\n",
              "       '293k', '17k', '74k', '14k', '317k', '78k', '924k', '902k', '818k',\n",
              "       '81k', '939k', '169k', '45k', '475k', '965k', '90M', '545k', '61k',\n",
              "       '283k', '655k', '714k', '93k', '872k', '121k', '322k', '1.0M',\n",
              "       '976k', '172k', '238k', '549k', '206k', '954k', '444k', '717k',\n",
              "       '210k', '609k', '308k', '705k', '306k', '904k', '473k', '175k',\n",
              "       '350k', '383k', '454k', '421k', '70k', '812k', '442k', '842k',\n",
              "       '417k', '412k', '459k', '478k', '335k', '782k', '721k', '430k',\n",
              "       '429k', '192k', '200k', '460k', '728k', '496k', '816k', '414k',\n",
              "       '506k', '887k', '613k', '243k', '569k', '778k', '683k', '592k',\n",
              "       '319k', '186k', '840k', '647k', '191k', '373k', '437k', '598k',\n",
              "       '716k', '585k', '982k', '222k', '219k', '55k', '948k', '323k',\n",
              "       '691k', '511k', '951k', '963k', '25k', '554k', '351k', '27k',\n",
              "       '82k', '208k', '913k', '514k', '551k', '29k', '103k', '898k',\n",
              "       '743k', '116k', '153k', '209k', '353k', '499k', '173k', '597k',\n",
              "       '809k', '122k', '411k', '400k', '801k', '787k', '237k', '50k',\n",
              "       '643k', '986k', '97k', '516k', '837k', '780k', '961k', '269k',\n",
              "       '20k', '498k', '600k', '749k', '642k', '881k', '72k', '656k',\n",
              "       '601k', '221k', '228k', '108k', '940k', '176k', '33k', '663k',\n",
              "       '34k', '942k', '259k', '164k', '458k', '245k', '629k', '28k',\n",
              "       '288k', '775k', '785k', '636k', '916k', '994k', '309k', '485k',\n",
              "       '914k', '903k', '608k', '500k', '54k', '562k', '847k', '957k',\n",
              "       '688k', '811k', '270k', '48k', '329k', '523k', '921k', '874k',\n",
              "       '981k', '784k', '280k', '24k', '518k', '754k', '892k', '154k',\n",
              "       '860k', '364k', '387k', '626k', '161k', '879k', '39k', '970k',\n",
              "       '170k', '141k', '160k', '144k', '143k', '190k', '376k', '193k',\n",
              "       '246k', '73k', '658k', '992k', '253k', '420k', '404k', '470k',\n",
              "       '226k', '240k', '89k', '234k', '257k', '861k', '467k', '157k',\n",
              "       '44k', '676k', '67k', '552k', '885k', '1020k', '582k', '619k'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "#Unique values of size\n",
        "df['Size'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "cQkpKxlBLnY5"
      },
      "outputs": [],
      "source": [
        "#Removing the secial characters '+' , ',' , 'k' , 'M' , 'Varies with device'\n",
        "\n",
        "df['Size']=df.Size.apply(lambda x:x.strip('+'))\n",
        "df['Size']=df['Size'].apply(lambda x:x.replace(',','')) \n",
        "df['Size']=df.Size.apply(lambda x: x.replace('k','')) \n",
        "df['Size']=df['Size'].apply(lambda x:x.replace('M','000')) \n",
        "df['Size']=df.Size.replace('Varies with device',np.NaN) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "mkuvl2bh1lZt"
      },
      "outputs": [],
      "source": [
        "#Changing the datatype of size column\n",
        "\n",
        "df['Size'].fillna(str(df['Size'].mode()[0]),inplace=True)\n",
        "df['Size']=df['Size'].astype(float)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "g. **Type**"
      ],
      "metadata": {
        "id": "-OwwZtd3DHAl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7fCDll2cbhG5",
        "outputId": "e537ccd0-c0be-4704-9197-f13acf8836b7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Free', 'Paid', nan], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "#unique values of type of apps\n",
        "\n",
        "df.Type.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "bInHtLbbc9_F",
        "outputId": "73ba29e8-cea3-45e0-b615-e080efb15321"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            App Category  Rating  Reviews     Size  Installs  \\\n",
              "9148  Command & Conquer: Rivals   FAMILY     0.0      0.0  11000.0         0   \n",
              "\n",
              "     Type Price Content Rating    Genres   Last Updated         Current Ver  \\\n",
              "9148  NaN     0   Everyone 10+  Strategy  June 28, 2018  Varies with device   \n",
              "\n",
              "             Android Ver  \n",
              "9148  Varies with device  "
            ],
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              "\n",
              "  <div id=\"df-6735aeb8-9d97-4c7d-a5c8-93c302b20850\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>App</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
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              "      <th>Installs</th>\n",
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              "      <th>Content Rating</th>\n",
              "      <th>Genres</th>\n",
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              "      <th>Android Ver</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9148</th>\n",
              "      <td>Command &amp; Conquer: Rivals</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>Strategy</td>\n",
              "      <td>June 28, 2018</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "#see if there are null values in type column\n",
        "\n",
        "df[df['Type'].isna()]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FC6U_LGqIqRw"
      },
      "source": [
        "*Since the Price of Row number 9148 is 0.0 ,hence we can fill the type with 'Free'.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "5d5wc64RI8mQ"
      },
      "outputs": [],
      "source": [
        "#fill the null value with free\n",
        "df['Type']=df['Type'].fillna('Free')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "h. **Price**"
      ],
      "metadata": {
        "id": "ALIrFSI3CMrI"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MsKi5zIty-Hf"
      },
      "source": [
        "*Remove special characters from Price*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_gtZuz4hGetn",
        "outputId": "718b1089-8b75-479b-9e03-30d7f7f5e8a0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['0', '$4.99', '$3.99', '$6.99', '$1.49', '$2.99', '$7.99', '$5.99',\n",
              "       '$3.49', '$1.99', '$9.99', '$7.49', '$0.99', '$9.00', '$5.49',\n",
              "       '$10.00', '$24.99', '$11.99', '$79.99', '$16.99', '$14.99',\n",
              "       '$1.00', '$29.99', '$12.99', '$2.49', '$10.99', '$1.50', '$19.99',\n",
              "       '$15.99', '$33.99', '$74.99', '$39.99', '$3.95', '$4.49', '$1.70',\n",
              "       '$8.99', '$2.00', '$3.88', '$25.99', '$399.99', '$17.99',\n",
              "       '$400.00', '$3.02', '$1.76', '$4.84', '$4.77', '$1.61', '$2.50',\n",
              "       '$1.59', '$6.49', '$1.29', '$5.00', '$13.99', '$299.99', '$379.99',\n",
              "       '$37.99', '$18.99', '$389.99', '$19.90', '$8.49', '$1.75',\n",
              "       '$14.00', '$4.85', '$46.99', '$109.99', '$154.99', '$3.08',\n",
              "       '$2.59', '$4.80', '$1.96', '$19.40', '$3.90', '$4.59', '$15.46',\n",
              "       '$3.04', '$4.29', '$2.60', '$3.28', '$4.60', '$28.99', '$2.95',\n",
              "       '$2.90', '$1.97', '$200.00', '$89.99', '$2.56', '$30.99', '$3.61',\n",
              "       '$394.99', '$1.26', '$1.20', '$1.04'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "df.Price.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "1455slHwHJIV"
      },
      "outputs": [],
      "source": [
        "#removing the special characters from price ',' , '$' and changing the datatype\n",
        "\n",
        "char_to_remove=[',','$']\n",
        "for char in char_to_remove:\n",
        "  df['Price']=df['Price'].apply(lambda x:x.replace(char,\"\"))\n",
        "df['Price']=df['Price'].astype(float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WtNTiXOJO8n6",
        "outputId": "5d8bc706-8247-46e5-ee68-8ee6e5d7ca43"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "App               0\n",
              "Category          0\n",
              "Rating            0\n",
              "Reviews           0\n",
              "Size              0\n",
              "Installs          0\n",
              "Type              0\n",
              "Price             0\n",
              "Content Rating    0\n",
              "Genres            0\n",
              "Last Updated      0\n",
              "Current Ver       8\n",
              "Android Ver       2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S3lvmlNvAILU"
      },
      "source": [
        "i. **Handling current and android version column**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64OJ2xAtILWt",
        "outputId": "fb6bb704-7615-4a76-8d1c-aa88be219e12"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Varies with device    1055\n",
              "1.0                    799\n",
              "1.1                    260\n",
              "1.2                    176\n",
              "2.0                    149\n",
              "                      ... \n",
              "2.27.1                   1\n",
              "1.6.11                   1\n",
              "20180517                 1\n",
              "3.1.00                   1\n",
              "2.0.148.0                1\n",
              "Name: Current Ver, Length: 2817, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "df['Current Ver'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PxNb7eKHIL9v"
      },
      "outputs": [],
      "source": [
        "df.loc[df['Current Ver']=='Varies with device','Current Ver']=np.nan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "jVtTbzTiNugO"
      },
      "outputs": [],
      "source": [
        "#Fill null values of current version with mode\n",
        "\n",
        "df['Current Ver'].fillna(str(df['Current Ver'].mode()[0]),inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mFZ3rPC1T511",
        "outputId": "f3de1583-6e0e-4edb-a722-a5ed087dc245"
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              "4.1 and up            2202\n",
              "4.0.3 and up          1395\n",
              "4.0 and up            1285\n",
              "Varies with device     990\n",
              "4.4 and up             818\n",
              "2.3 and up             616\n",
              "5.0 and up             512\n",
              "4.2 and up             372\n",
              "2.3.3 and up           273\n",
              "2.2 and up             239\n",
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              "5.1 and up              22\n",
              "1.5 and up              20\n",
              "4.4W and up             11\n",
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              "8.0 and up               6\n",
              "7.1 and up               3\n",
              "4.0.3 - 7.1.1            2\n",
              "5.0 - 8.0                2\n",
              "1.0 and up               2\n",
              "7.0 - 7.1.1              1\n",
              "4.1 - 7.1.1              1\n",
              "5.0 - 6.0                1\n",
              "2.2 - 7.1.1              1\n",
              "5.0 - 7.1.1              1\n",
              "Name: Android Ver, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "df['Android Ver'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LWt9ZGjVH-VO"
      },
      "outputs": [],
      "source": [
        "df.loc[df['Android Ver']=='Varies with device','Android Ver']=np.nan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "NV-JlOhlWTnP"
      },
      "outputs": [],
      "source": [
        "#Fill null values of android version with mode\n",
        "\n",
        "df['Android Ver'].fillna(df['Android Ver'].mode()[0],inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hqcztoMkW1gZ",
        "outputId": "abbc72b8-1a6f-4f69-cde0-be6eda0e6f08"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 10840 entries, 0 to 10840\n",
            "Data columns (total 13 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   App             10840 non-null  object \n",
            " 1   Category        10840 non-null  object \n",
            " 2   Rating          10840 non-null  float64\n",
            " 3   Reviews         10840 non-null  float64\n",
            " 4   Size            10840 non-null  float64\n",
            " 5   Installs        10840 non-null  int64  \n",
            " 6   Type            10840 non-null  object \n",
            " 7   Price           10840 non-null  float64\n",
            " 8   Content Rating  10840 non-null  object \n",
            " 9   Genres          10840 non-null  object \n",
            " 10  Last Updated    10840 non-null  object \n",
            " 11  Current Ver     10840 non-null  object \n",
            " 12  Android Ver     10840 non-null  object \n",
            "dtypes: float64(4), int64(1), object(8)\n",
            "memory usage: 1.4+ MB\n"
          ]
        }
      ],
      "source": [
        "#Let's check the information of our dataset after cleaning\n",
        "\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nMCu16TrA9CZ"
      },
      "source": [
        "## **Loading user_review dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "6MzOGJ7osezP"
      },
      "outputs": [],
      "source": [
        "#Loading the second dataset (User Review)\n",
        "df2=pd.read_csv('/content/drive/MyDrive/Capston Project/EDA/Play Store Review Analysis /User Reviews.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHXeZB4BBdqA"
      },
      "source": [
        "*Basic overview of user_review dataset*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "FP5kNl_vFmN_",
        "outputId": "5d656237-9d8e-43d6-db8d-5ec95ef7c334"
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              "                     App                                  Translated_Review  \\\n",
              "0  10 Best Foods for You  I like eat delicious food. That's I'm cooking ...   \n",
              "1  10 Best Foods for You    This help eating healthy exercise regular basis   \n",
              "2  10 Best Foods for You                                                NaN   \n",
              "3  10 Best Foods for You         Works great especially going grocery store   \n",
              "4  10 Best Foods for You                                       Best idea us   \n",
              "5  10 Best Foods for You                                           Best way   \n",
              "6  10 Best Foods for You                                            Amazing   \n",
              "7  10 Best Foods for You                                                NaN   \n",
              "8  10 Best Foods for You                               Looking forward app,   \n",
              "9  10 Best Foods for You              It helpful site ! It help foods get !   \n",
              "\n",
              "  Sentiment  Sentiment_Polarity  Sentiment_Subjectivity  \n",
              "0  Positive                1.00                0.533333  \n",
              "1  Positive                0.25                0.288462  \n",
              "2       NaN                 NaN                     NaN  \n",
              "3  Positive                0.40                0.875000  \n",
              "4  Positive                1.00                0.300000  \n",
              "5  Positive                1.00                0.300000  \n",
              "6  Positive                0.60                0.900000  \n",
              "7       NaN                 NaN                     NaN  \n",
              "8   Neutral                0.00                0.000000  \n",
              "9   Neutral                0.00                0.000000  "
            ],
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              "      <td>10 Best Foods for You</td>\n",
              "      <td>It helpful site ! It help foods get !</td>\n",
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      ],
      "source": [
        "#top 10 rows\n",
        "\n",
        "df2.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.tail(10)"
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      "execution_count": 36,
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-65389f81-9542-4948-ba4f-f5ab3f8f252e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-65389f81-9542-4948-ba4f-f5ab3f8f252e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "526FyZfY__FV",
        "outputId": "c9c6d952-48a2-43f5-9903-aceeff5ea25e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(64295, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "#See number of rows and columns of 2nd dataset\n",
        "df2.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lRWSVlNP-_jT",
        "outputId": "22d7880b-40e6-469e-eb03-b4365d3aef11"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 64295 entries, 0 to 64294\n",
            "Data columns (total 5 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   App                     64295 non-null  object \n",
            " 1   Translated_Review       37427 non-null  object \n",
            " 2   Sentiment               37432 non-null  object \n",
            " 3   Sentiment_Polarity      37432 non-null  float64\n",
            " 4   Sentiment_Subjectivity  37432 non-null  float64\n",
            "dtypes: float64(2), object(3)\n",
            "memory usage: 2.5+ MB\n"
          ]
        }
      ],
      "source": [
        "#basic info\n",
        "df2.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "FgJVjNmuEKHT",
        "outputId": "67279254-0948-4fa5-f7d8-6b33527c38f6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Sentiment_Polarity  Sentiment_Subjectivity\n",
              "count        37432.000000            37432.000000\n",
              "mean             0.182146                0.492704\n",
              "std              0.351301                0.259949\n",
              "min             -1.000000                0.000000\n",
              "25%              0.000000                0.357143\n",
              "50%              0.150000                0.514286\n",
              "75%              0.400000                0.650000\n",
              "max              1.000000                1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-319fa255-1b07-4d6f-942f-c9d57cfef8d7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sentiment_Polarity</th>\n",
              "      <th>Sentiment_Subjectivity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>37432.000000</td>\n",
              "      <td>37432.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.182146</td>\n",
              "      <td>0.492704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.351301</td>\n",
              "      <td>0.259949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.357143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.150000</td>\n",
              "      <td>0.514286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.650000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-319fa255-1b07-4d6f-942f-c9d57cfef8d7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-319fa255-1b07-4d6f-942f-c9d57cfef8d7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-319fa255-1b07-4d6f-942f-c9d57cfef8d7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "df2.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3uMGg3mBl7k"
      },
      "source": [
        "*Cleaning user_review dataset*"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dropping all those rows which have null values in all the columns"
      ],
      "metadata": {
        "id": "d7wlGtHnR9H2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "g0_kFDQp_CME"
      },
      "outputs": [],
      "source": [
        "#dropping the null rows\n",
        "df2.dropna (subset=['Translated_Review','Sentiment','Sentiment_Polarity','Sentiment_Subjectivity'],inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5xz2iF24AaXz",
        "outputId": "61ef6a89-4c49-4ccd-e9cb-62d48da4dad6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "App                       0\n",
              "Translated_Review         0\n",
              "Sentiment                 0\n",
              "Sentiment_Polarity        0\n",
              "Sentiment_Subjectivity    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "df2.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pNksqsWICt8e"
      },
      "source": [
        "## ***5.Exploratory Data Analysis***\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lJgKOI91On_3"
      },
      "source": [
        "*Heatmap to depict correlation between different variables*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "7uU1tg66_6za"
      },
      "outputs": [],
      "source": [
        "#checking correlation between variables\n",
        "corr=df.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "3WsPrE2d2Qep",
        "outputId": "45bba3dc-cad2-4c4d-fe1e-27674a95dd1b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3aa2036dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.heatmap(corr,cmap ='coolwarm')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IIMgHYkRO2n8"
      },
      "source": [
        "This shows that there exist +ive correlation between Installs and Reviews"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating bins for reviews column\n",
        "u=df['Reviews'].unique()\n",
        "len(pd.unique(df['Reviews']))\n",
        "bins=[]\n",
        "for i in range(0,80500000,2000000):\n",
        "   bins.append(i)\n",
        "bins"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r66_bqKm5vmW",
        "outputId": "45bd71f7-c84d-4383-9655-ba321f286ccf"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0,\n",
              " 2000000,\n",
              " 4000000,\n",
              " 6000000,\n",
              " 8000000,\n",
              " 10000000,\n",
              " 12000000,\n",
              " 14000000,\n",
              " 16000000,\n",
              " 18000000,\n",
              " 20000000,\n",
              " 22000000,\n",
              " 24000000,\n",
              " 26000000,\n",
              " 28000000,\n",
              " 30000000,\n",
              " 32000000,\n",
              " 34000000,\n",
              " 36000000,\n",
              " 38000000,\n",
              " 40000000,\n",
              " 42000000,\n",
              " 44000000,\n",
              " 46000000,\n",
              " 48000000,\n",
              " 50000000,\n",
              " 52000000,\n",
              " 54000000,\n",
              " 56000000,\n",
              " 58000000,\n",
              " 60000000,\n",
              " 62000000,\n",
              " 64000000,\n",
              " 66000000,\n",
              " 68000000,\n",
              " 70000000,\n",
              " 72000000,\n",
              " 74000000,\n",
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        "s = pd.DataFrame(pd.cut(df['Reviews'], bins=bins).value_counts())\n",
        "s\n"
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        "colab": {
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating bins for install column\n",
        "p = pd.DataFrame(pd.cut(df['Installs'], bins=bins).value_counts())\n",
        "p"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "IG2ruZ6gLZAZ",
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      "execution_count": 44,
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>(78000000, 80000000]</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00f56abb-8b4e-491c-933f-fc45dc99a889')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-00f56abb-8b4e-491c-933f-fc45dc99a889 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-00f56abb-8b4e-491c-933f-fc45dc99a889');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Maximum no. of reviews are **8871** from the range 0 to 20,00,000 having maximum no. of installs as **7666**.\n",
        "Thus validating the conclusion of heatmap that there exist **positive correlation** between Reviews and Installs.\n",
        "\n",
        "**Higher the reviews higher is the installation**. "
      ],
      "metadata": {
        "id": "78ek2vU2MFHw"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uJiNFEnQQo18"
      },
      "source": [
        "### ***To check no. of reviews of apps having 1 Billion installs***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d20116cb-0033-4bdc-980b-f40c79d6549f",
        "id": "iNgDLLePUZtg"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "App         20\n",
              "Installs    20\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ],
      "source": [
        "app_install[app_install['Installs']==1000000000].count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "VkjO7mEnPUSu"
      },
      "outputs": [],
      "source": [
        "#Creating dataset of apps and reviews having 1B installs\n",
        "app_install_review=df[['App','Reviews']][df['Installs']==1000000000].reset_index(drop=True) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "Dm0DJBlUPUSv",
        "outputId": "59661f75-4b77-4cd8-9f31-a133fbf60830"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         App     Reviews\n",
              "0                          Google Play Books   1433233.0\n",
              "1   Messenger – Text and Video Chat for Free  56642847.0\n",
              "2                         WhatsApp Messenger  69119316.0\n",
              "3               Google Chrome: Fast & Secure   9642995.0\n",
              "4                                      Gmail   4604324.0\n",
              "5                                   Hangouts   3419249.0\n",
              "6              Skype - free IM & video calls  10484169.0\n",
              "7                          Google Play Games   7165362.0\n",
              "8                             Subway Surfers  27722264.0\n",
              "9                                   Facebook  78158306.0\n",
              "10                                 Instagram  66577313.0\n",
              "11                                   Google+   4831125.0\n",
              "12                             Google Photos  10858556.0\n",
              "13                 Maps - Navigate & Explore   9235155.0\n",
              "14                        Google Street View   2129689.0\n",
              "15                                    Google   8033493.0\n",
              "16                              Google Drive   2731171.0\n",
              "17                                   YouTube  25655305.0\n",
              "18                   Google Play Movies & TV    906384.0\n",
              "19                               Google News    877635.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e78a3a11-6bf9-4427-8af1-921fcf9fc816\">\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App</th>\n",
              "      <th>Reviews</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Google Play Books</td>\n",
              "      <td>1433233.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Messenger – Text and Video Chat for Free</td>\n",
              "      <td>56642847.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>WhatsApp Messenger</td>\n",
              "      <td>69119316.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Google Chrome: Fast &amp; Secure</td>\n",
              "      <td>9642995.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gmail</td>\n",
              "      <td>4604324.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Hangouts</td>\n",
              "      <td>3419249.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Skype - free IM &amp; video calls</td>\n",
              "      <td>10484169.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Google Play Games</td>\n",
              "      <td>7165362.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Subway Surfers</td>\n",
              "      <td>27722264.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Facebook</td>\n",
              "      <td>78158306.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Instagram</td>\n",
              "      <td>66577313.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Google+</td>\n",
              "      <td>4831125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Google Photos</td>\n",
              "      <td>10858556.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Maps - Navigate &amp; Explore</td>\n",
              "      <td>9235155.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Google Street View</td>\n",
              "      <td>2129689.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Google</td>\n",
              "      <td>8033493.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Google Drive</td>\n",
              "      <td>2731171.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>YouTube</td>\n",
              "      <td>25655305.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Google Play Movies &amp; TV</td>\n",
              "      <td>906384.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Google News</td>\n",
              "      <td>877635.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e78a3a11-6bf9-4427-8af1-921fcf9fc816')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e78a3a11-6bf9-4427-8af1-921fcf9fc816 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e78a3a11-6bf9-4427-8af1-921fcf9fc816');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ],
      "source": [
        "app_install_review"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "Um8Ix-MKPUSv",
        "outputId": "6a76dd80-4af7-4e18-d195-73d34c02efd1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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8IGnl9NP7gNurqi8IgiAYTNVeNHsDf0weNHcDu1dcXxAEQZCoVMGb2XRgcpV1BEEQBPWJmaxBEAR9Sij4IAiCPiUUfBAEQZ9S9SDrqGDSfmeX2v/eQ7aoSJIgCILOES34IAiCPiUUfBAEQZ8SCj4IgqBPCQUfBEHQp4SCD4Ig6FNCwQdBEPQpoeCDIAj6lFDwQRAEfUoo+CAIgj4lFHwQBEGfEgo+CIKgTwkFHwRB0KeEgg+CIOhTKo0mKeleYBbwKvCKmUV2pyAIgi7RjXDB7zWzx7pQTxAEQVAgTDRBEAR9StUK3oALJE2TtGfFdQVBEAQFqjbRvNvM/i1pCeBCSXea2eXFHZLi3xNg4sSJFYsTBEHw+qHSFryZ/Tt9PgqcAbyjzj5HmtlkM5s8YcKEKsUJgiB4XVGZgpe0gKSFat+BDwC3VVVfEARBMJgqTTRLAmdIqtVzgpmdV2F9QRAEQYGGCl7S24EHzOzhtP4J4GPAfcABZvbEcGXN7G5gzQ7KGgRBEJSgmYnmN8BLAJI2BA4BjgOeBo6sVrQgCIKgHZqZaMYWWuk7AEea2WnAaZKmVytaEARB0A7NWvBjJdVeAu8DLi5s68Ys2CAIgqBFminpE4HLJD0GPA9cASBpBdxMEwRBEIxQGip4MztY0kXAUsAFZmZp0xhg76qFC4IgCFqnmRfNocDVwFVm9lztdzP7e9WCBUEQBO3RzETzT+DDwI+TP/vVabkKuNnMXqtWvCAIgqBVmplofgn8EkDS0sC70vIlYAlgfNUCBkEQBK3R1BNG3nRfA1fsGwCr4S37P1QrWhAEQdAOzWzwF+Kt9OnAtcAPzOyObggWBEEQtEczP/i7gdeAFdOygqTFK5cqCIIgaJtmNvi9ACSNB9bHzTT/I2kCcJuZ7Vq9iEEQBEEr5M5GfRH4Lz7Z6UVgWWCeqoQKgiAI2qehiUbSzyVdB/wH+B6wEPBrYGUzW6ML8gVBEAQt0qwFfw9wPDDdzF7tgjxBEARBh2g2yPqEmU0zs1clbVDcIOnzFcoVBEEQtEkzBb9P4fvhQ7Z9ssOyBEEQBB2kmYLXMN/rrQdBEAQjiGYK3ob5Xm+9LpLGSrpJ0lmlJAuCIAjaotkg6yqSbsFb68un76T1t2TW8UXgDiJuTRAEQVdppuBXbefgkpYFtgAOZrA9PwiCIKiYZjNZ72vz+IcC++L+80EQBEEXaRZsbBb1be0CzMyGNbtI2hJ41MymSdq4wX57AnsCTJw4MUfmIAiCIINmLfh2Wt4bAFtL2hyYDxgv6Xgz22VIHUcCRwJMnjw5a+A2CIIgaE6zUAVnS9pZ0gJlD2xm3zCzZc1sErAjcPFQ5R4EQRBURzM3ySOBLYF7JZ0s6SOSIshYEATBKKChgjezv5jZFGA54DTgE8D9ko6WtGluJWZ2qZlt2Z6oQRAEQRmateABMLP/mtlJZvYR4APAWsB5lUoWBEEQtEWWgpe0pKS9JV0F/Bk4H1inUsmCIAiCtmjmJrknPkC6Mm6i+ZqZXd0NwYIgCIL2aDaTdX3gh8BFZvZaF+QJgiAIOkQzE83FZnahmb0W8eCDIAhGFxEPPgiCoE+JePBBEAR9SuXx4IMgCILe0I148EEQBEEPqDQefBAEQdA7WooHL2kMMAVoN158EARBUBHNokmOl/QNSb+U9AE5ewN3A9t3R8QgCIKgFZqZaP4APAlcA+wBfBO3v3/YzKZXLFsQBEHQBs0U/FvMbA0ASb8D/gNMNLMXKpcsCIIgaItmbpIv176Y2avAg6HcgyAIRgfNWvBrSnomfRcwf1pvmpM1CIIg6C3NvGjGdkuQIAiCoLNkxYNvBUnzSbpe0s2SZkj6XlV1BUEQBHPSzETTDi8Cm5jZs5LmBq6UdK6ZXVthnUEQBEGiMgVvZgY8m1bnTkvErwmCIOgSlZloACSNlTQdeBS40Myuq7K+IAiCYIBKFbyZvWpmawHLAu+QtPrQfSTtKWmqpKkzZ86sUpwgCILXFZUq+Bpm9hRwCfChOtuONLPJZjZ5woQJ3RAnCILgdUGVXjQTJC2Svs8PbArcWVV9QRAEwWCq9KJZCjhW0lj8RXKymZ1VYX1BEARBgSq9aG4B1q7q+EEQBEFjumKDD4IgCLpPlSaaIKiESfudXWr/ew/ZoiJJgmBkEy34IAiCPiUUfBAEQZ8SCj4IgqBPCQUfBEHQp4SCD4Ig6FPCiyYIgr4kvK2iBR8EQdC3hIIPgiDoU0LBB0EQ9Cmh4IMgCPqUUPBBEAR9Sij4IAiCPiUUfBAEQZ8SCj4IgqBPCQUfBEHQp4SCD4Ig6FOqTLr9JkmXSLpd0gxJX6yqriAIgmBOqoxF8wrwFTO7UdJCwDRJF5rZ7RXWGQRBECQqa8Gb2X/M7Mb0fRZwB7BMVfUFQRAEg+mKDV7SJGBt4Lpu1BcEQRB0QcFLWhA4DfiSmT1TZ/uekqZKmjpz5syqxQmCIHjdUKmClzQ3rtz/aGan19vHzI40s8lmNnnChAlVihMEQfC6okovGgG/B+4ws59VVU8QBEFQnypb8BsAHwc2kTQ9LZtXWF8QBEFQoDI3STO7ElBVxw+CIAgaEzNZgyAI+pRQ8EEQBH1KKPggCII+JRR8EARBnxIKPgiCoE8JBR8EQdCnhIIPgiDoU0LBB0EQ9Cmh4IMgCPqUUPBBEAR9Sij4IAiCPiUUfBAEQZ8SCj4IgqBPCQUfBEHQp4SCD4Ig6FNCwQdBEPQpoeCDIAj6lCpzsh4l6VFJt1VVRxAEQTA8VbbgjwE+VOHxgyAIggZUmZP1ckmTqjp+ELyemLTf2aXL3HvIFhVIEowmKlPwQX9TVuGEsgmC7tPzQVZJe0qaKmnqzJkzey1OEARB39DzFryZHQkcCTB58mTrsThdJ1rCQRBURc8VfBAE1RMNidcnVbpJnghcA6ws6UFJn6qqriAIgmBOqvSimVLVsYMgCILmhIkmCILKCPfO3tJzL5ogCIKgGkLBB0EQ9Clhogm6TnTbg6A7RAs+CIKgTwkFHwRB0KeEgg+CIOhTQsEHQRD0KaHggyAI+pTwohnFhDfK6CLiwQTdJlrwQRAEfUoo+CAIgj4lTDSvY8JkEAT9TbTggyAI+pRowQdBMGKJXmZ7RAs+CIKgT4kWfBAEDYlW9OglWvBBEAR9SqUteEkfAn4BjAV+Z2aHVFVXtDKCIAgGU5mClzQW+BWwKfAgcIOkM83s9qrqDIIg6ATtzhIfKQ3OKk007wD+aWZ3m9lLwJ+AbSqsLwiCIChQpYJfBnigsP5g+i0IgiDoAjKzag4sbQt8yMz2SOsfB9Yzs88P2W9PYM+0ujJwV4dFWRx4rAdle1n3aJW7l3WH3K+fuker3MOxnJlNqLvFzCpZgHcC5xfWvwF8o6r6GsgxtRdle1n3aJU7ztnrR+44Z91ZqjTR3ACsKOnNkuYBdgTOrLC+IAiCoEBlXjRm9oqkzwPn426SR5nZjKrqC4IgCAZTqR+8mZ0DnFNlHRkc2aOyvax7tMrdy7pD7tdP3aNV7tJUNsgaBEEQ9JYIVRAEQdCnhILvEJLGSNq+13IErx8kLSrpbb2WIxi59J2Cl7SBpAXS910k/UzScpllx0n6jqTfpvUVJW2ZU9bMXgP2bVlwr+/dknZP3ydIenM7x+sGkraTtFD6/m1Jp0tap0T5dq7X8pLmTd83lvQFSYuUqHuO8yvp7bnle4GkSyWNl/QG4Ebgt5J+VqL8kpJ+L+nctL6apE9llr1S0sGSPlS75t2k1eej1eda0jnpnlywDZnbej7apps+md1YgFsAAWsCNwH/A1yWWfYkXEnfltbHAdNL1H0I8FXgTcAbaktm2f2BvwJ/T+tLA1dllPtoo6WE7AsAY9L3lYCtgblzznf6fDdwKbAFcF2Xrtd03FFgBeDvwE+Ac0rUfSOwTGF9I+DWEuW3TDI/ATwDzAKeKXm/7lpy/5vS5x7A94rXILP8ucD2wM1pfa7c/wy8GfgE8FvgVmAq8PMSdQvYBfhuWp8IvCOzbEvPR9q3pecaD61yIvAocDLwEWCekterreej3aXvWvDAK+ZndBvgl2b2KyC3tbG8mf0YeBnAzP6L35S57IArqMuBaWmZmln2I7hSfS7V/RB5cm/VYMnqfSQuB+aTtAxwAfBx4JiMcq+mzy2AI83sbGCeEvW2c71eM7NX8HN3uJl9DViqRN17AX+W9EZJmwOHAZuXKH8osCuwmJmNN7OFzGx8ifIAXyy5/1ySlsKV9FklywIsbmYnA6+BuzMzcA0bYmb3ABcCF+H3yzhg1RJ1H4FPgJyS1mfhAQlzaPX5gBafazP7i5lNASYBp+Evt/slHS1p08y6230+2qIfE37MkvQNvKWwoaQxwNyZZV+SND/gzQ1peeDF3IrNrB2TyktmZpJqdS+QWefubdRZRGb239RdP8LMfixpeka5f0v6DR419EfJZFKm4VC7Xh8H3lPyer0saQquZLdKv+WWxcxukPQF/IX2AvB+M5uZLzoP4K3CbrqiHYjPLbkqyf8W4B8lyj8naTEG7vH1gadzCkr6Fz7N/gTg98De5qbJXNYzs3Uk3QRgZk+mSZA5tPR81Mq2+Vz/F+8FnJTGPI7Flf3YjOLtPh9t0Y8KfgdgJ+BTZvawpIl41z2H/YHzgDdJ+iOwAbBbbsWSxgH7ABPNbE9JKwIrm1lOS+vkdCMsIunTwCfxrnCzOncxs+Ml7VNvu5nl2mcl6Z3AzkDNJptzA28PfAj4XzN7KrUuv5ZZJwxcr0+2cL12Bz4DHGxm9ySb7B+aFZL0V9LDnhiHK7nfS8LMts6sf1/gHEmXUVAYzc65pEtS/QJWkHRx+m5mtkmjsmZ2CnBKYf1u4GOZ8oLfn2cCy0u6CpgAbJtZ9jDc1DAFWBu4TNLlZvavzPIvpzDiNUU7gdSTyKCl5yPR7nO9JH6f74j3EE8uUb7d56Mt+s4PXtJmZnbukN8+Y2a/ziy/GLA+/sBda2bZgYEknYSbZT5hZqsnhX+1ma3VpJyAZYFVgA+kus83swsz6tzLzH4jaf96283se5myb4iPH1xlZj9KLcMvmdkXhtn/DY2OZ2ZP5NSbjrUcsKKZ/S2ds7FmNqtJmbHAcWa2c249hbIbNdpuZpdlHucC4FncHj1bUTU754VBZAFnUzALmdl9TcouCxyOKymAK4AvmtmDGfKOBb6Qyq+c6r/LzF5uVnbIcRbEX65fBZY1s5yGAJJ2xl/o6+Ct4G2Bb6eXVk75TSn5fBTKln6u04tkCn6uTgP+ZGZX59aZjnEQbs662syeK1O2I3TL2N+tBbga2KSwvi9wbmbZlgeB0v5T0+dNhd9uziybPbhX0XnbLue3wrZ7gLvT59Dl7hL1fhqPW/SvtL4icFFm2SspOehV5xhL4mMVWwJLlCx7WwfO+40l978QV65zpWU34MIS5a9vQ9afAtcBM/DW867AW0oeYxV8nOrzwKolyu1DYUC8Bdk/Cvws/YePZJa5GDetjGmj3t2Bo4A7gOtT/du0e9/kLv3Ygl8cH3z6Gt41WgWYYp50pFnZ/8NbYpuY2aqSFgUuMLMs1zlJVwPvw1vB6yRb34lm9o6Mssfig4w35NRVp/x8uGnlrcB8td/N7JOZ5W80s3Wa/dZpkp3/Hbhnwdrpt1vNbI2Mssfhg3xnkgbfIN8sJZ+38BPcu0HAe4CvmdmpmeV/DPzNzC7I2X+YY5xuZh8tsf90G9IjrPdbg/I/x8cpTmLwObsxo+y2wBVm9kiuvKlc27291EPdHvdYOgk4JVcOSUfgnlYnpp92wBsU/9OkXMfuf0lvxOX/KrComXXFzbTvbPBm9pikrYG/4eaSbS3/LdbOIBC0Z+tbD9hZ0n34g1ezyeZOZPkDcCfwQXwgbme81dAQSZvhJoJlJB1W2DQeeKVBuYY3fo7CSLxoZi+5lQokzcVg+3gj/pWWMeR7VBT5FvB2M3s01T0Bv2+yFDzwWeCrkl7EPTRq1yzbk6aMck88LmkXBpTVFODxEuVrL4IDi2IADW3/idOBnSS92cwOSuMlbzSz65uUm8bAmEOxTqXPtzSr2Nzs9b00yLkDbv9/0MzenyH3JnhvoWb7PxbvhVSOpN8BqwGP4Oa0bXH33K7QN6uKILgAACAASURBVApe0iwGK4Z58BtnW0m5D107g0CY2YWSbmTA1vdFy7fhfzC3nmFYwcy2k7SNmR0r6QT8hmrGQ7gr59b4g1hjFvDlBuV+2mBbrsIAf1C/CcyfbKyfw/2dm5IeeiSNM/d0KMuYmnJPPE4JD4d2WmFp3GVn3MRxYAll+Unchv5z/DxfTYkBQzN7b2sSA+7S+Bp+bQ/C75HTgIY9XGvPu2wojwIP49dqicwy/8TNrbXxjTel35qx7JBGzyBsmPGpISyGOys8hfc+HjN3Te0KfaPgO9TlOQw4A1hC0sGkQaDcwoVW7X/S50RJCwP3ZVzUdm1ltYGypyStjj8ETR8AM7sZuFnSH8vceG0qiiL74aalW3G/9HOA3+UUTF4/vwcWxM/1msBeZva5zLrPk3Q+g7vupaKfJjPeigw2i12eUfQIBpTlgWQqS3xQc5CXj6QNGJwes5G8C+M9zQ3TT5cBB5pZjqtkWz3cYXp9T5PxfEj6HG7imIB7EX3azG5vUqbmLbUQcIek69P6erg9vBnPM7jRUxoz+0iSZVW8EXeJpLFmtmw7x82l72zwAMlEU7uBL7UMN0W5//X6+Fv2fXgL/CIza2rmKBzjWtxDoDY7c3W8K7gw8NlGtlpJtzLQbZ0PnzV4l5m9NbPuPXAF8TbgaFzpfdfyvYfuoc5Lxsyadp/TC2U1Biu543LqbQdJ1+Ev4TML9vvbzGz1Esf4KO76B25fPqNE2T3wiUrL4rNq1weusSaujqnsjTVlWZD9ZjNbM6dcs98alD8NuA33YgGff7Bmjqkone93ATck2SfgY1RrZ9Y99PlYI8mS83z8EDjJzHLmZtTKtOUt1QkbvDwkwntwfbQIcC1+nx3VznGz6dZobrcWPFzARXhX9pO418EPM8ve1GbdpwNvLayvhttz30KJkAep7DrA77p43hYrLMsAX8Jbds3K7Q9cgtsYj8Z7DqeWqLfl6f6kKd+04LVU2H9JBmb9lvWiuRV/qU1P66sAp+fKjnfdb0zrExrdf/gM0K/gLfV9CssBZf5zvfsw997ETUpnAg8CB+P5k4f1tKpTvvTzAYxPn2+ot3Tq/h+m7ms7cIxf4j3DpauUdbilb0w0BTYH1rI0wy4NqNyE54RtxkWSPoY/pK10bVayQtYqM7td0ipmdndtEDEXM7tR0nq5+8uDbH0Cn1Y9+7panp0QMxs6UHeopGnAd5sU3ZYUR8bMdpdPCjk+V258uv9HcTfRsuf8AUnvAkzS3HhrukyPa6gXzeGSsr1ogBfM7AVJSJrXzO6UtHJm2bLmwHnwXtlcDB5Qfob8iUoAz0t6t5ldCbPNO8/nFDSzP6Z7otbD/bCV6OHS2vNxAv7yHW6gNqeHWRyfmwf3InrOmozLmdn6zY7dDDP7vHzew2rAQ/IZtXNZk3kenaIfFTx4V6jmerVwiXJ74a2iVyS9QHmviBlyV8s/pfUdgNvl05MbTibR4JmoY/AW/EMlZD8H7/4NmnSTyxD76BhgMnn3x/Nm9pqkVySNxwfB3lSi6nam+38G+AXe4/g3HnKgoevbENr1onkwvVj/DFwo6UkGBvIaUlZZmpsTLpN0jJndpxTh0MyezZS1xmeBY5MtXvhzslujApLGm9kzyd3xUQbGLJD0Bsuf1Fb6+TCzLdOA9EZmdn9mPUOPMfuFmI61DW5Oqxz5ZKk98R7H8rg579f4da++/taeq5GLPDbJIbjZQLjtaz8zO6lBmfXN7NoO1D0/7gVSs+lehQ+mvQCMa/QwavBM1FeAe4HTzOyFzLrbshfKp88Prf9/zeyuJuWOAL6JT+P+Cj6zc7plxsiRh+c9CB/sy5ruL+lHZvZ1SdtZ5izIYY4zyN8+jcPcbBk++HWOtRHemDjPGsy5UJs+4Wm84w+4wgCPDbOrmd1WUt7xqb5nMvY9KynaoeM0tQZQ01Z0Ok47z0fW3IhcimMfVaI25nl0pP5+U/AA8ngPNW+E683s4Sb7z1aOkq4xs3dWLWOnkfRlXLmexWBFmR0yoAMyTMJtpreUKFN6un8akH4bMK3Nl9pP0nGKXjS3mNnXM8qOBWaY2Sol66wpybo+4c2UpXwy3bfM7JK0vjHwAzN7V5NydWMVzRag8Qv1jc2eoVyS183K+H/ODpOgFiYCSprLzF5JA+k1ar3Tjco+55JWs+S5k9solHSdma1Xe6HI53ncaPnzW9qiX000b2fAi8Zo7lddfNjmG3avZgdxe+YBwHIMtoPn2AkvxAesnkrri+KxL3L941/C7cnfYqCVlWWjTPW15D4n6SPAxWb2tJndK2kRSR82sz9nyr20lfB6SZwHPAksKOkZBibMlDKpmdnXhnjRHGmZXjRm9qqkuyRNLGM6sPZ9wheoKfd0vEuVF1mxHTfi6ZJuw+3hpzW7J4YjvYyOxXuHwicE7mp5bqXrAbtIupf8iYDX46bOrQq/1Xqn25QUH+DH6bn8Cx6Pf6WMMpepxXkenaDvWvCSDsEV/B/TT1Nwt65vNihzM7Ax/na/OH2frfRzW8GS7sQnB02jEGO7zgBmvbL1pqBndyMl3Y3HzckOjjakfEvucx2Qu+Xp/pL+YmatPKjFY7wRVx6v4fdJdktV0uV4VMXrGTztv2k0SrXoEy7pDHwmZC1q5i7Aupb8rasg9Vbej5vhNsfHek4E/mJmWQO06TjTgJ1qZj9JK+GhPNbNKLtcvd+tQXC2ds0wqUf6RNGMJWlv4H/x/3FaxjHG4PM8ZgdJw73juqJ4+1HB38JgL5qxuIfHsG/61Cp4jcEt+RplbIzXmVm258uQstPwIEj3p/XlgDNyTRDJ1PFha21G53CKummME0m3DD23ZWyMcg+HBXCzUvZ0/3Rd/2ZtTLiS+7F/F3+pC8/odKBl+ihrGD9ry4hGqRZ9wlML8nsUfPeBA8zsyUyZ34IPTK+P93quAb5sHnY4p/w8wGa4sn8vPlckK6LnMPfKHL8NU3YN3A0V4I6cMQdJD+IBxurSyCyVyk/D41I9nda/gJvx9gB+ZRnzHXpNv5poSnnRmNmkDtV7SbLrns5gO3hO7IlvAVfKY4vXAl/tWaLu5/Cu9CVD6s5yk6R197mp8pygtcw8/0OJ2X/W4gzkZCJ5TdLCrZoM8IB0a9d6WPKQslfj0f9yZMgKKzwMD+E5C2akulfDZ7Tui98/dRV8UuS517QeJ+DXqtbi3xFvjWc1TMzjBt2Ou6OuS7mMTlPlsVlqbrQ70yTjWTId/gX3zJr9MpR0Px6VsdEg8VjctbScj/IA8xSU+w/w3tqm5olxGuoVDcT8r4eZWXjRtIJa8KLpYN2X1PnZct/08kiYNfetsrHod63VV/zZzI6tt3+d8mvh5plB7nPmoQwalVsA+A7ehTd8YtnBViL2tVqc7i/pL/hDdyGDTSRZCjANWG5syesltU4vbTZgWSg/NP4RuJllKvCVRq1i1ZlxW/utUc8pmTW+ypzzHXLvsXqt6JwZtG/CXwZT8B7XifgY0Z059aZjzIs3AIq9jyPMbNjsSvJYMC8B+xZ65WPwZ3x+M9u7Qdl2PcvOwGPILIvfZyub2ePysAPHNzItSaq3bX38Bf6oZUaobZe+U/BQ3otmJCC1FnxK0jZ4fJJfpfXr8VmRBnzdSroRqoT7XCdQe9P9d633e7OXmgY8StbCTSN/wc/XNrgXzW6Zsh+Ez+o8AX8p7oj7Ot+Im1k2blD2JPwlWvQJXxwf+7hyOAWQxot+zZzjPFm9Jkk/wgeo/4T/5x2ARUlZtOqNN6UX4TJ4JqM/5dY1TP2lvGhSb+FtQ8clkjfKrWY2bA+iAzb4eYHt8BfM3fhM7Zm4qWhXy0w4kkx538EbMAfbkIREVdJXCj5d9FfNzFKLYz087vNNXap/SeAHuGfIZqnb/U4z+31G2ZZi0cvTru1oZg+k9el4AKsFgaNzu4Kq70b3NO6KmB3/oyxyl8e34z2WtSStgrv9lQ2jW6bO/Rttt/wsWHO0fGut72atYrXoEy5pWs6gZIN672mwue54kzzb1xXtDgyqjhcNriiH7a016c00HCNSmoSVGktzYCUnTslzLqwB/MOSt1uT/T+Iz05+EVfs9Xr4ldI3Nnj5jLEfAc+mltXX8JbU2pKOMrMfNSnfkl/zEI7B3/LfSut/x5MTNFXwtB6pb56ack9cmVphT6hcYuLJaam5cG2J2zw/I+kU86z0VdDydH+1GCBtqAJX6+GG/ysPd1Cb+botrqCpJ9cQGZ6XdDhuax/amp1DuWtggtRf5ZEVz6CF+Q7Wgptmjrksk58CH7AhXjS4LX845pO0NnPa0QXM26iywjk5mzqB/PDkONmYTzrM8sOXdAPek/4JPpA9yHMqc1yubfpGwePBsZYnhQYFljNP/jEOvygNFby16Nc8hMXN7GRJ30jHfEXSq80KJVqNRb9occXMPl9YnZBZN7iJZJ1ayzG1cs/GxzCmAVUp+Jan++MvpBrz4d3phjNFi6j9cMM74x4pR+DX7VrcV3t+PCVdo7o3ppxP+FCzSDFxc5n5DgfhXjevpvXxwC8sc+Zxm8xthZnRZvZ3eQyhRvyH4T1hskyvNsSjKyna3GvcKs/hL+pt8aToQye2dccDx3oQ4ayKhQYRBcmMEoknx52FR6M8s7aUkOFSPBpjLULg+sBlmWVbitSH+/t/us7ve+E+xrmy34k/gLX1eYE7m50//MVwBm6bfBQPWbxsi9dwIzzxSMt5VnGTUu6+1+FmguK903ae1Vw58UG72vpKZWRvo94fprrfhucbvQv4fGbZN+f81qD8UXis/43T8jvgqG6c7zqy9DQHcreWfmrBz5+6cmOAeQrdulq3LIfvtCnDPriSXj7ZxieQGenPBgefgvxIfV8G/ixpJwZSga2LK+gPl5D9j8B1yTMFfPbfCcnM0yixwtH4ION2aX2X9NumJepG0jJ4wm7IDJam1gOkzcbMHtDgSIa5Pa6aTbbVPLilWrOSVsS7+yvgYR2+amb/zpW1UM83JP0Nf7k9CWxoZjnZjcBf3kO9Uk6lsYmlyGdxL5qa58sVeO+nUtRmID95buUHzezF1PN6G3CcZdjhe03fDLIO46I4G8ucEJMGSoseOI822r9O+blwLwGR5yUwDni5tl+yP2+Oz2g8vUS9mzBgU5xhZheXkTsd4+14QgfwxOENfZRTmVYnSH0DV3IHpvX7cZe0eYBjzeyHGXW3FCCtUP5UvPv/S3xA/ovAZDPbMbP8KXjPZycKeXDN7IsZZY/CX2RFn/Cxw70cJF0BHIf3MrfGB+9LD0SnAdP/S/WugZv4PmVmwyq8NPD9VtxMVzQNjceTlDe0ZTfx9NrX8sMzt4TaD+Q3HW88TMKjtv4Fj2u/eWclrYBedyFG0oKnBLsPt40eh7coty1RfjtgofT92/iElXWalLkcWDF9XwF3nTscNxNlJSrp4P8fCyyN56+cCEzMKHMR3mofm5Zd8NmNzcrdiMdVqa3fVJDhyi7938XxnssjuHnpeGCxEuVrMt+SPucmM0kE3sPaJ90jp+M9sXkb7D99yPqNLf7n64HVCusfJZniGpTZBu+VPZ4+a8thwLsy6rwKeFPxv+BjJRNz7pUOXu8FgQVbKFczuX4N2Lt47Uf60nMBRtIC3Ewhqw/eyiiTLaf2oL8bn2i1BSnrUIMytxa+H4RPgQZvyXbNToh3mx/DUwzegpsBbskotxxulqrZ4P+c+WK4ccj6boXvDW3RuPloucL6d9O1O5MSNuEOnLPr0+fleHrGxYG7S5SfB29Fr05h/GOYfe/EJ9usk5Y7iusl6hxb57eslxrea2jlPN0wZP2Xhe+5L0ThjYfvpvWJeOylnLKr40l/7kvLNGD1EvJfh0/wuq12f5E5VtOO3B25R7tV0WhYhipU3F6XrWQZaNH9EA9G1PRNX1SieEvnw4X1Uunn2vzv/8x90DtU39/rKTW8ZfuPZucM9xUHd+f8O24H3gM4v4QMxwKLFNYXpcSgX6pvUdzT6O70gtsrs+zGSdlcll4Q9+D28OH2v6TBcnFGfYcWvn9xyLZjMmVeCe+x3ZbW3wZ8O+fearDtX5l1/x8eYuGOwrW6IbPs1cB7h5z7q0tc59Xw3sqUtP5mfBJhpXJ3YulKJaNlwQexzscz3OwGnAv8qET5s4DfpId9kaSsGipp3Czwv3gX/ZGC4lqkywr+EjyVWNlyrT70P8C9KsYVflsA7/o3NE0Vz0s6xtcL69mmC+q8fOv91qB8y14ldNmLpnhehp6j3HOGv4zeQUmvIzrg6cWAmaR0/t16+5V9toD5i9er7HlvRe5OLGPoMySdLmkLebyK3DLzgscHxxX029JypGUkfyiwPf6C+KD5CPsbGDwoVY9P46aRSfgkkNqEm9Vwxd8t7gYulfQNSfvUloxyv8Xz3b4MYJ7sI2eQ8jt4i/d+SdOSB9G9+EuumTeTJC2YrvH78BdMjTLx/MekGcO1g76Bcl449cLF5g4YzuFFg9vwq0LDfC/DOJszdMawoY0LfBnYXdIlkn6alkvxRtSXMusuPU9E0jmS3gzcLek7kial5dv4/Z6FpK3wcYPz0vpaks6sSu5O0k9ukjWOAHYHDkteDkdbc6+Ka4B1JP3BzD6OD3q1wlLA2TbEnapRAfN42ofU+f1qvGvZLe5PyzxpyWWcmV0/xNWw6UNvPtFmP0nfwweXwbvyOREsD8UfuGfwru9UgOQa+58Ssv8UuCbdJ8JdWg9uVqjgVbKwBmcLGk/+C2ZoZMVdaBJZsU1qL7Mxhe+1izY28xiPJZfBmrLalozzbe6J9q4hnl5nWzlPr7JJysF7g+fjsfOXYuCFfAWuI3I5AO+5XApgZtPlYZerkrtj9I2b5FDk4Tyn4GEDHsBbmsdbHbdFebaaH+CDnHO0uC3TXXFUu1O1iKRz8Vmbp5iHWtgWd7vbrOJ6lwGWwLu7tSiDS+Et4+yZyJLeisc1B7dlN/L5r5XZBp9jsDU+sFtjFh6Mq+mLWQORFTdIP9UiKw6bz7Ud1IGcB0mpHYm70j6JjxvsYmb3dk7ShvWvwkCS8ossY56IPDn5d4AP4Yq+pvDMmsSDLxzjWjNbX4XgZcqMY9+q3J2iLxW8PK73LnhkvodwG+C7gTWsToQ/Se/G/ZC3Z/ADC34j5ExcmR2eVNK+wPNmdri6lNy3XVLXcV/mnLTTcEp1rx/6TiBpCQb/56wXhKR3mtk1Jetqyydcai3qaCdJk9/GmNmsLtTVbpLyeYD98LkKtQiatbK5QeV+j5sB98PDDnwBb0h8piq5O0a3jP3dWvDu0O24XXipIdumNin7qTbrbtmdapjjzdfF83YBPivzDjxkwFGUG2BegDQHYLQseAv8H3jckHvwFu6MEuV/jJtl5sYVwEz85daoTFs+4fTQKwNYEo/dc25aX63dZyajzntwe/k9haW23tAlFW+1346bQMe1IcM43HR3A25GO7jZs9mO3B09f92qqGt/qOAO1YO6W3anKhzjBnwCzFL4bNJuyT4tfRbdNodVHEnGYZcS9Z6OzxcY04PrdTMeO6jm3vpe4Pclyk9Pnx9Jim9hmntNteUTTg+9MnCvsu1r9eFjeCM2pgtu9nprr+Xo5dJ3g6xmdomk1XFlW+x2Nxzs7FDdt0v6Ot4aw8zuoUkUyzpshtu078Mz93SL2tjEfyRtgZu2GnUza6n2VsZDO9RMW1vhsyVzaWVQHABJP8X91meUqK/Iy+YZesZIGpPunUNLlK95vWyBj0E8PWSwuR7tRv/spVdGO9FS20ItJCk3s/e0WeehZvYlSX+lfljqypKrd4xev2E6vQD74z7dj+Cj6A8Dp3ap7q3w6Hz3pPW1aBKNMsm4XGF9eXw26ffx7OvdOm9b4i3Q1dP5mwZsnVHucgqmGVzxX95C/QsDn8EHxK/GlX6z2Z174CaP61LZhUvW+Td8+vrheFzyX1BuAswh+AzTm3BlP4HmM5fb8gmnxaijdY5TDFewfmaZS2kxWmoH7s9r8cxKU9O9+RIe7uJfuHtxFXWumz43qreMVLkH1d+Ni9PNBZ9iP4aBbuSSwIWZZeewgdb7rUH5aUlRZU8EYfCknXXTA7tBWr++1+cz4z/fRSGGCj65666Sx1gMD/Q1NSmvHZLSvTSz/MpJ2d6HR7ZsaKYjhVLAxw3G4KaGXfHBs1KzefFeztj0fRw+4Nlo/yXwF9gluJvmT5PivAZYMrPOVXAPnM8Dq7Z43c7CX477An/PLLNOKvN0+vw7nk6vG/fZ6RTMLXgP/VQ8Dv70iuv+KA3iBI1Uuc360ESDe6+8JukVeTKDR/GY38MiD/s6Dlh8iH/weDwXZS4v25zd9GbdZ0sR/ibiLbLNzWxGGv1fqHHR9pFnFRrWlcqaJ7A+DrhenqAY3H3wmBL1n4Er6D8AW5lZza/6JEk50SzH4gpvFXzC2M3APpL2suGjQv4Zj9/ynKTTzOxjeNiCVlgFmCSPIlpjWHOgtegTPsQr41G8xzF7mzX3JpkEPGEp166ZbSlpb3wy3U6NyqbyYxlovWZHS+0gK1nBFGduDl3FzO7OMIu1y1bAzyVdjmdoO8/yzSu9lLsvFfxUeYag3+It6mdJKbMasBc+o27pVKZ25p/BQ8nmMkMel32sPH73F2g+WWkvXLG/hLdev5xupB2Y02WzCopK9Hu4iSsbMzs4+cLX7J27W7kcuIfZMLkqzWxyvd9rSPo5blq6GM/jWrP9/0hSIzt+8cnKnbBSr/4/4Ca16QzEkTeaTG4DSAq9zESfaQyknZt9mLRuNP8fp1HIIiTpC/g9thbulVNvVm5R3lclTTGzn+MmxG4zQ563uJik/PY0n6DSl4yZ7S6P1b8Z7iX3K0kXmtkeGcV7Jjf0qR98jdRqGW8+fT5n/73N7PA26huHT6z6AP7gnQ8cZJlxp9MxtgY+iNt1f29dvEBlfPYljTezZ4bz923WohxyrJYGxSXtDpxsZs/V2bawmT09TLkbzWydod/LIukO3JY94h8iSbdaSl0n6Qd4JMqPmdl/lZnIO71Q58ZbsbPPuXUhv6haTFLeYRnmxl0vd8cDwy2eUaancveNgh9mtHo2uTdhrzxwRgJllJ2ks1I3/x4Gm3hE5szIdJz98eh+q+GzfzfD48FnZcJKJrUVGXy9GiaJTp4fzyVZ5wdq8X9qso/PrPsU4AsFs1LltOqVkUxhT+EpFtfGA2c9LmlVfIZ3joKv19MyazIZrlMks+XKzJmkvOp6N8Nb3hvj4yUnAxfkmml6JTf0l4Kv281PZN2ErSobNQk8ZBnuVCOBdlqzbdR5K7AmPjC9pjyj1vFm1jTln6Q98MHZZXEzyfrANV1UOJfgJo7rgRdrv1d5vSVdiw923oK/kNbAJ9YtDHzWzC4Ypty8eEKal/AJN0fjE7NWAXY1swsz6n6Lmd3d7LcqUJ0k5bjcDV/mHar7RLzXcq6Zvdhs/yFlN6ZHcgP950XTzkKLHjj4g3IjHsdmQ1pwp+rhf56FjzU8gwcJq32fBTyTUf5M3C7Z0kxBBpJmTMMHtUWTDENDrtd8DEw4WgU4vYvnrmX3uTbq7IhXRjpvb6cQDz+jzBxhhelCovDC/dH1JOWF+pbEx3u2pJAUaKTL3TeDrOpMUuLSHjiJN+JJpqfgHgln4z7N2YNRkrYzs1Oa/dZpzKxdT52f4t3XQyTdgA8mnWX54w6tDIrXeMHMXpCEpHnN7E55TtuuYGaXdauuAh3xykjX54acfdWZ6JntUipJeSeRtB3ubXQp3gA5XNLXLC+XbM/khv4y0bSdlFjSEcA38XjmX8GVzXQzyw4tmrrCU/CXzffMLMsLp555pBcmk1ZJbnSb4PHtP2SZduwhx5hEuUHxM/ABry+lup/EH6hKo3dKmkV919JSNvwW6z4Jz9tb9MpYHA+sd6WZvX24sm3U2Xb0zA7IUCpJeYfrvhnY1NzFtTZ7+G9mtmZG2Z7JDf2l4Keb2VqF9baUYwvKZl58yvoUPFzwmfg0+oa9iDSAszke4+OkwqbxuIfGO8rK3m2Sp8BWuLJZB2/B792kTEcGxQvH2wi3Q59nFYXcHQn00itDLUTP7GDdtfDKtf9dC69cyibeYt2zPZDSes2Mu0aDYrV9eyY39JeCvxNXrrV+6h9xc4mglBfNMngi6dnmK2vulXEcPsX/HLxFc1sJudfEB+oOxJNH15gFXGJmT+YeqxdIOhlPhnAe/oK6zFJ89iblOjEofhDeY7va6rhK9ivtemXIw2OvaGZHp9boguZxk4bb/9P4rOJ/yO1Av8fD5t6HJ0uv3E0yydErL5qf4Ml7apPLdsCD8mVlewsvmg7QIYXxI9JEBAoTV6yJV4Sk1xjwC67nMti0yy5p7tqFT65/b8rtPfQSSR/Eu6tdCTo1pO7d8QlW78RfiFfgcXD+0m1ZukW7XhnJU2wyPvC3kqSl8UBpGzQocxuwtpm9LJ/I9xV8rsfawP7WZlCvTLk3pofeKJI+RiE5i5md0Wj/QrmNCS+akbEwJK5Kl+u+FDfLvAGPGX0d8PNen5MK/++KeMiA2/CW0TJtHOuN+Kzh+4FZvf5vFZ+3trwycHdSMThe0i3NyhS+nwB8sbCeneS8l/97tF6vdpe+S7rdJndTbeLjRixsHifko8BxZrYenuarXzkK9zb6GO5iWnoGsaTfSboaT4IxF57vctHGpUY97Sbrfslc0xiAPDtTM16TtJQ8ZtP78CicNeYvUXc7dDtJOZJmSXqmzjJL0jOZh+m63EX6xk2yHTQQcOu/wHRJFzF44kqzgFudYC55TtHt8XAH/c5CZvbb9P0nklqx4y6GJ4x+Cvcsecy6EWO7twxN1r0z5ZJ1nyzpN8Aiybb+SeB3Tcp8N9UxFg9/PQNmD2xXPskp0e7/boWL8N7h6cBJZnZfC8fohdyz6RsbfDtI2rXRdjNrNdJgGRm2w5MDX2lmn5PnOv2JeaTDEY+kKpsWrQAAIABJREFUzfFB4eclfdSaJCrv1KB4OtaqePyeL+MuaMu28BdGBZ3wypC0KYV4SZY3i3Uu/KX8ZOG3BXAd0o04MD3xRpG0MN6r3hH3+T8Jd6TIirUUXjQdJo3yl0pKnDwJJpjZ7UN+fyvwqJnNLFH/G3GvEsPTsz3cyv8YbUj6JT54dyOeQKKhG2SHBsW3xAdZNwQWwZMrXGFmR2ULPgppxytD0o9siPdHvd+CwSTXyB3xlJw/MLOf9VikLPpRwf8fPrFgEzNbNXmkXGANJoBI+hP+Vr18yO/vweN7NI2XnfbfA+/OXoy3jjYCDsxROMm++Sl8xmAxcFZXJkSURdJ6ePLgmYXfvovHhvkfM/vTsIU7J8Mv8RbRFWb2UNX1jQQ64EVTb0LdLWb2tg6L2hHSJKtlzexXaf06BlIbft0qnukt6V14T/M9wJW4qeaKjHI9lXs2vR5l7vRCC0mJgakNtjXMyDRk37soZATCbcRZ2Y2AU4CD8FReuwIXAL/o9flsIO/NDM7k9DPcVjkBNzN1S4434jMst6JJNqV+WGjRKwP4LB7C4zk8UFltuQcP7tbz/zaM3FfhLsO19enpuZpIiWxrLdZ9b6pvP7xXvk5xGalyF5d+HGRtJSlxo3gsZUa8H8f9sWvMSr/lsIKZbSdpGzM7VtIJeOt0pDKXmb2YbLPHAM8D25rH8hnXDQEkfQpPUFLrMR0uKavHNIppNbbJCcC5wA9xhVVjluXbk0ubPzvAPGb2QGH9SjN7HHg80wOoHe7F9cgHGRizqGEUEqjUoZdyz6YfFfxhwBnAEpIOxl3nvt2kzD8lbW5m5xR/TGEEyngJ/BO4TtJf8BtgG+AWSfsAWGO7Xc2O+pQ8Jv3DeP7OkcqVydvojXji6g2Tct8IV/bdYF98As7jAJIWwzNo9bOCb8krwzz5ydO4uQFJS+CmwAUlLWhm92fUfQTJ/InPvJ6FZ4LqePybAoPcXs3s84XVCVSImW3cRvGeyV2k7xS8mf1R0jTcX1fAh83sjibFvgScLWl7vAsMPmD4Tjw8aC7/SkuN2ozKnIiNR6bxgm/jcWwWxL1qRiRmtlea8v4S8AhwqqRahptsz582W4Xt9JhGK5/FvTJqrrtX4Io3C0lb4ea0pfFoqcsBdzCQH7YR65nZOpJuAjCzJ9OAb5VcJ+nTNuBSC4CkvfA4/COVESF33wyyapjUcTWadUOTO9NOeEwZ8LyTJ1iJdHuFY433Km1Wxr4/MLNvpu+bWobL2khF0gQr4XGUyrQyKL5P+roWnvBiUI/JzHZrRf7XA/LIiJvg4SXWlvReYBcz+1RG2euAd+HeYesk8+cFlpnmsUV5l8BnPL+Ie2gBrAvMizfeHqmq7nYYKXL3k4KvpY6rm5TYMlPItSnDZDxTTq3F/jTwSTOb1qBMR/KDjlZq/1mFfLCSbrYGoVjl8VSGxcy+12k5e02nvDIkTTWzyUnRr53Mag3Pd6HszgxEDD2WZP7MrbsdJG3CQC9jhnnS8hFPr+XuGxONmb251zLgtt/PWXKjSiaMo/FIdEF9Sg+K96MCz2Bf3A+7xry47XsB/B7LVbJPSVoQj8L5R0mPUkig3YgWzZ8dISnGnih1SafjETTPtYxIqUV6KTf0kYKvoRaTEneIV63gI2tmV0pqVucSyeSgwvfZNBmY7QdaGRQHZk+WmqMLal3KydplOuWVsQ0eO/7L+NjHwviA6bAMMX8+ykDYXCS9IdcLZxRzBJ5Y5jB5ovWji55MI5m+MdHUUItJiQvl58F9i6H8LMFD8eBLJ+KKZwf8YToe6k+/H63mhmFepLOp918bHGsVBlqFF+W2CiWtW1idDx/cfcXM9s2te7Qg6Z9mtsIw2/5lZstXWHfPzZ8jAXnYgil4rKgH8DSTx5fREd2mHxX86cB3bCAg0mp4C2VfPCHzWg3Kbkx7swTbnn4/WpDHwL8NeKz2U2Fz0//a7qB4g+Neb6MgC1ZZJP0RT7pRzytjYzObknmceukGn8ZdLb9iZt0KHjaqSC64u+CpER/CYye9G1ijTXfKSulHBX+bma1e7zcNSetXp+w0YKda90vSSnjy7HWHK5Mhz5IjdaS/HSR9CTenPI3nBz3DSgSd6kSrcMhLYgzupXCYmXUt8Xa36JRXhjwL1oP4xCfhdv3l0zE/20hZ9dj82TPkuX9XBv4AHGNm/ylsm2pmk3smXBP6UcG3nJRYdWJy1PstQ4ZFcHPBTsCqZrZ0mfKjCXnUyx1x2+59eCCm6V2qu/iSeAWfdn+gmV3Zjfp7QbteGfU8ZmoNnwzvpbbMn6MVSe81s0a98xFL3w2yArvhSYm/lNavAr6KzxR9b5OyLcdulidD3gZX6mvjrpIfxr0V+hYzuzvN3J0ff4muhMfdyKKdVuEI8ZzqKh3wyvhvmtB3alrfFh8ngjoD1kN4CPjUcOZPPH5S32Fml6TZ5asxOBDgcb2TKo++a8HD7IHS0uFU1WLsZnncmPfgN/if8Afwn2UVkKR1ioOTQ9dHEkNa7g/g//tsMysVpqCVVqGktwMPWArFLOkTDCSBPuB14NXRMum6/QKfpW14iOUvA/8G1m3U+2nH/DmaSY4QG+MK/hxgM9wasG0v5cqh7xR8BwZK5wcmlnGDkjQdtwEfhycDeFDS3WW9CyT91sw+Pdz6SCINst6CzyJ9hiGtv1z3zlYGxeXZn95vZk9I2hB/ueyNz2xddTQ8eKORdsyfoxlJtwJr4hFq15S0JO49s2mPRWtKP5pofgp8YOhAKT4g1RBJWwM/AeYB3ixpLdymu3Wjcsl+uQruQvU3SY8BC5UdYB2qzEeqck8cyIBSX7CN46xUU+4AZna7pFWS6We4MmMLrfQdgCPN7DTgtPSyDYYgaV8z+7EG0lMOwvLSUu5G6+bP0czz5jN+X5GHIXkUbziOePpRwbcaThU89Ow7gEtT2emSsswsZnZnKr9/8s/eCbhB0oNm9q5m5aWehGJtGTM7oEOHmiGPR1NsFd6ezGXDmdbGSpor2ejfB+xZ2NaP93QnqM0taDkfqHk6xsNxU+RQ82flaft6yNTkOPFbPBjhs8A1vRUpj3400RyFT3UvDpSOtYzMSJKuNbP1NTguSsvZbpLSfk+OeUgtBN3qJZIOa7Q9s0VYM4l9joFxj6vwmYMvAOPquV5K+hawOe6DPxFPvmCSVgCONbMNsv/I64x2xnXaNX/2A5ImAePN7JYei5JFP7Z22gmnOkPSTngLccV0jKtzK5b0Y+D7eDz08/AYNF/OLN6LUKztMGwAtTK00io0s4PlseiXwl+CtVbKGNwWHwzPT+V5g0/F08/dVqYsLZo/RyPDeHjN3jZSHSCK9F0LHtryohmHT0OenXEeOMgyQwYX/Ik/gseR3we4vJFvcaFs10OxjgSiVdh9koLfHjeHjccV/fczynVknshoQX0wM73vFHwvFYakGWb21uRLf6qZndds8kihbM9CsfaSKmYPB3lIWgP3VtrBzJr2Ftsxfwa9oR8VfGmFIemvNJjk0cyLpnCcQ/DJTc/jg7WLAGeZ2XqZ5VsKujWaeb21CnuNpFXxhsTH8OxXJwGnmdmjGWVbmicyWklm2p8AK+AJy79qZv/urVTl6EcFX1phyPOIAnwUzzFaa6FMAR4xsyw7enoAFgCeNrNX5WFcF8x1lUwDq2+iMDYyGux87RCtwu4i6RrcY+kUM3uohfItmT9HI5KuwOe2XA5sDbzT/r+9ew+zrCrvPP790Qg2cnG4aHQwoi2XQVAQRC4dgyBRBxMR8YITb+HxFgmGRE3CaDBqjJpRJwZBUMDoKLdIDMpN1Ehii43AINAQBGF4CF4eQcUGQ4P0b/5Y69jnVNflnFNVZ+9z6vd5nnq69z61e78UVW+ts/Za72sf2WxUg5nEBD+fVTQbFQ6a7tws12/UkWm6czNc+17KOuPvs+HdROvn+eo7pFOAx9YdjU8Dfq+fOd16/ZIaFY6zpfa8ZOru3H5/ltskq2h6PUrSk11LptY18HM2U6gPrf4rsFzS3vDrColbA1v0ee+XAStsP9jn57fFJ4G3A6cC2L5OpXRDXwne9jpJJwGXsQRGhU2r0w5/w8Z1VfrZdb2kVtEAj5zy89zz8z0O764nLsHXkd9H6segjge+Iek2yv/EJwJv7OO651FG3ztOue9a4IQ+730DZc5+zrnQltnC9pVTdp32XTp2ulGhpIkdFbbAmZQNeR+l7D59HWV5aT/ms4lwHP2Q3p/nH3Udm9K8vNUmZopGC9eUeHNgt3r474NMFUh6Sd0yPzCVht3/TEn0v75nvw94myLpYuBYypzuMyQdRak4+II+r88qmhGSdLXtfSRdb3vP7nN9XJvnJWNmkkbwQzcllnSI7a9LmvoAZYUkbJ/fTwC2vyDpcEq97u63v7P2vKz+Afgg5Wn9QI19G/YW4DRgN0l3UWqy//4A1y+1UWHT1knaBLhF0rGUKpL91hKaz/RnNGCSEvx8mhI/m1Li93enec2UWtdzkvQJypz7c4BPUday91tL5pe2Z93+30b1ecVz69d4E9trB/wnhq7BH0N5K+V79DjgvZRphtf0c+E8pz+jAZM0RTN0U2JJLwW+1O+O1Vn+netsP63rzy2Bi23/Vh/XfoQyNXMBvVM0rX6Qo1I69f3A422/QKXc7wG2T+/z+qyiabmFmv6M0ZukBD90U2KVnosHUUoTfJ5SIuDhIWJYbftZKk0sjqRsJFkz0y+eKddOty16HJZJXkyZAvufLrWyN6XUzd6z4dCii6QzmXkzn20fM8u1q4BXdN4hq5RkPpQ6/Wn70IWOt02k8ar02m2SpmiOB76oUixso6bEs11o+8UqdZ5fTHnreoZKG7qzbF8+QAxfVikr+rc1BlOmauZke1zraW9v+1xJfwFg+1eS5vzlmFHhyH15mnNPoPzcLJvj2vlMf06Ck6mVXil9ENYCX6A842u1iRnBd2ieTYnrv7EdZf78D4FtbQ9c3L9OPTzS9r19fv42lOVrz66nLqc0G+nr+qZI+gZl2/tldRXN/sAHbf/2HNct6VFhk1Ta9p1A+V77KHD6bPsv5jP9OQk6G5zUW0a8rxpTTZukETww/6bEtVzAkZR6HduyoTlxv9cfCOxE/drWVTj9NOc9g7JE8mX1+FWUqY+2b43+E8pzgxU1ae9A+eU4l6U+Khy5WuvonZSm8H8LvMlzNDavVkt6/QzTn62fplgAD0laRp3iUqn0OhYr3SZuBD+M+jD0xZTaM3tTEtbZlDn9vr9Akj4LrACuBTrTFHYfzS+mboue6Vyb1G/644C/p9QnEX3uRF3qo8JRk3QeZcryw8C5bPj+BMCzNCqX9Bjgi5SH/xtNf3qAtpTjSGNc6TUJHlDpoXoJJalfOuxWeUk3AbsP8kuh69orgLe7drWXdBDwv2wfMEwsoyLpStv7DXHd0A/FY3CS/h9dNY46pzvH/ZQqWIjpz3GlMa30mgQPSFpu+z8X4N85DzjO9g+HuPbplMp129RTP6MUcmp1azBJHwUeQSk7e3/n/FzLO5f6qDDaT9K2s70+27uetkiCXwDaUE9+K2Avyrxk3+UG6lTHB22/ra7mwfYvFi/ihTPf5Z1LeVQY7SbpdsrPdXehpc5xX+96mpYEvwC0oZ78tPpZaqna8Hvholpckt5q++8krexMK0VEuyTBLwBJf0xpzn1Nn6sSpvs3TqGUHD6P3qmOvsokjJo29J8duxrZEYPQ9M237wXuGPbnfVQmbpnkMDT/ln07Av+bUnDremAVJeF/a4B5ukdSdr52T230XQenATdJugV4vKTu5wSdt69puddykt5g+7Sm4xgDJ1NW0FxH+f7ek7KkeRtJb7b9lSaDm01G8PRMscy3Zd9mwL7AgcAB9ePntndf2IjbQaXRyaWUdmY9bN8x+ohiEHn31R9J5wPvsr2mHu9O2dH6DuD8Ni9lzgieDXPkkj7s3vZ8X5I0SGXD5ZQuTtvUjx9Qyv/OqW6eeD1dm6RqbK2ttW37R0Drd/PFjDT3pwSwSye5A9i+UdJutm+T2v0lTILvNWzLvtMoK0HWAqsp0zMfsf2zAe79z5RKil9lyiaUiEUyXXns2Nia+ozs7Hr8cuDGWo6k1e0lM0XTRdLzKc0relr22b50jusuAbanzMt9C7gCuGHAXbCt3rUasVRJWk6pS9Upab2KMi//AKVl5X1NxTaXJPgpNGTLvlpS9KmU+fcDgT2AnwJX2D6xj+vfR3koe9FQgbeApN+o0zYRE6U+X9uVMWsMnwQ/xdRiYUC/xcI61+9IqS1/IPBCYDvbj57l89eyYfPEoygbpB5iw2qUrQf/r2hGHtrFJNI0jeEpu8xb3xg+c/BdZioWRikhMNt1x7Fh5P4QdYkkpULkrA9ZbW81v6hbpd1PnKLTvewS22slvZOy/O99c5WWWOI+DPyOpzSGp5TWaLUk+F77MlyxsJ0oG5SOH7QOjaTnAVvZ/scp518C/ML2ZQPG0qRPzv0p0bB32T5P0krguZSywacAz2o2rFYb28bwmaLpMp9iYfO45ypKca2fTDm/PaVPbKurScZ46TStkPQ3wPW2P9/dyCI2JukMSv337sbwy9q8hLkjI/he21OWPw1ULGyeNp+a3Os9707ji1gEd0k6FTgM+GBdVLBJwzG13ZspjeE7fR3+jbKKpvUygu8yU9GwfoqFzeOe36NMC/1qyvlHADfa3nmx7h1Lj6QtgOdTRu+3SHocsGebt9u3QVbRxFAkfQB4LHCs7fvruS2BvwPutv1nTcbXD0lPBHa2/dW6ZnhT22ubjiumV4tnraQkq1V5wDq7cV5Fk7dmXSTtL+k7ku6T9KCkhyUtdl32dwI/Bu6QdLWkq4HbgZ/U11pN0uspfWtPrad2pDTyiBaS9JeUZLUdZUryzLqaJmbWWUXz27afDTyP0qy89TKC71LrzryCsiJmX+DVlDoUfzGCey8HOj1Kb12IDlOjIOlaYD9gtTd0nL/e9p7NRhbTkXQz8HTbD9Tj5cC1tndtNrL2knTd1Oqo051rozxkncL2rZKW2X6YMrr5v8CiJ/ia0PsqTNYy62w/2Cm6JGlTZim9HI37AaU09QP1eHPgrubCGQtXSfoUvatoBilC2Jgk+F6/rA9TrpX0IeCHZBprLpdLOgFYLukwSs2OLzUcU8zsXkrxrMsov4gPA66U9DEA28fNdvESlVU0k6A+LPwxsBlwPKXk78m2b200sBaTtAlwDPA7lAdQlwKfGmKzWIyApNfM9rrtfxhVLLH4kuBbohYr+x/Ak22/R9JvAr9h+8qGQ5tTfdezGxuWkD3YcEgR8ybpRcCOtj9ej1cDO9SX/8z2eY0F16dMP7THyZQOUEfX47XAx5sLpz+SDge+D3wMOAm4VdILmo0qZiJpZ0n/KOlGSbd1PpqOq6XeAVzQdbw58EzgYOBNTQQ0qMzBt8ezbD+jPtTF9s/qyLjtPgw8pzONJWkFcCFwcaNRxUzOBE6kLPN7DvA6MtCbyWa27+w6/qbte4B7xmWXef7HdqmV9uY8t0gekrSMugKltvBbP6J7z8faKc8obqO8+4h2Wm77a5Tp2Ttsvxs4vOGY2uq/dB/YPrbrcAfGQBJ8r+mWQy76EsnqY8A/AY+R9NfAN4H3j+je83GVpIskvbY+wPsS8B1JR0o6sungYiPr6oPxWyQdK+nFwJZNB9VSq+tGvh6S3gi0/tkY5CErAHXO+L8DLwPO6Xppa0qdmP1GFMduwKGU1Shfs33TKO47H5LOnOVlj0PFvaVE0jOBm4BHA++lrBT7kO1vNxpYC0l6DGVX9jqgU85hH8pc/BG2f9xUbP1KggckPR3YC3gP8JddL60F/mXA5tmD3nvb2V63/dPFuvdCkLRdnZeMmEiSDqG04wRYY/vrTcYziCT4LpJ2tP0fU87t2l3sfxHueTsbWvZ1dI5t+8mLde+FIOkWSgesM4GLs/69nSRdMNvri1wSOxqSBN+l1ul4l+1z6/GfAsfY3r3ZyNqrrt9/LvAHlCVk5wKftv29RgOLHpJ+AtxJaTW3mintFRezJHY0Jwm+S62NfRqlTsdjKXOVf2r7vhHce7pm1fcCd0ytFd9Wkp5DqdfxKOC7wJ/bvqLZqAKgrtA6jLLP4mmUpaxn2V7TaGCxqJLgp5D0FsrKmfXAK2x/a0T3/TalAfJ1lNHVnsANlIdgb25rQwZJ2wG/D7yKUubhdMrmkL2A82w/qcHwYhq1i9PRlH6sf2X7pIZDikWSjU5dJH2VUm1vD0pR/9Ml/avtt43g9j+gTAetqbHsTnno+w7gfKCVCR64AvgsZVVB9/OLqyR9oqGYYho1sR9OSe47sWFpbkyojOC7SDrC9he7jpcBJ9h+7wjufYPtPaY7J+la23stdgzDkPQG26dNOfcB23/eVEyxMUmfoQxcLgLOtn1DwyHFCCTBTyFpJaX93JmStge2sn37CO57DvBT4Ox66uWUjjuvomyRfuZixzAMSRcBn7P9uXr8ceCRto9pNrLoJmk9cH897P6h76zW2nr0UcViS4LvIulESienXW3vIunxlHnkg0Zw7+WUWuor66lVlAJkDwBbjOJB7zBq3BcAZ1CaOf/c9lubjSoiIAm+R20/tzdwTVf7uZG15hqnzu1TNmhtRdnxt4q6UaztG7QiloI8ZO31oG1L6hT8GlnFuOk6t0tqc+f2q+nakFX/PLx+GGj1Bq2IpSAJHpD0ftsnAOdKOhV4dC0y9AfAJ0cURqdz+801pl0om1L2GdH9B5LljxHtlykaQNI1tp9R/34YXe3nbF82ohjGqnN7LVp1p+0f1eNXAy8B7gDenSmaiOYlwQOSvkvp0qLpXh9FspJ0BmVzVXfn9mVtrcYo6RrgubZ/KunZlNU/f0TZ4PTfbB/VaIARkQQPIGkdcBcb5pN//RIjKvhVN6G8hQ2raP6N0vB73WLfexiSvmv76fXvHwd+UptH0OZ1+xFLSebgixs7q2aaYnudpJOAyxiDVTTAMkmb1jo5hwJv6Hot31cRLZAfxJYYw1U0ZwGXS7ob+E/KOw4kPYVSJC0iGpYpGkDSa21/uuEYrgZeOXUVje1WrqIBkLQ/8DjgK7bvr+d2Aba0fc2sF0fEokuCb4lxW0UTEe2XKZr2uErSp+hdRXNVg/FExJjLCH4GkjTK9nPjtoomItovCX4G3ZufIiLG0SZNB9Bi0256WvCbSC+qXaQ6x6sl3VY/XjqKGCJiMiXBz2xUb23eQSm327E5pXn1wcCbRhRDREygPGSd2UhG8MBmtu/sOv6m7XuAe0ZZzTIiJk/m4GcgaRPb60dwn1ttP2WG175ve8VixxARkylTNDMYRXKvVtfSxD0kvRG4ckQxRMQEygi+YZIeQ+mGtA7o7P7chzIXf4TtHzcVW0SMtyT4StImwFG2z23o/ocAT62Ha2x/vYk4ImJyJMF3kXSV7X2bjiMiYiEkwXeR9AHgbuAc4P7O+XQniohxlATfRdLt05weScOPiIiFlgQfETGhskyyi6QtJL1T0mn1eGdJL2w6roiIYSTB9zoTeBA4sB7fBbyvuXAiIoaXBN9rhe0PAQ8B2P4loytZEBGxoJLgez0oaTm10JikFZQNSBERYyfFxnqdCFxCaXj9OeAg4LWNRhQRMaSsoplC0nbA/pSpmW/bvrvhkCIihpIE30XSdB2c7gXusP2rUccTETEfSfBdJH0beAZwHWUEvwewBtgGeLPtrzQYXkTEQPKQtdcPgL1t72t7H2Bv4DbgMOBDjUYWETGgJPheu9he0zmwfSOwm+3bGowpImIoWUXTa42kU4Cz6/HLgRslbU5dGx8RMS4yB9+lroH/Q2BlPbUKOBl4ANjC9n1NxRYRMagk+IiICZUpmi6SDgLeDTyRrq9NygVHxDjKCL6LpH8HjgeuBh7unLd9T2NBRUQMKSP4XvfavrjpICIiFkJG8F1qy75lwPl0FRmzfU1jQUVEDCkJvoukf5nmtG0fMvJgIiLmKQk+ImJCZSdrF0mPlXS6pIvr8e6Sjmk6roiIYSTB9/o0cCnw+Hr8PeCPG4smImIekuB7bW/7XGA9QC0R/PDsl0REtFMSfK/7a8OPTsu+/Sn14CMixk7Wwff6E+ACYIWkVcAOwFHNhhQRMZysoplC0qbArpSGHzfbThXJiBhLmaLpIumlwPJaE/4I4JwZ2vhFRLReEnyvd9leK2klcChwOnBKwzFFRAwlCb5XZ8XM4cAnbV8IbNZgPBERQ0uC73WXpFMpnZwuqp2c8jWKiLGUh6xdJG0BPB+43vYtkh4H7Gn7Kw2HFhExsCT4LpJWAP9he52kg4GnAZ+x/fNmI4uIGFymH3p9AXhY0lOA04AnAJ9vNqSIiOEkwfdaX8sTHAn8ve23A49rOKaIiKEkwfd6SNLRwKuBL9dzj2gwnoiIoSXB93odcADw17Zvl/Qk4LMNxxQRMZQ8ZJ1C0nLgN23f3HQsERHzkRF8F0m/C1wLXFKP95J0QbNRRUQMJwm+17uB/YCfA9i+FnhykwFFRAwrCb7XQ7an1n9f30gkERHzlHrwvdZIeiWwTNLOwHHAtxqOKSJiKBnB9/oj4KnAOuAs4BekJ2tEjKmsoomImFCZogHmWilj+/dGFUtExEJJgi8OAO6kTMusprTri4gYa5miASQtAw4DjqZUkLwQOKu27ouIGEt5yArYftj2JbZfA+wP3Ap8Q9KxDYcWETG0TNFUtXvT4ZRR/E7Ax4B/ajKmiIj5yBQNIOkzwB7ARcDZtm9oOKSIiHlLggckrQfur4fdXxABtr316KOKiJifJPiIiAmVh6wRERMqCT4iYkIlwUdETKgk+AhA0hGSLGm3pmOJWChJ8BHF0cA3658REyEJPpY8SVsCK4FjgFfUcwdL+ldJF0q6WdInJG1SX7tP0kclrZH0NUk7NBh+xIyS4CPgRcAltr8H3CNpn3rZ5lwtAAAA+ElEQVR+P0qPgN2BFcCR9fyjgKtsPxW4HDhxxPFG9CUJPqJMy5xd/342G6ZprrR9m+2HKZVGV9bz64Fz6t//T9f5iFZJLZpY0iRtCxwC7CnJwDLKbuYL6d3VzDTHc52PaFRG8LHUHQV81vYTbe9k+wnA7cBvAftJelKde3855SEslJ+bo+rfX9l1PqJVkuBjqTuajauGfqGe/w5wEnATJel3Pu9+SvK/gTL6f89oQo0YTGrRRExD0sHA22y/cJrX7rO95eijihhMRvARERMqI/iIiAmVEXxExIRKgo+ImFBJ8BEREyoJPiJiQiXBR0RMqCT4iIgJ9f8BBwKsGXg9oJwAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Visualizing through graph\n",
        "plt.bar(app_install_review['App'],app_install_review['Reviews'])\n",
        "plt.xlabel('App')\n",
        "plt.xticks(rotation=90)\n",
        "plt.ylabel('REVIEWS')\n",
        "plt.title('Review of apps with installation of 1Billion')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-bkFRY-OTWlJ"
      },
      "source": [
        "This graph suggests that **facebook** has the maximum no. of reviews when installs is 1B."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "ia4tvKlyqXBR",
        "outputId": "37166007-88f4-4f46-dd89-a8553f987070"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  App     Reviews\n",
              "0                                            Facebook  78158306.0\n",
              "1                                  WhatsApp Messenger  69119316.0\n",
              "2                                           Instagram  66577313.0\n",
              "3            Messenger – Text and Video Chat for Free  56642847.0\n",
              "4                                      Clash of Clans  44891723.0\n",
              "5             Clean Master- Space Cleaner & Antivirus  42916526.0\n",
              "6                                      Subway Surfers  27722264.0\n",
              "7                                             YouTube  25655305.0\n",
              "8   Security Master - Antivirus, VPN, AppLock, Boo...  24900999.0\n",
              "9                                        Clash Royale  23133508.0\n",
              "10                                   Candy Crush Saga  22426677.0\n",
              "11        UC Browser - Fast Download Private & Secure  17712922.0\n",
              "12                                           Snapchat  17014787.0\n",
              "13    360 Security - Free Antivirus, Booster, Cleaner  16771865.0\n",
              "14                                     My Talking Tom  14891223.0\n",
              "15                                        8 Ball Pool  14198297.0\n",
              "16  DU Battery Saver - Battery Charger & Battery Life  13479633.0\n",
              "17                        BBM - Free Calls & Messages  12842860.0\n",
              "18  Cache Cleaner-DU Speed Booster (booster & clea...  12759663.0\n",
              "19                                            Twitter  11667403.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aa0854b9-1c39-460e-8468-93260b4eb150\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Reviews</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Facebook</td>\n",
              "      <td>78158306.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>WhatsApp Messenger</td>\n",
              "      <td>69119316.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Instagram</td>\n",
              "      <td>66577313.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Messenger – Text and Video Chat for Free</td>\n",
              "      <td>56642847.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Clash of Clans</td>\n",
              "      <td>44891723.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Clean Master- Space Cleaner &amp; Antivirus</td>\n",
              "      <td>42916526.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Subway Surfers</td>\n",
              "      <td>27722264.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>YouTube</td>\n",
              "      <td>25655305.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Security Master - Antivirus, VPN, AppLock, Boo...</td>\n",
              "      <td>24900999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Clash Royale</td>\n",
              "      <td>23133508.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Candy Crush Saga</td>\n",
              "      <td>22426677.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>UC Browser - Fast Download Private &amp; Secure</td>\n",
              "      <td>17712922.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Snapchat</td>\n",
              "      <td>17014787.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>360 Security - Free Antivirus, Booster, Cleaner</td>\n",
              "      <td>16771865.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>My Talking Tom</td>\n",
              "      <td>14891223.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>8 Ball Pool</td>\n",
              "      <td>14198297.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>DU Battery Saver - Battery Charger &amp; Battery Life</td>\n",
              "      <td>13479633.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>BBM - Free Calls &amp; Messages</td>\n",
              "      <td>12842860.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Cache Cleaner-DU Speed Booster (booster &amp; clea...</td>\n",
              "      <td>12759663.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Twitter</td>\n",
              "      <td>11667403.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ],
      "source": [
        "app_reviews=df[['App','Reviews']].sort_values('Reviews',ascending=False).reset_index(drop=True).head(20)\n",
        "app_reviews"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JrA5XsGMRGNF"
      },
      "source": [
        "Facebook has the highest number of reviews among all the apps with reviews of 78158306.0  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nEM1psLhUo11"
      },
      "source": [
        "\n",
        "### ***To check no. of installs of different apps***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "4bZV3b3J7I2E",
        "outputId": "69ff8f7d-8cc1-46cd-868a-d62cab80804f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           App    Installs\n",
              "3896                            Subway Surfers  1000000000\n",
              "3943                                  Facebook  1000000000\n",
              "335   Messenger – Text and Video Chat for Free  1000000000\n",
              "3523                              Google Drive  1000000000\n",
              "3565                              Google Drive  1000000000\n",
              "...                                        ...         ...\n",
              "8081                                CX Network           0\n",
              "7434                             Pekalongan CJ           0\n",
              "5945                Ain Arabic Kids Alif Ba ta           0\n",
              "5486                    AP Series Solution Pro           0\n",
              "6692                             cronometra-br           0\n",
              "\n",
              "[10840 rows x 2 columns]"
            ],
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              "\n",
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              "  <thead>\n",
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              "      <th>3896</th>\n",
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              "    <tr>\n",
              "      <th>3943</th>\n",
              "      <td>Facebook</td>\n",
              "      <td>1000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>335</th>\n",
              "      <td>Messenger – Text and Video Chat for Free</td>\n",
              "      <td>1000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3523</th>\n",
              "      <td>Google Drive</td>\n",
              "      <td>1000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3565</th>\n",
              "      <td>Google Drive</td>\n",
              "      <td>1000000000</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>0</td>\n",
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              "      <th>7434</th>\n",
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              "      <td>AP Series Solution Pro</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>6692</th>\n",
              "      <td>cronometra-br</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10840 rows × 2 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-75c15c52-ef4b-49dd-8943-0cb4d9914855');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "#creating and sorting new dataset on the basis of Installs of app\n",
        "app_install=pd.DataFrame(df[['App','Installs']].sort_values(by=['Installs'],ascending=False))\n",
        "app_install"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v3qk6j3hVGVZ",
        "outputId": "32e2987a-24bc-4e6e-fa5e-5898c32b87c4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ],
      "source": [
        "#Unstalled Apps\n",
        "Unistalled_apps=pd.DataFrame(df[\"App\"][df['Installs']==0])\n",
        "Unistalled_apps['App'].count()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQk9LXdDXylp"
      },
      "source": [
        "*15 Apps has **zero** install*\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iwW7u8zCYTkh"
      },
      "source": [
        "### ***List of apps having Zero install***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "-HO_wRUMYjnJ",
        "outputId": "0eb86205-e6dd-4c54-8035-87d0f450eea7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               App\n",
              "4465  Popsicle Launcher for Android P 9.0 launcher\n",
              "5307                       Ak Parti Yardım Toplama\n",
              "5486                        AP Series Solution Pro\n",
              "5945                    Ain Arabic Kids Alif Ba ta\n",
              "6692                                 cronometra-br\n",
              "7434                                 Pekalongan CJ\n",
              "8081                                    CX Network\n",
              "8614                             Sweden Newspapers\n",
              "8871                        Test Application DT 02\n",
              "9148                     Command & Conquer: Rivals\n",
              "9337                      EG | Explore Folegandros\n",
              "9719                                  EP Cook Book\n",
              "9905                                   Eu sou Rico\n",
              "9917                                   Eu Sou Rico\n",
              "9934             I'm Rich/Eu sou Rico/أنا غني/我很有錢"
            ],
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              "      <td>Ain Arabic Kids Alif Ba ta</td>\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-edcdd4c2-fa73-420f-85af-12f059180f85 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-edcdd4c2-fa73-420f-85af-12f059180f85');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ],
      "source": [
        "Unistalled_apps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QxuGtwFo7JHR",
        "outputId": "d20116cb-0033-4bdc-980b-f40c79d6549f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "App         20\n",
              "Installs    20\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ],
      "source": [
        "app_install[app_install['Installs']==1000000000].count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ErApGNZYuU3"
      },
      "source": [
        "*20 Apps has **1 Billion** installs*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f12B4qcpvTSi"
      },
      "source": [
        "## **Which category has the highest Installs**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MN8R9FQEQQci",
        "outputId": "ebcb3ecb-10e3-4795-d0bd-bc4bf91f3920"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Category     Installs\n",
              "0        ART_AND_DESIGN    124338100\n",
              "1     AUTO_AND_VEHICLES     53130211\n",
              "2                BEAUTY     27197050\n",
              "3   BOOKS_AND_REFERENCE   1921469576\n",
              "4              BUSINESS   1001914865\n",
              "5                COMICS     56086150\n",
              "6         COMMUNICATION  32647276251\n",
              "7                DATING    264310807\n",
              "8             EDUCATION    871452000\n",
              "9         ENTERTAINMENT   2869160000\n",
              "10               EVENTS     15973161\n",
              "11               FAMILY  10258263505\n",
              "12              FINANCE    876648734\n",
              "13       FOOD_AND_DRINK    273898751\n",
              "14                 GAME  35086024415\n",
              "15   HEALTH_AND_FITNESS   1583072512\n",
              "16       HOUSE_AND_HOME    168712461\n",
              "17   LIBRARIES_AND_DEMO     62995910\n",
              "18            LIFESTYLE    537643539\n",
              "19  MAPS_AND_NAVIGATION    724281890\n",
              "20              MEDICAL     53257437\n",
              "21   NEWS_AND_MAGAZINES   7496317760\n",
              "22            PARENTING     31521110\n",
              "23      PERSONALIZATION   2325494782\n",
              "24          PHOTOGRAPHY  10088247655\n",
              "25         PRODUCTIVITY  14176091369\n",
              "26             SHOPPING   3247848785\n",
              "27               SOCIAL  14069867902\n",
              "28               SPORTS   1751174498\n",
              "29                TOOLS  11452771915\n",
              "30     TRAVEL_AND_LOCAL   6868887146\n",
              "31        VIDEO_PLAYERS   6222002720\n",
              "32              WEATHER    426100520"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ca8cfaf0-9fbc-4e0f-8bf4-9e7262b1b560\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>Category</th>\n",
              "      <th>Installs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>124338100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AUTO_AND_VEHICLES</td>\n",
              "      <td>53130211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BEAUTY</td>\n",
              "      <td>27197050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BOOKS_AND_REFERENCE</td>\n",
              "      <td>1921469576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BUSINESS</td>\n",
              "      <td>1001914865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>COMICS</td>\n",
              "      <td>56086150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>32647276251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>DATING</td>\n",
              "      <td>264310807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>EDUCATION</td>\n",
              "      <td>871452000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>2869160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>EVENTS</td>\n",
              "      <td>15973161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>FAMILY</td>\n",
              "      <td>10258263505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>FINANCE</td>\n",
              "      <td>876648734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>FOOD_AND_DRINK</td>\n",
              "      <td>273898751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>GAME</td>\n",
              "      <td>35086024415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>HEALTH_AND_FITNESS</td>\n",
              "      <td>1583072512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>HOUSE_AND_HOME</td>\n",
              "      <td>168712461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>LIBRARIES_AND_DEMO</td>\n",
              "      <td>62995910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>537643539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>MAPS_AND_NAVIGATION</td>\n",
              "      <td>724281890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>53257437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NEWS_AND_MAGAZINES</td>\n",
              "      <td>7496317760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>PARENTING</td>\n",
              "      <td>31521110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>PERSONALIZATION</td>\n",
              "      <td>2325494782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>10088247655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>PRODUCTIVITY</td>\n",
              "      <td>14176091369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>SHOPPING</td>\n",
              "      <td>3247848785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>14069867902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>SPORTS</td>\n",
              "      <td>1751174498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>TOOLS</td>\n",
              "      <td>11452771915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>6868887146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>6222002720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>WEATHER</td>\n",
              "      <td>426100520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca8cfaf0-9fbc-4e0f-8bf4-9e7262b1b560')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ca8cfaf0-9fbc-4e0f-8bf4-9e7262b1b560 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ca8cfaf0-9fbc-4e0f-8bf4-9e7262b1b560');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "#category wise app installation\n",
        "category_install=pd.DataFrame(df.groupby(by='Category',as_index=False)['Installs'].sum())\n",
        "category_install"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "1sAXofSCNDo8",
        "outputId": "8b342ddd-f7b4-42f7-a5c6-b12f72f085c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        " #Visualization through graph\n",
        "\n",
        "plt.figure(figsize=(14,7))\n",
        "plt.xlabel('Categories')\n",
        "plt.ylabel('Number of Installs')\n",
        "plt.title('Category v/s Installs')\n",
        "plt.xticks(rotation=90)\n",
        "sns.barplot(category_install.Category,category_install.Installs)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k8UVSjXbv59d"
      },
      "source": [
        "***Games are the most downloaded category followed by communication and tools.***\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fs1yFVmMDWmu"
      },
      "source": [
        "##**Top 3 gaming Apps**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "tTCuZkX2kI3h",
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              "              App\n",
              "0  Subway Surfers\n",
              "1  Subway Surfers\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "top_gaming_app=pd.DataFrame(df[df['Category']=='GAME'].sort_values(by=['Installs'],ascending=False).head(3)['App']).reset_index(drop=True)\n",
        "top_gaming_app"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bM6RdQgPDiH0"
      },
      "source": [
        "## Top 3 communication Apps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "NRt6TOMHnHro"
      },
      "outputs": [],
      "source": [
        "top_communication_app=pd.DataFrame(df[df['Category']=='COMMUNICATION'].sort_values(by=['Installs'],ascending=False).head(3)['App']).reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
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              "                                        App\n",
              "0  Messenger – Text and Video Chat for Free\n",
              "1             Skype - free IM & video calls\n",
              "2             Skype - free IM & video calls"
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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        "top_communication_app"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZvUXZxAD2Ey"
      },
      "source": [
        "## **Top 3 Tool Apps**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "2UUDF8W-noBq"
      },
      "outputs": [],
      "source": [
        "top_tool_app=pd.DataFrame(df[df['Category']=='TOOLS'].sort_values(by=['Installs'],ascending=False).head(3)['App']).reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
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        "id": "j8IVFROQEhmX",
        "outputId": "8035e65d-b971-4096-954d-1e8cdc63f581"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            App\n",
              "0                        Google\n",
              "1                        Google\n",
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            ],
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              "                                                     [key], {});\n",
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          "metadata": {},
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      "source": [
        "top_tool_app"
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    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
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        },
        "id": "-zUd7KPDoj2V",
        "outputId": "6a385a22-759c-4104-c5a3-bf8086038133"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             game                             communication  \\\n",
              "0  Subway Surfers  Messenger – Text and Video Chat for Free   \n",
              "1  Subway Surfers             Skype - free IM & video calls   \n",
              "2  Subway Surfers             Skype - free IM & video calls   \n",
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              "      <td>Google</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Subway Surfers</td>\n",
              "      <td>Skype - free IM &amp; video calls</td>\n",
              "      <td>Google</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Subway Surfers</td>\n",
              "      <td>Skype - free IM &amp; video calls</td>\n",
              "      <td>Gboard - the Google Keyboard</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c369237-e45e-4e57-aaad-405e9dbf6d5f')\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "#top 3 apps of top ncategories\n",
        "top=pd.DataFrame()\n",
        "top['game']=top_gaming_app['App']\n",
        "top['communication']=top_communication_app['App']\n",
        "top['tool']=top_tool_app['App']\n",
        "top"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8bKoQxrfviDv"
      },
      "source": [
        "## **Number of apps belonging to each Content Rating**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "QUxT3c5yCbp4",
        "outputId": "485f847c-9787-43a5-e0a5-7f719aeb854c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Content Rating  No of Apps\n",
              "0         Everyone        8714\n",
              "1             Teen        1208\n",
              "2       Mature 17+         499\n",
              "3     Everyone 10+         414\n",
              "4  Adults only 18+           3\n",
              "5          Unrated           2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9630c265-f496-4f5d-b46e-b535ea4710dc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>No of Apps</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Everyone</td>\n",
              "      <td>8714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Teen</td>\n",
              "      <td>1208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mature 17+</td>\n",
              "      <td>499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adults only 18+</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Unrated</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9630c265-f496-4f5d-b46e-b535ea4710dc')\"\n",
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              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9630c265-f496-4f5d-b46e-b535ea4710dc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9630c265-f496-4f5d-b46e-b535ea4710dc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "#apps with content rating\n",
        "content_rating=pd.DataFrame(df['Content Rating'].value_counts())\n",
        "content_rating=content_rating.reset_index()\n",
        "content_rating.columns=['Content Rating','No of Apps']\n",
        "content_rating"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "eAtp0gCOCbs2",
        "outputId": "cd9f3fba-a253-4df2-cf8f-07f54e62ca8f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#visualizing through pie chart\n",
        "labels=content_rating['Content Rating']\n",
        "sizes=content_rating['No of Apps']\n",
        "myexplode = [0, 0, 0, 0, 0.3, 0.3]\n",
        "plt.pie(sizes,labels=labels,explode=myexplode,autopct='%1.1f%%')\n",
        "plt.axis('equal')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnzUCJ-ZN-ld"
      },
      "source": [
        "## **How much Installs are made for each type of content Rating ?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "ME9c4XhlN8Ck",
        "outputId": "062d479d-d7f0-4e50-a2d0-604a71a18342"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Content Rating      Installs\n",
              "1         Everyone  114156659621\n",
              "4             Teen   34716350393\n",
              "2     Everyone 10+   13233881795\n",
              "3       Mature 17+    5524491178\n",
              "0  Adults only 18+       2000000\n",
              "5          Unrated         50500"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Installs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Everyone</td>\n",
              "      <td>114156659621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Teen</td>\n",
              "      <td>34716350393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>13233881795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Mature 17+</td>\n",
              "      <td>5524491178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adults only 18+</td>\n",
              "      <td>2000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Unrated</td>\n",
              "      <td>50500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          document.querySelector('#df-389cad18-d2db-42f5-81d8-1e2aab0e657f button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-389cad18-d2db-42f5-81d8-1e2aab0e657f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "#content rating wise installs\n",
        "install_content=df.groupby(by=['Content Rating'],as_index=False)['Installs'].sum()\n",
        "install_content.sort_values(by=['Installs'],inplace=True,ascending=False)\n",
        "install_content"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "k00jKXujN6sO",
        "outputId": "0a6af450-3f9b-4f9f-84b8-b0fb53d6e3c8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Visualizing through bar plot \n",
        "sns.barplot(x=install_content['Content Rating'],y=install_content['Installs'])\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6XTnxRysxs5Q"
      },
      "source": [
        "*Most of the apps available in playstore are for everyone while nearly 10% are for teens*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mdQDh1NY0toO"
      },
      "source": [
        "## **See the revenue of paid apps**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "ZjWTAoI0ZJjo"
      },
      "outputs": [],
      "source": [
        "# function for calculating revenue\n",
        "def revenue(a,b):\n",
        "  return a*b"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "oxiBGvS1ZlNE"
      },
      "outputs": [],
      "source": [
        "revenue_df=pd.DataFrame(df[['App']])\n",
        "revenue_df['Revenue']=df.apply(lambda x:revenue(x['Installs'],x['Price']),axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JLpATkbHZla9",
        "outputId": "fe0335da-7368-4694-80cb-bcd20d7bba3e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f3a9f0bab90>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_03af9_row0_col0, #T_03af9_row0_col1, #T_03af9_row1_col0, #T_03af9_row1_col1, #T_03af9_row2_col0, #T_03af9_row2_col1, #T_03af9_row3_col0, #T_03af9_row3_col1, #T_03af9_row4_col0, #T_03af9_row4_col1 {\n",
              "  background-color: black;\n",
              "  color: green;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_03af9_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >App</th>\n",
              "      <th class=\"col_heading level0 col1\" >Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_03af9_level0_row0\" class=\"row_heading level0 row0\" >4347</th>\n",
              "      <td id=\"T_03af9_row0_col0\" class=\"data row0 col0\" >Minecraft</td>\n",
              "      <td id=\"T_03af9_row0_col1\" class=\"data row0 col1\" >69900000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_03af9_level0_row1\" class=\"row_heading level0 row1\" >2241</th>\n",
              "      <td id=\"T_03af9_row1_col0\" class=\"data row1 col0\" >Minecraft</td>\n",
              "      <td id=\"T_03af9_row1_col1\" class=\"data row1 col1\" >69900000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_03af9_level0_row2\" class=\"row_heading level0 row2\" >5351</th>\n",
              "      <td id=\"T_03af9_row2_col0\" class=\"data row2 col0\" >I am rich</td>\n",
              "      <td id=\"T_03af9_row2_col1\" class=\"data row2 col1\" >39999000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_03af9_level0_row3\" class=\"row_heading level0 row3\" >5356</th>\n",
              "      <td id=\"T_03af9_row3_col0\" class=\"data row3 col0\" >I Am Rich Premium</td>\n",
              "      <td id=\"T_03af9_row3_col1\" class=\"data row3 col1\" >19999500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_03af9_level0_row4\" class=\"row_heading level0 row4\" >4034</th>\n",
              "      <td id=\"T_03af9_row4_col0\" class=\"data row4 col0\" >Hitman Sniper</td>\n",
              "      <td id=\"T_03af9_row4_col1\" class=\"data row4 col1\" >9900000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "# creating and sorting table\n",
        "revenue_df.sort_values(by=['Revenue'],ascending=False,inplace=True)\n",
        "revenue_df_top5=revenue_df.head()\n",
        "revenue_df_top5.style.set_properties(**{'background-color': 'black',\n",
        "                           'color': 'green'})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RaqEp5JI1A5K"
      },
      "source": [
        "*Minecraft generates the most revenue.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SaNoL0no1PJ9"
      },
      "source": [
        "## **Category wise app distribution**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EbBMEKryl87E",
        "outputId": "01a7a606-f0e8-48d4-91ee-d17b315c32b2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Category   App\n",
              "0        ART_AND_DESIGN    65\n",
              "1     AUTO_AND_VEHICLES    85\n",
              "2                BEAUTY    53\n",
              "3   BOOKS_AND_REFERENCE   231\n",
              "4              BUSINESS   460\n",
              "5                COMICS    60\n",
              "6         COMMUNICATION   387\n",
              "7                DATING   234\n",
              "8             EDUCATION   156\n",
              "9         ENTERTAINMENT   149\n",
              "10               EVENTS    64\n",
              "11               FAMILY  1972\n",
              "12              FINANCE   366\n",
              "13       FOOD_AND_DRINK   127\n",
              "14                 GAME  1144\n",
              "15   HEALTH_AND_FITNESS   341\n",
              "16       HOUSE_AND_HOME    88\n",
              "17   LIBRARIES_AND_DEMO    85\n",
              "18            LIFESTYLE   382\n",
              "19  MAPS_AND_NAVIGATION   137\n",
              "20              MEDICAL   463\n",
              "21   NEWS_AND_MAGAZINES   283\n",
              "22            PARENTING    60\n",
              "23      PERSONALIZATION   392\n",
              "24          PHOTOGRAPHY   335\n",
              "25         PRODUCTIVITY   424\n",
              "26             SHOPPING   260\n",
              "27               SOCIAL   295\n",
              "28               SPORTS   384\n",
              "29                TOOLS   843\n",
              "30     TRAVEL_AND_LOCAL   258\n",
              "31        VIDEO_PLAYERS   175\n",
              "32              WEATHER    82"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0f0559aa-29e0-4a4c-9601-1c55787294ec\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>App</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AUTO_AND_VEHICLES</td>\n",
              "      <td>85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BEAUTY</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BOOKS_AND_REFERENCE</td>\n",
              "      <td>231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BUSINESS</td>\n",
              "      <td>460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>COMICS</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>DATING</td>\n",
              "      <td>234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>EDUCATION</td>\n",
              "      <td>156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>EVENTS</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>FAMILY</td>\n",
              "      <td>1972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>FINANCE</td>\n",
              "      <td>366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>FOOD_AND_DRINK</td>\n",
              "      <td>127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>GAME</td>\n",
              "      <td>1144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>HEALTH_AND_FITNESS</td>\n",
              "      <td>341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>HOUSE_AND_HOME</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>LIBRARIES_AND_DEMO</td>\n",
              "      <td>85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>MAPS_AND_NAVIGATION</td>\n",
              "      <td>137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NEWS_AND_MAGAZINES</td>\n",
              "      <td>283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>PARENTING</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>PERSONALIZATION</td>\n",
              "      <td>392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>PRODUCTIVITY</td>\n",
              "      <td>424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>SHOPPING</td>\n",
              "      <td>260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>SPORTS</td>\n",
              "      <td>384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>TOOLS</td>\n",
              "      <td>843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>WEATHER</td>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f0559aa-29e0-4a4c-9601-1c55787294ec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0f0559aa-29e0-4a4c-9601-1c55787294ec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "# creating dataset for category wise no. of apps \n",
        "category_apps=pd.DataFrame(df.groupby(by='Category',as_index=False)['App'].count())\n",
        "category_apps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "ggkDg4GLooIX",
        "outputId": "647d9eb7-fcd4-4c29-81d4-7fb95b2c5709"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# plotting bar graph\n",
        "category_apps.plot(x='Category',y='App',figsize=(10,5),kind='bar')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KY-xTsO_1YnI"
      },
      "source": [
        "*From graph it can be clearly seen that playstore has most apps for family followed by games and tools*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Boc7nQnzGnyF"
      },
      "source": [
        "## **Free v/s Paid Apps**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "hEEwAcaIGuK1",
        "outputId": "9d014981-5b5c-4ae7-dee2-407cdc3721d7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Type    App\n",
              "0  Free  10040\n",
              "1  Paid    800"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-118097fa-1e96-4d77-8469-86a6298bea70\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Type</th>\n",
              "      <th>App</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Free</td>\n",
              "      <td>10040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Paid</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-118097fa-1e96-4d77-8469-86a6298bea70')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-118097fa-1e96-4d77-8469-86a6298bea70 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-118097fa-1e96-4d77-8469-86a6298bea70');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ],
      "source": [
        "# creating dataset for free and paid apps\n",
        "type_df=df.groupby(['Type'],as_index=False)['App'].count()\n",
        "type_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "o9y3Ahh6Gwun",
        "outputId": "60be4a88-a9a2-49e8-e3a2-f9bbfca78ff0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualizing through pie chart\n",
        "labels=type_df['Type']\n",
        "sizes=type_df['App']\n",
        "colors=['lightcoral','skyblue']\n",
        "explode=(0.1,0)\n",
        "plt.pie(sizes,explode=explode,labels=labels,colors=colors,autopct='%1.1f%%')\n",
        "#plt.axis('equal')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "scWGjZadHMJn"
      },
      "source": [
        "*Approximately 92% of the apps on playstore are free while 8% are paid apps*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NRDURQt210sW"
      },
      "source": [
        "## **No. of free and paid apps for each category.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "o_Vwcld3FDBw",
        "outputId": "58a97416-de72-4e8d-d683-696c7563a114"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Type             Category    Free   Paid\n",
              "0          ART_AND_DESIGN    62.0    3.0\n",
              "1       AUTO_AND_VEHICLES    82.0    3.0\n",
              "2                  BEAUTY    53.0    0.0\n",
              "3     BOOKS_AND_REFERENCE   203.0   28.0\n",
              "4                BUSINESS   446.0   14.0\n",
              "5                  COMICS    60.0    0.0\n",
              "6           COMMUNICATION   360.0   27.0\n",
              "7                  DATING   227.0    7.0\n",
              "8               EDUCATION   152.0    4.0\n",
              "9           ENTERTAINMENT   147.0    2.0\n",
              "10                 EVENTS    63.0    1.0\n",
              "11                 FAMILY  1781.0  191.0\n",
              "12                FINANCE   349.0   17.0\n",
              "13         FOOD_AND_DRINK   125.0    2.0\n",
              "14                   GAME  1061.0   83.0\n",
              "15     HEALTH_AND_FITNESS   325.0   16.0\n",
              "16         HOUSE_AND_HOME    88.0    0.0\n",
              "17     LIBRARIES_AND_DEMO    84.0    1.0\n",
              "18              LIFESTYLE   363.0   19.0\n",
              "19    MAPS_AND_NAVIGATION   132.0    5.0\n",
              "20                MEDICAL   354.0  109.0\n",
              "21     NEWS_AND_MAGAZINES   281.0    2.0\n",
              "22              PARENTING    58.0    2.0\n",
              "23        PERSONALIZATION   309.0   83.0\n",
              "24            PHOTOGRAPHY   313.0   22.0\n",
              "25           PRODUCTIVITY   396.0   28.0\n",
              "26               SHOPPING   258.0    2.0\n",
              "27                 SOCIAL   292.0    3.0\n",
              "28                 SPORTS   360.0   24.0\n",
              "29                  TOOLS   765.0   78.0\n",
              "30       TRAVEL_AND_LOCAL   246.0   12.0\n",
              "31          VIDEO_PLAYERS   171.0    4.0\n",
              "32                WEATHER    74.0    8.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fce608f9-682d-4c4b-9c81-1af84716dff5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Type</th>\n",
              "      <th>Category</th>\n",
              "      <th>Free</th>\n",
              "      <th>Paid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>62.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AUTO_AND_VEHICLES</td>\n",
              "      <td>82.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BEAUTY</td>\n",
              "      <td>53.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BOOKS_AND_REFERENCE</td>\n",
              "      <td>203.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BUSINESS</td>\n",
              "      <td>446.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>COMICS</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>360.0</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>DATING</td>\n",
              "      <td>227.0</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>EDUCATION</td>\n",
              "      <td>152.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>147.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>EVENTS</td>\n",
              "      <td>63.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>FAMILY</td>\n",
              "      <td>1781.0</td>\n",
              "      <td>191.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>FINANCE</td>\n",
              "      <td>349.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>FOOD_AND_DRINK</td>\n",
              "      <td>125.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>GAME</td>\n",
              "      <td>1061.0</td>\n",
              "      <td>83.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>HEALTH_AND_FITNESS</td>\n",
              "      <td>325.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>HOUSE_AND_HOME</td>\n",
              "      <td>88.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>LIBRARIES_AND_DEMO</td>\n",
              "      <td>84.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>363.0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>MAPS_AND_NAVIGATION</td>\n",
              "      <td>132.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>354.0</td>\n",
              "      <td>109.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NEWS_AND_MAGAZINES</td>\n",
              "      <td>281.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>PARENTING</td>\n",
              "      <td>58.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>PERSONALIZATION</td>\n",
              "      <td>309.0</td>\n",
              "      <td>83.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>313.0</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>PRODUCTIVITY</td>\n",
              "      <td>396.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>SHOPPING</td>\n",
              "      <td>258.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>292.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>SPORTS</td>\n",
              "      <td>360.0</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>TOOLS</td>\n",
              "      <td>765.0</td>\n",
              "      <td>78.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>246.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>171.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>WEATHER</td>\n",
              "      <td>74.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fce608f9-682d-4c4b-9c81-1af84716dff5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fce608f9-682d-4c4b-9c81-1af84716dff5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fce608f9-682d-4c4b-9c81-1af84716dff5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ],
      "source": [
        "# Category wise type of apps\n",
        "category_type=pd.DataFrame(df.groupby(by=['Category','Type'],as_index=False)['App'].count())\n",
        "df_app_count = category_type.pivot('Category', 'Type', 'App').fillna(0).reset_index()\n",
        "df_app_count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "YZ42UyW-Mt18",
        "outputId": "1c84afca-e881-447b-cf17-a68fb01a859e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Visualizing through stacked bar chart\n",
        "df_app_count.set_index('Category').plot(kind='bar', stacked=True, figsize=(14,7))\n",
        "plt.xlabel(\"Category\", fontsize=15)\n",
        "plt.ylabel(\"Count\", fontsize=15)\n",
        "plt.title(\"Count of applications in each category differentiated by their type\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qd9hVec12INy"
      },
      "source": [
        "*Some of the categories has just paid apps while all the categories has mostly free apps*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TVjJoPzR2drh"
      },
      "source": [
        "## **Type of apps having rating 5**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "jZayrq487dst",
        "outputId": "0bbcf279-a6e8-4858-e58d-748683057035"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              App  Rating  Type\n",
              "329                  Hojiboy Tojiboyev Life Hacks     5.0  Free\n",
              "612                 American Girls Mobile Numbers     5.0  Free\n",
              "615                                  Awake Dating     5.0  Free\n",
              "633                         Spine- The dating app     5.0  Free\n",
              "636    Girls Live Talk - Free Text and Video Chat     5.0  Free\n",
              "...                                           ...     ...   ...\n",
              "10721                           Mad Dash Fo' Cash     5.0  Free\n",
              "10742                       GKPB FP Online Church     5.0  Free\n",
              "10776                            Monster Ride Pro     5.0  Free\n",
              "10820                             Fr. Daoud Lamei     5.0  Free\n",
              "10837            Fr. Mike Schmitz Audio Teachings     5.0  Free\n",
              "\n",
              "[271 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-702f63e8-5d51-46e8-a005-6147ffb3c4aa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>329</th>\n",
              "      <td>Hojiboy Tojiboyev Life Hacks</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Free</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>612</th>\n",
              "      <td>American Girls Mobile Numbers</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Free</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>615</th>\n",
              "      <td>Awake Dating</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Free</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>633</th>\n",
              "      <td>Spine- The dating app</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Free</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>636</th>\n",
              "      <td>Girls Live Talk - Free Text and Video Chat</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Free</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10721</th>\n",
              "      <td>Mad Dash Fo' Cash</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Free</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10742</th>\n",
              "      <td>GKPB FP Online Church</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Free</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10776</th>\n",
              "      <td>Monster Ride Pro</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Free</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10820</th>\n",
              "      <td>Fr. Daoud Lamei</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Free</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10837</th>\n",
              "      <td>Fr. Mike Schmitz Audio Teachings</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Free</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>271 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-702f63e8-5d51-46e8-a005-6147ffb3c4aa')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-702f63e8-5d51-46e8-a005-6147ffb3c4aa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-702f63e8-5d51-46e8-a005-6147ffb3c4aa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ],
      "source": [
        "rating_app_type=pd.DataFrame(df[df['Rating']==5.0][['App','Rating','Type']])\n",
        "rating_app_type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-PSJa7jA22Yz",
        "outputId": "07b6233b-1c8d-4839-fda1-a60f6c7f2bd7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "App       28\n",
              "Rating    28\n",
              "Type      28\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ],
      "source": [
        "rating_app_type[rating_app_type['Type']=='Paid'].count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLAo8fNP3Xa1"
      },
      "source": [
        "## **Histogram of rating**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "sLkAtg3n8E1W",
        "outputId": "322f6192-9bda-42a3-facd-d3f1be47d057"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbabf137690>"
            ]
          },
          "metadata": {},
          "execution_count": 131
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df['Rating'].hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x5oiR3HB1jhk",
        "outputId": "e5175c3c-2fe3-4e22-e627-e39c041fae44"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "average rating is 4.2\n"
          ]
        }
      ],
      "source": [
        "# calculating average rating\n",
        "print('average rating is',np.median(df['Rating']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oVP-FhF9BkTD"
      },
      "source": [
        "### **Distribution of Size**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "z0vJx-uL17Wl",
        "outputId": "2f5d9459-3a79-4de2-8a37-4b4961a7a640"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# size histogram\n",
        "plt.hist(df['Size'])\n",
        "plt.xlabel('Size')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "A0rnnXb2MzSD",
        "outputId": "ad1d97ff-2a00-411e-ae35-33acfb2f2029"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f3a9e761d50>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# size histogram with mean and meadian line\n",
        "df['Size']=df['Size'].astype(int)\n",
        "sns.histplot(data=df['Size'],kde=True)\n",
        "plt.axvline(df['Size'].mean(),color='r')\n",
        "plt.axvline(df['Size'].median(),color='g')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above plot we can infer that data of size is **right tailed** because **mean>median**"
      ],
      "metadata": {
        "id": "3Bg5i9ZYNqIq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hp8NJrnIM39h",
        "outputId": "81f8b16a-759c-4adf-9a65-ababe38dc807"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Size of app is : 17435.5306967595 kb\n",
            "Median Size of app is : 11000.0 kb\n"
          ]
        }
      ],
      "source": [
        "print('Mean Size of app is :',df['Size'].mean(),'kb')\n",
        "print('Median Size of app is :',df['Size'].median(),'kb')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BopUyI8IRgG"
      },
      "source": [
        "*Most of the apps are of size between 1kb to 20 mb.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cLq0BcUc3f8A"
      },
      "source": [
        "## **Average rating of each category**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uQ-zkLZ4yaX9",
        "outputId": "88813374-5df2-427d-bad9-9dfc38a78eb5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Category    Rating\n",
              "0             EDUCATION  4.360897\n",
              "1        ART_AND_DESIGN  4.156923\n",
              "2         ENTERTAINMENT  4.126174\n",
              "3                  GAME  4.110227\n",
              "4                COMICS  4.016667\n",
              "5           PHOTOGRAPHY  3.966866\n",
              "6              SHOPPING  3.899231\n",
              "7               WEATHER  3.881707\n",
              "8                SOCIAL  3.736271\n",
              "9    HEALTH_AND_FITNESS  3.725220\n",
              "10        VIDEO_PLAYERS  3.715429\n",
              "11               FAMILY  3.713945\n",
              "12  MAPS_AND_NAVIGATION  3.667153\n",
              "13              FINANCE  3.646448\n",
              "14       HOUSE_AND_HOME  3.625000\n",
              "15     TRAVEL_AND_LOCAL  3.599612\n",
              "16    AUTO_AND_VEHICLES  3.598824\n",
              "17            PARENTING  3.583333\n",
              "18       FOOD_AND_DRINK  3.576378\n",
              "19        COMMUNICATION  3.524548\n",
              "20                TOOLS  3.524081\n",
              "21               SPORTS  3.508594\n",
              "22         PRODUCTIVITY  3.486321\n",
              "23      PERSONALIZATION  3.473214\n",
              "24   NEWS_AND_MAGAZINES  3.402120\n",
              "25               BEAUTY  3.390566\n",
              "26            LIFESTYLE  3.365969\n",
              "27  BOOKS_AND_REFERENCE  3.348918\n",
              "28               DATING  3.308974\n",
              "29   LIBRARIES_AND_DEMO  3.195294\n",
              "30              MEDICAL  3.166739\n",
              "31               EVENTS  3.118750\n",
              "32             BUSINESS  2.714783"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b39d3aef-0196-47a3-a4e0-2f3e6604fc76\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>EDUCATION</td>\n",
              "      <td>4.360897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.156923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>4.126174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GAME</td>\n",
              "      <td>4.110227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>COMICS</td>\n",
              "      <td>4.016667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>3.966866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>SHOPPING</td>\n",
              "      <td>3.899231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>WEATHER</td>\n",
              "      <td>3.881707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>3.736271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>HEALTH_AND_FITNESS</td>\n",
              "      <td>3.725220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>3.715429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>FAMILY</td>\n",
              "      <td>3.713945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>MAPS_AND_NAVIGATION</td>\n",
              "      <td>3.667153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>FINANCE</td>\n",
              "      <td>3.646448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>HOUSE_AND_HOME</td>\n",
              "      <td>3.625000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>3.599612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>AUTO_AND_VEHICLES</td>\n",
              "      <td>3.598824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>PARENTING</td>\n",
              "      <td>3.583333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>FOOD_AND_DRINK</td>\n",
              "      <td>3.576378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>3.524548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>TOOLS</td>\n",
              "      <td>3.524081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>SPORTS</td>\n",
              "      <td>3.508594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>PRODUCTIVITY</td>\n",
              "      <td>3.486321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>PERSONALIZATION</td>\n",
              "      <td>3.473214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>NEWS_AND_MAGAZINES</td>\n",
              "      <td>3.402120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>BEAUTY</td>\n",
              "      <td>3.390566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>3.365969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>BOOKS_AND_REFERENCE</td>\n",
              "      <td>3.348918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>DATING</td>\n",
              "      <td>3.308974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>LIBRARIES_AND_DEMO</td>\n",
              "      <td>3.195294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>3.166739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>EVENTS</td>\n",
              "      <td>3.118750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>BUSINESS</td>\n",
              "      <td>2.714783</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b39d3aef-0196-47a3-a4e0-2f3e6604fc76')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b39d3aef-0196-47a3-a4e0-2f3e6604fc76');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "category_rating=df.groupby('Category')['Rating'].mean().sort_values(ascending=False).reset_index()\n",
        "category_rating"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "id": "roeWXqRjKOFs",
        "outputId": "7e20fec3-18b8-45e6-9981-c06cd62ec7a7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Visualizing through swarm plot\n",
        "fig,ax=plt.subplots()\n",
        "fig.set_size_inches(15,15)\n",
        "ax=sns.swarmplot(x='Rating',y='Category',data=category_rating)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ImGHzE6w3yyZ"
      },
      "source": [
        "*Education has the maximum average ratings of 4.3 followed by ART_AND_DESIGN\t4.153125 &\tENTERTAINMENT\t4.135294*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MUlYHar082Wl"
      },
      "source": [
        "## **Generes with Number of Apps**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "06q5re_b4GSr"
      },
      "outputs": [],
      "source": [
        "generes_count=pd.DataFrame(df['Genres'].value_counts().reset_index())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "CDj1ltP4fprc"
      },
      "outputs": [],
      "source": [
        "generes_count.columns=(['Geners','No. of apps'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "4OTLZOhBgABQ",
        "outputId": "9a629ed8-ba9c-4b62-e1ce-c9f908bf6cce"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Geners  No. of apps\n",
              "0                              Tools          826\n",
              "1                      Entertainment          561\n",
              "2                          Education          510\n",
              "3                           Business          420\n",
              "4                            Medical          395\n",
              "..                               ...          ...\n",
              "113        Art & Design;Pretend Play            1\n",
              "114           Lifestyle;Pretend Play            1\n",
              "115                Comics;Creativity            1\n",
              "116  Art & Design;Action & Adventure            1\n",
              "117              Strategy;Creativity            1\n",
              "\n",
              "[118 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-36f9a56a-547b-4fb8-87f2-b8bcd2a76c27\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Geners</th>\n",
              "      <th>No. of apps</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Tools</td>\n",
              "      <td>826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Entertainment</td>\n",
              "      <td>561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Education</td>\n",
              "      <td>510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Business</td>\n",
              "      <td>420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Medical</td>\n",
              "      <td>395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>Lifestyle;Pretend Play</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>Comics;Creativity</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>Art &amp; Design;Action &amp; Adventure</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>Strategy;Creativity</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>118 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-36f9a56a-547b-4fb8-87f2-b8bcd2a76c27')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-36f9a56a-547b-4fb8-87f2-b8bcd2a76c27 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-36f9a56a-547b-4fb8-87f2-b8bcd2a76c27');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ],
      "source": [
        "generes_count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 962
        },
        "id": "eL86dH9S7si2",
        "outputId": "bf9b54bc-e992-45fb-ddd1-a38992cebeb4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Visualizing through bar graph\n",
        "fig,ax=plt.subplots()\n",
        "fig.set_size_inches(10,15)\n",
        "plt.xticks(rotation=80)\n",
        "ax=sns.barplot(x='Geners',y='No. of apps',data=generes_count.head(20))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lXJTcTXy_qtM"
      },
      "source": [
        "*Tools are the Most trending Genres among all genres , followed by Entertainment and Education.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xWnMDvnVBOfP"
      },
      "source": [
        "# **How Rating Changes with Price**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "hWVXw5KeA4lJ",
        "outputId": "57782d04-dc8f-48ac-a19e-0a86b31151b8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Rating  Price\n",
              "0         4.1    0.0\n",
              "1         3.9    0.0\n",
              "2         4.7    0.0\n",
              "3         4.5    0.0\n",
              "4         4.3    0.0\n",
              "...       ...    ...\n",
              "10836     4.5    0.0\n",
              "10837     5.0    0.0\n",
              "10838     0.0    0.0\n",
              "10839     4.5    0.0\n",
              "10840     4.5    0.0\n",
              "\n",
              "[10840 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-200ead20-34e7-4902-ab68-83392b7e7e87\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Rating</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.9</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.3</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10836</th>\n",
              "      <td>4.5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10837</th>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10838</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10839</th>\n",
              "      <td>4.5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10840</th>\n",
              "      <td>4.5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10840 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-200ead20-34e7-4902-ab68-83392b7e7e87')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-200ead20-34e7-4902-ab68-83392b7e7e87 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-200ead20-34e7-4902-ab68-83392b7e7e87');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "price_rating=pd.DataFrame(df[['Rating','Price']])\n",
        "price_rating"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "3HqAQ1w7A7yV",
        "outputId": "a0551823-671f-4cb6-aa74-f19b5df2cff2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1008x648 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x648 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualizing through scatter plot\n",
        "sns.scatterplot(x=\"Price\", y=\"Rating\",data=df)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "plt.figure(figsize=(14,9))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Etb5CU2mSdEc"
      },
      "source": [
        "*Different Ratings are to free apps while high paid apps are given much good ratings.* "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "awD3wDU-E0Oj"
      },
      "source": [
        "## **How Rating and Installs are related?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "afgmBQ1FCn3d",
        "outputId": "33be7bbc-65f4-47ba-fd1d-16a058cac8ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Rating      Installs\n",
              "0      0.0  4.053968e+03\n",
              "1      1.0  3.231250e+02\n",
              "2      1.2  1.000000e+03\n",
              "3      1.4  2.333333e+03\n",
              "4      1.5  3.683333e+02\n",
              "5      1.6  3.800000e+04\n",
              "6      1.7  1.021250e+04\n",
              "7      1.8  6.493750e+04\n",
              "8      1.9  2.992308e+04\n",
              "9      2.0  4.200125e+05\n",
              "10     2.1  2.275000e+04\n",
              "11     2.2  1.521857e+05\n",
              "12     2.3  1.320500e+04\n",
              "13     2.4  6.758421e+04\n",
              "14     2.5  1.227667e+05\n",
              "15     2.6  2.170400e+04\n",
              "16     2.7  1.543920e+05\n",
              "17     2.8  8.355119e+04\n",
              "18     2.9  1.598022e+05\n",
              "19     3.0  4.148660e+05\n",
              "20     3.1  1.544326e+06\n",
              "21     3.2  4.480375e+05\n",
              "22     3.3  9.309000e+05\n",
              "23     3.4  8.548043e+05\n",
              "24     3.5  1.461534e+06\n",
              "25     3.6  1.424815e+06\n",
              "26     3.7  7.205232e+06\n",
              "27     3.8  2.093969e+06\n",
              "28     3.9  1.584731e+07\n",
              "29     4.0  1.876856e+07\n",
              "30     4.1  1.201301e+07\n",
              "31     4.2  1.803592e+07\n",
              "32     4.3  3.505054e+07\n",
              "33     4.4  3.022520e+07\n",
              "34     4.5  3.250675e+07\n",
              "35     4.6  1.515379e+07\n",
              "36     4.7  8.006764e+06\n",
              "37     4.8  1.892953e+06\n",
              "38     4.9  5.043977e+05\n",
              "39     5.0  3.285511e+02"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b1164976-5862-41f3-a27e-b3f1b235e077\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "      <th>Installs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>4.053968e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3.231250e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.2</td>\n",
              "      <td>1.000000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.4</td>\n",
              "      <td>2.333333e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.5</td>\n",
              "      <td>3.683333e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.6</td>\n",
              "      <td>3.800000e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.7</td>\n",
              "      <td>1.021250e+04</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.8</td>\n",
              "      <td>6.493750e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.9</td>\n",
              "      <td>2.992308e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2.0</td>\n",
              "      <td>4.200125e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2.1</td>\n",
              "      <td>2.275000e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2.2</td>\n",
              "      <td>1.521857e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2.3</td>\n",
              "      <td>1.320500e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2.4</td>\n",
              "      <td>6.758421e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2.5</td>\n",
              "      <td>1.227667e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2.6</td>\n",
              "      <td>2.170400e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2.7</td>\n",
              "      <td>1.543920e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2.8</td>\n",
              "      <td>8.355119e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2.9</td>\n",
              "      <td>1.598022e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>3.0</td>\n",
              "      <td>4.148660e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>3.1</td>\n",
              "      <td>1.544326e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>3.2</td>\n",
              "      <td>4.480375e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>3.3</td>\n",
              "      <td>9.309000e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>3.4</td>\n",
              "      <td>8.548043e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>3.5</td>\n",
              "      <td>1.461534e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>3.6</td>\n",
              "      <td>1.424815e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>3.7</td>\n",
              "      <td>7.205232e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>3.8</td>\n",
              "      <td>2.093969e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>3.9</td>\n",
              "      <td>1.584731e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>4.0</td>\n",
              "      <td>1.876856e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>4.1</td>\n",
              "      <td>1.201301e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>4.2</td>\n",
              "      <td>1.803592e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>4.3</td>\n",
              "      <td>3.505054e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>4.4</td>\n",
              "      <td>3.022520e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>4.5</td>\n",
              "      <td>3.250675e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>4.6</td>\n",
              "      <td>1.515379e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>4.7</td>\n",
              "      <td>8.006764e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>4.8</td>\n",
              "      <td>1.892953e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>4.9</td>\n",
              "      <td>5.043977e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.285511e+02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "rating_install=df.groupby(['Rating'],as_index=False)['Installs'].mean()\n",
        "rating_install"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "6YrZ2iGQ22CE",
        "outputId": "07e41b91-16de-4efe-daff-414a665346aa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Visualizing through bar graph\n",
        "plt.bar(rating_install['Rating'],rating_install['Installs'])\n",
        "plt.title('Installs vs Rating')\n",
        "plt.xlabel('Rating')\n",
        "plt.ylabel('Installs')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wgGQW3veIdb0"
      },
      "source": [
        "# **Have a view on Last Updated of Apps**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eIK9usa5LkRh",
        "outputId": "ca500ff1-c7aa-4f91-ee13-4ad66fc37a90"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ],
      "source": [
        "df['Last Updated']=pd.to_datetime(df['Last Updated'])\n",
        "df['Last Updated'].isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "id": "j5ChyTGNlIeW"
      },
      "outputs": [],
      "source": [
        "updated_app=pd.DataFrame(df[['App','Last Updated']].sort_values(by=['Last Updated'],ascending=False))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "id": "nm99Iv-1meMl"
      },
      "outputs": [],
      "source": [
        "update_count=pd.DataFrame(updated_app.groupby(by=['Last Updated'],as_index=False).count())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "wpi3DqrwKL6K",
        "outputId": "acf4dc65-33ca-40dd-8549-63f29f475867"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(*args, **kw)>"
            ]
          },
          "metadata": {},
          "execution_count": 107
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#plotting last updated apps\n",
        "plt.plot(update_count[\"Last Updated\"],update_count[\"App\"])\n",
        "plt.show"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RfV5QoJ4Jxau"
      },
      "source": [
        "*Mostly Apps are updated in the year 2017,2018,2019.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "FCw9q6NaLZAc",
        "outputId": "c05eb31a-f891-43eb-e224-2a2961380608"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Last Updated  App\n",
              "1371   2018-08-03  326"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b86d96cf-cb49-4f99-b35e-33ed09335f6b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Last Updated</th>\n",
              "      <th>App</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1371</th>\n",
              "      <td>2018-08-03</td>\n",
              "      <td>326</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b86d96cf-cb49-4f99-b35e-33ed09335f6b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b86d96cf-cb49-4f99-b35e-33ed09335f6b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b86d96cf-cb49-4f99-b35e-33ed09335f6b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "# maximum no. of apps updated on which date \n",
        "update_count[update_count['App']==update_count[\"App\"].max()]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KUGCwMmEO16I"
      },
      "source": [
        "## **EDA on User Review Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "usL15A-rfZ3-",
        "outputId": "03d7e77e-65d7-48d2-9b25-f50d2d67d028"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(865, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ],
      "source": [
        "df4=df2.groupby(by=['App'],as_index=False)['Translated_Review'].count().reset_index(drop=True)\n",
        "df4.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "kzpRzG68FOPg",
        "outputId": "79857c1b-e885-4e4e-c287-756238f4d32e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   App  Translated_Review\n",
              "0                                10 Best Foods for You                194\n",
              "1                     104 找工作 - 找工作 找打工 找兼職 履歷健檢 履歷診療室                 40\n",
              "2                                                 11st                 39\n",
              "3                           1800 Contacts - Lens Store                 80\n",
              "4                      1LINE – One Line with One Touch                 38\n",
              "..                                                 ...                ...\n",
              "860  Hotels.com: Book Hotel Rooms & Find Vacation D...                 68\n",
              "861     Hotspot Shield Free VPN Proxy & Wi-Fi Security                 34\n",
              "862                                            Hotstar                 32\n",
              "863                     Hotwire Hotel & Car Rental App                 33\n",
              "864                     Housing-Real Estate & Property                 21\n",
              "\n",
              "[865 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1eb0e3d0-4085-4097-95ae-9b01b1cc3a9e\">\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>App</th>\n",
              "      <th>Translated_Review</th>\n",
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              "      <th>0</th>\n",
              "      <td>10 Best Foods for You</td>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>104 找工作 - 找工作 找打工 找兼職 履歷健檢 履歷診療室</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11st</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1800 Contacts - Lens Store</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1LINE – One Line with One Touch</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>860</th>\n",
              "      <td>Hotels.com: Book Hotel Rooms &amp; Find Vacation D...</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>861</th>\n",
              "      <td>Hotspot Shield Free VPN Proxy &amp; Wi-Fi Security</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>862</th>\n",
              "      <td>Hotstar</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>863</th>\n",
              "      <td>Hotwire Hotel &amp; Car Rental App</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>864</th>\n",
              "      <td>Housing-Real Estate &amp; Property</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>865 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1eb0e3d0-4085-4097-95ae-9b01b1cc3a9e')\"\n",
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              "  </svg>\n",
              "      </button>\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1eb0e3d0-4085-4097-95ae-9b01b1cc3a9e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1eb0e3d0-4085-4097-95ae-9b01b1cc3a9e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "n_ytOUkhwZc-",
        "outputId": "8d6d13d6-ebf3-44ab-faf2-04dd0bd998c8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            App  Translated_Review\n",
              "256  Bowmasters                312"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fb5567f6-7f83-42c2-9312-c62752c414d8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Translated_Review</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>Bowmasters</td>\n",
              "      <td>312</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb5567f6-7f83-42c2-9312-c62752c414d8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fb5567f6-7f83-42c2-9312-c62752c414d8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fb5567f6-7f83-42c2-9312-c62752c414d8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ],
      "source": [
        "# maximum translated app review\n",
        "df4[df4['Translated_Review']==max(df4['Translated_Review'])]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "Z-b7UzERu6FI",
        "outputId": "c7b59cf8-24fb-45ed-97a8-cac52d3e67a8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a9e439410>"
            ]
          },
          "metadata": {},
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Visualizing through histogram\n",
        "plt.figure(figsize=(15,7))\n",
        "sns.histplot(df4['Translated_Review'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's merge the two datasets**"
      ],
      "metadata": {
        "id": "wtADnizR-knL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "id": "HleqBgaGrSvl"
      },
      "outputs": [],
      "source": [
        "# To merge dataset 1 and datset 2\n",
        "new_df = pd.merge(df2, df[[\"App\",\"Type\",\"Price\",\"Reviews\"]] , how='inner', on=\"App\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "8EhfUF2z-tWE",
        "outputId": "2ba4695c-fc36-49ff-f701-87a2fbf017e0"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  App  \\\n",
              "0               10 Best Foods for You   \n",
              "1               10 Best Foods for You   \n",
              "2               10 Best Foods for You   \n",
              "3               10 Best Foods for You   \n",
              "4               10 Best Foods for You   \n",
              "...                               ...   \n",
              "72600  Housing-Real Estate & Property   \n",
              "72601  Housing-Real Estate & Property   \n",
              "72602  Housing-Real Estate & Property   \n",
              "72603  Housing-Real Estate & Property   \n",
              "72604  Housing-Real Estate & Property   \n",
              "\n",
              "                                       Translated_Review Sentiment  \\\n",
              "0      I like eat delicious food. That's I'm cooking ...  Positive   \n",
              "1      I like eat delicious food. That's I'm cooking ...  Positive   \n",
              "2        This help eating healthy exercise regular basis  Positive   \n",
              "3        This help eating healthy exercise regular basis  Positive   \n",
              "4             Works great especially going grocery store  Positive   \n",
              "...                                                  ...       ...   \n",
              "72600  Most ads older many agents ..not much owner po...  Positive   \n",
              "72601  If photos posted portal load, fit purpose. I'm...  Positive   \n",
              "72602  Dumb app, I wanted post property rent give opt...  Negative   \n",
              "72603  I property business got link SMS happy perform...  Positive   \n",
              "72604  Useless app, I searched flats kondapur, Hydera...  Negative   \n",
              "\n",
              "       Sentiment_Polarity  Sentiment_Subjectivity  Type  Price  Reviews  \n",
              "0                1.000000                0.533333  Free    0.0   2490.0  \n",
              "1                1.000000                0.533333  Free    0.0   2490.0  \n",
              "2                0.250000                0.288462  Free    0.0   2490.0  \n",
              "3                0.250000                0.288462  Free    0.0   2490.0  \n",
              "4                0.400000                0.875000  Free    0.0   2490.0  \n",
              "...                   ...                     ...   ...    ...      ...  \n",
              "72600            0.173333                0.486667  Free    0.0  28301.0  \n",
              "72601            0.225000                0.447222  Free    0.0  28301.0  \n",
              "72602           -0.287500                0.250000  Free    0.0  28301.0  \n",
              "72603            0.800000                1.000000  Free    0.0  28301.0  \n",
              "72604           -0.316667                0.400000  Free    0.0  28301.0  \n",
              "\n",
              "[72605 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5e3c1ff1-4f93-41cf-bb97-e9b69b9ea9f3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Translated_Review</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Sentiment_Polarity</th>\n",
              "      <th>Sentiment_Subjectivity</th>\n",
              "      <th>Type</th>\n",
              "      <th>Price</th>\n",
              "      <th>Reviews</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10 Best Foods for You</td>\n",
              "      <td>I like eat delicious food. That's I'm cooking ...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2490.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10 Best Foods for You</td>\n",
              "      <td>I like eat delicious food. That's I'm cooking ...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2490.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10 Best Foods for You</td>\n",
              "      <td>This help eating healthy exercise regular basis</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.288462</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2490.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10 Best Foods for You</td>\n",
              "      <td>This help eating healthy exercise regular basis</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.288462</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2490.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10 Best Foods for You</td>\n",
              "      <td>Works great especially going grocery store</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2490.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72600</th>\n",
              "      <td>Housing-Real Estate &amp; Property</td>\n",
              "      <td>Most ads older many agents ..not much owner po...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.173333</td>\n",
              "      <td>0.486667</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28301.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72601</th>\n",
              "      <td>Housing-Real Estate &amp; Property</td>\n",
              "      <td>If photos posted portal load, fit purpose. I'm...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.225000</td>\n",
              "      <td>0.447222</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28301.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72602</th>\n",
              "      <td>Housing-Real Estate &amp; Property</td>\n",
              "      <td>Dumb app, I wanted post property rent give opt...</td>\n",
              "      <td>Negative</td>\n",
              "      <td>-0.287500</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28301.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72603</th>\n",
              "      <td>Housing-Real Estate &amp; Property</td>\n",
              "      <td>I property business got link SMS happy perform...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28301.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72604</th>\n",
              "      <td>Housing-Real Estate &amp; Property</td>\n",
              "      <td>Useless app, I searched flats kondapur, Hydera...</td>\n",
              "      <td>Negative</td>\n",
              "      <td>-0.316667</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28301.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>72605 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e3c1ff1-4f93-41cf-bb97-e9b69b9ea9f3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5e3c1ff1-4f93-41cf-bb97-e9b69b9ea9f3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5e3c1ff1-4f93-41cf-bb97-e9b69b9ea9f3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nK2TCN-Y-ukR",
        "outputId": "7199fa46-b861-4618-c385-190a972368ef"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 72605 entries, 0 to 72604\n",
            "Data columns (total 8 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   App                     72605 non-null  object \n",
            " 1   Translated_Review       72605 non-null  object \n",
            " 2   Sentiment               72605 non-null  object \n",
            " 3   Sentiment_Polarity      72605 non-null  float64\n",
            " 4   Sentiment_Subjectivity  72605 non-null  float64\n",
            " 5   Type                    72605 non-null  object \n",
            " 6   Price                   72605 non-null  float64\n",
            " 7   Reviews                 72605 non-null  float64\n",
            "dtypes: float64(4), object(4)\n",
            "memory usage: 5.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "sOqBlHKp-wjy",
        "outputId": "e3ede7b1-424c-4efe-801a-69d164cf7ef7"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Sentiment_Polarity  Sentiment_Subjectivity         Price       Reviews\n",
              "count        72605.000000            72605.000000  72605.000000  7.260500e+04\n",
              "mean             0.157342                0.494230      0.087035  2.559316e+06\n",
              "std              0.332670                0.240091      1.064462  6.780453e+06\n",
              "min             -1.000000                0.000000      0.000000  1.000000e+01\n",
              "25%              0.000000                0.380000      0.000000  2.466600e+04\n",
              "50%              0.129427                0.509740      0.000000  1.955580e+05\n",
              "75%              0.356534                0.633333      0.000000  1.855262e+06\n",
              "max              1.000000                1.000000     29.990000  7.815831e+07"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9323934e-9ff3-4c43-b049-8c8daa6fbf00\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>Sentiment_Polarity</th>\n",
              "      <th>Sentiment_Subjectivity</th>\n",
              "      <th>Price</th>\n",
              "      <th>Reviews</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>72605.000000</td>\n",
              "      <td>72605.000000</td>\n",
              "      <td>72605.000000</td>\n",
              "      <td>7.260500e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.157342</td>\n",
              "      <td>0.494230</td>\n",
              "      <td>0.087035</td>\n",
              "      <td>2.559316e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.332670</td>\n",
              "      <td>0.240091</td>\n",
              "      <td>1.064462</td>\n",
              "      <td>6.780453e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.380000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.466600e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.129427</td>\n",
              "      <td>0.509740</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.955580e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.356534</td>\n",
              "      <td>0.633333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.855262e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>29.990000</td>\n",
              "      <td>7.815831e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9323934e-9ff3-4c43-b049-8c8daa6fbf00')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9323934e-9ff3-4c43-b049-8c8daa6fbf00 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9323934e-9ff3-4c43-b049-8c8daa6fbf00');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing Sentiment_Polarity through histogram\n",
        "new_df['Sentiment_Polarity'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "EKbZtMcv-37S",
        "outputId": "f51a4564-7235-43bd-db2f-41274441579b"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a9e230d10>"
            ]
          },
          "metadata": {},
          "execution_count": 125
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribution plot of Sentiment Subjectivity"
      ],
      "metadata": {
        "id": "OjUTehQRJ0F-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing Sentiment_Subjectivity through histogram\n",
        "sns.distplot(new_df['Sentiment_Subjectivity'], hist=True)\n",
        "plt.xlabel(\"Subjectivity\")\n",
        "plt.title('Distribution of Subjectivity')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "_c8xZrwX_KaB",
        "outputId": "d3f3eb8f-cd71-4f93-b0ba-54b63f033e53"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of Subjectivity')"
            ]
          },
          "metadata": {},
          "execution_count": 126
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df[new_df['Sentiment_Subjectivity']==-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "FIFFbqVgBSbT",
        "outputId": "87947f5f-d584-432e-81bd-004ca61e4c05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [App, Translated_Review, Sentiment, Sentiment_Polarity, Sentiment_Subjectivity, Type, Price, Reviews]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e75afee2-b4bb-4926-8710-80413637e5af\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Translated_Review</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Sentiment_Polarity</th>\n",
              "      <th>Sentiment_Subjectivity</th>\n",
              "      <th>Type</th>\n",
              "      <th>Price</th>\n",
              "      <th>Reviews</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e75afee2-b4bb-4926-8710-80413637e5af')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e75afee2-b4bb-4926-8710-80413637e5af button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e75afee2-b4bb-4926-8710-80413637e5af');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*There are no apps with sentiment subjectivity of -1*"
      ],
      "metadata": {
        "id": "Yw7T7w62Bi6q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.Sentiment.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sXIEUYUX_Yly",
        "outputId": "6fee85e2-ba08-4754-b01c-e6b95972f85c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Positive    23073\n",
              "Negative     8005\n",
              "Neutral      4851\n",
              "Name: Sentiment, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **See the percent of Sentiment Counts**"
      ],
      "metadata": {
        "id": "ZqEt8dkv_wh7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing Sentiment Counts through pie chart\n",
        "new_df['Sentiment'].value_counts().plot(kind='pie', shadow=True, autopct='%1.2f%%', pctdistance=1.1, labeldistance=1.2,colors=['Blue','red','purple'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "DAoWSa2JACUm",
        "outputId": "5119de1b-65a5-4613-f270-d37214ad59fe"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a9e0cc290>"
            ]
          },
          "metadata": {},
          "execution_count": 127
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Here we can see that there are almost 64% positive Reviews given to apps ,i.e., most of the apps have positive effect on users.*"
      ],
      "metadata": {
        "id": "REMN4ypFA5i7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Sentiment of apps for type as free and paid**"
      ],
      "metadata": {
        "id": "SZo4UTgKoxnR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentiment_type=pd.DataFrame(new_df.groupby(['Sentiment','Type'])['App'].count()).reset_index()\n",
        "sentiment_type"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "XvM2axJciVwr",
        "outputId": "675a4349-3cf6-4dab-9f42-4db38950bec0"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Sentiment  Type    App\n",
              "0  Negative  Free  18020\n",
              "1  Negative  Paid    114\n",
              "2   Neutral  Free   8234\n",
              "3   Neutral  Paid     42\n",
              "4  Positive  Free  45569\n",
              "5  Positive  Paid    626"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6179cc20-dae3-4cc8-aaec-279d41b9b75e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Sentiment</th>\n",
              "      <th>Type</th>\n",
              "      <th>App</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Negative</td>\n",
              "      <td>Free</td>\n",
              "      <td>18020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Negative</td>\n",
              "      <td>Paid</td>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Neutral</td>\n",
              "      <td>Free</td>\n",
              "      <td>8234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Neutral</td>\n",
              "      <td>Paid</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Positive</td>\n",
              "      <td>Free</td>\n",
              "      <td>45569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Positive</td>\n",
              "      <td>Paid</td>\n",
              "      <td>626</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6179cc20-dae3-4cc8-aaec-279d41b9b75e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating dataset for sentiment and type of apps\n",
        "sentiment_type1=sentiment_type.pivot('Sentiment','Type','App').fillna(0).reset_index()\n",
        "sentiment_type1\n"
      ],
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          "height": 143
        },
        "id": "M-NDIpG0l4b9",
        "outputId": "714e4a55-fa59-47a4-ab5f-2a6454c1795b"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Type Sentiment   Free  Paid\n",
              "0     Negative  18020   114\n",
              "1      Neutral   8234    42\n",
              "2     Positive  45569   626"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Type</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Free</th>\n",
              "      <th>Paid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Positive</td>\n",
              "      <td>45569</td>\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-cff95303-bd2c-4032-90fc-c55bfa1d65fb');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing sentiments of free apps  through bar chart\n",
        "x=sentiment_type1['Sentiment']\n",
        "y=sentiment_type1['Free']\n",
        "sns.barplot(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "fjK1jtH6kF_A",
        "outputId": "8c0ed003-53d3-47d4-fad9-6b3f2b987323"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a9e805490>"
            ]
          },
          "metadata": {},
          "execution_count": 131
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing sentiments of paid apps  through bar chart\n",
        "x=sentiment_type1['Sentiment']\n",
        "y=sentiment_type1['Paid']\n",
        "sns.barplot(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "0Y0fursckdV-",
        "outputId": "bbf699f3-0b92-404d-b994-80d235c8338d"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a9eb15a50>"
            ]
          },
          "metadata": {},
          "execution_count": 132
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Both types of apps have high positive sentiments compare to negative and nuetral.*\n",
        "*Paid apps generally have low negative and nuetral sentiments.*"
      ],
      "metadata": {
        "id": "zivHijOzqHo_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**CONCLUSION:**\n",
        "\n",
        "\n",
        "*   Facebook has the maximum number of reviews.\n",
        "*   There are total 15 apps which are unistalled.\n",
        "*   There are total 20 apps having 1 B installs.\n",
        "*   Games are the most downloaded category followed by communication and tools.\n",
        "*   Most of the apps are from content rating as everyone equivalent to 81.8%.\n",
        "*   Content rating as Everyone has the maximum no. of installs.\n",
        "*   Minecraft application generates the maximum revenue.\n",
        "*   Most of the apps are for family followed by games and tools.\n",
        "*   92% of the apps on playstore are free while 8% are paid apps.\n",
        "*   Average rating of apps on playstore is 4.2\n",
        "*   Most of the apps are of size between 1Kb to 20Mb.\n",
        "*   Education category has maximum average rating as 4.3\n",
        "*   Tools are the most famous generes having 826 apps on playstore.\n",
        "*   Free apps has varying ratings while paid apps generally have high ratings.\n",
        "*   Mostly apps are updated in the year 2017,2018,2019.\n",
        "*   Bowmaster app has the maximum translated reiews as 312.\n",
        "*   Sentiment subjectivity generally lies b/w 0.2 to 1\n",
        "*   There are no apps having Sentiment subjectivity as -1.\n",
        "*   There are almost 64% positive Reviews given to apps ,i.e., most of the \n",
        "    apps have positive effect on users.\n",
        "\n",
        "*  Both types of apps have high positive sentiments compare to negative and nuetral. Paid apps generally have low negative and nuetral sentiments.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "miKwDs25LrtY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**THANK YOU**"
      ],
      "metadata": {
        "id": "5g_HyFhIdPEC"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}