Google_Play_Store_App_Analytics.ipynb

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        "<a href=\"https://colab.research.google.com/github/skdarkey/Selorm_Portfolio/blob/main/Google_Play_Store_App_Analytics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "key": "4"
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      "source": [
        "# Introduction\n",
        "\n",
        "In this notebook, I did a comprehensive analysis of the Android app market by comparing thousands of apps in the Google Play store.\n",
        "\n",
        "\n",
        "* The goal is to understand the android application store to enable me price my future applications reasonably. My colleague developers can also make use of this analytics to place the apps in the appropriate categories and adopt best pricing mechanisms backed by data to ensure success of their apps."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sDT5lMxadiT3"
      },
      "source": [
        "# About the Dataset of Google Play Store Apps & Reviews"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5CiydsdNdiT3"
      },
      "source": [
        "**Data Source:** <br>\n",
        "The original App and review data was scraped from the Google Play Store by Lavanya Gupta in 2018. Original data can be found here [here](\n",
        "https://www.kaggle.com/lava18/google-play-store-apps) on Kaggle."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# Import Statements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_5mfFH4_diT4"
      },
      "source": [
        "import pandas as pd\n",
        "import plotly.express as px"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KegC9nPDdiT7"
      },
      "source": [
        "# Notebook Presentation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPL7rhJndiT7"
      },
      "source": [
        "# Show numeric output in decimal format e.g., 2.15\n",
        "pd.options.display.float_format = '{:,.2f}'.format"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uc9LOZu9diT-"
      },
      "source": [
        "# Read the Dataset"
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "zU0Zisv_diT-"
      },
      "source": [
        "data = \"https://raw.githubusercontent.com/skdarkey/Datasets/main/Google_Play_Store_Apps_Analytics.csv\"\n",
        "#df_apps = pd.read_csv('apps.csv')\n",
        "df_apps = pd.read_csv(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iKvSUwQldiUA"
      },
      "source": [
        "# Data Cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MWTzYZIds5L9"
      },
      "source": [
        "Knowing the number of rows and columns the `df_apps` have? What the column names are?"
      ]
    },
    {
      "cell_type": "code",
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        "id": "HJI7UsPrnuRf",
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      },
      "source": [
        "df_apps.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10841, 12)"
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          "execution_count": 4
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        "outputId": "7e4fa2dd-8ef7-448f-baf4-198e7fa74b45"
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      "source": [
        "# Getting the column names in the data set\n",
        "df_apps.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['App', 'Category', 'Rating', 'Reviews', 'Size_MBs', 'Installs', 'Type',\n",
              "       'Price', 'Content_Rating', 'Genres', 'Last_Updated', 'Android_Ver'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 5
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      ]
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        "outputId": "5354f327-9972-4fd7-9636-f4d43a0aa16c"
      },
      "source": [
        "# Inspecting random data in the data set\n",
        "df_apps.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                App       Category  Rating  Reviews  Size_MBs     Installs  \\\n",
              "805     Prestige AH        MEDICAL     4.0        2     29.00          100   \n",
              "1415     CX Capture       BUSINESS     NaN        1      5.80          100   \n",
              "1593         DG-App          TOOLS     5.0        1      4.50          500   \n",
              "457    FN Web Radio  COMMUNICATION     NaN        0      1.60           10   \n",
              "10381    Google Pay        FINANCE     4.2   347838      9.15  100,000,000   \n",
              "\n",
              "       Type Price Content_Rating         Genres       Last_Updated  \\\n",
              "805    Free     0       Everyone        Medical       May 11, 2017   \n",
              "1415   Free     0       Everyone       Business   January 23, 2018   \n",
              "1593   Free     0       Everyone          Tools  November 29, 2017   \n",
              "457    Free     0       Everyone  Communication       May 18, 2018   \n",
              "10381  Free     0       Everyone        Finance      July 26, 2018   \n",
              "\n",
              "              Android_Ver  \n",
              "805          4.0.3 and up  \n",
              "1415         4.0.3 and up  \n",
              "1593           4.1 and up  \n",
              "457            2.3 and up  \n",
              "10381  Varies with device  "
            ],
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              "      <th>805</th>\n",
              "      <td>Prestige AH</td>\n",
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              "      <td>November 29, 2017</td>\n",
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              "      <td>Google Pay</td>\n",
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              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
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              "      <td>July 26, 2018</td>\n",
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          "metadata": {},
          "execution_count": 6
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hh81sMcvdiUI"
      },
      "source": [
        "### Droping some Unused Columns\n",
        "\n",
        "** `Last_Updated` and `Android_Version` from the DataFrame will not be used."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7EMSKAOnyzI"
      },
      "source": [
        "df_apps.drop(['Last_Updated', 'Android_Ver'], axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQGe1DIKdiUM"
      },
      "source": [
        "### Finding and Removing NaN values in Ratings column\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "grcP1IOZn1Wv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92a4c437-413c-4262-c33a-6c1ab5b94c5f"
      },
      "source": [
        "# getting to know number of columns containing 'Not-a-Number' values in the Dataset.\n",
        "df_apps.Rating.isna().values.sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1474"
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          "metadata": {},
          "execution_count": 11
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        "outputId": "f0a9bda1-9465-4213-c159-3bfa0eb2c962"
      },
      "source": [
        "#droping columns with NaN Values in the data set\n",
        "df_apps_clean = df_apps.dropna()\n",
        "print(df_apps_clean.head())\n",
        "df_apps_clean.isna().values.any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                App Category  Rating  Reviews  \\\n",
            "21                              KBA-EZ Health Guide  MEDICAL    5.00        4   \n",
            "28                                         Ra Ga Ba     GAME    5.00        2   \n",
            "47                                          Mu.F.O.     GAME    5.00        2   \n",
            "82                                 Brick Breaker BR     GAME    5.00        7   \n",
            "99  Anatomy & Physiology Vocabulary Exam Review App  MEDICAL    5.00        1   \n",
            "\n",
            "    Size_MBs Installs  Type  Price Content_Rating   Genres  \n",
            "21     25.00        1  Free      0       Everyone  Medical  \n",
            "28     20.00        1  Paid  $1.49       Everyone   Arcade  \n",
            "47     16.00        1  Paid  $0.99       Everyone   Arcade  \n",
            "82     19.00        5  Free      0       Everyone   Arcade  \n",
            "99      4.60        5  Free      0       Everyone  Medical  \n"
          ]
        },
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          "data": {
            "text/plain": [
              "False"
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          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pD26SWlxdiUV"
      },
      "source": [
        "### Finding and Removing Duplicates\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lE7X6KG3n5pR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bbd736e-f369-42d1-ab81-ecd5b810c0bf"
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      "source": [
        "#Checking for duplicates in the data\n",
        "df_apps_clean.duplicated().values.any()                 #Are there any duplicates in the data\n",
        "df_apps_clean.duplicated().values.sum()                 #In total, how many duplicated rows do we have"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "476"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wtt09kiRn6GS"
      },
      "source": [
        "# providing specific column names to be used to check and remove duplicates\n",
        "df_apps_clean = df_apps_clean.drop_duplicates(subset=['App', 'Type', 'Price'])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "30yZCwtKn5xC",
        "colab": {
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          "height": 80
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        "outputId": "ade274df-6b1a-4539-9e7e-e9a8fe1df66e"
      },
      "source": [
        "# Checking for a specific name with duplicates.\n",
        "df_apps_clean[df_apps_clean.App == 'Instagram']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             App Category  Rating   Reviews  Size_MBs       Installs  Type  \\\n",
              "10806  Instagram   SOCIAL    4.50  66577313      5.30  1,000,000,000  Free   \n",
              "\n",
              "      Price Content_Rating  Genres  \n",
              "10806     0           Teen  Social  "
            ],
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10806</th>\n",
              "      <td>Instagram</td>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>4.50</td>\n",
              "      <td>66577313</td>\n",
              "      <td>5.30</td>\n",
              "      <td>1,000,000,000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
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              "  "
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        "id": "AagS_wgWn5ib",
        "colab": {
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          "height": 300
        },
        "outputId": "43c70f1c-95af-421a-e539-3a8b4553fe0d"
      },
      "source": [
        "df_apps_clean.shape\n",
        "df_apps_clean.describe()  # getting descriptive stats"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Rating       Reviews  Size_MBs\n",
              "count 8,199.00      8,199.00  8,199.00\n",
              "mean      4.17    255,072.97     20.09\n",
              "std       0.54  1,985,241.40     21.65\n",
              "min       1.00          1.00      0.01\n",
              "25%       4.00        126.00      4.90\n",
              "50%       4.30      2,992.00     11.00\n",
              "75%       4.50     43,612.50     28.00\n",
              "max       5.00 78,158,306.00    100.00"
            ],
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              "      <th>count</th>\n",
              "      <td>8,199.00</td>\n",
              "      <td>8,199.00</td>\n",
              "      <td>8,199.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.17</td>\n",
              "      <td>255,072.97</td>\n",
              "      <td>20.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.54</td>\n",
              "      <td>1,985,241.40</td>\n",
              "      <td>21.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <th>50%</th>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.50</td>\n",
              "      <td>43,612.50</td>\n",
              "      <td>28.00</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.00</td>\n",
              "      <td>78,158,306.00</td>\n",
              "      <td>100.00</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJrM69CfdiUh"
      },
      "source": [
        "# Finding the Highest Rated Apps\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rp7vFHBMn7st",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16c83605-0605-4ae3-d9d0-84ed9e57074f"
      },
      "source": [
        "# Identifying highest rated Apps\n",
        "High_Rated = df_apps_clean.sort_values('Rating', ascending=False)\n",
        "\n",
        "# Identifying highest rated Apps that also have highest reviews\n",
        "High_Rated_AndReviewed = High_Rated.sort_values('Reviews', ascending=False)\n",
        "\n",
        "#Print samples of each to compare\n",
        "print(High_Rated.head())\n",
        "print(High_Rated_AndReviewed.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                      App     Category  Rating  Reviews  Size_MBs Installs  \\\n",
            "21    KBA-EZ Health Guide      MEDICAL    5.00        4     25.00        1   \n",
            "1230         Sway Medical      MEDICAL    5.00        3     22.00      100   \n",
            "1227    AJ Men's Grooming    LIFESTYLE    5.00        2     22.00      100   \n",
            "1224       FK Dedinje BGD       SPORTS    5.00       36      2.60      100   \n",
            "1223      CB VIDEO VISION  PHOTOGRAPHY    5.00       13      2.60      100   \n",
            "\n",
            "      Type Price Content_Rating       Genres  \n",
            "21    Free     0       Everyone      Medical  \n",
            "1230  Free     0       Everyone      Medical  \n",
            "1227  Free     0       Everyone    Lifestyle  \n",
            "1224  Free     0       Everyone       Sports  \n",
            "1223  Free     0       Everyone  Photography  \n",
            "                                            App       Category  Rating  \\\n",
            "10805                                  Facebook         SOCIAL    4.10   \n",
            "10785                        WhatsApp Messenger  COMMUNICATION    4.40   \n",
            "10806                                 Instagram         SOCIAL    4.50   \n",
            "10784  Messenger – Text and Video Chat for Free  COMMUNICATION    4.00   \n",
            "10650                            Clash of Clans           GAME    4.60   \n",
            "\n",
            "        Reviews  Size_MBs       Installs  Type Price Content_Rating  \\\n",
            "10805  78158306      5.30  1,000,000,000  Free     0           Teen   \n",
            "10785  69119316      3.50  1,000,000,000  Free     0       Everyone   \n",
            "10806  66577313      5.30  1,000,000,000  Free     0           Teen   \n",
            "10784  56642847      3.50  1,000,000,000  Free     0       Everyone   \n",
            "10650  44891723     98.00    100,000,000  Free     0   Everyone 10+   \n",
            "\n",
            "              Genres  \n",
            "10805         Social  \n",
            "10785  Communication  \n",
            "10806         Social  \n",
            "10784  Communication  \n",
            "10650       Strategy  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "28o1Tro4diUi"
      },
      "source": [
        "# Find 5 Largest Apps in terms of Size (MBs)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5r0pJCOn9Vu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9d9c30c3-acfb-4ce8-81bf-c7f7cefd1172"
      },
      "source": [
        "LargestApps = df_apps_clean.sort_values('Size_MBs', ascending=False)\n",
        "LargestApps[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  App            Category  Rating  Reviews  \\\n",
              "9942   Talking Babsy Baby: Baby Games           LIFESTYLE    4.00   140995   \n",
              "10687          Hungry Shark Evolution                GAME    4.50  6074334   \n",
              "9943            Miami crime simulator                GAME    4.00   254518   \n",
              "9944     Gangster Town: Vice District              FAMILY    4.30    65146   \n",
              "3144                       Vi Trainer  HEALTH_AND_FITNESS    3.60      124   \n",
              "\n",
              "       Size_MBs     Installs  Type Price Content_Rating  \\\n",
              "9942     100.00   10,000,000  Free     0       Everyone   \n",
              "10687    100.00  100,000,000  Free     0           Teen   \n",
              "9943     100.00   10,000,000  Free     0     Mature 17+   \n",
              "9944     100.00   10,000,000  Free     0     Mature 17+   \n",
              "3144     100.00        5,000  Free     0       Everyone   \n",
              "\n",
              "                       Genres  \n",
              "9942   Lifestyle;Pretend Play  \n",
              "10687                  Arcade  \n",
              "9943                   Action  \n",
              "9944               Simulation  \n",
              "3144         Health & Fitness  "
            ],
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              "  <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_MBs</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Type</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content_Rating</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9942</th>\n",
              "      <td>Talking Babsy Baby: Baby Games</td>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>4.00</td>\n",
              "      <td>140995</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10,000,000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Lifestyle;Pretend Play</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10687</th>\n",
              "      <td>Hungry Shark Evolution</td>\n",
              "      <td>GAME</td>\n",
              "      <td>4.50</td>\n",
              "      <td>6074334</td>\n",
              "      <td>100.00</td>\n",
              "      <td>100,000,000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>Arcade</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9943</th>\n",
              "      <td>Miami crime simulator</td>\n",
              "      <td>GAME</td>\n",
              "      <td>4.00</td>\n",
              "      <td>254518</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10,000,000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Mature 17+</td>\n",
              "      <td>Action</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9944</th>\n",
              "      <td>Gangster Town: Vice District</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>4.30</td>\n",
              "      <td>65146</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10,000,000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Mature 17+</td>\n",
              "      <td>Simulation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3144</th>\n",
              "      <td>Vi Trainer</td>\n",
              "      <td>HEALTH_AND_FITNESS</td>\n",
              "      <td>3.60</td>\n",
              "      <td>124</td>\n",
              "      <td>100.00</td>\n",
              "      <td>5,000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Health &amp; Fitness</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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          "metadata": {},
          "execution_count": 18
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "*Based on the data, there seems to be an upper limit in size for apps that can be placed on the Google App store.*"
      ],
      "metadata": {
        "id": "37ZxxcRqbTO6"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C3gTckh7diUk"
      },
      "source": [
        "# Finding the top 50 Apps with Most Reviews\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TLt98G0Sn_De",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22646e98-ad90-4e46-e48e-001c41daa9bb"
      },
      "source": [
        "# Identifying highest reviewed Apps\n",
        "Highest_Reviewed = df_apps_clean.sort_values('Reviews', ascending=False)\n",
        "HighR50 = Highest_Reviewed[:50]\n",
        "print(HighR50)\n",
        "\n",
        "# Are there any paid applications in the top 50 most reviewed apps\n",
        "HighR50[HighR50.Type == 'Paid'].values.any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                     App            Category  \\\n",
            "10805                                           Facebook              SOCIAL   \n",
            "10785                                 WhatsApp Messenger       COMMUNICATION   \n",
            "10806                                          Instagram              SOCIAL   \n",
            "10784           Messenger – Text and Video Chat for Free       COMMUNICATION   \n",
            "10650                                     Clash of Clans                GAME   \n",
            "10744            Clean Master- Space Cleaner & Antivirus               TOOLS   \n",
            "10835                                     Subway Surfers                GAME   \n",
            "10828                                            YouTube       VIDEO_PLAYERS   \n",
            "10746  Security Master - Antivirus, VPN, AppLock, Boo...               TOOLS   \n",
            "10584                                       Clash Royale                GAME   \n",
            "10763                                   Candy Crush Saga                GAME   \n",
            "10770        UC Browser - Fast Download Private & Secure       COMMUNICATION   \n",
            "10735                                           Snapchat              SOCIAL   \n",
            "10489    360 Security - Free Antivirus, Booster, Cleaner               TOOLS   \n",
            "10731                                     My Talking Tom                GAME   \n",
            "10594                                        8 Ball Pool                GAME   \n",
            "10302  DU Battery Saver - Battery Charger & Battery Life               TOOLS   \n",
            "10354                        BBM - Free Calls & Messages       COMMUNICATION   \n",
            "10549  Cache Cleaner-DU Speed Booster (booster & clea...               TOOLS   \n",
            "10757                                            Twitter  NEWS_AND_MAGAZINES   \n",
            "10721                                    Viber Messenger       COMMUNICATION   \n",
            "10578                                     Shadow Fight 2                GAME   \n",
            "10813                                      Google Photos         PHOTOGRAPHY   \n",
            "10724                        LINE: Free Calls & Messages       COMMUNICATION   \n",
            "10717                                                Pou                GAME   \n",
            "10792                      Skype - free IM & video calls       COMMUNICATION   \n",
            "10628                                         Pokémon GO                GAME   \n",
            "10388           Minion Rush: Despicable Me Official Game                GAME   \n",
            "10694                                            Yes day                GAME   \n",
            "10695                                            Hay Day              FAMILY   \n",
            "10644                           Dream League Soccer 2018                GAME   \n",
            "10696                                  My Talking Angela                GAME   \n",
            "10660             VivaVideo - Video Editor & Photo Movie       VIDEO_PLAYERS   \n",
            "10786                       Google Chrome: Fast & Secure       COMMUNICATION   \n",
            "10817                          Maps - Navigate & Explore    TRAVEL_AND_LOCAL   \n",
            "10672                                  Hill Climb Racing                GAME   \n",
            "10734                                      Facebook Lite              SOCIAL   \n",
            "10649                                Asphalt 8: Airborne                GAME   \n",
            "10699                          Mobile Legends: Bang Bang                GAME   \n",
            "10322  Battery Doctor-Battery Life Saver & Battery Co...               TOOLS   \n",
            "10396                                     Piano Tiles 2™                GAME   \n",
            "10777                                       Temple Run 2                GAME   \n",
            "10822                                             Google               TOOLS   \n",
            "10359  Truecaller: Caller ID, SMS spam blocking & Dialer       COMMUNICATION   \n",
            "10711                         SHAREit - Transfer & Share               TOOLS   \n",
            "10389   Sniper 3D Gun Shooter: Free Shooting Games - FPS                GAME   \n",
            "10676                                   Farm Heroes Saga                GAME   \n",
            "10576   PicsArt Photo Studio: Collage Maker & Pic Editor         PHOTOGRAPHY   \n",
            "10461  PhotoGrid: Video & Pic Collage Maker, Photo Ed...         PHOTOGRAPHY   \n",
            "10502   GO Launcher - 3D parallax Themes & HD Wallpapers     PERSONALIZATION   \n",
            "\n",
            "       Rating   Reviews  Size_MBs       Installs  Type Price Content_Rating  \\\n",
            "10805    4.10  78158306      5.30  1,000,000,000  Free     0           Teen   \n",
            "10785    4.40  69119316      3.50  1,000,000,000  Free     0       Everyone   \n",
            "10806    4.50  66577313      5.30  1,000,000,000  Free     0           Teen   \n",
            "10784    4.00  56642847      3.50  1,000,000,000  Free     0       Everyone   \n",
            "10650    4.60  44891723     98.00    100,000,000  Free     0   Everyone 10+   \n",
            "10744    4.70  42916526      3.40    500,000,000  Free     0       Everyone   \n",
            "10835    4.50  27722264     76.00  1,000,000,000  Free     0   Everyone 10+   \n",
            "10828    4.30  25655305      4.65  1,000,000,000  Free     0           Teen   \n",
            "10746    4.70  24900999      3.40    500,000,000  Free     0       Everyone   \n",
            "10584    4.60  23133508     97.00    100,000,000  Free     0   Everyone 10+   \n",
            "10763    4.40  22426677     74.00    500,000,000  Free     0       Everyone   \n",
            "10770    4.50  17712922     40.00    500,000,000  Free     0           Teen   \n",
            "10735    4.00  17014787      5.30    500,000,000  Free     0           Teen   \n",
            "10489    4.60  16771865      3.40    100,000,000  Free     0       Everyone   \n",
            "10731    4.50  14891223     36.00    500,000,000  Free     0       Everyone   \n",
            "10594    4.50  14198297     52.00    100,000,000  Free     0       Everyone   \n",
            "10302    4.50  13479633     14.00    100,000,000  Free     0       Everyone   \n",
            "10354    4.30  12842860      3.50    100,000,000  Free     0       Everyone   \n",
            "10549    4.50  12759663     15.00    100,000,000  Free     0       Everyone   \n",
            "10757    4.30  11667403      6.30    500,000,000  Free     0     Mature 17+   \n",
            "10721    4.30  11334799      3.50    500,000,000  Free     0       Everyone   \n",
            "10578    4.60  10979062     88.00    100,000,000  Free     0   Everyone 10+   \n",
            "10813    4.50  10858556      6.90  1,000,000,000  Free     0       Everyone   \n",
            "10724    4.20  10790289      3.50    500,000,000  Free     0       Everyone   \n",
            "10717    4.30  10485308     24.00    500,000,000  Free     0       Everyone   \n",
            "10792    4.10  10484169      3.50  1,000,000,000  Free     0       Everyone   \n",
            "10628    4.10  10424925     85.00    100,000,000  Free     0       Everyone   \n",
            "10388    4.50  10216538     36.00    100,000,000  Free     0   Everyone 10+   \n",
            "10694    4.50  10055521     94.00    100,000,000  Free     0       Everyone   \n",
            "10695    4.50  10053186     94.00    100,000,000  Free     0       Everyone   \n",
            "10644    4.60   9882639     74.00    100,000,000  Free     0       Everyone   \n",
            "10696    4.50   9881829     99.00    100,000,000  Free     0       Everyone   \n",
            "10660    4.60   9879473     40.00    100,000,000  Free     0           Teen   \n",
            "10786    4.30   9642995      3.50  1,000,000,000  Free     0       Everyone   \n",
            "10817    4.30   9235155     12.00  1,000,000,000  Free     0       Everyone   \n",
            "10672    4.40   8923587     63.00    100,000,000  Free     0       Everyone   \n",
            "10734    4.30   8606259      5.30    500,000,000  Free     0           Teen   \n",
            "10649    4.50   8389714     92.00    100,000,000  Free     0           Teen   \n",
            "10699    4.40   8219586     99.00    100,000,000  Free     0           Teen   \n",
            "10322    4.50   8190074     17.00    100,000,000  Free     0       Everyone   \n",
            "10396    4.70   8118880     36.00    100,000,000  Free     0       Everyone   \n",
            "10777    4.30   8118609     62.00    500,000,000  Free     0       Everyone   \n",
            "10822    4.40   8033493      3.40  1,000,000,000  Free     0       Everyone   \n",
            "10359    4.50   7820209      3.50    100,000,000  Free     0       Everyone   \n",
            "10711    4.60   7790693     17.00    500,000,000  Free     0       Everyone   \n",
            "10389    4.60   7671249     36.00    100,000,000  Free     0     Mature 17+   \n",
            "10676    4.40   7614130     70.00    100,000,000  Free     0       Everyone   \n",
            "10576    4.50   7594559     34.00    100,000,000  Free     0           Teen   \n",
            "10461    4.60   7529865      6.90    100,000,000  Free     0       Everyone   \n",
            "10502    4.50   7464996      6.15    100,000,000  Free     0       Everyone   \n",
            "\n",
            "                          Genres  \n",
            "10805                     Social  \n",
            "10785              Communication  \n",
            "10806                     Social  \n",
            "10784              Communication  \n",
            "10650                   Strategy  \n",
            "10744                      Tools  \n",
            "10835                     Arcade  \n",
            "10828    Video Players & Editors  \n",
            "10746                      Tools  \n",
            "10584                   Strategy  \n",
            "10763                     Casual  \n",
            "10770              Communication  \n",
            "10735                     Social  \n",
            "10489                      Tools  \n",
            "10731                     Casual  \n",
            "10594                     Sports  \n",
            "10302                      Tools  \n",
            "10354              Communication  \n",
            "10549                      Tools  \n",
            "10757           News & Magazines  \n",
            "10721              Communication  \n",
            "10578                     Action  \n",
            "10813                Photography  \n",
            "10724              Communication  \n",
            "10717                     Casual  \n",
            "10792              Communication  \n",
            "10628                  Adventure  \n",
            "10388  Casual;Action & Adventure  \n",
            "10694                     Casual  \n",
            "10695                     Casual  \n",
            "10644                     Sports  \n",
            "10696                     Casual  \n",
            "10660    Video Players & Editors  \n",
            "10786              Communication  \n",
            "10817             Travel & Local  \n",
            "10672                     Racing  \n",
            "10734                     Social  \n",
            "10649                     Racing  \n",
            "10699                     Action  \n",
            "10322                      Tools  \n",
            "10396                     Arcade  \n",
            "10777                     Action  \n",
            "10822                      Tools  \n",
            "10359              Communication  \n",
            "10711                      Tools  \n",
            "10389                     Action  \n",
            "10676                     Casual  \n",
            "10576                Photography  \n",
            "10461                Photography  \n",
            "10502            Personalization  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "*Facebook, WhatsApp, Messenger, Instagram have the highest reviews. I believe all these apps belong to one company 'Meta'. How? Interestingly too, there seems to be no paid app in the top 50 most reviewed apps in the app store.*"
      ],
      "metadata": {
        "id": "dIS7VY7IbuXd"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFoRqpezjebe"
      },
      "source": [
        "# Visualising Apps' Content Ratings using Plotly Pie and Donut Charts -\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c0IIxNGnoFKM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af38face-8c34-42e4-e030-48498260b379"
      },
      "source": [
        "# Counting the number of Rating occurences for each content-Type category of the Apps\n",
        "ratings = df_apps_clean.Content_Rating.value_counts()\n",
        "ratings\n",
        "# df_apps_clean[df_apps_clean.Content_Rating == 'Adults only 18+']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Everyone           6621\n",
              "Teen                912\n",
              "Mature 17+          357\n",
              "Everyone 10+        305\n",
              "Adults only 18+       3\n",
              "Unrated               1\n",
              "Name: Content_Rating, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*It seems Adults only apps are so few. May be adults are not very tech savvy. Apps with contents targeted at everyone and teens seem so popular.*"
      ],
      "metadata": {
        "id": "QBIOOFIPc-u_"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kil2mdfoFOr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "outputId": "5911b306-0031-47e5-e19c-6ff63658b516"
      },
      "source": [
        "# Creating a chart to display the content rating and percentage of apps in each content category\n",
        "fig = px.pie(labels=ratings.index, values=ratings.values,\n",
        "             title='Content Rating', names=ratings.index)\n",
        "\n",
        "fig.update_traces(textposition='outside', textinfo='percent+label')\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/plotly/express/_core.py:137: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  return args[\"labels\"][column]\n"
          ]
        },
        {
          "output_type": "display_data",
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ZaHYNy2oFEE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "outputId": "f699d197-8fcd-4763-b9b2-8ca512912026"
      },
      "source": [
        "# How about the same data on doughnut chart\n",
        "fig = px.pie(labels=ratings.index, values=ratings.values,\n",
        "             title='Content Rating', names=ratings.index,\n",
        "             hole = 0.6)  # hole for doughnut\n",
        "\n",
        "fig.update_traces(textposition='inside', textfont_size = 15, textinfo='percent')\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/plotly/express/_core.py:137: FutureWarning:\n",
            "\n",
            "Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "\n"
          ]
        },
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RoPpW37xdiUm"
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      "source": [
        "# Examining the Number of Installs\n",
        "\n",
        "Here I want to know the number of installs per app.\n",
        "\n",
        "*Answering the question how many apps had several millions of installations? vs How many apps just had a single install?*"
      ]
    },
    {
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          "height": 977
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        "outputId": "2e5a8eef-3c5e-4673-fd30-967be18d96a5"
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      "source": [
        "# Using the describe method or the info method to know the data type of the install column\n",
        "df_apps_clean.Installs.describe()\n",
        "df_apps_clean.info()\n",
        "\n",
        "#Using the group and count method to know the number of Apps installed at each level. Egs; Apps that have only 1 install, or over 1000 installs\n",
        "df_apps_clean[['App', 'Installs']].groupby('Installs').count()\n",
        "\n",
        "#High_install = df_apps_clean.sort_values('Installs', ascending=False)\n",
        "#High_install.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 8199 entries, 21 to 10835\n",
            "Data columns (total 10 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   App             8199 non-null   object \n",
            " 1   Category        8199 non-null   object \n",
            " 2   Rating          8199 non-null   float64\n",
            " 3   Reviews         8199 non-null   int64  \n",
            " 4   Size_MBs        8199 non-null   float64\n",
            " 5   Installs        8199 non-null   object \n",
            " 6   Type            8199 non-null   object \n",
            " 7   Price           8199 non-null   object \n",
            " 8   Content_Rating  8199 non-null   object \n",
            " 9   Genres          8199 non-null   object \n",
            "dtypes: float64(2), int64(1), object(7)\n",
            "memory usage: 704.6+ KB\n"
          ]
        },
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              "                App\n",
              "Installs           \n",
              "1                 3\n",
              "1,000           698\n",
              "1,000,000      1417\n",
              "1,000,000,000    20\n",
              "10               69\n",
              "10,000          988\n",
              "10,000,000      933\n",
              "100             303\n",
              "100,000        1096\n",
              "100,000,000     189\n",
              "5                 9\n",
              "5,000           425\n",
              "5,000,000       607\n",
              "50               56\n",
              "50,000          457\n",
              "50,000,000      202\n",
              "500             199\n",
              "500,000         504\n",
              "500,000,000      24"
            ],
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              "      <th>50,000</th>\n",
              "      <td>457</td>\n",
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              "    <tr>\n",
              "      <th>50,000,000</th>\n",
              "      <td>202</td>\n",
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      "source": [
        "# Removing the comma in the figures above.\n",
        "df_apps_clean.Installs = df_apps_clean.Installs.astype(str).str.replace(',', \"\")\n",
        "df_apps_clean.Installs.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7933     1000000\n",
              "9496    10000000\n",
              "2460        1000\n",
              "4076       10000\n",
              "1319         100\n",
              "Name: Installs, dtype: object"
            ]
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          "metadata": {},
          "execution_count": 24
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    },
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      "cell_type": "code",
      "metadata": {
        "id": "cFjptR-JoJOE"
      },
      "source": [
        "#Converting the new figures now to numeric data types\n",
        "df_apps_clean.Installs = pd.to_numeric(df_apps_clean.Installs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8AMmxKHgoJGT",
        "colab": {
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          "height": 676
        },
        "outputId": "22a30af1-bc2f-4561-9b35-13c69f6fcd96"
      },
      "source": [
        "df_apps_clean[['App', 'Installs']].groupby('Installs').count()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "             App\n",
              "Installs        \n",
              "1              3\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7T4sfNpFdiUv"
      },
      "source": [
        "### Finding the Most Expensive Apps\n",
        "\n",
        "* The approach here is to filter out any Junk, and calculating a Sales Revenue Estimate.\n",
        "\n",
        "\n",
        "* Converting the price column to numeric data. Then investigate the top 20 most expensive apps in the dataset.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqhX2WCJoQEp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2c43224-0d72-4561-de30-885e21935470"
      },
      "source": [
        "# Converting the price column to numeric\n",
        "df_apps_clean.Price = pd.to_numeric(df_apps_clean.Price.str.replace('$', ''))\n",
        "\n",
        "df_apps_clean.Price.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-27-b496e0f105a6>:2: FutureWarning:\n",
            "\n",
            "The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21   0.00\n",
              "28   1.49\n",
              "47   0.99\n",
              "82   0.00\n",
              "99   0.00\n",
              "Name: Price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rgposh-CdiU4"
      },
      "source": [
        "### The most expensive apps"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c8_n8z8XoUML",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "54ccd564-6299-4147-cbc3-159a068c9683"
      },
      "source": [
        "# Sorting the apps by price to find the most expensive\n",
        "PriceSorted = df_apps_clean.sort_values('Price', ascending=False)\n",
        "PriceSorted[:20]\n",
        "\n",
        "# removing some expensive replica apps\n",
        "df_apps_clean = df_apps_clean[df_apps_clean['Price'] < 250]\n",
        "\n",
        "df_apps_clean.sort_values('Price', ascending=False).head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            App   Category  Rating  Reviews  Size_MBs  \\\n",
              "2281  Vargo Anesthesia Mega App    MEDICAL    4.60       92     32.00   \n",
              "1407               LTC AS Legal    MEDICAL    4.00        6      1.30   \n",
              "2629           I am Rich Person  LIFESTYLE    4.20      134      1.80   \n",
              "2481    A Manual of Acupuncture    MEDICAL    3.50      214     68.00   \n",
              "2463         PTA Content Master    MEDICAL    4.20       64     41.00   \n",
              "\n",
              "      Installs  Type  Price Content_Rating     Genres  \n",
              "2281      1000  Paid  79.99       Everyone    Medical  \n",
              "1407       100  Paid  39.99       Everyone    Medical  \n",
              "2629      1000  Paid  37.99       Everyone  Lifestyle  \n",
              "2481      1000  Paid  33.99       Everyone    Medical  \n",
              "2463      1000  Paid  29.99       Everyone    Medical  "
            ],
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              "      <td>MEDICAL</td>\n",
              "      <td>4.00</td>\n",
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              "      <td>39.99</td>\n",
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              "      <td>37.99</td>\n",
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              "      <td>Lifestyle</td>\n",
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              "    <tr>\n",
              "      <th>2481</th>\n",
              "      <td>A Manual of Acupuncture</td>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>3.50</td>\n",
              "      <td>214</td>\n",
              "      <td>68.00</td>\n",
              "      <td>1000</td>\n",
              "      <td>Paid</td>\n",
              "      <td>33.99</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Medical</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2463</th>\n",
              "      <td>PTA Content Master</td>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>4.20</td>\n",
              "      <td>64</td>\n",
              "      <td>41.00</td>\n",
              "      <td>1000</td>\n",
              "      <td>Paid</td>\n",
              "      <td>29.99</td>\n",
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              "          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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VGu85CLb4OOH"
      },
      "source": [
        "### Estimating Highest Grossing Paid Apps"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3mDi45iIoVzE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "63eeb3d8-4818-4e61-b498-bb1b08482fbc"
      },
      "source": [
        "# Adding a new column and calculating the estimated revenue for the apps\n",
        "df_apps_clean['Revenue_Estimate'] = df_apps_clean.Installs.mul(df_apps_clean.Price)\n",
        "\n",
        "# Sorting the Revenue Estimates to see which apps have gotten the most revenues\n",
        "df_apps_clean.sort_values('Revenue_Estimate', ascending=False)[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                App     Category  Rating  Reviews  Size_MBs  \\\n",
              "9220                      Minecraft       FAMILY    4.50  2376564     19.00   \n",
              "8825                  Hitman Sniper         GAME    4.60   408292     29.00   \n",
              "7151  Grand Theft Auto: San Andreas         GAME    4.40   348962     26.00   \n",
              "7477            Facetune - For Free  PHOTOGRAPHY    4.40    49553     48.00   \n",
              "7977        Sleep as Android Unlock    LIFESTYLE    4.50    23966      0.85   \n",
              "6594            DraStic DS Emulator         GAME    4.60    87766     12.00   \n",
              "6082                   Weather Live      WEATHER    4.50    76593      4.75   \n",
              "7954                    Bloons TD 5       FAMILY    4.60   190086     94.00   \n",
              "7633        Five Nights at Freddy's         GAME    4.60   100805     50.00   \n",
              "6746     Card Wars - Adventure Time       FAMILY    4.30   129603     23.00   \n",
              "\n",
              "      Installs  Type  Price Content_Rating                     Genres  \\\n",
              "9220  10000000  Paid   6.99   Everyone 10+  Arcade;Action & Adventure   \n",
              "8825  10000000  Paid   0.99     Mature 17+                     Action   \n",
              "7151   1000000  Paid   6.99     Mature 17+                     Action   \n",
              "7477   1000000  Paid   5.99       Everyone                Photography   \n",
              "7977   1000000  Paid   5.99       Everyone                  Lifestyle   \n",
              "6594   1000000  Paid   4.99       Everyone                     Action   \n",
              "6082    500000  Paid   5.99       Everyone                    Weather   \n",
              "7954   1000000  Paid   2.99       Everyone                   Strategy   \n",
              "7633   1000000  Paid   2.99           Teen                     Action   \n",
              "6746   1000000  Paid   2.99   Everyone 10+    Card;Action & Adventure   \n",
              "\n",
              "      Revenue_Estimate  \n",
              "9220     69,900,000.00  \n",
              "8825      9,900,000.00  \n",
              "7151      6,990,000.00  \n",
              "7477      5,990,000.00  \n",
              "7977      5,990,000.00  \n",
              "6594      4,990,000.00  \n",
              "6082      2,995,000.00  \n",
              "7954      2,990,000.00  \n",
              "7633      2,990,000.00  \n",
              "6746      2,990,000.00  "
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              "      <th>App</th>\n",
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              "      <th>7477</th>\n",
              "      <td>Facetune - For Free</td>\n",
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              "      <td>49553</td>\n",
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              "      <th>6594</th>\n",
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              "      <td>4.60</td>\n",
              "      <td>87766</td>\n",
              "      <td>12.00</td>\n",
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              "      <th>6082</th>\n",
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              "      <td>Bloons TD 5</td>\n",
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              "      <td>4.60</td>\n",
              "      <td>190086</td>\n",
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              "      <td>2.99</td>\n",
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              "      <td>Strategy</td>\n",
              "      <td>2,990,000.00</td>\n",
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              "    <tr>\n",
              "      <th>7633</th>\n",
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              "      <td>4.60</td>\n",
              "      <td>100805</td>\n",
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              "      <th>6746</th>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "dc": {
          "key": "18"
        },
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "lvpM2MyTdiU6"
      },
      "source": [
        "# Analysing App Categories using Plotly Bar Charts & Scatter Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3L6hK0aLoYxY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d282ceb-caaa-4e1a-8221-15e39d22f405"
      },
      "source": [
        "#Finding the number of unique categories in the data.\n",
        "df_apps_clean.Category.nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "33"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mg50Ts9joYz1"
      },
      "source": [
        "# Number of apps by category\n",
        "top10_category = df_apps_clean.Category.value_counts()[:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZZizlGjoYfn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "035e4db5-b04c-4368-9462-01e0bf6ac104"
      },
      "source": [
        "top10_category"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FAMILY             1606\n",
              "GAME                910\n",
              "TOOLS               719\n",
              "PRODUCTIVITY        301\n",
              "PERSONALIZATION     298\n",
              "LIFESTYLE           297\n",
              "FINANCE             296\n",
              "MEDICAL             292\n",
              "PHOTOGRAPHY         263\n",
              "BUSINESS            262\n",
              "Name: Category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0tTurR7qdiU9"
      },
      "source": [
        "### Vertical Bar Chart\n",
        "* Visualising categories with the highest number of Apps."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFSMH00AoaMB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "bc1fbf0a-1310-450a-f440-48abb39c4b50"
      },
      "source": [
        "# Ploting the graphs using plotly express\n",
        "bar = px.bar(x = top10_category.index,\n",
        "             y = top10_category.values)\n",
        "bar.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*It can be seen that Family category has the highest number of Apps*"
      ],
      "metadata": {
        "id": "3J4-fLsHmGUO"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "57mowetLdiU_"
      },
      "source": [
        "### Most Popular Categories (highest Downloads)\n",
        "* By Horizontal Bar Chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHO7eTJmocLm"
      },
      "source": [
        "# grouping the apps by category and sum the number of installs per category to give idea on most popular categories\n",
        "category_installs = df_apps_clean.groupby('Category').agg({'Installs': pd.Series.sum})\n",
        "category_installs.sort_values('Installs', ascending=True, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h7aUARLtocCy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "5fd9a5fc-c330-4161-9b7e-459ce6b7f281"
      },
      "source": [
        "#Plotting the bars horizontally\n",
        "h_bar = px.bar(x = category_installs.Installs,\n",
        "               y = category_installs.index,\n",
        "               orientation='h')\n",
        "h_bar.show()\n"
      ],
      "execution_count": null,
      "outputs": [
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    },
    {
      "cell_type": "markdown",
      "source": [
        "*It is seen now that the GAMES category has the highest number of downloads and hence popular even more than the Family category that has about 2 times the number of games apps*"
      ],
      "metadata": {
        "id": "CD2PPnqlmdzA"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uxk24qMk8vrz"
      },
      "source": [
        "### Category Concentration - Downloads vs. Competition\n",
        "\n",
        "* The goal is to know the number of apps per category and total installs of those apps"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVvGPDY7oe5A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3bb61300-6103-42e8-9597-c42aeeec3606"
      },
      "source": [
        "#Creating a new dataframe\n",
        "CategoryNumber = df_apps_clean.groupby('Category').agg({'App': pd.Series.count})\n",
        "\n",
        "#Using .merge() I joined the 2 dataframes\n",
        "cat_merged_df = pd.merge(CategoryNumber, category_installs, on='Category', how=\"inner\")\n",
        "\n",
        "#Checking the new number of row and columns\n",
        "print(f'The dimensions of the DataFrame are: {cat_merged_df.shape}')\n",
        "\n",
        "#Sorting\n",
        "cat_merged_df.sort_values('Installs', ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The dimensions of the DataFrame are: (33, 2)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      App     Installs\n",
              "Category                              \n",
              "GAME                  910  13858762717\n",
              "COMMUNICATION         257  11039241530\n",
              "TOOLS                 719   8099724500\n",
              "PRODUCTIVITY          301   5788070180\n",
              "SOCIAL                203   5487841475\n",
              "PHOTOGRAPHY           263   4649143130\n",
              "FAMILY               1606   4437554490\n",
              "VIDEO_PLAYERS         148   3916897200\n",
              "TRAVEL_AND_LOCAL      187   2894859300\n",
              "NEWS_AND_MAGAZINES    204   2369110650\n",
              "ENTERTAINMENT         102   2113660000\n",
              "BOOKS_AND_REFERENCE   169   1665791655\n",
              "PERSONALIZATION       298   1532352930\n",
              "SHOPPING              180   1400331540\n",
              "HEALTH_AND_FITNESS    243   1134006220\n",
              "SPORTS                260   1096431465\n",
              "BUSINESS              262    692018120\n",
              "LIFESTYLE             297    503611120\n",
              "MAPS_AND_NAVIGATION   118    503267560\n",
              "FINANCE               296    455249400\n",
              "WEATHER                72    361096500\n",
              "EDUCATION             118    352852000\n",
              "FOOD_AND_DRINK         94    211677750\n",
              "DATING                134    140912410\n",
              "ART_AND_DESIGN         61    114233100\n",
              "HOUSE_AND_HOME         62     97082000\n",
              "AUTO_AND_VEHICLES      73     53129800\n",
              "LIBRARIES_AND_DEMO     64     52083000\n",
              "COMICS                 54     44931100\n",
              "MEDICAL               292     39162676\n",
              "PARENTING              50     31116110\n",
              "BEAUTY                 42     26916200\n",
              "EVENTS                 45     15949410"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb391755-4d69-40e3-b003-65a17f4361e6\">\n",
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              "      <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>App</th>\n",
              "      <th>Installs</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Category</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>GAME</th>\n",
              "      <td>910</td>\n",
              "      <td>13858762717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COMMUNICATION</th>\n",
              "      <td>257</td>\n",
              "      <td>11039241530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TOOLS</th>\n",
              "      <td>719</td>\n",
              "      <td>8099724500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PRODUCTIVITY</th>\n",
              "      <td>301</td>\n",
              "      <td>5788070180</td>\n",
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              "      <th>SOCIAL</th>\n",
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              "      <th>PHOTOGRAPHY</th>\n",
              "      <td>263</td>\n",
              "      <td>4649143130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FAMILY</th>\n",
              "      <td>1606</td>\n",
              "      <td>4437554490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VIDEO_PLAYERS</th>\n",
              "      <td>148</td>\n",
              "      <td>3916897200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TRAVEL_AND_LOCAL</th>\n",
              "      <td>187</td>\n",
              "      <td>2894859300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NEWS_AND_MAGAZINES</th>\n",
              "      <td>204</td>\n",
              "      <td>2369110650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ENTERTAINMENT</th>\n",
              "      <td>102</td>\n",
              "      <td>2113660000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BOOKS_AND_REFERENCE</th>\n",
              "      <td>169</td>\n",
              "      <td>1665791655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PERSONALIZATION</th>\n",
              "      <td>298</td>\n",
              "      <td>1532352930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SHOPPING</th>\n",
              "      <td>180</td>\n",
              "      <td>1400331540</td>\n",
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              "      <th>HEALTH_AND_FITNESS</th>\n",
              "      <td>243</td>\n",
              "      <td>1134006220</td>\n",
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              "      <td>260</td>\n",
              "      <td>1096431465</td>\n",
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              "      <th>BUSINESS</th>\n",
              "      <td>262</td>\n",
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              "    <tr>\n",
              "      <th>LIFESTYLE</th>\n",
              "      <td>297</td>\n",
              "      <td>503611120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MAPS_AND_NAVIGATION</th>\n",
              "      <td>118</td>\n",
              "      <td>503267560</td>\n",
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              "    <tr>\n",
              "      <th>FINANCE</th>\n",
              "      <td>296</td>\n",
              "      <td>455249400</td>\n",
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              "    <tr>\n",
              "      <th>WEATHER</th>\n",
              "      <td>72</td>\n",
              "      <td>361096500</td>\n",
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              "      <th>EDUCATION</th>\n",
              "      <td>118</td>\n",
              "      <td>352852000</td>\n",
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              "      <th>FOOD_AND_DRINK</th>\n",
              "      <td>94</td>\n",
              "      <td>211677750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DATING</th>\n",
              "      <td>134</td>\n",
              "      <td>140912410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ART_AND_DESIGN</th>\n",
              "      <td>61</td>\n",
              "      <td>114233100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HOUSE_AND_HOME</th>\n",
              "      <td>62</td>\n",
              "      <td>97082000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AUTO_AND_VEHICLES</th>\n",
              "      <td>73</td>\n",
              "      <td>53129800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LIBRARIES_AND_DEMO</th>\n",
              "      <td>64</td>\n",
              "      <td>52083000</td>\n",
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              "      <th>COMICS</th>\n",
              "      <td>54</td>\n",
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              "    <tr>\n",
              "      <th>MEDICAL</th>\n",
              "      <td>292</td>\n",
              "      <td>39162676</td>\n",
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              "    <tr>\n",
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        "#Now I plot the new new dataframe as scatter plot to measure Category concentration of installed applications\n",
        "\n",
        "scatter = px.scatter(cat_merged_df,\n",
        "                     x = 'App',\n",
        "                     y = 'Installs',\n",
        "                     title = 'Category Concentration',\n",
        "                     size = 'App',\n",
        "                     hover_name= cat_merged_df.index,\n",
        "                     color = 'Installs')\n",
        "scatter.update_layout(xaxis_title = \"Number of Apps in Category (Lower = More Concentration)\",\n",
        "                      yaxis_title = \"Installs\",\n",
        "                      yaxis=dict(type = 'log'))\n",
        "scatter.show()"
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        "\n",
        "*The chart here is showing that categories like Family, Tools and Game have many different apps sharing the high number of downloads. But for categories like video players and entertainment (upper right), only few apps dominate the downloads.The bottom left categories do not have high apps nor high downloads. So increasing your new application download rate, it is wise to place it in the Family, Games and Tools category.*"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dmXDF75WdiVC"
      },
      "source": [
        "### Extracting Nested Data from a Column\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Lo69kS6ogdm",
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      "source": [
        "# No of unique Genres\n",
        "df_apps_clean.Genres.nunique()\n",
        "# Getting a sample of them to inspect\n",
        "df_apps_clean.Genres.sample(10)\n",
        "# Counting the No of genres in categories. Nested values are present\n",
        "df_apps_clean.Genres.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Tools                                718\n",
              "Entertainment                        467\n",
              "Education                            429\n",
              "Productivity                         301\n",
              "Personalization                      298\n",
              "                                    ... \n",
              "Adventure;Brain Games                  1\n",
              "Travel & Local;Action & Adventure      1\n",
              "Art & Design;Pretend Play              1\n",
              "Music & Audio;Music & Video            1\n",
              "Lifestyle;Pretend Play                 1\n",
              "Name: Genres, Length: 114, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Separating the nested genre names by Spliting the strings and then stack them\n",
        "stack = df_apps_clean.Genres.str.split(';', expand=True).stack()\n",
        "stack.head()\n",
        "\n",
        "#printing a sample to inspect\n",
        "print(f'I now have a single Genre column with shape: {stack.shape}')\n",
        "\n",
        "# Count and Check split was successful\n",
        "num_genres = stack.value_counts()\n",
        "print(f'Number of genres: {len(num_genres)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xfSfmgcDuU8s",
        "outputId": "b0f77ea7-ba14-4908-d309-f66a568032e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I now have a single Genre column with shape: (8564,)\n",
            "Number of genres: 53\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3zaKvfydiVI"
      },
      "source": [
        "# Examining the Competition in Genres\n",
        "* Exploring also colour Scales in Plotly\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c5xbfMgxojoA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "2a4c5b0f-0215-4b28-8bbd-1fa8f7d3ae32"
      },
      "source": [
        "# plotting the genre data on vertical\n",
        "Genre_Apps = df_apps_clean.groupby('Genres').agg({'App': pd.Series.count})\n",
        "#Genre_Apps.head()\n",
        "\n",
        "#plotting my charts with continuous color scale in plotly\n",
        "v_bar = px.bar(\n",
        "               x = num_genres.index[:15],           #index = number of category names\n",
        "               y = num_genres.values[:15],\n",
        "               title = 'Top Genres',\n",
        "               hover_name= num_genres.index[:15],\n",
        "               color = num_genres.values[:15],\n",
        "               color_continuous_scale = 'Agsunset')\n",
        "v_bar.update_layout(xaxis_title = \"Genres\",\n",
        "               yaxis_title = \"Number of Apps\",\n",
        "               coloraxis_showscale=False)\n",
        "\n",
        "v_bar.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BIWG_10PyIzL"
      },
      "source": [
        "# Analysing Free vs. Paid Apps per Category\n",
        "*  Exploring Grouped Bar Charts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ANMs7xywolDH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff1ef48b-be53-4eb2-86e5-30d4dcd0645b"
      },
      "source": [
        "#Grouping the data by category, Type and aggregating the number of apps in the categories by type (Free of paid)\n",
        "df_free_vs_paid = df_apps_clean.groupby([\"Category\", \"Type\"], as_index=False).agg({'App': pd.Series.count})\n",
        "df_free_vs_paid.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
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      "cell_type": "code",
      "metadata": {
        "id": "tqOntrS7onGt",
        "colab": {
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          "height": 542
        },
        "outputId": "693a90f8-0265-4951-8484-dc1d87ab1f36"
      },
      "source": [
        "# Plotling the free vs paid app per category and grouped bar charts\n",
        "\n",
        "g_bar = px.bar(df_free_vs_paid,\n",
        "               x = 'Category',\n",
        "               y= 'App',\n",
        "               title = 'Free vs Paid Apps by Category',\n",
        "               color = 'Type',\n",
        "               barmode = 'group')\n",
        "g_bar.update_layout(xaxis_title = 'Category',\n",
        "                    yaxis_title = 'Number of Apps',\n",
        "                    xaxis = {'categoryorder': 'total descending'},\n",
        "                    yaxis = dict(type = 'log'))\n",
        "g_bar.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
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    {
      "cell_type": "markdown",
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        "id": "nbYh2tU-VZ-i"
      },
      "source": [
        "# Exploring Lost Downloads for Paid Apps\n",
        "* Here the goal is to know how much downloads do paid application sacrifice. Thats comparing to being free apps.\n",
        "\n"
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        "outputId": "fa955470-024f-4ba9-a2fc-cd8e09700d43"
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      "source": [
        "# And how many downloads are you giving up by making your app paid?\n",
        "\n",
        "#Using the Box plots\n",
        "box= px.box(df_apps_clean,\n",
        "            y='Installs',\n",
        "            x='Type',\n",
        "            color='Type',\n",
        "            notched=True,\n",
        "            points = 'all',\n",
        "            title = 'How Many Downloads are Paid Apps Giving Up?')\n",
        "box.update_layout(yaxis=dict(type='log'))\n",
        "\n",
        "box.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
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      "cell_type": "markdown",
      "source": [
        "*Using the box plot, the median number of installations compare for a free app is 500k while that of paid app is 5k. The difference is large. Meaning if you choose to make your app paid, you are potentially giving up One hundred times rate of downloads.*\n",
        "\n",
        " *Things must be well thought out. The monetization strategies should decide such as ad revenue should be considered here. But, lets first of all know if paid apps make money.*"
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      "source": [
        "# Investigating Revenue by App Category."
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      "metadata": {
        "id": "W1iVoT8Dos7x",
        "colab": {
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          "height": 542
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        "outputId": "314a09da-ec9b-4656-d825-637dfd200b0b"
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      "source": [
        "#How much revenue can we estimate per category\n",
        "df_paid_apps = df_apps_clean[df_apps_clean['Type'] == 'Paid']\n",
        "\n",
        "box = px.box(df_paid_apps,\n",
        "             x = 'Category',\n",
        "             y = 'Revenue_Estimate',\n",
        "             title = \"How much can paid Apps Earn?\")\n",
        "box.update_layout(xaxis_title = \"Category\",\n",
        "                  yaxis_title = 'Paid App Ballpark Revenue',\n",
        "                  xaxis = {'categoryorder': 'min ascending'},\n",
        "                  yaxis = dict(type='log'))\n",
        "box.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</body>\n",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Looking at the hover text, the median app in Tools category for example makes 5990 usd. Meaning many apps in the Tool category do not make much money, if you should take into consideration the amount spent in developing the app. However, the best apps in the same category can earn up to 3 Million dollars. Apps in Entertainment generally earn around 400K USD.*\n",
        "\n",
        "*Hovering on the bars reveals a lot of earning potential informations for apps in each category.*\n"
      ],
      "metadata": {
        "id": "7lSKj_F2OG_o"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uT32ylj4WS9B"
      },
      "source": [
        "# Finally, examining Paid App Pricing Strategies by Category\n",
        "\n",
        "* Meaning, how much can one charge for a new app if you want it paid.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AdBr0TUhouqs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3389f490-0b05-414a-e9a9-1231b86c6bae"
      },
      "source": [
        "# App Pricing by Category\n",
        "paid_app_median_price = df_paid_apps.Price.median()\n",
        "print(f\"The median price for a paid app is {paid_app_median_price}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The median price for a paid app is 2.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJnABRw3ovwr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "dcbf5415-50b5-46f2-8ff3-edf956a07c14"
      },
      "source": [
        "# ploting price distribution for paid apps by category.\n",
        "box = px.box(df_paid_apps,\n",
        "             x = 'Category',\n",
        "             y = \"Price\",\n",
        "             title = 'Price per Category')\n",
        "\n",
        "box.update_layout(xaxis_title = \"Category\",\n",
        "                  yaxis_title = 'Paid App Price',\n",
        "                  xaxis = {'categoryorder': 'max descending'},\n",
        "                  yaxis = dict(type='log'))\n",
        "box.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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      "source": [
        "*The median price for a paid app is `$`2.99. The Dating Apps category has the highest average price of about `$`7 USD. The Social, Art_And_Design, and Auto_And_Vehicles category on the other hand have the lowest average price. This must be because there are a lot of free Social app, their revenue strategies is usually ad based. Medical apps also seem to be well patronised and have an average of `$`5.50. The spread seems wide for Maps and Navigation apps, with a median of 2.99 with a lower limit at 0.99 USD and a third quartile limit of 10.5 USD.*"
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      "cell_type": "markdown",
      "source": [
        "That is a total wrap on the app market. Buy this analysis, if you want to launch your app next so you can be better placed to succeed 😎\n"
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