Play_Store_App_Review_Analysis_Capstone_Project.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/Preetirai-tech/Play-Store-App-Review-Analysis/blob/main/Play_Store_App_Review_Analysis_Capstone_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gAI6udmVXZj_"
      },
      "source": [
        "# Imported the necessary libraries that can be needed for EDA."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "sbBkALaeM74d"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mWoAxbXXqY_"
      },
      "source": [
        "### Lets read the file as play_df and reviews_df for play_store data and user_reviews data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "wE4J1HfUXWpL"
      },
      "outputs": [],
      "source": [
        "play_df = pd.read_csv(\"/content/drive/MyDrive/AlmaBetter /Capstone Projects/EDA/Play Store Data.csv\")\n",
        "reviews_df = pd.read_csv(\"/content/drive/MyDrive/AlmaBetter /Capstone Projects/EDA/User Reviews.csv\")"
      ]
    },
    {
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      "execution_count": 3,
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              "      <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",
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              "                                                 App  ...   Android Ver\n",
              "0     Photo Editor & Candy Camera & Grid & ScrapBook  ...  4.0.3 and up\n",
              "1                                Coloring book moana  ...  4.0.3 and up\n",
              "2  U Launcher Lite – FREE Live Cool Themes, Hide ...  ...  4.0.3 and up\n",
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              "4              Pixel Draw - Number Art Coloring Book  ...    4.4 and up\n",
              "\n",
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              "(10841, 13)"
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              "                     App  ... Sentiment_Subjectivity\n",
              "0  10 Best Foods for You  ...               0.533333\n",
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              "2  10 Best Foods for You  ...                    NaN\n",
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              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10 Best Foods for You</td>\n",
              "      <td>Best idea us</td>\n",
              "      <td>Positive</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10 Best Foods for You</td>\n",
              "      <td>Best way</td>\n",
              "      <td>Positive</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64222</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64223</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64226</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64227</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64230</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>37427 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae53378a-1a3c-4b24-93e3-c2c49f247fdf')\"\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",
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              "      height: 32px;\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-ae53378a-1a3c-4b24-93e3-c2c49f247fdf 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-ae53378a-1a3c-4b24-93e3-c2c49f247fdf');\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",
              "  "
            ],
            "text/plain": [
              "                                  App  ... Sentiment_Subjectivity\n",
              "0               10 Best Foods for You  ...               0.533333\n",
              "1               10 Best Foods for You  ...               0.288462\n",
              "3               10 Best Foods for You  ...               0.875000\n",
              "4               10 Best Foods for You  ...               0.300000\n",
              "5               10 Best Foods for You  ...               0.300000\n",
              "...                               ...  ...                    ...\n",
              "64222  Housing-Real Estate & Property  ...               0.486667\n",
              "64223  Housing-Real Estate & Property  ...               0.447222\n",
              "64226  Housing-Real Estate & Property  ...               0.250000\n",
              "64227  Housing-Real Estate & Property  ...               1.000000\n",
              "64230  Housing-Real Estate & Property  ...               0.400000\n",
              "\n",
              "[37427 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "#lets drop nan values from reviews dataframe which have no reviews\n",
        "reviews_df.drop(reviews_df[reviews_df['Translated_Review'].isna()].index,inplace=True)\n",
        "reviews_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "vlssBHKfYaws",
        "outputId": "e371734d-ab57-4c4c-e294-9fec4b21d6e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d3739955-6ed6-44cb-a56c-50cb2abb9713\">\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>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",
              "      <th>Translated_Review</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Sentiment_Polarity</th>\n",
              "      <th>Sentiment_Subjectivity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</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",
              "      <td>A kid's excessive ads. The types ads allowed a...</td>\n",
              "      <td>Negative</td>\n",
              "      <td>-0.250</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14M</td>\n",
              "      <td>500,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>January 15, 2018</td>\n",
              "      <td>2.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>It bad &gt;:(</td>\n",
              "      <td>Negative</td>\n",
              "      <td>-0.725</td>\n",
              "      <td>0.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</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",
              "      <td>like</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</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",
              "      <td>I love colors inspyering</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</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",
              "      <td>I hate</td>\n",
              "      <td>Negative</td>\n",
              "      <td>-0.800</td>\n",
              "      <td>0.900000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d3739955-6ed6-44cb-a56c-50cb2abb9713')\"\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",
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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-d3739955-6ed6-44cb-a56c-50cb2abb9713 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-d3739955-6ed6-44cb-a56c-50cb2abb9713');\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",
              "  "
            ],
            "text/plain": [
              "                   App  ... Sentiment_Subjectivity\n",
              "0  Coloring book moana  ...               1.000000\n",
              "1  Coloring book moana  ...               0.833333\n",
              "2  Coloring book moana  ...               0.000000\n",
              "3  Coloring book moana  ...               0.600000\n",
              "4  Coloring book moana  ...               0.900000\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "#Lets merge two dataframes\n",
        "combined_df = pd.merge(play_df,reviews_df,how='inner',left_on='App',right_on='App')\n",
        "combined_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Ac-Qrltgkri",
        "outputId": "a314c1be-b794-4b70-ac30-451e25c7c2c9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 72605 entries, 0 to 72604\n",
            "Data columns (total 17 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   App                     72605 non-null  object \n",
            " 1   Category                72605 non-null  object \n",
            " 2   Rating                  72566 non-null  float64\n",
            " 3   Reviews                 72605 non-null  object \n",
            " 4   Size                    72605 non-null  object \n",
            " 5   Installs                72605 non-null  object \n",
            " 6   Type                    72605 non-null  object \n",
            " 7   Price                   72605 non-null  object \n",
            " 8   Content Rating          72605 non-null  object \n",
            " 9   Genres                  72605 non-null  object \n",
            " 10  Last Updated            72605 non-null  object \n",
            " 11  Current Ver             72605 non-null  object \n",
            " 12  Android Ver             72605 non-null  object \n",
            " 13  Translated_Review       72605 non-null  object \n",
            " 14  Sentiment               72605 non-null  object \n",
            " 15  Sentiment_Polarity      72605 non-null  float64\n",
            " 16  Sentiment_Subjectivity  72605 non-null  float64\n",
            "dtypes: float64(3), object(14)\n",
            "memory usage: 10.0+ MB\n"
          ]
        }
      ],
      "source": [
        "combined_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yx_Xjit4grSm",
        "outputId": "64a767a6-9404-4e2a-ee09-34265ffad59a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type',\n",
              "       'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver',\n",
              "       'Android Ver', 'Translated_Review', 'Sentiment', 'Sentiment_Polarity',\n",
              "       'Sentiment_Subjectivity'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "combined_df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Km4M6FHfg82G"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "wokH07MOg-2W",
        "outputId": "ce2564f9-6b1d-460f-9f6b-f997997c34fd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b8f082a7-4ac3-4bfe-a789-0f2d47091bff\">\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",
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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",
              "      <th>Translated_Review</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Sentiment_Polarity</th>\n",
              "      <th>Sentiment_Subjectivity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14000.0</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",
              "      <td>A kid's excessive ads. The types ads allowed a...</td>\n",
              "      <td>Negative</td>\n",
              "      <td>-0.250</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14000.0</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",
              "      <td>It bad &gt;:(</td>\n",
              "      <td>Negative</td>\n",
              "      <td>-0.725</td>\n",
              "      <td>0.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14000.0</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",
              "      <td>like</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14000.0</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",
              "      <td>I love colors inspyering</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14000.0</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",
              "      <td>I hate</td>\n",
              "      <td>Negative</td>\n",
              "      <td>-0.800</td>\n",
              "      <td>0.900000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8f082a7-4ac3-4bfe-a789-0f2d47091bff')\"\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-b8f082a7-4ac3-4bfe-a789-0f2d47091bff 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-b8f082a7-4ac3-4bfe-a789-0f2d47091bff');\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",
              "  "
            ],
            "text/plain": [
              "                   App  ... Sentiment_Subjectivity\n",
              "0  Coloring book moana  ...               1.000000\n",
              "1  Coloring book moana  ...               0.833333\n",
              "2  Coloring book moana  ...               0.000000\n",
              "3  Coloring book moana  ...               0.600000\n",
              "4  Coloring book moana  ...               0.900000\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# So in the size column we can see we have values with M and K so lets replace them with their equivalent value with respect to k and convert into float\n",
        "combined_df['Size']=combined_df['Size'].str.replace('M','000')\n",
        "combined_df['Size']=combined_df['Size'].str.replace('k','')\n",
        "\n",
        "#apps['size']=apps['size'].str.replace('.','')\n",
        "combined_df['Size']=combined_df['Size'].replace(\"Varies with device\",np.nan)\n",
        "combined_df['Size']=combined_df['Size'].astype('float')\n",
        "combined_df.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "YdLWuCAAiych"
      },
      "outputs": [],
      "source": [
        "# Lets clean the Installs and Price columns as these have +, ',' ,$ present which can be treated as string. Then we will convert the last updated column to date format\n",
        "combined_df['Installs'] = combined_df['Installs'].str.replace('+','')\n",
        "combined_df['Installs'] = combined_df['Installs'].str.replace(',','')\n",
        "combined_df['Installs'] = combined_df['Installs'].astype(int)\n",
        "combined_df['Reviews'] = combined_df['Reviews'].astype(int)\n",
        "combined_df['Price'] = combined_df['Price'].str.replace('$','')\n",
        "combined_df['Price'] = combined_df['Price'].astype(float)\n",
        "combined_df['Last Updated'] = pd.to_datetime(combined_df['Last Updated'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "UMM1lyWRpfLn",
        "outputId": "e9135abb-2e37-4124-b332-ca0731240f04"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-2456f989-8155-4fcd-b9f6-a4a5add17226\">\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>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",
              "      <th>Translated_Review</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Sentiment_Polarity</th>\n",
              "      <th>Sentiment_Subjectivity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14000.0</td>\n",
              "      <td>500000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>2018-01-15</td>\n",
              "      <td>2.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>A kid's excessive ads. The types ads allowed a...</td>\n",
              "      <td>Negative</td>\n",
              "      <td>-0.250</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14000.0</td>\n",
              "      <td>500000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>2018-01-15</td>\n",
              "      <td>2.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>It bad &gt;:(</td>\n",
              "      <td>Negative</td>\n",
              "      <td>-0.725</td>\n",
              "      <td>0.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14000.0</td>\n",
              "      <td>500000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>2018-01-15</td>\n",
              "      <td>2.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>like</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14000.0</td>\n",
              "      <td>500000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>2018-01-15</td>\n",
              "      <td>2.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>I love colors inspyering</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14000.0</td>\n",
              "      <td>500000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>2018-01-15</td>\n",
              "      <td>2.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>I hate</td>\n",
              "      <td>Negative</td>\n",
              "      <td>-0.800</td>\n",
              "      <td>0.900000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2456f989-8155-4fcd-b9f6-a4a5add17226')\"\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-2456f989-8155-4fcd-b9f6-a4a5add17226 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-2456f989-8155-4fcd-b9f6-a4a5add17226');\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",
              "  "
            ],
            "text/plain": [
              "                   App  ... Sentiment_Subjectivity\n",
              "0  Coloring book moana  ...               1.000000\n",
              "1  Coloring book moana  ...               0.833333\n",
              "2  Coloring book moana  ...               0.000000\n",
              "3  Coloring book moana  ...               0.600000\n",
              "4  Coloring book moana  ...               0.900000\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "combined_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3mgyZR0G0tO",
        "outputId": "4820466b-8827-4b26-fd50-36ee99ffb492"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 72605 entries, 0 to 72604\n",
            "Data columns (total 17 columns):\n",
            " #   Column                  Non-Null Count  Dtype         \n",
            "---  ------                  --------------  -----         \n",
            " 0   App                     72605 non-null  object        \n",
            " 1   Category                72605 non-null  object        \n",
            " 2   Rating                  72566 non-null  float64       \n",
            " 3   Reviews                 72605 non-null  int64         \n",
            " 4   Size                    45046 non-null  float64       \n",
            " 5   Installs                72605 non-null  int64         \n",
            " 6   Type                    72605 non-null  object        \n",
            " 7   Price                   72605 non-null  float64       \n",
            " 8   Content Rating          72605 non-null  object        \n",
            " 9   Genres                  72605 non-null  object        \n",
            " 10  Last Updated            72605 non-null  datetime64[ns]\n",
            " 11  Current Ver             72605 non-null  object        \n",
            " 12  Android Ver             72605 non-null  object        \n",
            " 13  Translated_Review       72605 non-null  object        \n",
            " 14  Sentiment               72605 non-null  object        \n",
            " 15  Sentiment_Polarity      72605 non-null  float64       \n",
            " 16  Sentiment_Subjectivity  72605 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(5), int64(2), object(9)\n",
            "memory usage: 10.0+ MB\n"
          ]
        }
      ],
      "source": [
        "combined_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2c3P63mmG10m",
        "outputId": "e3beed92-8069-46c6-83cc-033a90522ccb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GAME                   19125\n",
              "FAMILY                  5910\n",
              "HEALTH_AND_FITNESS      4503\n",
              "SPORTS                  3504\n",
              "DATING                  3198\n",
              "PRODUCTIVITY            3121\n",
              "TRAVEL_AND_LOCAL        3101\n",
              "PHOTOGRAPHY             2480\n",
              "ENTERTAINMENT           2433\n",
              "BUSINESS                2347\n",
              "MEDICAL                 2211\n",
              "NEWS_AND_MAGAZINES      2204\n",
              "SHOPPING                2026\n",
              "FINANCE                 2009\n",
              "COMMUNICATION           1949\n",
              "TOOLS                   1794\n",
              "EDUCATION               1694\n",
              "SOCIAL                  1398\n",
              "LIFESTYLE               1332\n",
              "PERSONALIZATION         1312\n",
              "FOOD_AND_DRINK           846\n",
              "BOOKS_AND_REFERENCE      815\n",
              "HOUSE_AND_HOME           697\n",
              "ART_AND_DESIGN           382\n",
              "BEAUTY                   338\n",
              "LIBRARIES_AND_DEMO       333\n",
              "VIDEO_PLAYERS            331\n",
              "AUTO_AND_VEHICLES        289\n",
              "PARENTING                260\n",
              "WEATHER                  233\n",
              "MAPS_AND_NAVIGATION      192\n",
              "EVENTS                   158\n",
              "COMICS                    80\n",
              "Name: Category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Number of apps per categories\n",
        "combined_df['Category'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "tQnbt2K1_oVz",
        "outputId": "700f71e0-aae5-446a-b7e6-20a2356afd8e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Number of Apps per cateory')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig = plt.figure(figsize =(12,6))\n",
        "combined_df['Category'].value_counts().plot(kind='bar')\n",
        "plt.ylabel('No. of Apps')\n",
        "plt.xlabel('Category')\n",
        "plt.title('Number of Apps per cateory')\n",
        "# plt.savefig('plot.png', dpi=300, bbox_inches='tight')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m74EcMQCG_aI",
        "outputId": "6ee873d5-650e-4989-b5c7-4afe7ae9193c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Category\n",
              "ART_AND_DESIGN         4.303141\n",
              "AUTO_AND_VEHICLES      4.577855\n",
              "BEAUTY                 4.248817\n",
              "BOOKS_AND_REFERENCE    4.327853\n",
              "BUSINESS               4.273157\n",
              "COMICS                 4.262500\n",
              "COMMUNICATION          4.226424\n",
              "DATING                 4.116667\n",
              "EDUCATION              4.486954\n",
              "ENTERTAINMENT          4.028607\n",
              "EVENTS                 4.336076\n",
              "FAMILY                 4.380017\n",
              "FINANCE                4.262817\n",
              "FOOD_AND_DRINK         4.418676\n",
              "GAME                   4.460444\n",
              "HEALTH_AND_FITNESS     4.423118\n",
              "HOUSE_AND_HOME         4.208321\n",
              "LIBRARIES_AND_DEMO     4.057057\n",
              "LIFESTYLE              4.126502\n",
              "MAPS_AND_NAVIGATION    4.323958\n",
              "MEDICAL                4.163950\n",
              "NEWS_AND_MAGAZINES     4.177132\n",
              "PARENTING              4.250769\n",
              "PERSONALIZATION        4.327439\n",
              "PHOTOGRAPHY            4.378911\n",
              "PRODUCTIVITY           4.395867\n",
              "SHOPPING               4.344965\n",
              "SOCIAL                 4.212947\n",
              "SPORTS                 4.275000\n",
              "TOOLS                  4.271628\n",
              "TRAVEL_AND_LOCAL       4.293099\n",
              "VIDEO_PLAYERS          4.241994\n",
              "WEATHER                4.281116\n",
              "Name: Rating, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Average rating for every category\n",
        "combined_df.groupby('Category').Rating.agg('mean')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
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        },
        "id": "1z90fu3QMjVn",
        "outputId": "22dd5257-9ffd-4b4f-8cc3-d8895a0f278e"
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      "outputs": [
        {
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          "data": {
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              "\n",
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              "      <th>5</th>\n",
              "      <td>COMICS</td>\n",
              "      <td>4.2</td>\n",
              "      <td>4.7</td>\n",
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              "      <th>6</th>\n",
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              "      <td>4.7</td>\n",
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              "      <th>8</th>\n",
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              "      <td>3.5</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
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              "      <th>9</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.6</td>\n",
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              "      <td>4.0</td>\n",
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              "      <th>11</th>\n",
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              "      <td>3.6</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
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              "      <th>12</th>\n",
              "      <td>FINANCE</td>\n",
              "      <td>3.6</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
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              "      <th>13</th>\n",
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              "      <td>3.6</td>\n",
              "      <td>4.7</td>\n",
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              "      <th>14</th>\n",
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              "      <td>3.4</td>\n",
              "      <td>4.7</td>\n",
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              "      <th>15</th>\n",
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              "      <td>3.7</td>\n",
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              "      <td>4.6</td>\n",
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              "      <th>18</th>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.6</td>\n",
              "    </tr>\n",
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              "      <th>19</th>\n",
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              "      <td>4.2</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>2.6</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NEWS_AND_MAGAZINES</td>\n",
              "      <td>3.7</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>PARENTING</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>PERSONALIZATION</td>\n",
              "      <td>3.9</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
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              "      <td>3.8</td>\n",
              "      <td>4.8</td>\n",
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              "      <td>4.7</td>\n",
              "    </tr>\n",
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              "      <th>26</th>\n",
              "      <td>SHOPPING</td>\n",
              "      <td>3.8</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>3.6</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
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              "      <td>3.5</td>\n",
              "      <td>4.6</td>\n",
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              "      <td>4.8</td>\n",
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              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>3.8</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>WEATHER</td>\n",
              "      <td>3.9</td>\n",
              "      <td>4.5</td>\n",
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            ],
            "text/plain": [
              "               Category  min_rating_per_category  max_rating_per_category\n",
              "0        ART_AND_DESIGN                      3.9                      4.7\n",
              "1     AUTO_AND_VEHICLES                      4.2                      4.9\n",
              "2                BEAUTY                      3.9                      4.6\n",
              "3   BOOKS_AND_REFERENCE                      3.5                      4.7\n",
              "4              BUSINESS                      3.8                      4.6\n",
              "5                COMICS                      4.2                      4.7\n",
              "6         COMMUNICATION                      3.7                      4.6\n",
              "7                DATING                      3.3                      4.7\n",
              "8             EDUCATION                      3.5                      4.7\n",
              "9         ENTERTAINMENT                      3.0                      4.6\n",
              "10               EVENTS                      4.0                      4.5\n",
              "11               FAMILY                      3.6                      4.8\n",
              "12              FINANCE                      3.6                      4.8\n",
              "13       FOOD_AND_DRINK                      3.6                      4.7\n",
              "14                 GAME                      3.4                      4.7\n",
              "15   HEALTH_AND_FITNESS                      3.7                      4.9\n",
              "16       HOUSE_AND_HOME                      3.4                      4.7\n",
              "17   LIBRARIES_AND_DEMO                      3.1                      4.6\n",
              "18            LIFESTYLE                      3.2                      4.6\n",
              "19  MAPS_AND_NAVIGATION                      4.2                      4.8\n",
              "20              MEDICAL                      2.6                      4.8\n",
              "21   NEWS_AND_MAGAZINES                      3.7                      4.7\n",
              "22            PARENTING                      4.0                      4.7\n",
              "23      PERSONALIZATION                      3.9                      4.7\n",
              "24          PHOTOGRAPHY                      3.8                      4.8\n",
              "25         PRODUCTIVITY                      3.1                      4.7\n",
              "26             SHOPPING                      3.8                      4.7\n",
              "27               SOCIAL                      3.6                      4.8\n",
              "28               SPORTS                      3.5                      4.6\n",
              "29                TOOLS                      3.4                      4.8\n",
              "30     TRAVEL_AND_LOCAL                      3.1                      4.7\n",
              "31        VIDEO_PLAYERS                      3.8                      4.8\n",
              "32              WEATHER                      3.9                      4.5"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# Lowest and hightest rating of each category\n",
        "combined_df.groupby('Category').agg(min_rating_per_category = ('Rating','min'),max_rating_per_category = ('Rating','max')).reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
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        },
        "id": "MsL1GaqZedxf",
        "outputId": "56551429-341a-4c9d-ab7c-112417b1a3e6"
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              "    .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-e6234038-84fc-43c5-b97a-766d0252bf2d 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-e6234038-84fc-43c5-b97a-766d0252bf2d');\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",
              "  "
            ],
            "text/plain": [
              "               Category                 App    Installs\n",
              "90        COMMUNICATION               Gmail  1000000000\n",
              "94        COMMUNICATION            Hangouts  1000000000\n",
              "561  NEWS_AND_MAGAZINES         Google News  1000000000\n",
              "627         PHOTOGRAPHY       Google Photos  1000000000\n",
              "702              SOCIAL            Facebook  1000000000\n",
              "708              SOCIAL             Google+  1000000000\n",
              "781               TOOLS              Google  1000000000\n",
              "824    TRAVEL_AND_LOCAL  Google Street View  1000000000"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# Maximum apps installed in each category\n",
        "max_apps_installed = combined_df.groupby(['Category','App'])['Installs'].max().reset_index()\n",
        "max_apps_installed[max_apps_installed['Installs'] == max_apps_installed['Installs'].max()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zbWZq0Zhe5C5",
        "outputId": "5b9d2b2e-ecf7-42af-f364-8175596fb07d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Type\n",
              "Free    4.333644\n",
              "Paid    4.333376\n",
              "Name: Rating, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# to find out the average rating of the paid apps and free apps and compare them\n",
        "combined_df.groupby('Type')['Rating'].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "azVknit9lqZA"
      },
      "source": [
        "##### The result depicts that the average rating of free and paid apps are very close and yet the average rating of free apps is more . Hence, free apps are performing well"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TYunzaEuAZoj"
      },
      "source": [
        "### Category with most number of paid apps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 989
        },
        "id": "bTdl5pxtlyT4",
        "outputId": "a7d109b6-161c-4337-bb66-7d58ce63b9eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b39f167e-5b03-4239-833f-9701ec2c06c8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\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>Category</th>\n",
              "      <th>App</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
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              "      <th>2</th>\n",
              "      <td>GAME</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>PERSONALIZATION</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TOOLS</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>BOOKS_AND_REFERENCE</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>PRODUCTIVITY</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>SPORTS</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>FINANCE</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>HEALTH_AND_FITNESS</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>BUSINESS</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>WEATHER</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>DATING</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>MAPS_AND_NAVIGATION</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>EDUCATION</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>AUTO_AND_VEHICLES</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>FOOD_AND_DRINK</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>NEWS_AND_MAGAZINES</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>PARENTING</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>SHOPPING</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>EVENTS</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>LIBRARIES_AND_DEMO</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b39f167e-5b03-4239-833f-9701ec2c06c8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
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              "    .colab-df-convert {\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-b39f167e-5b03-4239-833f-9701ec2c06c8 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-b39f167e-5b03-4239-833f-9701ec2c06c8');\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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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "               Category  App\n",
              "0                FAMILY  191\n",
              "1               MEDICAL  109\n",
              "2                  GAME   83\n",
              "3       PERSONALIZATION   83\n",
              "4                 TOOLS   78\n",
              "5   BOOKS_AND_REFERENCE   28\n",
              "6          PRODUCTIVITY   28\n",
              "7         COMMUNICATION   27\n",
              "8                SPORTS   24\n",
              "9           PHOTOGRAPHY   22\n",
              "10            LIFESTYLE   19\n",
              "11              FINANCE   17\n",
              "12   HEALTH_AND_FITNESS   16\n",
              "13             BUSINESS   14\n",
              "14     TRAVEL_AND_LOCAL   12\n",
              "15              WEATHER    8\n",
              "16               DATING    7\n",
              "17  MAPS_AND_NAVIGATION    5\n",
              "18        VIDEO_PLAYERS    4\n",
              "19            EDUCATION    4\n",
              "20    AUTO_AND_VEHICLES    3\n",
              "21       ART_AND_DESIGN    3\n",
              "22               SOCIAL    3\n",
              "23        ENTERTAINMENT    2\n",
              "24       FOOD_AND_DRINK    2\n",
              "25   NEWS_AND_MAGAZINES    2\n",
              "26            PARENTING    2\n",
              "27             SHOPPING    2\n",
              "28               EVENTS    1\n",
              "29   LIBRARIES_AND_DEMO    1"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "play_df[play_df['Type'] == 'Paid'].groupby(['Category'])['App'].count().sort_values(ascending=False).reset_index()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CznV_FhzGmz7"
      },
      "source": [
        "### Let's visualize the results using horizontal bar plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "joE4LuLyAjj-",
        "outputId": "58a97451-beab-4326-e850-ac3aa8af179c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df_paid = play_df[play_df['Type'] == 'Paid'].groupby(['Category'])['App'].count().sort_values(ascending=False).reset_index()\n",
        "\n",
        "fig = plt.figure(figsize =(12,10))\n",
        "plt.barh(df_paid.Category,df_paid.App)\n",
        "plt.xlabel('No. of paid Apps')\n",
        "plt.ylabel('Category')\n",
        "plt.title('Category with most number of paid Apps')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GDEqOJZ3HHiq"
      },
      "source": [
        "### Comparison of size of app and number of installs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "vs3YBHQUHMIT",
        "outputId": "f0183ad7-72c8-4bc7-b9c9-3234d191e091"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-105cf721-f976-4e0c-bddf-2cd25faedb25\">\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>Size</th>\n",
              "      <th>Installs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.2</td>\n",
              "      <td>43700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.3</td>\n",
              "      <td>370000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.5</td>\n",
              "      <td>998000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.6</td>\n",
              "      <td>54000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.7</td>\n",
              "      <td>40000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>95000.0</td>\n",
              "      <td>2222000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>96000.0</td>\n",
              "      <td>38000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>97000.0</td>\n",
              "      <td>202509000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>98000.0</td>\n",
              "      <td>13200000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>99000.0</td>\n",
              "      <td>152000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>166 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-105cf721-f976-4e0c-bddf-2cd25faedb25')\"\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-105cf721-f976-4e0c-bddf-2cd25faedb25 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-105cf721-f976-4e0c-bddf-2cd25faedb25');\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",
              "  "
            ],
            "text/plain": [
              "        Size      Installs\n",
              "0        1.2      43700000\n",
              "1        1.3     370000000\n",
              "2        1.5     998000000\n",
              "3        1.6      54000000\n",
              "4        1.7      40000000\n",
              "..       ...           ...\n",
              "161  95000.0    2222000000\n",
              "162  96000.0      38000000\n",
              "163  97000.0  202509000000\n",
              "164  98000.0   13200000000\n",
              "165  99000.0     152000000\n",
              "\n",
              "[166 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "combined_df.drop([10472]).groupby(['Size'])['Installs'].sum().reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "fIEaL1UdHS-R",
        "outputId": "40001525-2933-4e0e-8a01-65552e0fd7d8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df_size_vs_install = combined_df.drop([10472]).groupby(['Size'])['Installs'].sum().reset_index()\n",
        "\n",
        "plt.figure(figsize =(15,6))\n",
        "plt.scatter(df_size_vs_install.Size, df_size_vs_install.Installs)\n",
        "plt.xlabel('Install size')\n",
        "plt.ylabel('Number of Installs')\n",
        "plt.title('Install size vs Number of Installs')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DOOjeprtKjDF"
      },
      "source": [
        "### **Most downloaded genres in each category**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xzqaKqo6Hct3",
        "outputId": "a7161ed2-6516-4837-fee7-ef5fd63c3b77"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-44a72692-ea44-49bc-afdc-4c8da7a93c46\">\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_x</th>\n",
              "      <th>Genres</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>Art &amp; Design</td>\n",
              "      <td>688100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AUTO_AND_VEHICLES</td>\n",
              "      <td>Auto &amp; Vehicles</td>\n",
              "      <td>448300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BEAUTY</td>\n",
              "      <td>Beauty</td>\n",
              "      <td>498500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BOOKS_AND_REFERENCE</td>\n",
              "      <td>Books &amp; Reference</td>\n",
              "      <td>35380100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BUSINESS</td>\n",
              "      <td>Business</td>\n",
              "      <td>19544600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>COMICS</td>\n",
              "      <td>Comics</td>\n",
              "      <td>70100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>Communication</td>\n",
              "      <td>714217000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>DATING</td>\n",
              "      <td>Dating</td>\n",
              "      <td>3739360000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>EDUCATION</td>\n",
              "      <td>Education;Education</td>\n",
              "      <td>96180000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>Entertainment</td>\n",
              "      <td>14583950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>EVENTS</td>\n",
              "      <td>Events</td>\n",
              "      <td>45590000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>FAMILY</td>\n",
              "      <td>Casual</td>\n",
              "      <td>141850000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>FINANCE</td>\n",
              "      <td>Finance</td>\n",
              "      <td>50408490000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>FOOD_AND_DRINK</td>\n",
              "      <td>Food &amp; Drink</td>\n",
              "      <td>3032550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>GAME</td>\n",
              "      <td>Casual</td>\n",
              "      <td>838915000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>HEALTH_AND_FITNESS</td>\n",
              "      <td>Health &amp; Fitness</td>\n",
              "      <td>71948300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>HOUSE_AND_HOME</td>\n",
              "      <td>House &amp; Home</td>\n",
              "      <td>688880000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>LIBRARIES_AND_DEMO</td>\n",
              "      <td>Libraries &amp; Demo</td>\n",
              "      <td>646400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>Lifestyle</td>\n",
              "      <td>5335270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>MAPS_AND_NAVIGATION</td>\n",
              "      <td>Maps &amp; Navigation</td>\n",
              "      <td>336000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1208837000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NEWS_AND_MAGAZINES</td>\n",
              "      <td>News &amp; Magazines</td>\n",
              "      <td>497925800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>PARENTING</td>\n",
              "      <td>Parenting</td>\n",
              "      <td>96200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>PERSONALIZATION</td>\n",
              "      <td>Personalization</td>\n",
              "      <td>24988600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>Photography</td>\n",
              "      <td>846456000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>PRODUCTIVITY</td>\n",
              "      <td>Productivity</td>\n",
              "      <td>283493300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>SHOPPING</td>\n",
              "      <td>Shopping</td>\n",
              "      <td>66968000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>Social</td>\n",
              "      <td>347549100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>SPORTS</td>\n",
              "      <td>Sports</td>\n",
              "      <td>39204890000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>TOOLS</td>\n",
              "      <td>Tools</td>\n",
              "      <td>237093800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>Travel &amp; Local</td>\n",
              "      <td>69388500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>Video Players &amp; Editors</td>\n",
              "      <td>4078000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>WEATHER</td>\n",
              "      <td>Weather</td>\n",
              "      <td>4921000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "             Category_x                   Genres      Installs\n",
              "0        ART_AND_DESIGN             Art & Design     688100000\n",
              "1     AUTO_AND_VEHICLES          Auto & Vehicles     448300000\n",
              "2                BEAUTY                   Beauty     498500000\n",
              "3   BOOKS_AND_REFERENCE        Books & Reference   35380100000\n",
              "4              BUSINESS                 Business   19544600000\n",
              "5                COMICS                   Comics      70100000\n",
              "6         COMMUNICATION            Communication  714217000000\n",
              "7                DATING                   Dating    3739360000\n",
              "8             EDUCATION      Education;Education   96180000000\n",
              "9         ENTERTAINMENT            Entertainment   14583950000\n",
              "10               EVENTS                   Events      45590000\n",
              "11               FAMILY                   Casual  141850000000\n",
              "12              FINANCE                  Finance   50408490000\n",
              "13       FOOD_AND_DRINK             Food & Drink    3032550000\n",
              "14                 GAME                   Casual  838915000000\n",
              "15   HEALTH_AND_FITNESS         Health & Fitness   71948300000\n",
              "16       HOUSE_AND_HOME             House & Home     688880000\n",
              "17   LIBRARIES_AND_DEMO         Libraries & Demo     646400000\n",
              "18            LIFESTYLE                Lifestyle    5335270000\n",
              "19  MAPS_AND_NAVIGATION        Maps & Navigation     336000000\n",
              "20              MEDICAL                  Medical    1208837000\n",
              "21   NEWS_AND_MAGAZINES         News & Magazines  497925800000\n",
              "22            PARENTING                Parenting      96200000\n",
              "23      PERSONALIZATION          Personalization   24988600000\n",
              "24          PHOTOGRAPHY              Photography  846456000000\n",
              "25         PRODUCTIVITY             Productivity  283493300000\n",
              "26             SHOPPING                 Shopping   66968000000\n",
              "27               SOCIAL                   Social  347549100000\n",
              "28               SPORTS                   Sports   39204890000\n",
              "29                TOOLS                    Tools  237093800000\n",
              "30     TRAVEL_AND_LOCAL           Travel & Local   69388500000\n",
              "31        VIDEO_PLAYERS  Video Players & Editors    4078000000\n",
              "32              WEATHER                  Weather    4921000000"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "df_group1 = combined_df[['Category','Genres','Installs']]\n",
        "df_group2 = df_group1.groupby(['Category','Genres'])['Installs'].sum().reset_index()\n",
        "df_group3 = df_group2.groupby(['Category'])['Installs'].max().reset_index()\n",
        "df_group4 = pd.merge(df_group2,df_group3,on='Installs',how='inner')\n",
        "df_group4.drop('Category_y', axis=1, inplace=True)\n",
        "df_group4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "id": "BeBX9c1hKvyX",
        "outputId": "6305563c-bad4-46ef-ebf8-6d018ade941b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "plt.bar(df_group4['Genres'], df_group4['Installs'])\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('Most downloaded genres in each category')\n",
        "plt.xlabel('Genres')\n",
        "plt.ylabel('Number of Installs')\n",
        "plt.savefig('Most downloaded genres in each category.png')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ez0gng6GP4EH"
      },
      "source": [
        "### Average Rating of paid and free apps and their comparison per category and overall"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "Tbig7VbqP9xa",
        "outputId": "f5465054-c27f-4120-cdd9-35e0de614db5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Average Rating')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(15,6))\n",
        "combined_df.groupby(['Category','Type'])['Rating'].agg('mean').unstack().plot(ax=ax, kind='bar')\n",
        "plt.title('Average Rating of paid and free apps and their comparison per category and overall')\n",
        "plt.xlabel('Category')\n",
        "plt.ylabel('Average Rating')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u2nR8lUvM1UG"
      },
      "source": [
        "### Average rating of paid apps and free apps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "ehxiVgrqQBpH",
        "outputId": "e4efee6b-942d-4a8a-9f45-903316dfeb38"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Type')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PFjwP2H+o/UuSJC1HXqFdkiSpoUV7rpL8+RyLrwXOqqpz25ckSZI0vcbpuToAOBLYvb89l+6sv+OTHD1gbZIkSVNnnDlXewAPqKrrAZIcA3wY+C3gLOB1w5UnSZI0XcbpudqV7nsCZ9wE3KmqfjRruSRJ0mZvnJ6rk4CvJPmv/vFjgJOTbAecP1hlkiRJU2jRcFVVr07yMeA3+0VHVtXa/v5hg1UmSZI0hca9ztXZwKUz2yfZq6q+M1hVkiRJU2qcSzH8KXAM8D3gFiBAAfcbtjRJkqTpM07P1VHAflX1g6GLkSRJmnbjnC14Cd1FQyVJkrSIcXquLgJOT/JhRi69UFX/MFhVkiRJU2qccPWd/rZ1f5MkSdI8xrkUw6uWohBJkqSVYN5wleSNVfXCJB+kOzvwVqrqsYNWJkmSNIUW6rn6t/7f1y9FIZIkSSvBvOGqqs7q796/qt40ui7JUcDnhixMkiRpGo1zKYanz7Hs8MZ1SJIkrQgLzbk6FPgj4C5JThtZtT1w1dCFSZIkTaOF5lx9CVgP7AK8YWT5dcB5QxYlSZI0rRaac3UxcDHwG0tXjiRJ0nRbdM5Vkl9P8tUk1ye5McktSTYsRXGSJEnTZpwJ7W8GDgX+H7At8Gzgn4csSpIkaVqNE66oqguBVVV1S1W9A3jEsGVJkiRNp3G+W/CHSbYGzk3yOrpJ7mOFMkmSpM3NOCHpaf12zwduAPYEHj9kUZIkSdNq0XBVVRdX1Y+rakP/Jc6vBp4yfGmSJEnTZ95wlWTPJMcl+VCSZyfZLskbgG8Cuy5diZIkSdNjoTlX76L7/sD30U1gXwucC9yvqi5fgtokSZKmzkLhaueqOra///EkTwQOq6qfDl+WJEnSdFrwbMEkOwHpH/4AuGOSAFSV3y8oSZI0y0Lh6o7AWfw8XAGc3f9bwF2HKkqSJGlaLfTdgmuWsA5JkqQVwYuBSpIkNWS4kiRJashwJUmS1NBY4SrJQUme0d9fneQuw5YlSZI0nRYNV0mOAV4MvLRftBXw7iGLkiRJmlbj9Fw9Dngs3Zc2U1WXAdsPWZQkSdK0Gidc3VhVRXdtK5JsN2xJkiRJ02uccHVqkrcBOyZ5DvAp4Phhy5IkSZpOC379DUBVvT7J7wIbgP2AV1bVJwevTJIkaQotGq4A+jBloJIkSVrEouEqyXX0861GXAusBf6iqi4aojBJkqRpNE7P1RuB7wIn032J81OAu9F9ifMJwEOHKk6SJGnajDOh/bFV9baquq6qNlTVccDvV9UpwE4D1ydJkjRVxglXP0zypCRb9LcnAT/u180eLpQkSdqsjROuDgOeBnwf+F5//6lJtgWeP2BtkiRJU2ecSzFcBDxmntVfaFuOJEnSdBvnbMFtgGcB9wa2mVleVc8csC5JkqSpNM6w4L8Bvwz8PvA5YA/guiGLkiRJmlbjhKt9quoVwA1V9U7gYOCBiz0pyZ5JPpvk/CRfT3LUphYrSZK03I1znaub+n+vSXIf4HJg1zGedzPdRUbPTrI9cFaST1bV+bexVkmSpGVvnHB1XJKdgJcDpwF3AF6x2JOqaj2wvr9/XZILgN0Bw5UkSVqxFgxXSbYANlTV1cAZwF1vSyNJ1gD7A1+ZY90RwBEAe+21123ZvSRJ0rKx4JyrqvopcPSmNJDkDsD7gBdW1YY52jiuqg6oqgNWr169KU1JkiRN3DgT2j+V5EX9BPWdZ27j7DzJVnTB6qSqev8mVSpJkjQFxplz9eT+3+eNLCsWGSJMEuDtwAVV9Q+3rTxJkqTpMs4V2u9yG/f9ILqvyvnfJOf2y15WVR+5jfuTJEla9sa5QvvtgT8H9qqqI5LsC+xXVR9a6HlV9QUgbcqUJEmaDuPMuXoHcCPwm/3jS4HXDFaRJEnSFBsnXN2tql5HfzHRqvoh9khJkiTNaZxwdWOSbekmsZPkbsBPBq1KkiRpSo1ztuCxwMeAPZOcRDdR/fABa5IkSZpa45wt+IkkZwG/TjcceFRVXTl4ZZIkSVNonLMFPwicDJxWVTcMX5IkSdL0GmfO1euBBwPnJ/mPJE9Iss3AdUmSJE2lcYYFPwd8Lskq4GHAc4ATgB0Grk2SJGnqjDOhnf5swcfQfRXOA4B3DlmUJEnStBpnztWpwIF0Zwy+GfhcVf106MIkSZKm0Tg9V28HDq2qWwCSHJTk0Kp63iLPkyRJ2uyMM+fq40n2T3Io8CTg28D7B69MkiRpCs0brpLcHTi0v10JnAKkqn57iWqTJEmaOgv1XH0D+Dzw6Kq6ECDJny1JVZIkSVNqoetcPR5YD3w2yfFJHo5f2CxJkrSgecNVVf1nVT0FuAfwWeCFwK5J3pLk95aqQEmSpGmy6BXaq+qGqjq5qh4D7AGcA7x48MokSZKm0Dhff/MzVXV1VR1XVQ8fqiBJkqRptlHhSpIkSQszXEmSJDVkuJIkSWrIcCVJktSQ4UqSJKkhw5UkSVJDhitJkqSGDFeSJEkNGa4kSZIaMlxJkiQ1ZLiSJElqyHAlSZLUkOFKkiSpIcOVJElSQ4YrSZKkhgxXkiRJDRmuJEmSGjJcSZIkNWS4kiRJashwJUmS1JDhSpIkqSHDlSRJUkOGK0mSpIYMV5IkSQ0ZriRJkhoyXEmSJDVkuJIkSWrIcCVJktSQ4UqSJKkhw5UkSVJDhitJkqSGDFeSJEkNDRaukpyQ5PtJvjZUG5IkScvNkD1XJwKPGHD/kiRJy85g4aqqzgCuGmr/kiRJy5FzriRJkhqaeLhKckSStUnWXnHFFZMuR5IkaZNMPFxV1XFVdUBVHbB69epJlyNJkrRJJh6uJEmSVpIhL8XwHuC/gf2SfDfJs4ZqS5IkabnYcqgdV9WhQ+1bkiRpuXJYUJIkqSHDlSRJUkOGK0mSpIYMV5IkSQ0ZriRJkhoyXEmSJDVkuJIkSWrIcCVJktSQ4UqSJKkhw5UkSVJDhitJkqSGDFeSJEkNGa4kSZIaMlxJkiQ1ZLiSJElqyHAlSZLUkOFKkiSpIcOVJElSQ4YrSZKkhgxXkiRJDRmuJEmSGjJcSZIkNWS4kiRJashwJUmS1JDhSpIkqSHDlSRJUkOGK0mSpIYMV5IkSQ0ZriRJkhoyXEmSJDVkuJIkSWrIcCVJktSQ4UqSJKkhw5UkSVJDhitJkqSGDFeSJEkNGa4kSZIaMlxJkiQ1ZLiSJElqyHAlSZLUkOFKkiSpIcOVJElSQ4YrSZKkhgxXkiRJDRmuJEmSGjJcSZIkNWS4kiRJashwJUmS1JDhSpIkqSHDlSRJUkOGK0mSpIYGDVdJHpHkm0kuTPKSIduSJElaDgYLV0lWAf8MPBK4F3BoknsN1Z4kSdJyMGTP1YHAhVV1UVXdCLwXOGTA9iRJkiZuyHC1O3DJyOPv9sskSZJWrC0nXUCSI4Aj+ofXJ/nmJOvR1NgFuHLSRSw3+btJVyBNPX+2zMGfLfPae66FQ4arS4E9Rx7v0S+7lao6DjhuwDq0AiVZW1UHTLoOSSuLP1vUwpDDgl8F9k1ylyRbA08BThuwPUmSpIkbrOeqqm5O8nzg48Aq4ISq+vpQ7UmSJC0Hg865qqqPAB8Zsg1tthxKljQEf7Zok6WqJl2DJEnSiuHX30iSJDVkuJIkSWrIcCVJktSQ4UpTI52nJnll/3ivJAdOui5JkkY5oV1TI8lbgJ8CD6uqeybZCfhEVf3ahEuTNIWSXAfM+0uwqnZYwnK0gkz862+kjfDAqnpAknMAqurq/gK1krTRqmp7gCSvBtYD/wYEOAy48wRL05RzWFDT5KYkq+j/0kyymq4nS5I2xWOr6l+q6rqq2lBVbwEOmXRRml6GK02TfwQ+ANwpyV8DXwBeO9mSJK0ANyQ5LMmqJFskOQy4YdJFaXo550pTJck9gIfTdd1/uqoumHBJkqZckjXAm4AH0fWMfxF4YVWtm1xVmmaGK02VJAcB+1bVO/phwTtU1bcnXZckSTMMV5oaSY4BDgD2q6q7J9kN+PeqetCES5M0hZIcXVWvS/JPzHHWYFW9YAJlaQXwbEFNk8cB+wNnA1TVZUm2n2xJkqbYzLSCtROtQiuO4UrT5MaqqiQzZwtuN+mCJE2vqvpg/+87J12LVhbDlabJqUneBuyY5DnAM4HjJ1yTpCnXz998MXAvYJuZ5VX1sIkVpalmuNJUSBLgFOAewAZgP+CVVfXJiRYmaSU4ie7ny8HAkcDTgSsmWpGmmhPaNTWS/G9V3XfSdUhaWZKcVVW/muS8qrpfv+yrfrWWbisvIqppcnYSf9hJau2m/t/1SQ5Osj+w8yQL0nSz50pTI8k3gH2BdXRXTw5QM39pStJtkeTRwOeBPYF/AnYAXlVVp020ME0tw5WWvSR7VdV3kuw91/qqunipa5I0/ZJsQzfHah/gf4G3V9XNk61KK4HhSstekrOr6gH9/fdV1R9OuiZJ0y/JKXRDgp8HHglcXFVHTbYqrQSeLahpkJH7d51YFZJWmnvNnCST5O3AmROuRyuEE9o1DWqe+5K0KWYmsuNwoFpyWFDLXpJb+PkE9m2BH86sopvQvsOkapM0vUZ+tsCtf774s0WbxHAlSZLUkMOCkiRJDRmuJEmSGvJsQUlTJ8kvAZ/uH/4ycAs//y64A6vqxokUJkk450rSlEtyLHB9Vb1+0rVIEjgsKGll2DbJt5NsBZBkh5nHSU5P8qYk5yb5WpID+222S3JCkjOTnJPkkMkegqSVwnAlaSX4EXA6cHD/+CnA+6tq5jpGt6+q+wN/ApzQL/sr4DNVdSDw28DfJ9lu6UqWtFIZriStFP8KPKO//wzgHSPr3gNQVWcAOyTZEfg94CVJzqULZtsAey1ZtZJWLCe0S1oRquqLSdYkeSiwqqq+Nrp69uZ0F4r8w6r65lLVKGnzYM+VpJXkXcDJ3LrXCuDJAEkOAq6tqmuBjwN/miT9uv2XslBJK5fhStJKchKwE/0w4IgfJzkHeCvwrH7Zq4GtgPOSfL1/LEmbzEsxSFoxkjwBOKSqnjay7HTgRVW1dmKFSdqsOOdK0oqQ5J+ARwKPmnQtkjZv9lxJkiQ15JwrSZKkhgxXkiRJDRmuJEmSGjJcSZIkNWS4kiRJashwJUmS1ND/B1+2k5XZS3vmAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(10,5))\n",
        "combined_df.groupby('Type')['Rating'].agg('mean').plot(kind='bar',ax=ax)\n",
        "plt.title('Average rating of paid apps and free apps')\n",
        "plt.ylabel('Average Rating')\n",
        "plt.xlabel('Type')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fjJXd4mLNWzH"
      },
      "source": [
        "### **To find the percentage of involvement of audience in each app and compare every category**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "OQaOucXCM_pl",
        "outputId": "1cea10fc-367c-4a2b-cae3-585e7197a6c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-45efbe62-81c8-4026-b4e3-ad81669c32a9\">\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>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",
              "      <th>Translated_Review</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Sentiment_Polarity</th>\n",
              "      <th>Sentiment_Subjectivity</th>\n",
              "      <th>involvement</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14000.0</td>\n",
              "      <td>500000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>2018-01-15</td>\n",
              "      <td>2.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>A kid's excessive ads. The types ads allowed a...</td>\n",
              "      <td>Negative</td>\n",
              "      <td>-0.250</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.998066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14000.0</td>\n",
              "      <td>500000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>2018-01-15</td>\n",
              "      <td>2.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>It bad &gt;:(</td>\n",
              "      <td>Negative</td>\n",
              "      <td>-0.725</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.998066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14000.0</td>\n",
              "      <td>500000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>2018-01-15</td>\n",
              "      <td>2.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>like</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.998066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14000.0</td>\n",
              "      <td>500000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>2018-01-15</td>\n",
              "      <td>2.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>I love colors inspyering</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.998066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14000.0</td>\n",
              "      <td>500000</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>2018-01-15</td>\n",
              "      <td>2.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>I hate</td>\n",
              "      <td>Negative</td>\n",
              "      <td>-0.800</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.998066</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45efbe62-81c8-4026-b4e3-ad81669c32a9')\"\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",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\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",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-45efbe62-81c8-4026-b4e3-ad81669c32a9 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-45efbe62-81c8-4026-b4e3-ad81669c32a9');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                   App        Category  ...  Sentiment_Subjectivity  involvement\n",
              "0  Coloring book moana  ART_AND_DESIGN  ...                1.000000     0.998066\n",
              "1  Coloring book moana  ART_AND_DESIGN  ...                0.833333     0.998066\n",
              "2  Coloring book moana  ART_AND_DESIGN  ...                0.000000     0.998066\n",
              "3  Coloring book moana  ART_AND_DESIGN  ...                0.600000     0.998066\n",
              "4  Coloring book moana  ART_AND_DESIGN  ...                0.900000     0.998066\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "#lets first install a new column named involvement and we will calculate involvement as (total install-no. of reviews)/total installs\n",
        "#lets define a function\n",
        "def involvement():\n",
        "  return (combined_df['Installs']-combined_df['Reviews'])/combined_df['Installs']\n",
        "#lets apply it to the columns\n",
        "combined_df['involvement'] = involvement()\n",
        "combined_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1AYFZX3XSHJy"
      },
      "source": [
        "### **Now lets check which app category has more audience involvement. This can be done by grouping the category and finding the average involvement**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "VO9AchZ3N1Zv"
      },
      "outputs": [],
      "source": [
        "involvement_df = combined_df.groupby('Category')['involvement'].mean().reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "nzTu5j-KSk4n",
        "outputId": "766569b2-421f-416f-afde-14eb7ed35d94"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize = (15,5))\n",
        "plt.plot(involvement_df['Category'],involvement_df['involvement'])\n",
        "plt.xticks(rotation = '90')\n",
        "plt.title('Comparison of audience involvement in each category')\n",
        "plt.xlabel('Category')\n",
        "plt.ylabel('Involvement')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xLAgjTv5ZbrY"
      },
      "source": [
        "### **Lets check the data by setting the Y axis from 0 to 1.00 so to check the data correctly**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "wELQitivSpeG",
        "outputId": "2a1e5355-86dc-4213-fd70-14c0aeb50b4a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize = (15,5))\n",
        "plt.plot(involvement_df['Category'],involvement_df['involvement'])\n",
        "plt.ylim(0,1.00)\n",
        "plt.xticks(rotation = '90')\n",
        "plt.xlabel('Category')\n",
        "plt.ylabel('Involvement')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mBpB2RTLf0OK"
      },
      "source": [
        "### **Now lets check the number of paid apps and number of free apps to total apps**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "gVZ4aa_7ZzWe"
      },
      "outputs": [],
      "source": [
        "# There is one index missing from the dataframe so lets reset and drop the old index column\n",
        "combined_df.reset_index(inplace=True)\n",
        "combined_df.drop(['index'],inplace = True,axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "s775eLKBgS_D",
        "outputId": "09fdf4b4-f18d-44bb-ead7-a058b4aa7f1b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Apps')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "paid_list=[]\n",
        "free_list = []\n",
        "total_list = [i for i in play_df['App']]\n",
        "for i in range(len(play_df['Type'])):\n",
        "  if play_df['Type'][i]=='Paid':\n",
        "    paid_list.append(play_df['App'][i])\n",
        "  if play_df['Type'][i]=='Free':\n",
        "    free_list.append(play_df['App'][i])\n",
        "  else:\n",
        "    continue\n",
        "\n",
        "p = len(paid_list)\n",
        "q = len(free_list)\n",
        "r = len(total_list)\n",
        "    \n",
        "name = ('paid','free','total')\n",
        "values = (p,q,r)\n",
        "plt.bar(name,values)\n",
        "plt.title(' number of paid apps and number of free apps to total apps')\n",
        "plt.xlabel('Type')\n",
        "plt.ylabel('Number of Apps')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YjbTIFN3lZg_"
      },
      "source": [
        "### **Sentiment polarity is the inclination of positivity or negativity so lets see whats the average sentiment polarity per category**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "JeAtbTP6gZM0",
        "outputId": "a397fd18-cd74-42a9-e90b-ba59bfb3cd48"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7dfc0e1c-d44f-4fbe-95ca-3b8453618b5a\">\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>Sentiment_Polarity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>0.234735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AUTO_AND_VEHICLES</td>\n",
              "      <td>0.348683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BEAUTY</td>\n",
              "      <td>0.193360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BOOKS_AND_REFERENCE</td>\n",
              "      <td>0.226526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BUSINESS</td>\n",
              "      <td>0.247749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>COMICS</td>\n",
              "      <td>0.468482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>0.182965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>DATING</td>\n",
              "      <td>0.164817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>EDUCATION</td>\n",
              "      <td>0.251392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>0.133400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>EVENTS</td>\n",
              "      <td>0.377933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>FAMILY</td>\n",
              "      <td>0.137005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>FINANCE</td>\n",
              "      <td>0.169447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>FOOD_AND_DRINK</td>\n",
              "      <td>0.253251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>GAME</td>\n",
              "      <td>0.060506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>HEALTH_AND_FITNESS</td>\n",
              "      <td>0.278884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>HOUSE_AND_HOME</td>\n",
              "      <td>0.237435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>LIBRARIES_AND_DEMO</td>\n",
              "      <td>0.240771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>0.187299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>MAPS_AND_NAVIGATION</td>\n",
              "      <td>0.267218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>0.226565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NEWS_AND_MAGAZINES</td>\n",
              "      <td>0.106667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>PARENTING</td>\n",
              "      <td>0.318880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>PERSONALIZATION</td>\n",
              "      <td>0.252074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>0.220280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>PRODUCTIVITY</td>\n",
              "      <td>0.169384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>SHOPPING</td>\n",
              "      <td>0.181029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>0.101338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>SPORTS</td>\n",
              "      <td>0.172202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>TOOLS</td>\n",
              "      <td>0.209034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>0.168372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>0.143543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>WEATHER</td>\n",
              "      <td>0.282033</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7dfc0e1c-d44f-4fbe-95ca-3b8453618b5a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
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              "    [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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              "          document.querySelector('#df-7dfc0e1c-d44f-4fbe-95ca-3b8453618b5a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7dfc0e1c-d44f-4fbe-95ca-3b8453618b5a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
              "  "
            ],
            "text/plain": [
              "               Category  Sentiment_Polarity\n",
              "0        ART_AND_DESIGN            0.234735\n",
              "1     AUTO_AND_VEHICLES            0.348683\n",
              "2                BEAUTY            0.193360\n",
              "3   BOOKS_AND_REFERENCE            0.226526\n",
              "4              BUSINESS            0.247749\n",
              "5                COMICS            0.468482\n",
              "6         COMMUNICATION            0.182965\n",
              "7                DATING            0.164817\n",
              "8             EDUCATION            0.251392\n",
              "9         ENTERTAINMENT            0.133400\n",
              "10               EVENTS            0.377933\n",
              "11               FAMILY            0.137005\n",
              "12              FINANCE            0.169447\n",
              "13       FOOD_AND_DRINK            0.253251\n",
              "14                 GAME            0.060506\n",
              "15   HEALTH_AND_FITNESS            0.278884\n",
              "16       HOUSE_AND_HOME            0.237435\n",
              "17   LIBRARIES_AND_DEMO            0.240771\n",
              "18            LIFESTYLE            0.187299\n",
              "19  MAPS_AND_NAVIGATION            0.267218\n",
              "20              MEDICAL            0.226565\n",
              "21   NEWS_AND_MAGAZINES            0.106667\n",
              "22            PARENTING            0.318880\n",
              "23      PERSONALIZATION            0.252074\n",
              "24          PHOTOGRAPHY            0.220280\n",
              "25         PRODUCTIVITY            0.169384\n",
              "26             SHOPPING            0.181029\n",
              "27               SOCIAL            0.101338\n",
              "28               SPORTS            0.172202\n",
              "29                TOOLS            0.209034\n",
              "30     TRAVEL_AND_LOCAL            0.168372\n",
              "31        VIDEO_PLAYERS            0.143543\n",
              "32              WEATHER            0.282033"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "combined_df.groupby('Category')['Sentiment_Polarity'].mean().reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "z9W-iT-AljkD",
        "outputId": "82814249-eb5a-4d12-d62c-55dcb2ec11ce"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "\n",
        "x = sorted([i for i in combined_df['Category'].unique()])\n",
        "y = [i for i in combined_df.groupby('Category')['Sentiment_Polarity'].mean()]\n",
        "plt.figure(figsize=(15,5))\n",
        "plt.xticks(rotation = 90)\n",
        "plt.plot(x,y)\n",
        "plt.title('average sentiment polarity per category')\n",
        "plt.xlabel('Category')\n",
        "plt.ylabel('Average sentiment polarity')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "LQA5Jny9m0WM",
        "outputId": "0c38643c-4e2d-471e-bac1-c171efd59d35"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "z = [i for i in combined_df.groupby('Category')['Sentiment_Subjectivity'].mean()]\n",
        "x = sorted([i for i in combined_df['Category'].unique()])\n",
        "plt.figure(figsize = (15,5))\n",
        "plt.xticks(rotation = 90)\n",
        "plt.plot(x,z)\n",
        "plt.title('Average sentiment subjectivity per category')\n",
        "plt.xlabel('Category')\n",
        "plt.ylabel('Average sentiment subjectivity.png ')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TlhUHeAzqmGI"
      },
      "source": [
        "### So from the above, it can be seen that the comics and the events categories have more subjectivity than the game category, that means the comics and the events categories have more opinion based reviews than the games."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "19jOlQyzsAAp"
      },
      "source": [
        "### **Sentiment polarity indicates how much a review is inclined to positivity or negativity and sentiment subjectivity indicates how much factual or emotional that review is. So lets see if there is any co-relation between the two**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Yv5SCs4eqdSZ",
        "outputId": "81020c72-e28a-4f84-d2f4-d0b15e9ec9ba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-31d771cd-0a8b-4b16-9e78-e33836fcdf3a\">\n",
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              "      <th></th>\n",
              "      <th>Sentiment_Polarity</th>\n",
              "      <th>Sentiment_Subjectivity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.250000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.725000</td>\n",
              "      <td>0.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.800000</td>\n",
              "      <td>0.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72600</th>\n",
              "      <td>0.475000</td>\n",
              "      <td>0.512500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72601</th>\n",
              "      <td>0.566667</td>\n",
              "      <td>0.733333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72602</th>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72603</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72604</th>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.450000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>72605 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-31d771cd-0a8b-4b16-9e78-e33836fcdf3a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          const element = document.querySelector('#df-31d771cd-0a8b-4b16-9e78-e33836fcdf3a');\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",
              "  "
            ],
            "text/plain": [
              "       Sentiment_Polarity  Sentiment_Subjectivity\n",
              "0               -0.250000                1.000000\n",
              "1               -0.725000                0.833333\n",
              "2                0.000000                0.000000\n",
              "3                0.500000                0.600000\n",
              "4               -0.800000                0.900000\n",
              "...                   ...                     ...\n",
              "72600            0.475000                0.512500\n",
              "72601            0.566667                0.733333\n",
              "72602            0.200000                0.200000\n",
              "72603            0.000000                0.000000\n",
              "72604            0.850000                0.450000\n",
              "\n",
              "[72605 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "sentiment_df = combined_df[['Sentiment_Polarity','Sentiment_Subjectivity']].dropna()\n",
        "sentiment_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "q6Fj6ElRsZvU",
        "outputId": "27ee8aab-5b2d-4855-c6f1-9a9496e3f4f4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "      <th></th>\n",
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              "      <th>Sentiment_Subjectivity</th>\n",
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              "    <tr>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.227486</td>\n",
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              "    <tr>\n",
              "      <th>Sentiment_Subjectivity</th>\n",
              "      <td>0.227486</td>\n",
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              "  </tbody>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
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              "      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-005a82fc-e451-4792-a0fa-bbb8e85e5ef5 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-005a82fc-e451-4792-a0fa-bbb8e85e5ef5');\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",
              "  "
            ],
            "text/plain": [
              "                        Sentiment_Polarity  Sentiment_Subjectivity\n",
              "Sentiment_Polarity                1.000000                0.227486\n",
              "Sentiment_Subjectivity            0.227486                1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "sentiment_df.corr()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-AFyeLdFAie"
      },
      "source": [
        "### **Lets create box plots for two sentiment polarity and sentiment subjectivity to know where most of the values lie**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "_Nuqi2PKsnLX",
        "outputId": "602eb29b-3621-4f8b-873a-049f2c470981"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "x = [i for i in sentiment_df['Sentiment_Polarity']]\n",
        "y = [i for i in sentiment_df['Sentiment_Subjectivity']]\n",
        "plt.boxplot((x,y),patch_artist=True,labels = ['sentiment_polarity','sentiment_subjectivity'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g36Ah5sgFVdG"
      },
      "source": [
        "### **Lets see which category has how much positiveness and negativeness by comparing the sentiment with the category**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "y24xiBKpFPm3",
        "outputId": "d2f5dae9-8841-4f98-98fe-0b7d7f14ab44"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-2b88a948-a600-4986-9bb2-54ae4f909fb8\">\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>Sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72600</th>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72601</th>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72602</th>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72603</th>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72604</th>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>72605 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2b88a948-a600-4986-9bb2-54ae4f909fb8')\"\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",
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              "\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             Category Sentiment\n",
              "0      ART_AND_DESIGN  Negative\n",
              "1      ART_AND_DESIGN  Negative\n",
              "2      ART_AND_DESIGN   Neutral\n",
              "3      ART_AND_DESIGN  Positive\n",
              "4      ART_AND_DESIGN  Negative\n",
              "...               ...       ...\n",
              "72600     PHOTOGRAPHY  Positive\n",
              "72601     PHOTOGRAPHY  Positive\n",
              "72602     PHOTOGRAPHY  Positive\n",
              "72603     PHOTOGRAPHY   Neutral\n",
              "72604     PHOTOGRAPHY  Positive\n",
              "\n",
              "[72605 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "#first lets make a dataframe by cleaning the na values in sentiment column from combined_df\n",
        "sentiment_category_df = combined_df[['Category','Sentiment']].dropna()\n",
        "sentiment_category_df"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "MZsF_knlFdPe",
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        {
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          "data": {
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              "\n",
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              "      <th></th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Category</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Negative</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Negative</td>\n",
              "      <td>BEAUTY</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Negative</td>\n",
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              "      <td>134</td>\n",
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              "      <th>4</th>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Positive</td>\n",
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              "      <td>1824</td>\n",
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              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>Positive</td>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Positive</td>\n",
              "      <td>WEATHER</td>\n",
              "      <td>165</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>99 rows × 3 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\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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              "  "
            ],
            "text/plain": [
              "   Sentiment             Category  count\n",
              "0   Negative       ART_AND_DESIGN     61\n",
              "1   Negative    AUTO_AND_VEHICLES     17\n",
              "2   Negative               BEAUTY     65\n",
              "3   Negative  BOOKS_AND_REFERENCE    134\n",
              "4   Negative             BUSINESS    342\n",
              "..       ...                  ...    ...\n",
              "94  Positive               SPORTS   2201\n",
              "95  Positive                TOOLS   1090\n",
              "96  Positive     TRAVEL_AND_LOCAL   1824\n",
              "97  Positive        VIDEO_PLAYERS    190\n",
              "98  Positive              WEATHER    165\n",
              "\n",
              "[99 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "#lets create a completely new dataframe by grouping the category and sentiment\n",
        "new_df = sentiment_category_df.groupby(['Sentiment','Category']).size().to_frame(name = 'count').reset_index()\n",
        "new_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Nlr92nvFFnuB",
        "outputId": "c2a5052e-2b00-49a7-9917-115b0482b52b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Sentiment</th>\n",
              "      <th>Negative</th>\n",
              "      <th>Neutral</th>\n",
              "      <th>Positive</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Category</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ART_AND_DESIGN</th>\n",
              "      <td>61</td>\n",
              "      <td>62</td>\n",
              "      <td>259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AUTO_AND_VEHICLES</th>\n",
              "      <td>17</td>\n",
              "      <td>36</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BEAUTY</th>\n",
              "      <td>65</td>\n",
              "      <td>88</td>\n",
              "      <td>185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>134</td>\n",
              "      <td>130</td>\n",
              "      <td>551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BUSINESS</th>\n",
              "      <td>342</td>\n",
              "      <td>555</td>\n",
              "      <td>1450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COMICS</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>372</td>\n",
              "      <td>285</td>\n",
              "      <td>1292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DATING</th>\n",
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              "      <td>1971</td>\n",
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              "      <th>EDUCATION</th>\n",
              "      <td>216</td>\n",
              "      <td>132</td>\n",
              "      <td>1346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ENTERTAINMENT</th>\n",
              "      <td>588</td>\n",
              "      <td>359</td>\n",
              "      <td>1486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>16</td>\n",
              "      <td>17</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FAMILY</th>\n",
              "      <td>1559</td>\n",
              "      <td>463</td>\n",
              "      <td>3888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FINANCE</th>\n",
              "      <td>442</td>\n",
              "      <td>255</td>\n",
              "      <td>1312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FOOD_AND_DRINK</th>\n",
              "      <td>128</td>\n",
              "      <td>107</td>\n",
              "      <td>611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME</th>\n",
              "      <td>7270</td>\n",
              "      <td>814</td>\n",
              "      <td>11041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HEALTH_AND_FITNESS</th>\n",
              "      <td>577</td>\n",
              "      <td>482</td>\n",
              "      <td>3444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HOUSE_AND_HOME</th>\n",
              "      <td>114</td>\n",
              "      <td>148</td>\n",
              "      <td>435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LIBRARIES_AND_DEMO</th>\n",
              "      <td>51</td>\n",
              "      <td>44</td>\n",
              "      <td>238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LIFESTYLE</th>\n",
              "      <td>261</td>\n",
              "      <td>269</td>\n",
              "      <td>802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MAPS_AND_NAVIGATION</th>\n",
              "      <td>27</td>\n",
              "      <td>36</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MEDICAL</th>\n",
              "      <td>362</td>\n",
              "      <td>315</td>\n",
              "      <td>1534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NEWS_AND_MAGAZINES</th>\n",
              "      <td>646</td>\n",
              "      <td>244</td>\n",
              "      <td>1314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PARENTING</th>\n",
              "      <td>23</td>\n",
              "      <td>52</td>\n",
              "      <td>185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PERSONALIZATION</th>\n",
              "      <td>217</td>\n",
              "      <td>196</td>\n",
              "      <td>899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHOTOGRAPHY</th>\n",
              "      <td>485</td>\n",
              "      <td>286</td>\n",
              "      <td>1709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PRODUCTIVITY</th>\n",
              "      <td>596</td>\n",
              "      <td>381</td>\n",
              "      <td>2144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SHOPPING</th>\n",
              "      <td>422</td>\n",
              "      <td>267</td>\n",
              "      <td>1337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SOCIAL</th>\n",
              "      <td>400</td>\n",
              "      <td>268</td>\n",
              "      <td>730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SPORTS</th>\n",
              "      <td>821</td>\n",
              "      <td>482</td>\n",
              "      <td>2201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TOOLS</th>\n",
              "      <td>347</td>\n",
              "      <td>357</td>\n",
              "      <td>1090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TRAVEL_AND_LOCAL</th>\n",
              "      <td>760</td>\n",
              "      <td>517</td>\n",
              "      <td>1824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VIDEO_PLAYERS</th>\n",
              "      <td>83</td>\n",
              "      <td>58</td>\n",
              "      <td>190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WEATHER</th>\n",
              "      <td>33</td>\n",
              "      <td>35</td>\n",
              "      <td>165</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2cc0ba5-6d68-487b-8ae6-6efe6c86e27e')\"\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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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\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",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "Sentiment            Negative  Neutral  Positive\n",
              "Category                                        \n",
              "ART_AND_DESIGN             61       62       259\n",
              "AUTO_AND_VEHICLES          17       36       236\n",
              "BEAUTY                     65       88       185\n",
              "BOOKS_AND_REFERENCE       134      130       551\n",
              "BUSINESS                  342      555      1450\n",
              "COMICS                      2        6        72\n",
              "COMMUNICATION             372      285      1292\n",
              "DATING                    697      530      1971\n",
              "EDUCATION                 216      132      1346\n",
              "ENTERTAINMENT             588      359      1486\n",
              "EVENTS                     16       17       125\n",
              "FAMILY                   1559      463      3888\n",
              "FINANCE                   442      255      1312\n",
              "FOOD_AND_DRINK            128      107       611\n",
              "GAME                     7270      814     11041\n",
              "HEALTH_AND_FITNESS        577      482      3444\n",
              "HOUSE_AND_HOME            114      148       435\n",
              "LIBRARIES_AND_DEMO         51       44       238\n",
              "LIFESTYLE                 261      269       802\n",
              "MAPS_AND_NAVIGATION        27       36       129\n",
              "MEDICAL                   362      315      1534\n",
              "NEWS_AND_MAGAZINES        646      244      1314\n",
              "PARENTING                  23       52       185\n",
              "PERSONALIZATION           217      196       899\n",
              "PHOTOGRAPHY               485      286      1709\n",
              "PRODUCTIVITY              596      381      2144\n",
              "SHOPPING                  422      267      1337\n",
              "SOCIAL                    400      268       730\n",
              "SPORTS                    821      482      2201\n",
              "TOOLS                     347      357      1090\n",
              "TRAVEL_AND_LOCAL          760      517      1824\n",
              "VIDEO_PLAYERS              83       58       190\n",
              "WEATHER                    33       35       165"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "# Now lets make a pivot to see which category has how much positive,negative and neutral reviews\n",
        "pivotted_new_df = new_df.pivot(index='Category',columns='Sentiment',values='count')\n",
        "pivotted_new_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "Hoa_M_KbGIoD",
        "outputId": "8552ef8e-b8a3-456f-b247-6d3e95f11b65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\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>Sentiment</th>\n",
              "      <th>Negative</th>\n",
              "      <th>Neutral</th>\n",
              "      <th>Positive</th>\n",
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              "      <th>Category</th>\n",
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              "      <th></th>\n",
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              "  <tbody>\n",
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              "      <th>GAME</th>\n",
              "      <td>7270</td>\n",
              "      <td>814</td>\n",
              "      <td>11041</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-03049293-077b-4050-a580-9c05fa13d643');\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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              "  "
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            "text/plain": [
              "Sentiment  Negative  Neutral  Positive\n",
              "Category                              \n",
              "GAME           7270      814     11041"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "#Lets find out the most negative reviewed category\n",
        "pivotted_new_df[pivotted_new_df['Negative']==pivotted_new_df['Negative'].max()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "keMri4qiKAh5",
        "outputId": "f6bd0671-8711-4d55-aa76-600393eba8c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-85713d25-9eef-4a02-bf5c-fa203d2b7a43\">\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Sentiment</th>\n",
              "      <th>Negative</th>\n",
              "      <th>Neutral</th>\n",
              "      <th>Positive</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Category</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>GAME</th>\n",
              "      <td>7270</td>\n",
              "      <td>814</td>\n",
              "      <td>11041</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85713d25-9eef-4a02-bf5c-fa203d2b7a43')\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-85713d25-9eef-4a02-bf5c-fa203d2b7a43');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "Sentiment  Negative  Neutral  Positive\n",
              "Category                              \n",
              "GAME           7270      814     11041"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "# Lets find out the most positive reviewed category\n",
        "pivotted_new_df[pivotted_new_df['Positive']==pivotted_new_df['Positive'].max()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "Xs9VhOkxLIC4",
        "outputId": "d85b7930-02c3-4a80-a1d3-0a731ec8196a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c85b6f79-f9a8-4821-b408-ae4df423b51c\">\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>Sentiment</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>COMICS</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>72</td>\n",
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              "  </tbody>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "       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-c85b6f79-f9a8-4821-b408-ae4df423b51c 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-c85b6f79-f9a8-4821-b408-ae4df423b51c');\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",
              "  "
            ],
            "text/plain": [
              "Sentiment  Negative  Neutral  Positive\n",
              "Category                              \n",
              "COMICS            2        6        72"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "# Lets find out the minimum number of negative reviewed Category\n",
        "pivotted_new_df[pivotted_new_df['Negative']==pivotted_new_df['Negative'].min()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "mO1KnmajckLo",
        "outputId": "f6887b77-86f9-4012-a0a6-e4037f15a87e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-95db9af0-2bf7-4d7b-83c5-84c99d9c5b9b\">\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>Sentiment</th>\n",
              "      <th>Negative</th>\n",
              "      <th>Neutral</th>\n",
              "      <th>Positive</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Category</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>COMICS</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-95db9af0-2bf7-4d7b-83c5-84c99d9c5b9b')\"\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-95db9af0-2bf7-4d7b-83c5-84c99d9c5b9b 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-95db9af0-2bf7-4d7b-83c5-84c99d9c5b9b');\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",
              "  "
            ],
            "text/plain": [
              "Sentiment  Negative  Neutral  Positive\n",
              "Category                              \n",
              "COMICS            2        6        72"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "# Lets find out the minimum number of positive reviewed Category\n",
        "pivotted_new_df[pivotted_new_df['Positive']==pivotted_new_df['Positive'].min()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "evKnvVHlc-Ee",
        "outputId": "250a166b-b55e-4756-fdf4-8e584f1def70"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Lets plot the pivot\n",
        "pivotted_new_df.plot(kind = 'bar',figsize = (20,5), width =0.7)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.title('Sentiment per category')\n",
        "plt.xlabel('Category')\n",
        "plt.ylabel('Number of Reviews')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P5CrzOZc3jbB"
      },
      "source": [
        "### **Lets see which category is popular amongst the teens and this can be done by selecting the content rating which specifies the teen and grouping by category then taking the average installs**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "xC-BXVjH3Tyy",
        "outputId": "3e33c033-cf6d-4b68-cc47-fb238bd50487"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d9f2dd47-fc62-412f-97cb-36a3a5d9be65\">\n",
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              "      <th>Category</th>\n",
              "      <th>Installs</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>AUTO_AND_VEHICLES</td>\n",
              "      <td>10000000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BOOKS_AND_REFERENCE</td>\n",
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              "      <th>2</th>\n",
              "      <td>COMICS</td>\n",
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              "      <th>3</th>\n",
              "      <td>DATING</td>\n",
              "      <td>1000000</td>\n",
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              "      <th>4</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>5639919</td>\n",
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              "      <th>5</th>\n",
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              "      <td>4809523</td>\n",
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              "      <th>9</th>\n",
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              "      <td>675675</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>HOUSE_AND_HOME</td>\n",
              "      <td>10000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>NEWS_AND_MAGAZINES</td>\n",
              "      <td>685599853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>PERSONALIZATION</td>\n",
              "      <td>63664596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>SHOPPING</td>\n",
              "      <td>60229226</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>SPORTS</td>\n",
              "      <td>5000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>3392156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>1000000</td>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d9f2dd47-fc62-412f-97cb-36a3a5d9be65');\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",
              "  "
            ],
            "text/plain": [
              "               Category   Installs\n",
              "0     AUTO_AND_VEHICLES   10000000\n",
              "1   BOOKS_AND_REFERENCE  100000000\n",
              "2                COMICS     876250\n",
              "3                DATING    1000000\n",
              "4         ENTERTAINMENT    5639919\n",
              "5                EVENTS     100000\n",
              "6                FAMILY   23007640\n",
              "7        FOOD_AND_DRINK    4809523\n",
              "8                  GAME   46558481\n",
              "9    HEALTH_AND_FITNESS     675675\n",
              "10       HOUSE_AND_HOME      10000\n",
              "11            LIFESTYLE     100000\n",
              "12              MEDICAL      50000\n",
              "13   NEWS_AND_MAGAZINES  685599853\n",
              "14      PERSONALIZATION   63664596\n",
              "15             SHOPPING   60229226\n",
              "16               SOCIAL  637555888\n",
              "17               SPORTS    5000000\n",
              "18     TRAVEL_AND_LOCAL    3392156\n",
              "19        VIDEO_PLAYERS    1000000"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "teen_df = combined_df[combined_df['Content Rating']=='Teen'].groupby('Category').agg({'Installs':'mean'}).astype(int).reset_index()\n",
        "teen_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "TKXwLGzM36zd",
        "outputId": "d010ed41-4391-4e7b-aaeb-647714082a84"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "plt.bar(teen_df['Category'],teen_df['Installs'])\n",
        "plt.xticks(rotation = '90')\n",
        "plt.title('Category popular among teens')\n",
        "plt.xlabel('Category')\n",
        "plt.ylabel('Number of Installs')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QcLXYDwmIjhL"
      },
      "source": [
        "### **So here we saw an interesting result that when we consider average install we find two categories are dominating they are 'news and magazines' and 'social'**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qAQgZmUCI0uO"
      },
      "source": [
        "### **Lets see the result by considering the total number of apps installed**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "0GHUMpl0ISnO"
      },
      "outputs": [],
      "source": [
        "teen_df1 = combined_df[combined_df['Content Rating']=='Teen'].groupby('Category').agg({'Installs':'sum'}).astype(int).reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "1PzITsE6JB-g",
        "outputId": "244de26e-462d-46bd-9c85-1a52259a3728"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "plt.bar(teen_df1['Category'],teen_df1['Installs'])\n",
        "plt.xticks(rotation = '90')\n",
        "plt.title('')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "vb9_sSCZJE5m"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### So we saw that we have three popular categories amongst the teens from theabove analysis. In two categories the genres are same but in Game category there are multiple genres so lets see which genre in game is mostly downloaded by the teens"
      ],
      "metadata": {
        "id": "NXZN83XT0UFM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets filter by content rating and category then group by genres and taking the average installs per genre \n",
        "game_df = combined_df[(combined_df['Content Rating']=='Teen') & (combined_df['Category']=='GAME')].groupby('Genres')['Installs'].mean().reset_index()\n",
        "game_df['Installs'] = game_df['Installs'].astype(int)\n",
        "game_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "vccDZGYJ0nd6",
        "outputId": "56a60cc1-7d7d-4c5f-b985-049a4d0e3ea5"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b753411c-d79e-41b8-a835-9a2dc5540e4b\">\n",
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              "      <th></th>\n",
              "      <th>Genres</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Racing</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      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-b753411c-d79e-41b8-a835-9a2dc5540e4b 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-b753411c-d79e-41b8-a835-9a2dc5540e4b');\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",
              "  "
            ],
            "text/plain": [
              "         Genres   Installs\n",
              "0        Action   49275752\n",
              "1     Adventure   10000000\n",
              "2        Arcade   50000000\n",
              "3        Puzzle   10000000\n",
              "4        Racing  100000000\n",
              "5  Role Playing    3134328\n",
              "6    Simulation   37705627\n",
              "7      Strategy    1000000"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#lets plot the above df\n",
        "plt.bar(game_df['Genres'],game_df['Installs'])\n",
        "plt.xticks(rotation = '90')\n",
        "plt.title('Popular game genres in teens')\n",
        "plt.xlabel('Game genres')\n",
        "plt.ylabel('Number of Installs')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "yS7T_ayL0vF4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "d9a45033-73f9-4e98-ea74-6a1fe39d40f2"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_df.groupby('Content Rating')['Content Rating'].count()"
      ],
      "metadata": {
        "id": "hTh-XDrs5AGZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "937c929a-adb1-402d-cdfc-f6775f295d7e"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Content Rating\n",
              "Adults only 18+       36\n",
              "Everyone           54395\n",
              "Everyone 10+        3040\n",
              "Mature 17+          4480\n",
              "Teen               10654\n",
              "Name: Content Rating, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### We can see from above we have next highest as mature 17+ so lets say whats most popular in 17+"
      ],
      "metadata": {
        "id": "2Kcj4ZrvuUON"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mature_df1 = combined_df[combined_df['Content Rating']=='Mature 17+'].groupby('Category').agg({'Installs':'mean'}).astype(int).reset_index()\n",
        "mature_df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "X9OW0gdiuL8z",
        "outputId": "31978cf2-fc43-4917-aa26-3d3a3fd27983"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e3ffd95b-a7ff-401b-a57f-104e7d985179\">\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>Installs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BEAUTY</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>50000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>DATING</td>\n",
              "      <td>1184386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>10000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GAME</td>\n",
              "      <td>43534722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NEWS_AND_MAGAZINES</td>\n",
              "      <td>1000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>PERSONALIZATION</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>10000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>80555371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>SPORTS</td>\n",
              "      <td>1000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>10000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e3ffd95b-a7ff-401b-a57f-104e7d985179')\"\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",
              "    }\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",
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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-e3ffd95b-a7ff-401b-a57f-104e7d985179 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-e3ffd95b-a7ff-401b-a57f-104e7d985179');\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",
              "  "
            ],
            "text/plain": [
              "              Category  Installs\n",
              "0               BEAUTY    100000\n",
              "1        COMMUNICATION  50000000\n",
              "2               DATING   1184386\n",
              "3        ENTERTAINMENT  10000000\n",
              "4                 GAME  43534722\n",
              "5              MEDICAL     50000\n",
              "6   NEWS_AND_MAGAZINES   1000000\n",
              "7      PERSONALIZATION    100000\n",
              "8          PHOTOGRAPHY  10000000\n",
              "9               SOCIAL  80555371\n",
              "10              SPORTS   1000000\n",
              "11    TRAVEL_AND_LOCAL  10000000"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#lets plot these\n",
        "plt.plot(mature_df1['Category'],mature_df1['Installs'])\n",
        "plt.xticks(rotation = '90')\n",
        "plt.title('Popular categories for 17+')\n",
        "plt.xlabel('Category')\n",
        "plt.ylabel('Number of Installs')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Rhj2v0R3ueEk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "b61fe63f-2410-4d27-bb43-b720350ca906"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_df[combined_df['Category']=='COMMUNICATION'].groupby('Genres')['Genres'].count()"
      ],
      "metadata": {
        "id": "oA90IEmDEd8X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "223352fa-f09a-4705-844b-4ebe3855fa4a"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Genres\n",
              "Communication    1949\n",
              "Name: Genres, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_df[combined_df['Category']=='SOCIAL'].groupby('Genres')['Genres'].count()"
      ],
      "metadata": {
        "id": "gJpk8gR9_mNA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e03817b-56b9-4bf9-9bbc-7ada697ab442"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Genres\n",
              "Social    1398\n",
              "Name: Genres, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mature_game_df = combined_df[(combined_df['Category']=='GAME') & (combined_df['Content Rating']=='Mature 17+')]\n",
        "grouped_df = mature_game_df[mature_game_df['Content Rating']=='Mature 17+'].groupby('Genres')['Installs'].mean().reset_index()\n",
        "#lets plot the dataframe\n",
        "plt.bar(grouped_df['Genres'],grouped_df['Installs'])\n",
        "plt.title('Action vs RolePlaying')\n",
        "plt.xlabel('Genre')\n",
        "plt.ylabel('Number of Installs')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "-4SViYryYKnB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "569f7634-f81f-454b-ade2-59159077e76f"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### We can clearly see that among 17+ people mostly social,communication and game category is popular and in the game category Action is most popular"
      ],
      "metadata": {
        "id": "Ej8wJ29QbLLi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "T2RhI8MZbFuD"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Play Store App Review Analysis - Capstone Project.ipynb",
      "provenance": [],
      "mount_file_id": "1mbPL_Cayoy8rWqenS5dSiJj8gmxbprM6",
      "authorship_tag": "ABX9TyPewhqS0lHHNSfIctwehIOK",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}