AndroidAppMarket_Code1.ipynb

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  "metadata": {
    "colab": {
      "name": "AndroidAppMarket_Code1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMqwRLc8BgZBzQj1NVxUp1t",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Shubhi550/TheAndroidAppMarket/blob/master/AndroidAppMarket_Code1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G-rQoQBPY1g0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "9cce385f-4157-4657-93e0-8b21f5459546"
      },
      "source": [
        "#dataframes & mathematical calculations\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "#plotly\n",
        "import plotly.offline as py\n",
        "py.init_notebook_mode(connected=True)\n",
        "import plotly.graph_objs as go\n",
        "\n",
        "#to solve blank output problem of graphs in colab\n",
        "import plotly.io as pio\n",
        "pio.renderers.default = 'colab'\n",
        "from plotly.offline import init_notebook_mode, iplot\n",
        "\n",
        "#matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "\n"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bKyCGWgYZNLf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "65dca316-161c-4068-9f0e-ca0ad86263d6"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "#Some rows of the data contain same apps(so our dataframe contains dublicate rows)\n",
        "df_with_duplicates = pd.read_csv('/content/drive/My Drive/googleplaystore.csv')\n",
        "df_with_duplicates.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Size</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Type</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Genres</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Current Ver</th>\n",
              "      <th>Android Ver</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.1</td>\n",
              "      <td>159</td>\n",
              "      <td>19M</td>\n",
              "      <td>10,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design</td>\n",
              "      <td>7-Jan-18</td>\n",
              "      <td>1.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14M</td>\n",
              "      <td>500,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>15-Jan-18</td>\n",
              "      <td>2.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.7</td>\n",
              "      <td>87510</td>\n",
              "      <td>8.7M</td>\n",
              "      <td>5,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design</td>\n",
              "      <td>1-Aug-18</td>\n",
              "      <td>1.2.4</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sketch - Draw &amp; Paint</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.5</td>\n",
              "      <td>215644</td>\n",
              "      <td>25M</td>\n",
              "      <td>50,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>Art &amp; Design</td>\n",
              "      <td>8-Jun-18</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>4.2 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.3</td>\n",
              "      <td>967</td>\n",
              "      <td>2.8M</td>\n",
              "      <td>100,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Creativity</td>\n",
              "      <td>20-Jun-18</td>\n",
              "      <td>1.1</td>\n",
              "      <td>4.4 and up</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 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",
              "3                              Sketch - Draw & Paint  ...    4.2 and up\n",
              "4              Pixel Draw - Number Art Coloring Book  ...    4.4 and up\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "VYGvLnfAdtR-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "outputId": "8befffd5-8e95-42d9-d6da-f3132e039d1b"
      },
      "source": [
        "df = df_with_duplicates.drop_duplicates()\n",
        "df"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Size</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Type</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Genres</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Current Ver</th>\n",
              "      <th>Android Ver</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.1</td>\n",
              "      <td>159</td>\n",
              "      <td>19M</td>\n",
              "      <td>10,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
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              "      <td>1.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14M</td>\n",
              "      <td>500,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>15-Jan-18</td>\n",
              "      <td>2.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.7</td>\n",
              "      <td>87510</td>\n",
              "      <td>8.7M</td>\n",
              "      <td>5,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design</td>\n",
              "      <td>1-Aug-18</td>\n",
              "      <td>1.2.4</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sketch - Draw &amp; Paint</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.5</td>\n",
              "      <td>215644</td>\n",
              "      <td>25M</td>\n",
              "      <td>50,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>Art &amp; Design</td>\n",
              "      <td>8-Jun-18</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>4.2 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.3</td>\n",
              "      <td>967</td>\n",
              "      <td>2.8M</td>\n",
              "      <td>100,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Creativity</td>\n",
              "      <td>20-Jun-18</td>\n",
              "      <td>1.1</td>\n",
              "      <td>4.4 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10836</th>\n",
              "      <td>Sya9a Maroc - FR</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>4.5</td>\n",
              "      <td>38</td>\n",
              "      <td>53M</td>\n",
              "      <td>5,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Education</td>\n",
              "      <td>25-Jul-17</td>\n",
              "      <td>1.48</td>\n",
              "      <td>4.1 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10837</th>\n",
              "      <td>Fr. Mike Schmitz Audio Teachings</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4</td>\n",
              "      <td>3.6M</td>\n",
              "      <td>100+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Education</td>\n",
              "      <td>6-Jul-18</td>\n",
              "      <td>1</td>\n",
              "      <td>4.1 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10838</th>\n",
              "      <td>Parkinson Exercices FR</td>\n",
              "      <td>MEDICAL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>9.5M</td>\n",
              "      <td>1,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Medical</td>\n",
              "      <td>20-Jan-17</td>\n",
              "      <td>1</td>\n",
              "      <td>2.2 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10839</th>\n",
              "      <td>The SCP Foundation DB fr nn5n</td>\n",
              "      <td>BOOKS_AND_REFERENCE</td>\n",
              "      <td>4.5</td>\n",
              "      <td>114</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>1,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Mature 17+</td>\n",
              "      <td>Books &amp; Reference</td>\n",
              "      <td>19-Jan-15</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10840</th>\n",
              "      <td>iHoroscope - 2018 Daily Horoscope &amp; Astrology</td>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>4.5</td>\n",
              "      <td>398307</td>\n",
              "      <td>19M</td>\n",
              "      <td>10,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Lifestyle</td>\n",
              "      <td>25-Jul-18</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10358 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                     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",
              "3                                  Sketch - Draw & Paint  ...          4.2 and up\n",
              "4                  Pixel Draw - Number Art Coloring Book  ...          4.4 and up\n",
              "...                                                  ...  ...                 ...\n",
              "10836                                   Sya9a Maroc - FR  ...          4.1 and up\n",
              "10837                   Fr. Mike Schmitz Audio Teachings  ...          4.1 and up\n",
              "10838                             Parkinson Exercices FR  ...          2.2 and up\n",
              "10839                      The SCP Foundation DB fr nn5n  ...  Varies with device\n",
              "10840      iHoroscope - 2018 Daily Horoscope & Astrology  ...  Varies with device\n",
              "\n",
              "[10358 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab_type": "code",
        "colab": {
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          "height": 170
        },
        "outputId": "55ae6bf2-cdce-4111-c63c-4212942c5c93"
      },
      "source": [
        "df['Category'].unique()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ART_AND_DESIGN', 'AUTO_AND_VEHICLES', 'BEAUTY',\n",
              "       'BOOKS_AND_REFERENCE', 'BUSINESS', 'COMICS', 'COMMUNICATION',\n",
              "       'DATING', 'EDUCATION', 'ENTERTAINMENT', 'EVENTS', 'FINANCE',\n",
              "       'FOOD_AND_DRINK', 'HEALTH_AND_FITNESS', 'HOUSE_AND_HOME',\n",
              "       'LIBRARIES_AND_DEMO', 'LIFESTYLE', 'GAME', 'FAMILY', 'MEDICAL',\n",
              "       'SOCIAL', 'SHOPPING', 'PHOTOGRAPHY', 'SPORTS', 'TRAVEL_AND_LOCAL',\n",
              "       'TOOLS', 'PERSONALIZATION', 'PRODUCTIVITY', 'PARENTING', 'WEATHER',\n",
              "       'VIDEO_PLAYERS', 'NEWS_AND_MAGAZINES', 'MAPS_AND_NAVIGATION',\n",
              "       '1.9'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
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        },
        "outputId": "d2aace0e-8a0d-4a59-9131-e2fc6f87ee3f"
      },
      "source": [
        "df['Category']"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0             ART_AND_DESIGN\n",
              "1             ART_AND_DESIGN\n",
              "2             ART_AND_DESIGN\n",
              "3             ART_AND_DESIGN\n",
              "4             ART_AND_DESIGN\n",
              "                ...         \n",
              "10836                 FAMILY\n",
              "10837                 FAMILY\n",
              "10838                MEDICAL\n",
              "10839    BOOKS_AND_REFERENCE\n",
              "10840              LIFESTYLE\n",
              "Name: Category, Length: 10358, dtype: object"
            ]
          },
          "metadata": {
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          "execution_count": 40
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    {
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        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a5156e89-f402-4593-bf1a-9deee10a7da0"
      },
      "source": [
        "len(df)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10358"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwwAx_c3egCI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "4213e02f-bde6-4dc3-96b5-a5878db634a6"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 10358 entries, 0 to 10840\n",
            "Data columns (total 13 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   App             10358 non-null  object \n",
            " 1   Category        10358 non-null  object \n",
            " 2   Rating          8893 non-null   float64\n",
            " 3   Reviews         10358 non-null  object \n",
            " 4   Size            10358 non-null  object \n",
            " 5   Installs        10358 non-null  object \n",
            " 6   Type            10357 non-null  object \n",
            " 7   Price           10358 non-null  object \n",
            " 8   Content Rating  10357 non-null  object \n",
            " 9   Genres          10358 non-null  object \n",
            " 10  Last Updated    10358 non-null  object \n",
            " 11  Current Ver     10350 non-null  object \n",
            " 12  Android Ver     10355 non-null  object \n",
            "dtypes: float64(1), object(12)\n",
            "memory usage: 1.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YObi4fFW8bP8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "99f6bf34-b28a-4662-a371-58752337bc0b"
      },
      "source": [
        "#list the names of all the columns\n",
        "df.columns.values.tolist()\n"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['App',\n",
              " 'Category',\n",
              " 'Rating',\n",
              " 'Reviews',\n",
              " 'Size',\n",
              " 'Installs',\n",
              " 'Type',\n",
              " 'Price',\n",
              " 'Content Rating',\n",
              " 'Genres',\n",
              " 'Last Updated',\n",
              " 'Current Ver',\n",
              " 'Android Ver']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FG7CXrnG2wiN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d53a01b6-d619-4007-a1dd-c34d01af3192"
      },
      "source": [
        "#list the names of the columns which have nan values\n",
        "df.columns[df.isna().any()].tolist()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Rating', 'Type', 'Content Rating', 'Current Ver', 'Android Ver']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xUQe3rnuMcpn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Proceeding towards Data Cleaning \n",
        "#info() gave details of columns data type\n",
        "#Installs, Size & Price columns are objects(but they should be int/float). They are objects because they contains some special chars(, + $)\n",
        "#These special chars should be removed from the columns -- Installs, Price"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozzlqnREMjt_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "outputId": "a15ca990-6662-42da-e9f2-c04a64e2f208"
      },
      "source": [
        "chars_to_remove = ['+' , ',' , '$']       #List of chars to be removed\n",
        "cols_to_clean = ['Installs','Price']      #List of columns to be cleaned\n",
        "\n",
        "#lopping inside chars_to_remove, which is present inside cols_to_clean\n",
        "for col in cols_to_clean:\n",
        "  for char in chars_to_remove:\n",
        "    df[col] = df[col].astype(str).replace(char, '') #to replace + , $ with an empty string i.e ''(without space) \n",
        "    df[col] = pd.to_numeric(df[col], errors='coerce') #convert the columns to numeric data type\n",
        "    \n",
        "\n",
        "df.info()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 10358 entries, 0 to 10840\n",
            "Data columns (total 13 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   App             10358 non-null  object \n",
            " 1   Category        10358 non-null  object \n",
            " 2   Rating          8893 non-null   float64\n",
            " 3   Reviews         10358 non-null  object \n",
            " 4   Size            10358 non-null  object \n",
            " 5   Installs        1 non-null      float64\n",
            " 6   Type            10357 non-null  object \n",
            " 7   Price           9592 non-null   float64\n",
            " 8   Content Rating  10357 non-null  object \n",
            " 9   Genres          10358 non-null  object \n",
            " 10  Last Updated    10358 non-null  object \n",
            " 11  Current Ver     10350 non-null  object \n",
            " 12  Android Ver     10355 non-null  object \n",
            "dtypes: float64(3), object(10)\n",
            "memory usage: 1.1+ MB\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8BD5lzLNTE_X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3cab9325-323b-420f-9af2-42eb79d372cd"
      },
      "source": [
        "# x.unique() = to find unique elements of the list/array - x\n",
        "# here, list = Column-Category in df dataset\n",
        "# Print the total number of unique categories\n",
        "num_categories = len(df['Category'].unique())\n",
        "print('Number of categories = ', num_categories)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of categories =  34\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AaMy82W-PL4_",
        "colab_type": "code",
        "colab": {
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          "height": 542
        },
        "outputId": "7b38ed5a-b804-45ad-eb07-38830f483c16"
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      "source": [
        "# Count the number of apps in each 'Category' and sort them in descending order\n",
        "num_apps_in_category = df['Category'].value_counts().sort_values(ascending = False)\n",
        "\n",
        "#Distribution of different apps across different categories\n",
        "data = [go.Bar(x = num_apps_in_category.index,      # index = category name\n",
        "               y = num_apps_in_category.values)]    # value = count\n",
        "\n",
        "py.iplot(data)"
      ],
      "execution_count": 48,
      "outputs": [
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          "metadata": {
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    },
    {
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        "colab_type": "code",
        "colab": {
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          "height": 297
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        "outputId": "fc7fedc7-56f0-41a0-f251-e328f58134de"
      },
      "source": [
        "df.describe() #we can also find the average app ratings like this"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8893.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9592.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.189542</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.545452</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.500000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Rating  Installs   Price\n",
              "count  8893.000000       1.0  9592.0\n",
              "mean      4.189542       0.0     0.0\n",
              "std       0.545452       NaN     0.0\n",
              "min       1.000000       0.0     0.0\n",
              "25%       4.000000       0.0     0.0\n",
              "50%       4.300000       0.0     0.0\n",
              "75%       4.500000       0.0     0.0\n",
              "max      19.000000       0.0     0.0"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 49
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          "height": 221
        },
        "outputId": "ee154522-a235-4fb0-91f5-469fe02881dd"
      },
      "source": [
        "print(df['Rating'].mean)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<bound method Series.mean of 0        4.1\n",
            "1        3.9\n",
            "2        4.7\n",
            "3        4.5\n",
            "4        4.3\n",
            "        ... \n",
            "10836    4.5\n",
            "10837    5.0\n",
            "10838    NaN\n",
            "10839    4.5\n",
            "10840    4.5\n",
            "Name: Rating, Length: 10358, dtype: float64>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab_type": "code",
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          "height": 542
        },
        "outputId": "091acfeb-ef0b-4185-f14b-f59d848f57e1"
      },
      "source": [
        "fig = go.Figure(\n",
        "    data=[go.Histogram(x = df['Rating'])],\n",
        "    layout=go.Layout(\n",
        "        title=go.layout.Title(text=\"Distribution of apps according to their ratings\")\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.show()\n",
        "\n"
      ],
      "execution_count": 51,
      "outputs": [
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      "source": [
        "# Filter rows where both Rating and Size values are not null\n",
        "apps_with_size_and_rating_present = df[(~df['Rating'].isnull()) & (~df['Size'].isnull())]\n",
        "\n",
        "# Subset for categories with at least 250 apps\n",
        "large_categories = apps_with_size_and_rating_present.groupby(['Category']).filter(lambda x: len(x) >= 250).reset_index()\n",
        "\n",
        "\n",
        "# Subset apps whose 'Type' is 'Paid'\n",
        "paid_apps = apps_with_size_and_rating_present[apps_with_size_and_rating_present['Type'] == 'Paid']\n"
      ],
      "execution_count": 52,
      "outputs": []
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      "source": [
        "#Size vs Rating for Large Categories\n",
        "sample_data={'Size':large_categories['Size'],\n",
        "      'Rating': large_categories['Rating']}\n",
        "df= pd.DataFrame(sample_data)\n",
        "sns.scatterplot(x=\"Size\", y=\"Rating\", data=df)"
      ],
      "execution_count": 53,
      "outputs": [
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          "output_type": "execute_result",
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}