Google_playstore.ipynb

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    },
    "colab": {
      "name": "Google_playstore.ipynb",
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      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dimi-fn/Google-Playstore-Apps/blob/master/Google_playstore.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OoH1VXsjcNlm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "2131a232-0ab1-47f1-8ecd-6fa659548d24"
      },
      "source": [
        "# Import statements\n",
        "# Import necessary python libraries and packages\n",
        "\n",
        "\n",
        "# For data analysis & manipulation\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# For visualising distributional values\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tNjT7pqMcNlu",
        "colab_type": "code",
        "colab": {
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          "height": 50
        },
        "outputId": "c7dca5d9-f115-438f-86a8-1df1469570c2"
      },
      "source": [
        "# Python version\n",
        "\n",
        "import sys\n",
        "print (\"The Python version is: {}\".format(sys.version))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Python version is: 3.6.9 (default, Jul 17 2020, 12:50:27) \n",
            "[GCC 8.4.0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P507x5zMcNl1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ed0061eb-40da-4449-87a6-a466625a821a"
      },
      "source": [
        "# Generating the version of a wide variety of packages/libraries used\n",
        "\n",
        "pd.__version__\n",
        "pd.show_versions(as_json=False)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use \"pip install psycopg2-binary\" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.\n",
            "  \"\"\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "INSTALLED VERSIONS\n",
            "------------------\n",
            "commit           : None\n",
            "python           : 3.6.9.final.0\n",
            "python-bits      : 64\n",
            "OS               : Linux\n",
            "OS-release       : 4.19.112+\n",
            "machine          : x86_64\n",
            "processor        : x86_64\n",
            "byteorder        : little\n",
            "LC_ALL           : None\n",
            "LANG             : en_US.UTF-8\n",
            "LOCALE           : en_US.UTF-8\n",
            "\n",
            "pandas           : 1.0.5\n",
            "numpy            : 1.18.5\n",
            "pytz             : 2018.9\n",
            "dateutil         : 2.8.1\n",
            "pip              : 19.3.1\n",
            "setuptools       : 49.6.0\n",
            "Cython           : 0.29.21\n",
            "pytest           : 3.6.4\n",
            "hypothesis       : None\n",
            "sphinx           : 1.8.5\n",
            "blosc            : None\n",
            "feather          : 0.4.1\n",
            "xlsxwriter       : None\n",
            "lxml.etree       : 4.2.6\n",
            "html5lib         : 1.0.1\n",
            "pymysql          : None\n",
            "psycopg2         : 2.7.6.1 (dt dec pq3 ext lo64)\n",
            "jinja2           : 2.11.2\n",
            "IPython          : 5.5.0\n",
            "pandas_datareader: 0.8.1\n",
            "bs4              : 4.6.3\n",
            "bottleneck       : 1.3.2\n",
            "fastparquet      : None\n",
            "gcsfs            : None\n",
            "lxml.etree       : 4.2.6\n",
            "matplotlib       : 3.2.2\n",
            "numexpr          : 2.7.1\n",
            "odfpy            : None\n",
            "openpyxl         : 2.5.9\n",
            "pandas_gbq       : 0.11.0\n",
            "pyarrow          : 0.14.1\n",
            "pytables         : None\n",
            "pytest           : 3.6.4\n",
            "pyxlsb           : None\n",
            "s3fs             : None\n",
            "scipy            : 1.4.1\n",
            "sqlalchemy       : 1.3.19\n",
            "tables           : 3.4.4\n",
            "tabulate         : 0.8.7\n",
            "xarray           : 0.15.1\n",
            "xlrd             : 1.1.0\n",
            "xlwt             : 1.3.0\n",
            "xlsxwriter       : None\n",
            "numba            : 0.48.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QAZJ8sfIhnmM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Code to read csv file into colaboratory:\n",
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S7HGdKWQh3sG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WFioYw7OcNl9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "00f937c2-4495-4c3a-e7f1-6e0f54945271"
      },
      "source": [
        "# Assigning the dataset with the name: \"app\"\n",
        "\n",
        "downloaded = drive.CreateFile({'id':'1s5mJCccMkkVGSAVrzRYn0rdP-gJz849C'}) \n",
        "downloaded.GetContentFile('Google-Playstore-Full.csv') \n",
        "\n",
        "app = pd.read_csv('Google-Playstore-Full.csv') "
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (2,3,11,12,13) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JTD9WTUcNmC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "95e139b0-fe18-4d20-e454-71937e2af39d"
      },
      "source": [
        "# The type of this dataset is a dataframe\n",
        "type(app)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "erUfJO9TcNmI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "eb84cb5a-a589-46b8-de68-b64d101f79f5"
      },
      "source": [
        "# The columns of this dataframe are \"series\"\n",
        "type(app[\"Installs\"])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9APotmTVcNmN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "c66c3703-5a2b-4526-aeb7-fada6721d238"
      },
      "source": [
        "# First 5 rows of the dataframe\n",
        "\n",
        "app.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "      <th>Unnamed: 11</th>\n",
              "      <th>Unnamed: 12</th>\n",
              "      <th>Unnamed: 13</th>\n",
              "      <th>Unnamed: 14</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DoorDash - Food Delivery</td>\n",
              "      <td>FOOD_AND_DRINK</td>\n",
              "      <td>4.548561573</td>\n",
              "      <td>305034</td>\n",
              "      <td>5,000,000+</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 29, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TripAdvisor Hotels Flights Restaurants Attract...</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>4.400671482</td>\n",
              "      <td>1.20792e+06</td>\n",
              "      <td>100,000,000+</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 29, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Peapod</td>\n",
              "      <td>SHOPPING</td>\n",
              "      <td>3.656329393</td>\n",
              "      <td>1967</td>\n",
              "      <td>100,000+</td>\n",
              "      <td>1.4M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>September 20, 2018</td>\n",
              "      <td>5.0 and up</td>\n",
              "      <td>2.2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>foodpanda - Local Food Delivery</td>\n",
              "      <td>FOOD_AND_DRINK</td>\n",
              "      <td>4.107232571</td>\n",
              "      <td>389154</td>\n",
              "      <td>10,000,000+</td>\n",
              "      <td>16M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 22, 2019</td>\n",
              "      <td>4.2 and up</td>\n",
              "      <td>4.18.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>My CookBook Pro (Ad Free)</td>\n",
              "      <td>FOOD_AND_DRINK</td>\n",
              "      <td>4.647752285</td>\n",
              "      <td>2291</td>\n",
              "      <td>10,000+</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>$5.99</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            App Name  ... Unnamed: 14\n",
              "0                           DoorDash - Food Delivery  ...         NaN\n",
              "1  TripAdvisor Hotels Flights Restaurants Attract...  ...         NaN\n",
              "2                                             Peapod  ...         NaN\n",
              "3                    foodpanda - Local Food Delivery  ...         NaN\n",
              "4                          My CookBook Pro (Ad Free)  ...         NaN\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f050eccdcNmT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "c776d978-55bf-4538-9bdd-ab73662030aa"
      },
      "source": [
        "# Getting the last five rows\n",
        "\n",
        "app.tail()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "      <th>Unnamed: 11</th>\n",
              "      <th>Unnamed: 12</th>\n",
              "      <th>Unnamed: 13</th>\n",
              "      <th>Unnamed: 14</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>267047</th>\n",
              "      <td>Community Healthplex</td>\n",
              "      <td>HEALTH_AND_FITNESS</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>100+</td>\n",
              "      <td>4.2M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>July 27, 2018</td>\n",
              "      <td>5.0 and up</td>\n",
              "      <td>1.0.4.228</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>267048</th>\n",
              "      <td>Pet ads: Buy &amp; Sell</td>\n",
              "      <td>BUSINESS</td>\n",
              "      <td>2.6</td>\n",
              "      <td>5</td>\n",
              "      <td>500+</td>\n",
              "      <td>8.4M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>October 22, 2018</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>1.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>267049</th>\n",
              "      <td>Collectors Market: Buy &amp; Sell</td>\n",
              "      <td>BUSINESS</td>\n",
              "      <td>3.28571</td>\n",
              "      <td>7</td>\n",
              "      <td>1,000+</td>\n",
              "      <td>7.9M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 13, 2019</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>2.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>267050</th>\n",
              "      <td>Car Market, Buy &amp; Sell</td>\n",
              "      <td>BUSINESS</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1,000+</td>\n",
              "      <td>8.2M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>August 27, 2018</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>1.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>267051</th>\n",
              "      <td>Selfie with Ariana Grande</td>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>4.61111</td>\n",
              "      <td>18</td>\n",
              "      <td>1,000+</td>\n",
              "      <td>7.8M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>October 3, 2018</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             App Name  ... Unnamed: 14\n",
              "267047           Community Healthplex  ...         NaN\n",
              "267048            Pet ads: Buy & Sell  ...         NaN\n",
              "267049  Collectors Market: Buy & Sell  ...         NaN\n",
              "267050         Car Market, Buy & Sell  ...         NaN\n",
              "267051      Selfie with Ariana Grande  ...         NaN\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E02DdjQjcNma",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1699548b-e5e9-455e-b9d4-5b2d3e4d2763"
      },
      "source": [
        "# Getting the number of rows and columns of the dataframe\n",
        "\n",
        "app.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(267052, 15)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ruc3CUgcNme",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Removing the columns with index position: 11, 12, 13, 14. They do not seem to offer any substantial value to the data analysis\n",
        "\n",
        "app=app.drop(\"Unnamed: 11\", axis=1)\n",
        "app=app.drop(\"Unnamed: 12\", axis=1)\n",
        "app=app.drop(\"Unnamed: 13\", axis=1)\n",
        "app=app.drop(\"Unnamed: 14\", axis=1)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UeibMlFqcNmk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "83cea696-badc-4f4a-f54b-e86ddd137058"
      },
      "source": [
        "# Number of rows and columns after removing the useless columns\n",
        "\n",
        "app.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(267052, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECJS-X_wcNmq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "b72fa24e-03bc-4e24-c1c7-bfc09ae15a86"
      },
      "source": [
        "# Columns after removing the useless ones\n",
        "\n",
        "app.columns"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['App Name', 'Category', 'Rating', 'Reviews', 'Installs', 'Size',\n",
              "       'Price', 'Content Rating', 'Last Updated', 'Minimum Version',\n",
              "       'Latest Version'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HA20SY8OcNmv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b49502db-2064-42c0-f3cb-5c90de051bf4"
      },
      "source": [
        "# Number of app categories\n",
        "\n",
        "app[\"Category\"].nunique()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "67"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVmwIoE4cNm1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "55ab29c3-7707-40a4-82bd-ea29d7bd179a"
      },
      "source": [
        "# The app categories\n",
        "\n",
        "app.Category.unique()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['FOOD_AND_DRINK', 'TRAVEL_AND_LOCAL', 'SHOPPING', 'LIFESTYLE',\n",
              "       'GAME_ACTION', 'GAME_CASUAL', 'GAME_ROLE_PLAYING', 'GAME_PUZZLE',\n",
              "       'GAME_RACING', 'GAME_ADVENTURE', 'GAME_ARCADE', 'GAME_STRATEGY',\n",
              "       'GAME_SPORTS', 'GAME_SIMULATION', 'GAME_MUSIC', 'MUSIC_AND_AUDIO',\n",
              "       'FINANCE', 'EVENTS', 'ENTERTAINMENT', 'EDUCATION',\n",
              "       'GAME_EDUCATIONAL', 'BOOKS_AND_REFERENCE', 'NEWS_AND_MAGAZINES',\n",
              "       'PHOTOGRAPHY', 'VIDEO_PLAYERS', 'GAME_WORD', 'ART_AND_DESIGN',\n",
              "       'GAME_TRIVIA', 'GAME_BOARD', 'BUSINESS', 'PRODUCTIVITY',\n",
              "       'COMMUNICATION', 'HEALTH_AND_FITNESS', 'HOUSE_AND_HOME', 'SOCIAL',\n",
              "       'BEAUTY', 'GAME_CASINO', 'MAPS_AND_NAVIGATION', 'PERSONALIZATION',\n",
              "       'GAME_CARD', 'TOOLS', 'SPORTS', 'AUTO_AND_VEHICLES',\n",
              "       'LIBRARIES_AND_DEMO', 'COMICS', 'PARENTING', 'DATING', 'WEATHER',\n",
              "       'MEDICAL', ' Podcasts', ')', ' Channel 2 News', nan,\n",
              "       ' Breaking News', '6', 'Gate ALARM', ' Alfabe �?ren',\n",
              "       ' T�rk Alfabesi', ' not notified you follow -', ' Mexpost)',\n",
              "       ' Romantic Song Music Love Songs', ' ETEA & MDCAT', ' Tour Guide',\n",
              "       'TRAVEL', ' Speaker Pro 2019', ' Islamic Name Boy & Girl+Meaning',\n",
              "       ' Accounting', ' super loud speaker booster'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3Xqj1jvcNm7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "02248daa-1fd9-401f-ac98-7ed337f9d9fe"
      },
      "source": [
        "# Viewing the number of classes (gradation) of the number of installations\n",
        "# There are 38 different classes\n",
        "\n",
        "app[\"Installs\"].nunique()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NljuZ3SZcNnA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "dd9b6925-e913-4a1e-b2be-671f682b0ffa"
      },
      "source": [
        "# The gradation of installations in the dataframe\n",
        "\n",
        "# There seem to be some input mistakes, such as \"EDUCATION\", which should not belong in this column. They will be edited\n",
        "\n",
        "\n",
        "app.Installs.unique()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['5,000,000+', '100,000,000+', '100,000+', '10,000,000+', '10,000+',\n",
              "       '1,000,000+', '50,000,000+', '500,000+', '50,000+', '5,000+',\n",
              "       '1,000+', '500,000,000+', '1,000,000,000+', '5,000,000,000+',\n",
              "       '100+', '500+', '50+', '5+', '10+', '1+', 'EDUCATION', '6',\n",
              "       '11976', '0+', '20', '156', ' Xmax X', '166', '1', '54', '71',\n",
              "       '59', '13', '117', '511', '927', '4.823529243', '10'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28KOeszWcNnG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d36e0d99-d09b-4c2a-b0a6-b166daca285c"
      },
      "source": [
        "# There are a lot of app sizes\n",
        "\n",
        "app[\"Size\"].nunique()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1248"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvudevvTcNnL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "12375576-942c-41ff-e963-9121912b2682"
      },
      "source": [
        "# Viewing the content rating; who is permitted to download these apps\n",
        "\n",
        "# There are some invalid contents. They will be edited\n",
        "\n",
        "app[\"Content Rating\"].unique()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Everyone', 'Teen', 'Everyone 10+', 'Mature 17+',\n",
              "       'Adults only 18+', '100,000+', 'Unrated', '$0.99', '0', '3702',\n",
              "       '$2.49', '17M'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cvfx6vSGcNnU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f9ffd3c6-e109-49cd-8180-e662f777affe"
      },
      "source": [
        "# the number of categories of the age content rating\n",
        "\n",
        "len(app[\"Content Rating\"].unique())"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esxlzRr4cNnY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "1bbcd72c-b80b-487c-8bb9-61190fb55912"
      },
      "source": [
        "#current first five rows\n",
        "\n",
        "app.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DoorDash - Food Delivery</td>\n",
              "      <td>FOOD_AND_DRINK</td>\n",
              "      <td>4.548561573</td>\n",
              "      <td>305034</td>\n",
              "      <td>5,000,000+</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 29, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TripAdvisor Hotels Flights Restaurants Attract...</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>4.400671482</td>\n",
              "      <td>1.20792e+06</td>\n",
              "      <td>100,000,000+</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 29, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Peapod</td>\n",
              "      <td>SHOPPING</td>\n",
              "      <td>3.656329393</td>\n",
              "      <td>1967</td>\n",
              "      <td>100,000+</td>\n",
              "      <td>1.4M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>September 20, 2018</td>\n",
              "      <td>5.0 and up</td>\n",
              "      <td>2.2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>foodpanda - Local Food Delivery</td>\n",
              "      <td>FOOD_AND_DRINK</td>\n",
              "      <td>4.107232571</td>\n",
              "      <td>389154</td>\n",
              "      <td>10,000,000+</td>\n",
              "      <td>16M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 22, 2019</td>\n",
              "      <td>4.2 and up</td>\n",
              "      <td>4.18.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>My CookBook Pro (Ad Free)</td>\n",
              "      <td>FOOD_AND_DRINK</td>\n",
              "      <td>4.647752285</td>\n",
              "      <td>2291</td>\n",
              "      <td>10,000+</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>$5.99</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            App Name  ...      Latest Version\n",
              "0                           DoorDash - Food Delivery  ...  Varies with device\n",
              "1  TripAdvisor Hotels Flights Restaurants Attract...  ...  Varies with device\n",
              "2                                             Peapod  ...               2.2.0\n",
              "3                    foodpanda - Local Food Delivery  ...              4.18.2\n",
              "4                          My CookBook Pro (Ad Free)  ...  Varies with device\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gLnpt3WucNnc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "f941c126-e20f-4d5f-98ac-6fd2bc4c14c3"
      },
      "source": [
        "app.isnull().sum()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "App Name           1\n",
              "Category           1\n",
              "Rating             0\n",
              "Reviews            1\n",
              "Installs           0\n",
              "Size               0\n",
              "Price              0\n",
              "Content Rating     0\n",
              "Last Updated       0\n",
              "Minimum Version    1\n",
              "Latest Version     3\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrUVGyAycNnh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9b5d7ff7-432c-45d1-eca5-23c99f6bd4f7"
      },
      "source": [
        "# There are totally 11 empty data entries which will be dropped\n",
        "len(app.isnull().sum())"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a0X_YNCUcNnm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Dropping the entries where there are missing values\n",
        "\n",
        "app=app.dropna()"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uqfcZu5PcNnt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "2c79a843-fc1c-4458-bdbe-d39aeab0d30d"
      },
      "source": [
        "app.isnull().any()\n",
        "\n",
        "# False for every category means that there are no longer missing values"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "App Name           False\n",
              "Category           False\n",
              "Rating             False\n",
              "Reviews            False\n",
              "Installs           False\n",
              "Size               False\n",
              "Price              False\n",
              "Content Rating     False\n",
              "Last Updated       False\n",
              "Minimum Version    False\n",
              "Latest Version     False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bpQlTWeCcNn1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d1b03337-eb23-4bfc-c8e5-5037fa4180e5"
      },
      "source": [
        "# Ensuring there are no missing values in any column, in any data of every column\n",
        "\n",
        "app.isnull().any().any()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B43z8t9wcNoL",
        "colab_type": "text"
      },
      "source": [
        "# Cleaning of the Data - Exploring and Managing the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kD2YJZdgcNoM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "917d358e-a424-4d7b-e6e9-96bfdae5ebc8"
      },
      "source": [
        "# Start of cleaning\n",
        "# There were given some commands to locate any invalid data\n",
        "# I noticed that are some misplacing, e.g. here, \"4\" should move to \"Rating\", and \"GAME_STRATEGY\" should move to \"Category\"\n",
        "\n",
        "# Wherever the data are misplaced but valid, the data will be kept and edited (correcting the entry positions)\n",
        "# Wherever the data are misplaced and invalid too (with lot's of mistakes), the data will be removed\n",
        "\n",
        "app[app[\"Rating\"]== \"GAME_STRATEGY\"]"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13504</th>\n",
              "      <td>Never have I ever 18+</td>\n",
              "      <td>)</td>\n",
              "      <td>GAME_STRATEGY</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>100+</td>\n",
              "      <td>2.4M</td>\n",
              "      <td>$0.99</td>\n",
              "      <td>Mature 17+</td>\n",
              "      <td>December 30, 2018</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     App Name Category  ...    Minimum Version Latest Version\n",
              "13504  Never have I ever 18+         )  ...  December 30, 2018   4.0.3 and up\n",
              "\n",
              "[1 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qo2w_7utcNoQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# dropping the invalid entry\n",
        "\n",
        "app.drop(index=13504, inplace=True)  "
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1jtvbd2cNoU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "outputId": "882c803e-f95e-40a1-c5b6-880086bc97df"
      },
      "source": [
        "# Now the column \"Rating\" is fixed\n",
        "\n",
        "app[app[\"Rating\"]== \"GAME_STRATEGY\"]"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [App Name, Category, Rating, Reviews, Installs, Size, Price, Content Rating, Last Updated, Minimum Version, Latest Version]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLViuuJ1cNod",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        },
        "outputId": "a84e9255-2c18-4e97-84b9-ff876eaae1e2"
      },
      "source": [
        "# Noticing the same pattern. Wrong entry data in the columns\n",
        "\n",
        "app[app[\"Rating\"]== \"NEWS_AND_MAGAZINES\"]"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>23457</th>\n",
              "      <td>Israel News</td>\n",
              "      <td>Channel 2 News</td>\n",
              "      <td>NEWS_AND_MAGAZINES</td>\n",
              "      <td>3.8578</td>\n",
              "      <td>11976</td>\n",
              "      <td>1,000,000+</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>March 16, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48438</th>\n",
              "      <td>Mojo Times: Bihar Hindi Video News</td>\n",
              "      <td>Breaking News</td>\n",
              "      <td>NEWS_AND_MAGAZINES</td>\n",
              "      <td>4.77564</td>\n",
              "      <td>156</td>\n",
              "      <td>10,000+</td>\n",
              "      <td>6.9M</td>\n",
              "      <td>0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>March 30, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 App Name  ...      Latest Version\n",
              "23457                         Israel News  ...  Varies with device\n",
              "48438  Mojo Times: Bihar Hindi Video News  ...          4.1 and up\n",
              "\n",
              "[2 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9j1cjSyCcNoh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Here the data are misplaced but valid\n",
        "\n",
        "# I am manually fixing the misplacing data values\n",
        "\n",
        "app.loc[23457, [\"Category\", \"Rating\", \"Reviews\", \"Installs\", \"Size\", \"Price\", \"Content Rating\", \"Last Updated\", \"Minimum Version\", \"Latest Version\"]] = \"NEWS_AND_MAGAZINES\", \"3.857798815\", \"11976\", \"1,000,000+\", \"Varies with device\", \"0\", \"Everyone 10+\", \"March 16, 2019\", \"Varies with device\", \"NaN\" "
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8w6PDIYucNol",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "95cb1fe2-d0bf-4aa7-f9bb-f00014262fc6"
      },
      "source": [
        "app.loc[23457]"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "App Name                  Israel News\n",
              "Category           NEWS_AND_MAGAZINES\n",
              "Rating                    3.857798815\n",
              "Reviews                         11976\n",
              "Installs                   1,000,000+\n",
              "Size               Varies with device\n",
              "Price                               0\n",
              "Content Rating           Everyone 10+\n",
              "Last Updated           March 16, 2019\n",
              "Minimum Version    Varies with device\n",
              "Latest Version                    NaN\n",
              "Name: 23457, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HT-eKomfcNoq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "app.loc[48438, [\"Category\", \"Rating\", \"Reviews\", \"Installs\", \"Size\", \"Price\", \"Content Rating\", \"Last Updated\", \"Minimum Version\", \"Latest Version\"]] = \"NEWS_AND_MAGAZINES\", \"4.775640965\", \"156\", \"10,000+\", \"6.9M\", \"0\", \"Teen\", \"March 30, 2019\",\"4.1 and up\", \"NaN\" "
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hNHaWLj_cNov",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "5d40194d-b495-493a-877b-84aa9f9533c0"
      },
      "source": [
        "app.loc[48438]"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "App Name           Mojo Times: Bihar Hindi Video News\n",
              "Category                           NEWS_AND_MAGAZINES\n",
              "Rating                                    4.775640965\n",
              "Reviews                                           156\n",
              "Installs                                      10,000+\n",
              "Size                                             6.9M\n",
              "Price                                               0\n",
              "Content Rating                                   Teen\n",
              "Last Updated                           March 30, 2019\n",
              "Minimum Version                            4.1 and up\n",
              "Latest Version                                    NaN\n",
              "Name: 48438, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YRDP1Hr3cNo5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "b02b37a8-b3fd-49a7-cfea-aaf3c809d5eb"
      },
      "source": [
        "# Here is an example of misplaced data with a lot of mistakes. It does not seem important to be fixed, it will be dropped\n",
        "\n",
        "app[app[\"Rating\"]== \"ENTERTAINMENT\"]"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>113151</th>\n",
              "      <td>Steins</td>\n",
              "      <td>Gate ALARM</td>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>4.71687</td>\n",
              "      <td>166</td>\n",
              "      <td>500+</td>\n",
              "      <td>67M</td>\n",
              "      <td>$0.99</td>\n",
              "      <td>Teen</td>\n",
              "      <td>November 12, 2018</td>\n",
              "      <td>4.4 and up</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       App Name    Category  ...    Minimum Version Latest Version\n",
              "113151   Steins  Gate ALARM  ...  November 12, 2018     4.4 and up\n",
              "\n",
              "[1 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fRXyY0fIcNo9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "app.drop(index=113151, inplace=True)  "
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ILhP7zfcNpB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "outputId": "6cbe28ed-5c80-4b97-8166-42fc63d7c8d0"
      },
      "source": [
        "# Ensuring that there are no longer wrong entries in the column \"Rating\"\n",
        "\n",
        "app[app[\"Rating\"]== \"ENTERTAINMENT\"]"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [App Name, Category, Rating, Reviews, Installs, Size, Price, Content Rating, Last Updated, Minimum Version, Latest Version]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j2C77A68cNpO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "1a79e4c3-80ae-42a1-d1f2-d1f1f823940b"
      },
      "source": [
        "app[app[\"Rating\"]== \"EDUCATION\"]"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>125479</th>\n",
              "      <td>2-6 Ya? E?itici �ocuk Zeka Oyunlar?</td>\n",
              "      <td>Alfabe �?ren</td>\n",
              "      <td>EDUCATION</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>10+</td>\n",
              "      <td>57M</td>\n",
              "      <td>$2.49</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>October 31, 2017</td>\n",
              "      <td>2.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125480</th>\n",
              "      <td>2-6 Ya? E?itici �ocuk Zeka Oyunlar?</td>\n",
              "      <td>T�rk Alfabesi</td>\n",
              "      <td>EDUCATION</td>\n",
              "      <td>4.33333</td>\n",
              "      <td>54</td>\n",
              "      <td>50,000+</td>\n",
              "      <td>43M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>October 31, 2017</td>\n",
              "      <td>2.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180371</th>\n",
              "      <td>eShagird - Online academy</td>\n",
              "      <td>ETEA &amp; MDCAT</td>\n",
              "      <td>EDUCATION</td>\n",
              "      <td>4.50427</td>\n",
              "      <td>117</td>\n",
              "      <td>10,000+</td>\n",
              "      <td>6.9M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 2, 2019</td>\n",
              "      <td>4.0 and up</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   App Name  ... Latest Version\n",
              "125479  2-6 Ya? E?itici �ocuk Zeka Oyunlar?  ...     2.3 and up\n",
              "125480  2-6 Ya? E?itici �ocuk Zeka Oyunlar?  ...     2.3 and up\n",
              "180371            eShagird - Online academy  ...     4.0 and up\n",
              "\n",
              "[3 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0De3fNFcNpS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "outputId": "c574a33c-323a-4a7e-c477-e5c213d772fd"
      },
      "source": [
        "# Dropping these data entries which do not seem important and they have a lot of mistakes\n",
        "\n",
        "app.drop(index=125479, inplace=True)  \n",
        "app.drop(index=125480, inplace=True)  \n",
        "app.drop(index=180371, inplace=True) \n",
        "app[app[\"Rating\"]== \"EDUCATION\"]"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [App Name, Category, Rating, Reviews, Installs, Size, Price, Content Rating, Last Updated, Minimum Version, Latest Version]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7KQjhf1cNpe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "3b468d53-256a-42cc-d4f8-8784cb7acc1b"
      },
      "source": [
        "# In this line respecting the column \"Rating\" there are misplaced but valid data\n",
        "\n",
        "# Data will be fixed manually, putting them in the correct position\n",
        "\n",
        "app[app[\"Rating\"]== \"SOCIAL\"]"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>165230</th>\n",
              "      <td>Shytter -Twitter client</td>\n",
              "      <td>not notified you follow -</td>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>4.09859</td>\n",
              "      <td>71</td>\n",
              "      <td>5,000+</td>\n",
              "      <td>7.7M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 30, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       App Name  ... Latest Version\n",
              "165230  Shytter -Twitter client  ...     4.1 and up\n",
              "\n",
              "[1 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYS3TzXfcNpj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Fixing the data entry positions manually\n",
        "\n",
        "app.loc[165230, [\"Category\", \"Rating\", \"Reviews\", \"Installs\", \"Size\", \"Price\", \"Content Rating\", \"Last Updated\", \"Minimum Version\", \"Latest Version\"]] = \"SOCIAL\", \"4.098591328\", \"71\", \"5,000+\", \"7.7M\", \"0\", \"Everyone\", \"March 30, 2019\",\"4.1 and up\", \"NaN\" "
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hj269oKvcNpo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "outputId": "f414a0dd-2c73-4bfa-ad75-2884ef5cb86b"
      },
      "source": [
        "app[app[\"Rating\"]== \"SOCIAL\"]"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [App Name, Category, Rating, Reviews, Installs, Size, Price, Content Rating, Last Updated, Minimum Version, Latest Version]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2M9QVCbcNpx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "de74e54a-01c9-476e-d195-ec4925fafc7b"
      },
      "source": [
        "app[app[\"Rating\"]== \"PRODUCTIVITY\"]"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>168914</th>\n",
              "      <td>CorreosTrack 2.0 (Correos de M�xico</td>\n",
              "      <td>Mexpost)</td>\n",
              "      <td>PRODUCTIVITY</td>\n",
              "      <td>4.38983</td>\n",
              "      <td>59</td>\n",
              "      <td>10,000+</td>\n",
              "      <td>16M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>December 21, 2018</td>\n",
              "      <td>4.1 and up</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   App Name  ... Latest Version\n",
              "168914  CorreosTrack 2.0 (Correos de M�xico  ...     4.1 and up\n",
              "\n",
              "[1 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0HY8VF_0cNp0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "outputId": "217697d9-06f1-4a4f-d264-b91e98308627"
      },
      "source": [
        "# Fixing the data entry positions manually for the index position 168914\n",
        "\n",
        "app.loc[168914, [\"Category\", \"Rating\", \"Reviews\", \"Installs\", \"Size\", \"Price\", \"Content Rating\", \"Last Updated\", \"Minimum Version\", \"Latest Version\"]] = \"PRODUCTIVITY\", \"4.389830589\", \"59\", \"10,000+\", \"16M\", \"0\", \"Everyone\", \"December 21, 2018\",\"4.1 and up\", \"NaN\"\n",
        "app[app[\"Rating\"]== \"PRODUCTIVITY\"] # Ensuring that column \"Rating\" is fixed from this kind of data entry"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [App Name, Category, Rating, Reviews, Installs, Size, Price, Content Rating, Last Updated, Minimum Version, Latest Version]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u_oyrSP4cNqG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186
        },
        "outputId": "5312956a-888d-41ad-aee4-81b8e4b3533d"
      },
      "source": [
        "app[app[\"Rating\"]== \"MUSIC_AND_AUDIO\"]"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>177165</th>\n",
              "      <td>Music Love Song</td>\n",
              "      <td>Romantic Song Music Love Songs</td>\n",
              "      <td>MUSIC_AND_AUDIO</td>\n",
              "      <td>4.53846</td>\n",
              "      <td>13</td>\n",
              "      <td>1,000+</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>October 24, 2018</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193869</th>\n",
              "      <td>Equalizer &amp; Volume Booster</td>\n",
              "      <td>Speaker Pro 2019</td>\n",
              "      <td>MUSIC_AND_AUDIO</td>\n",
              "      <td>4.63209</td>\n",
              "      <td>511</td>\n",
              "      <td>10,000+</td>\n",
              "      <td>2.5M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>September 25, 2018</td>\n",
              "      <td>2.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>257773</th>\n",
              "      <td>High Volume Booster</td>\n",
              "      <td>super loud speaker booster</td>\n",
              "      <td>MUSIC_AND_AUDIO</td>\n",
              "      <td>4.400000095</td>\n",
              "      <td>10</td>\n",
              "      <td>1,000+</td>\n",
              "      <td>3.5M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>November 7, 2018</td>\n",
              "      <td>4.0 and up</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           App Name  ...      Latest Version\n",
              "177165              Music Love Song  ...  Varies with device\n",
              "193869  Equalizer & Volume Booster   ...          2.3 and up\n",
              "257773         High Volume Booster   ...          4.0 and up\n",
              "\n",
              "[3 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-ATjiURcNqJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "outputId": "a8def986-372b-4093-da7e-df3eaea87076"
      },
      "source": [
        "# Same logic here. Misplaced but valid data. They will be edited manually\n",
        "\n",
        "app.loc[177165, [\"Category\", \"Rating\", \"Reviews\", \"Installs\", \"Size\", \"Price\", \"Content Rating\", \"Last Updated\", \"Minimum Version\", \"Latest Version\"]] = \"MUSIC_AND_AUDIO\", \"4.538461685\", \"13\", \"1,000+\", \"Varies with device\", \"0\", \"Teen\", \"October 24, 2018\",\"Varies with device\", \"NaN\"\n",
        "app.loc[193869, [\"Category\", \"Rating\", \"Reviews\", \"Installs\", \"Size\", \"Price\", \"Content Rating\", \"Last Updated\", \"Minimum Version\", \"Latest Version\"]] = \"MUSIC_AND_AUDIO\", \"4.632093906\", \"511\", \"10,000+\", \"2.5M\", \"0\", \"Everyone\", \"September 25, 2018\",\"2.3 and up\", \"NaN\"\n",
        "app.loc[257773, [\"Category\", \"Rating\", \"Reviews\", \"Installs\", \"Size\", \"Price\", \"Content Rating\", \"Last Updated\", \"Minimum Version\", \"Latest Version\"]] = \"MUSIC_AND_AUDIO\", \"4.400000095\", \"10\", \"1,000+\", \"3.5M\", \"0\", \"Everyone\", \"November 7, 2018\",\"4.0 and up\", \"NaN\"\n",
        "app[app[\"Rating\"]== \"PRODUCTIVITY\"]"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [App Name, Category, Rating, Reviews, Installs, Size, Price, Content Rating, Last Updated, Minimum Version, Latest Version]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8MmKdnf-cNqR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "9a6b39c8-51c1-44a6-e753-b4fc316177d7"
      },
      "source": [
        "app[app[\"Rating\"]== \"TRAVEL_AND_LOCAL\"]"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>190759</th>\n",
              "      <td>Friend in Iceland</td>\n",
              "      <td>Tour Guide</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1,000+</td>\n",
              "      <td>27M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>October 16, 2017</td>\n",
              "      <td>4.0 and up</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 App Name     Category  ...   Minimum Version Latest Version\n",
              "190759  Friend in Iceland   Tour Guide  ...  October 16, 2017     4.0 and up\n",
              "\n",
              "[1 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZKoJYzjcNqV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "outputId": "406bbceb-137a-4d80-e5fd-131b224d8efa"
      },
      "source": [
        "# Fixing the entries in index position 190759 manually (misplaced but substantial values)\n",
        "\n",
        "app.loc[190759, [\"Category\", \"Rating\", \"Reviews\", \"Installs\", \"Size\", \"Price\", \"Content Rating\", \"Last Updated\", \"Minimum Version\", \"Latest Version\"]] = \"TRAVEL_AND_LOCAL\", \"5\", \"6\", \"1,000+\", \"27M\", \"0\", \"Everyone\", \"October 16, 2017\", \"4.0 and up\", \"NaN\"\n",
        "\n",
        "app[app[\"Rating\"]== \"TRAVEL_AND_LOCAL\"]"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [App Name, Category, Rating, Reviews, Installs, Size, Price, Content Rating, Last Updated, Minimum Version, Latest Version]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77u6B-s5cNqf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "4fb64c5a-4282-402a-93ee-e40020b0748b"
      },
      "source": [
        "app[app[\"Rating\"]== \"LIFESTYLE\"]"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>194165</th>\n",
              "      <td>Muslim Baby Names</td>\n",
              "      <td>Islamic Name Boy &amp; Girl+Meaning</td>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>4.38835</td>\n",
              "      <td>927</td>\n",
              "      <td>100,000+</td>\n",
              "      <td>3.7M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>May 23, 2018</td>\n",
              "      <td>4.0 and up</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 App Name  ... Latest Version\n",
              "194165  Muslim Baby Names  ...     4.0 and up\n",
              "\n",
              "[1 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1esWb7VcNqj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "outputId": "f4377563-b3de-409b-fa5f-1e6c4e418356"
      },
      "source": [
        "# same logic as previously\n",
        "\n",
        "app.loc[194165, [\"Category\", \"Rating\", \"Reviews\", \"Installs\", \"Size\", \"Price\", \"Content Rating\", \"Last Updated\", \"Minimum Version\", \"Latest Version\"]] = \"LIFESTYLE\", \"4.388349533\", \"927\", \"100,000+\", \"3.7M\", \"0\", \"Everyone\", \"May 23, 2018\", \"4.0 and up\", \"NaN\"\n",
        "app[app[\"Rating\"]== \"LIFESTYLE\"]"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [App Name, Category, Rating, Reviews, Installs, Size, Price, Content Rating, Last Updated, Minimum Version, Latest Version]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TlOxf0PcNqs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "f780dabd-b2f7-43e7-b3eb-e6994c7a7a1d"
      },
      "source": [
        "app[app[\"Rating\"]== \" Economics\"]"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>232811</th>\n",
              "      <td>Learn Accounts - Finance</td>\n",
              "      <td>Accounting</td>\n",
              "      <td>Economics</td>\n",
              "      <td>BOOKS_AND_REFERENCE</td>\n",
              "      <td>4.823529243</td>\n",
              "      <td>17</td>\n",
              "      <td>1,000+</td>\n",
              "      <td>17M</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>October 22, 2018</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        App Name     Category  ... Minimum Version    Latest Version\n",
              "232811  Learn Accounts - Finance   Accounting  ...        Everyone  October 22, 2018\n",
              "\n",
              "[1 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qeYiFn50cNqu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "outputId": "4451ced7-1315-4988-9026-d57545b31447"
      },
      "source": [
        "# Applying the same logic. Correting the misplaced (but valid) data\n",
        "\n",
        "app.loc[232811, [\"Category\", \"Rating\", \"Reviews\", \"Installs\", \"Size\", \"Price\", \"Content Rating\", \"Last Updated\", \"Minimum Version\", \"Latest Version\"]] = \" Economics\", \"4.823529243\", \"17\", \"1,000+\", \"17M\", \"0\", \"Everyone\", \"October 22, 2018\", \"NaN\", \"NaN\"\n",
        "app[app[\"Rating\"]== \" Economics\"]"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [App Name, Category, Rating, Reviews, Installs, Size, Price, Content Rating, Last Updated, Minimum Version, Latest Version]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9utOPNIScNqw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "outputId": "c57cb7ac-28a1-422b-b0c0-99286452eac4"
      },
      "source": [
        "# Here we had an entry in column \"Rating\" which was 7. But we want \"Rating<=5\". \n",
        "# It was fixed so now there is no longer rating with numbers more than \"5\"\n",
        "\n",
        "app[app[\"Rating\"]==7.000000]"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [App Name, Category, Rating, Reviews, Installs, Size, Price, Content Rating, Last Updated, Minimum Version, Latest Version]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QehYYweKcNqy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "outputId": "36d47156-4d34-4168-9485-c678cb3a3730"
      },
      "source": [
        "app.drop(index=99584, inplace=True)\n",
        "app[app[\"Rating\"]==7.000000]"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [App Name, Category, Rating, Reviews, Installs, Size, Price, Content Rating, Last Updated, Minimum Version, Latest Version]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "343BiWC5cNq0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Converting the column \"Rating\" to float so that we can apply statistics \n",
        "\n",
        "app.Rating= app.Rating.astype(float) "
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGOKdk0XcNq2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "5522f71d-c394-482e-dadd-d6c9cb25974d"
      },
      "source": [
        "app.describe()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>267040.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.269390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.586244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.017699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.382165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.648649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Rating\n",
              "count  267040.000000\n",
              "mean        4.269390\n",
              "std         0.586244\n",
              "min         1.000000\n",
              "25%         4.017699\n",
              "50%         4.382165\n",
              "75%         4.648649\n",
              "max         5.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dC3uU8qlcNq4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Converting the data in the column \"Reviews\" to float to that we can apply statistics\n",
        "\n",
        "app.Reviews= app.Reviews.astype(float) "
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RUQii6sjcNq5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "e32bda08-75e6-4693-fd03-567fa79f393f"
      },
      "source": [
        "app.describe()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>267040.000000</td>\n",
              "      <td>2.670400e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.269390</td>\n",
              "      <td>1.459628e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.586244</td>\n",
              "      <td>4.110638e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.017699</td>\n",
              "      <td>1.600000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.382165</td>\n",
              "      <td>9.300000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.648649</td>\n",
              "      <td>6.560000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.621429e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Rating       Reviews\n",
              "count  267040.000000  2.670400e+05\n",
              "mean        4.269390  1.459628e+04\n",
              "std         0.586244  4.110638e+05\n",
              "min         1.000000  1.000000e+00\n",
              "25%         4.017699  1.600000e+01\n",
              "50%         4.382165  9.300000e+01\n",
              "75%         4.648649  6.560000e+02\n",
              "max         5.000000  8.621429e+07"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozY_CxEccNq8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# I want to convert the column \"Installs\" into float\n",
        "\n",
        "# I firstly have to remove the \"+\"\n",
        " \n",
        "app.Installs= app[\"Installs\"].str.replace(\"+\", \"\")"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GC2jBktUcNrC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# I had some problems converting the column into float, even when i removed \"+\"\n",
        "\n",
        "# I am removing the commas\n",
        "\n",
        "app.Installs= app[\"Installs\"].str.replace(\",\", \"\")"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Dk5D2MacNrF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "app[\"Installs\"] = pd.to_numeric(app[\"Installs\"])"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KQQx2HZfcNrI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Removing \"$\" from the data entries in the column \"Price\" so that it can be converted to float\n",
        "\n",
        "app[\"Price\"]= app[\"Price\"].str.replace(\"$\", \"\")"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ge4e7pr_cNrN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert the data in \"Price\" to float\n",
        "\n",
        "\n",
        "app[\"Price\"]= app.Price.astype(float)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sL6xktWjcNrQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "cf154ffc-b568-4f71-f25f-398585d3df24"
      },
      "source": [
        "# the data in the column \"Prics\" successfully converted to float\n",
        "\n",
        "# In these columns i can do various statistical applications\n",
        "app.describe()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>267040.000000</td>\n",
              "      <td>2.670400e+05</td>\n",
              "      <td>2.670400e+05</td>\n",
              "      <td>267040.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.269390</td>\n",
              "      <td>1.459628e+04</td>\n",
              "      <td>6.410638e+05</td>\n",
              "      <td>0.227872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.586244</td>\n",
              "      <td>4.110638e+05</td>\n",
              "      <td>2.046801e+07</td>\n",
              "      <td>3.559421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.017699</td>\n",
              "      <td>1.600000e+01</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.382165</td>\n",
              "      <td>9.300000e+01</td>\n",
              "      <td>1.000000e+04</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.648649</td>\n",
              "      <td>6.560000e+02</td>\n",
              "      <td>5.000000e+04</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.621429e+07</td>\n",
              "      <td>5.000000e+09</td>\n",
              "      <td>399.990000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Rating       Reviews      Installs          Price\n",
              "count  267040.000000  2.670400e+05  2.670400e+05  267040.000000\n",
              "mean        4.269390  1.459628e+04  6.410638e+05       0.227872\n",
              "std         0.586244  4.110638e+05  2.046801e+07       3.559421\n",
              "min         1.000000  1.000000e+00  0.000000e+00       0.000000\n",
              "25%         4.017699  1.600000e+01  1.000000e+03       0.000000\n",
              "50%         4.382165  9.300000e+01  1.000000e+04       0.000000\n",
              "75%         4.648649  6.560000e+02  5.000000e+04       0.000000\n",
              "max         5.000000  8.621429e+07  5.000000e+09     399.990000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3k0BvM0icNrT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "93ed81f5-ebb6-47e6-84db-ca72fb2f190b"
      },
      "source": [
        "# procedure for converting the column \"Size\" to float\n",
        "# there are sizes counted in mb, kb, in numbers without measurement unit and with \"varies with device\"\n",
        "app.Size.unique()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Varies with device', '1.4M', '16M', ..., '601k', '715k', '311k'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3y0iuCZ2cNrX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# removing the \"m\" which is the mb for the size\n",
        "\n",
        "app.Size= app[\"Size\"].str.replace(\"M\", \"\")"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BEMB5gAwcNrc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# assigning \"Varies with device\" with a number like \"-1\" so that i can seperate it later\n",
        "\n",
        "# app.Size= app[\"Size\"].str.replace(\"Varies with device\", \"-1\")"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJL-zTNacNrl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Segmenting the column of the size\n",
        "\n",
        "y= app.iloc[:, 5:6]\n"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjp3QjUHcNro",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# I tried to fix the last problems in converting the column \"Size\" to float\n",
        "# Here i am trying to remove \"k\" (kbs) and the blanks, and to convert kbs to mbs\n",
        "# It keeps giving me errors and i cannot fix it\n",
        "# i will not use the column \"Size\" for statistical applications\n",
        "\n",
        "#for x in y:\n",
        "#    x = str(x)\n",
        "#    x= x.replace(\" \", \"\")\n",
        "#    x= x.replace(\",\", \".\")\n",
        "#    if \"k\" in x:\n",
        "#        x= x.replace(\"k\", \"\")\n",
        "#        x=x.replace(\" k\", \"\")\n",
        "#        x=x.replace(\"k \", \"\")\n",
        "#        x= float(x)\n",
        "#        x= x/1024"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofqCtTQicNrr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d27bd2fc-6267-476b-ac68-b15c5533d271"
      },
      "source": [
        "# There are 11,728 apps whose size varies with device\n",
        "\n",
        "len(app[app[\"Size\"]== \"Varies with device\"])"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11728"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4lebuGmcNrv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "2edc45e4-2787-4c7b-8042-cc1f105e361f"
      },
      "source": [
        "app.Size.describe()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                 267040\n",
              "unique                  1236\n",
              "top       Varies with device\n",
              "freq                   11728\n",
              "Name: Size, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLAOMY78cNry",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dd9dc4f6-83e3-492b-ab7a-b20fe1b37696"
      },
      "source": [
        "print (\"Apps whose size varies with device are {}% of the dataset\".format(11728/267040*100))"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Apps whose size varies with device are 4.391851408028759% of the dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CDlQQ65ycNr8",
        "colab_type": "text"
      },
      "source": [
        "# Statistical Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NRd5M0HWcNr9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3b1e4eb3-ab32-4425-8806-eee2c67f8888"
      },
      "source": [
        "#ensuring the shape of dataframe before proceeding to further statistics and visualization\n",
        "\n",
        "app.shape"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(267040, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtCMHv0rcNsB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "6c6f2e61-9cec-49a1-861a-fae24c620c25"
      },
      "source": [
        "# the columns, the data of which we can do statistic manipulation\n",
        "\n",
        "app.describe()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>267040.000000</td>\n",
              "      <td>2.670400e+05</td>\n",
              "      <td>2.670400e+05</td>\n",
              "      <td>267040.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.269390</td>\n",
              "      <td>1.459628e+04</td>\n",
              "      <td>6.410638e+05</td>\n",
              "      <td>0.227872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.586244</td>\n",
              "      <td>4.110638e+05</td>\n",
              "      <td>2.046801e+07</td>\n",
              "      <td>3.559421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.017699</td>\n",
              "      <td>1.600000e+01</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.382165</td>\n",
              "      <td>9.300000e+01</td>\n",
              "      <td>1.000000e+04</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.648649</td>\n",
              "      <td>6.560000e+02</td>\n",
              "      <td>5.000000e+04</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.621429e+07</td>\n",
              "      <td>5.000000e+09</td>\n",
              "      <td>399.990000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Rating       Reviews      Installs          Price\n",
              "count  267040.000000  2.670400e+05  2.670400e+05  267040.000000\n",
              "mean        4.269390  1.459628e+04  6.410638e+05       0.227872\n",
              "std         0.586244  4.110638e+05  2.046801e+07       3.559421\n",
              "min         1.000000  1.000000e+00  0.000000e+00       0.000000\n",
              "25%         4.017699  1.600000e+01  1.000000e+03       0.000000\n",
              "50%         4.382165  9.300000e+01  1.000000e+04       0.000000\n",
              "75%         4.648649  6.560000e+02  5.000000e+04       0.000000\n",
              "max         5.000000  8.621429e+07  5.000000e+09     399.990000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ev_n7qybcNsH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "68482c97-96ff-4ac6-d365-860c76725d8b"
      },
      "source": [
        "app.info() # data type for each column"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 267040 entries, 0 to 267051\n",
            "Data columns (total 11 columns):\n",
            " #   Column           Non-Null Count   Dtype  \n",
            "---  ------           --------------   -----  \n",
            " 0   App Name         267040 non-null  object \n",
            " 1   Category         267040 non-null  object \n",
            " 2   Rating           267040 non-null  float64\n",
            " 3   Reviews          267040 non-null  float64\n",
            " 4   Installs         267040 non-null  int64  \n",
            " 5   Size             267040 non-null  object \n",
            " 6   Price            267040 non-null  float64\n",
            " 7   Content Rating   267040 non-null  object \n",
            " 8   Last Updated     267040 non-null  object \n",
            " 9   Minimum Version  267040 non-null  object \n",
            " 10  Latest Version   267040 non-null  object \n",
            "dtypes: float64(3), int64(1), object(7)\n",
            "memory usage: 24.4+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGKRWTtscNsK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0e4c0ae6-3505-49b8-fc39-e4aee4e3f223"
      },
      "source": [
        "#reinsuring there are no any missing values\n",
        "app.isnull().any().any()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ByGpLyKncNsN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#******************************************************************************************************\n",
        "# Reviewing the unique values and the number of unique values in each column after the cleaning process"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mEg_mU4gcNsR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "d7eb8511-634d-4f17-f165-7b49c230c184"
      },
      "source": [
        "# Values in \"Category\"\n",
        "app[\"Category\"].unique()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['FOOD_AND_DRINK', 'TRAVEL_AND_LOCAL', 'SHOPPING', 'LIFESTYLE',\n",
              "       'GAME_ACTION', 'GAME_CASUAL', 'GAME_ROLE_PLAYING', 'GAME_PUZZLE',\n",
              "       'GAME_RACING', 'GAME_ADVENTURE', 'GAME_ARCADE', 'GAME_STRATEGY',\n",
              "       'GAME_SPORTS', 'GAME_SIMULATION', 'GAME_MUSIC', 'MUSIC_AND_AUDIO',\n",
              "       'FINANCE', 'EVENTS', 'ENTERTAINMENT', 'EDUCATION',\n",
              "       'GAME_EDUCATIONAL', 'BOOKS_AND_REFERENCE', 'NEWS_AND_MAGAZINES',\n",
              "       'PHOTOGRAPHY', 'VIDEO_PLAYERS', 'GAME_WORD', 'ART_AND_DESIGN',\n",
              "       'GAME_TRIVIA', 'GAME_BOARD', 'BUSINESS', 'PRODUCTIVITY',\n",
              "       'COMMUNICATION', 'HEALTH_AND_FITNESS', 'HOUSE_AND_HOME', 'SOCIAL',\n",
              "       'BEAUTY', 'GAME_CASINO', 'MAPS_AND_NAVIGATION', 'PERSONALIZATION',\n",
              "       'GAME_CARD', 'TOOLS', 'SPORTS', 'AUTO_AND_VEHICLES',\n",
              "       'LIBRARIES_AND_DEMO', 'COMICS', 'PARENTING', 'DATING', 'WEATHER',\n",
              "       'MEDICAL', 'TRAVEL', ' Economics'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NMpypUcacNsT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d90b20c6-e27b-4d4f-82c9-5a63702c34d0"
      },
      "source": [
        "# There are 51 different categories\n",
        "\n",
        "app[\"Category\"].nunique()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "51"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zk-ELt2LcNsW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "80d44473-1719-407b-8806-5469b0a76adb"
      },
      "source": [
        "# Unique values of Rating\n",
        "app[\"Rating\"].unique()"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4.54856157, 4.40067148, 3.65632939, ..., 4.60038614, 3.28787875,\n",
              "       4.06870222])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbzyDrN8cNsY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a3149d02-2c79-4e3c-a73e-651973b5707c"
      },
      "source": [
        "# There are 99,845 unique values of Rating\n",
        "\n",
        "app[\"Rating\"].nunique()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100205"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZS1ZzrRKcNsa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "896db27c-c42f-4107-9755-6e77aba8d7f7"
      },
      "source": [
        "# Unique values of the column \"Reviews\"\n",
        "app[\"Reviews\"].unique()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 305034., 1207922.,    1967., ...,  296774.,    7974.,   69123.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zfW08LRkcNsd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e19522d7-a676-45c9-998a-0342f202ddce"
      },
      "source": [
        "# There are 24,531 different reviews\n",
        "\n",
        "app[\"Reviews\"].nunique()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24531"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IvVboARycNsj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "c79bd5bb-342b-44d5-b3ae-189762031c94"
      },
      "source": [
        "# Unique values of installations\n",
        "\n",
        "app[\"Installs\"].unique()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([   5000000,  100000000,     100000,   10000000,      10000,\n",
              "          1000000,   50000000,     500000,      50000,       5000,\n",
              "             1000,  500000000, 1000000000, 5000000000,        100,\n",
              "              500,         50,          5,         10,          1,\n",
              "                0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4NPNoyvQcNsn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1ca53934-f652-484a-f681-0eb116eec30d"
      },
      "source": [
        "# There are 21 different classes of installations\n",
        "\n",
        "app[\"Installs\"].nunique()"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M2zeVLQQcNsq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "293e4d1f-47f3-4253-9c0b-40bf6f0c4768"
      },
      "source": [
        "# Unique values in the column \"Size\"\n",
        "app[\"Size\"].unique()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Varies with device', '1.4', '16', ..., '601k', '715k', '311k'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_yIqc6cFcNsr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "59eaeaaa-e6b7-4459-ec8e-edb337769d1f"
      },
      "source": [
        "# There are 1,236 different sizes for the apps\n",
        "\n",
        "app[\"Size\"].nunique()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1236"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9yzzQwH0cNsu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "da800ef9-5394-4de6-bb25-5bef8edc62c6"
      },
      "source": [
        "# There are 488 different prices \n",
        "app[\"Price\"].nunique()"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "488"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLgb0zMZcNsx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "2c77eeb5-5d4a-490b-d07f-1edd7f9a06e4"
      },
      "source": [
        "# Unique values of the column \"Content Rating\"\n",
        "app[\"Content Rating\"].unique()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Everyone', 'Teen', 'Everyone 10+', 'Mature 17+',\n",
              "       'Adults only 18+', 'Unrated'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KyYt0CPcNs0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e4590375-7d52-44ce-90f0-8358467e71c3"
      },
      "source": [
        "# There are 6 different content ratings\n",
        "\n",
        "len(app[\"Content Rating\"].unique())"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IMVmRaC-cNs7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "54edae4d-ea03-4f16-8b82-d0f707529397"
      },
      "source": [
        "print(\"***************************************************\")\n",
        "\n",
        "print(\"Minimum number of ratings: %.2f\" %app[\"Rating\"].min())\n",
        "print(\"Maximum number of ratings: %.2f\" %app[\"Rating\"].max())\n",
        "\n",
        "print(\"***************************************************\")\n",
        "\n",
        "print(\"Minimum number of reviews: %.2f\" %app[\"Reviews\"].min())\n",
        "print(\"Maximum number of reviews: %.2f\" %app[\"Reviews\"].max())\n",
        "\n",
        "print(\"***************************************************\")\n",
        "\n",
        "print(\"Minimum number of installs: %.2f\" %app[\"Installs\"].min())\n",
        "print(\"Maximum number of installs: %.2f\" %app[\"Installs\"].max())\n",
        "\n",
        "print(\"***************************************************\")\n",
        "\n",
        "\n",
        "print(\"Minimum number of prices: %.2f\" %app[\"Price\"].min())\n",
        "print(\"Maximum number of prices: %.2f\" %app[\"Price\"].max())\n",
        "\n",
        "print(\"***************************************************\")\n",
        "\n"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "***************************************************\n",
            "Minimum number of ratings: 1.00\n",
            "Maximum number of ratings: 5.00\n",
            "***************************************************\n",
            "Minimum number of reviews: 1.00\n",
            "Maximum number of reviews: 86214292.00\n",
            "***************************************************\n",
            "Minimum number of installs: 0.00\n",
            "Maximum number of installs: 5000000000.00\n",
            "***************************************************\n",
            "Minimum number of prices: 0.00\n",
            "Maximum number of prices: 399.99\n",
            "***************************************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jVT6iVfHcNs_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8be3ab64-d401-4cfc-d270-d0a28d811372"
      },
      "source": [
        "# Getting the measures of central tendency for all the installation grouped by \"Category\"\n",
        "\n",
        "app.groupby(\"Category\").Installs.agg([\"min\", \"mean\", \"median\", \"max\"])"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>mean</th>\n",
              "      <th>median</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Category</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Economics</th>\n",
              "      <td>1000</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ART_AND_DESIGN</th>\n",
              "      <td>1</td>\n",
              "      <td>2.105946e+05</td>\n",
              "      <td>5000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AUTO_AND_VEHICLES</th>\n",
              "      <td>1</td>\n",
              "      <td>1.824824e+05</td>\n",
              "      <td>5000</td>\n",
              "      <td>10000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BEAUTY</th>\n",
              "      <td>0</td>\n",
              "      <td>1.409585e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BOOKS_AND_REFERENCE</th>\n",
              "      <td>0</td>\n",
              "      <td>1.008366e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BUSINESS</th>\n",
              "      <td>0</td>\n",
              "      <td>1.455845e+05</td>\n",
              "      <td>1000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COMICS</th>\n",
              "      <td>5</td>\n",
              "      <td>3.832805e+05</td>\n",
              "      <td>50000</td>\n",
              "      <td>10000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COMMUNICATION</th>\n",
              "      <td>0</td>\n",
              "      <td>2.419759e+06</td>\n",
              "      <td>10000</td>\n",
              "      <td>1000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DATING</th>\n",
              "      <td>1</td>\n",
              "      <td>4.595702e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EDUCATION</th>\n",
              "      <td>0</td>\n",
              "      <td>7.466526e+04</td>\n",
              "      <td>5000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ENTERTAINMENT</th>\n",
              "      <td>0</td>\n",
              "      <td>3.573559e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>1000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EVENTS</th>\n",
              "      <td>1</td>\n",
              "      <td>4.602683e+04</td>\n",
              "      <td>1000</td>\n",
              "      <td>10000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FINANCE</th>\n",
              "      <td>0</td>\n",
              "      <td>2.236191e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FOOD_AND_DRINK</th>\n",
              "      <td>1</td>\n",
              "      <td>1.930530e+05</td>\n",
              "      <td>5000</td>\n",
              "      <td>50000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_ACTION</th>\n",
              "      <td>0</td>\n",
              "      <td>4.063549e+06</td>\n",
              "      <td>100000</td>\n",
              "      <td>500000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_ADVENTURE</th>\n",
              "      <td>1</td>\n",
              "      <td>1.155658e+06</td>\n",
              "      <td>10000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_ARCADE</th>\n",
              "      <td>1</td>\n",
              "      <td>2.960377e+06</td>\n",
              "      <td>10000</td>\n",
              "      <td>1000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_BOARD</th>\n",
              "      <td>5</td>\n",
              "      <td>1.159017e+06</td>\n",
              "      <td>10000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_CARD</th>\n",
              "      <td>1</td>\n",
              "      <td>8.000250e+05</td>\n",
              "      <td>100000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_CASINO</th>\n",
              "      <td>5</td>\n",
              "      <td>1.769368e+06</td>\n",
              "      <td>100000</td>\n",
              "      <td>50000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_CASUAL</th>\n",
              "      <td>0</td>\n",
              "      <td>2.696059e+06</td>\n",
              "      <td>100000</td>\n",
              "      <td>500000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_EDUCATIONAL</th>\n",
              "      <td>0</td>\n",
              "      <td>6.160472e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_MUSIC</th>\n",
              "      <td>5</td>\n",
              "      <td>2.268880e+06</td>\n",
              "      <td>100000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_PUZZLE</th>\n",
              "      <td>0</td>\n",
              "      <td>1.184301e+06</td>\n",
              "      <td>10000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_RACING</th>\n",
              "      <td>1</td>\n",
              "      <td>5.540269e+06</td>\n",
              "      <td>500000</td>\n",
              "      <td>500000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_ROLE_PLAYING</th>\n",
              "      <td>10</td>\n",
              "      <td>1.139481e+06</td>\n",
              "      <td>100000</td>\n",
              "      <td>50000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_SIMULATION</th>\n",
              "      <td>5</td>\n",
              "      <td>1.807155e+06</td>\n",
              "      <td>100000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_SPORTS</th>\n",
              "      <td>10</td>\n",
              "      <td>3.580514e+06</td>\n",
              "      <td>100000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_STRATEGY</th>\n",
              "      <td>1</td>\n",
              "      <td>2.544039e+06</td>\n",
              "      <td>100000</td>\n",
              "      <td>500000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_TRIVIA</th>\n",
              "      <td>1</td>\n",
              "      <td>6.068273e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GAME_WORD</th>\n",
              "      <td>0</td>\n",
              "      <td>1.046894e+06</td>\n",
              "      <td>100000</td>\n",
              "      <td>50000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HEALTH_AND_FITNESS</th>\n",
              "      <td>0</td>\n",
              "      <td>2.837032e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>500000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HOUSE_AND_HOME</th>\n",
              "      <td>1</td>\n",
              "      <td>3.353328e+05</td>\n",
              "      <td>5000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LIBRARIES_AND_DEMO</th>\n",
              "      <td>1</td>\n",
              "      <td>1.630496e+05</td>\n",
              "      <td>5000</td>\n",
              "      <td>10000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LIFESTYLE</th>\n",
              "      <td>0</td>\n",
              "      <td>1.463108e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MAPS_AND_NAVIGATION</th>\n",
              "      <td>1</td>\n",
              "      <td>2.986137e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MEDICAL</th>\n",
              "      <td>1</td>\n",
              "      <td>5.832896e+04</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MUSIC_AND_AUDIO</th>\n",
              "      <td>0</td>\n",
              "      <td>3.264714e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>1000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NEWS_AND_MAGAZINES</th>\n",
              "      <td>0</td>\n",
              "      <td>4.623790e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>1000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PARENTING</th>\n",
              "      <td>5</td>\n",
              "      <td>1.447583e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PERSONALIZATION</th>\n",
              "      <td>0</td>\n",
              "      <td>3.970350e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHOTOGRAPHY</th>\n",
              "      <td>0</td>\n",
              "      <td>1.292555e+06</td>\n",
              "      <td>50000</td>\n",
              "      <td>1000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PRODUCTIVITY</th>\n",
              "      <td>0</td>\n",
              "      <td>1.391869e+06</td>\n",
              "      <td>10000</td>\n",
              "      <td>1000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SHOPPING</th>\n",
              "      <td>0</td>\n",
              "      <td>6.134706e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SOCIAL</th>\n",
              "      <td>1</td>\n",
              "      <td>1.490510e+06</td>\n",
              "      <td>10000</td>\n",
              "      <td>1000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SPORTS</th>\n",
              "      <td>1</td>\n",
              "      <td>1.497417e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>50000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TOOLS</th>\n",
              "      <td>0</td>\n",
              "      <td>1.104303e+06</td>\n",
              "      <td>10000</td>\n",
              "      <td>5000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TRAVEL</th>\n",
              "      <td>10000</td>\n",
              "      <td>1.000000e+04</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TRAVEL_AND_LOCAL</th>\n",
              "      <td>0</td>\n",
              "      <td>1.222950e+06</td>\n",
              "      <td>10000</td>\n",
              "      <td>5000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VIDEO_PLAYERS</th>\n",
              "      <td>1</td>\n",
              "      <td>3.554788e+06</td>\n",
              "      <td>10000</td>\n",
              "      <td>5000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WEATHER</th>\n",
              "      <td>5</td>\n",
              "      <td>5.188793e+05</td>\n",
              "      <td>10000</td>\n",
              "      <td>100000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       min          mean  median         max\n",
              "Category                                                    \n",
              " Economics            1000  1.000000e+03    1000        1000\n",
              "ART_AND_DESIGN           1  2.105946e+05    5000   100000000\n",
              "AUTO_AND_VEHICLES        1  1.824824e+05    5000    10000000\n",
              "BEAUTY                   0  1.409585e+05   10000    10000000\n",
              "BOOKS_AND_REFERENCE      0  1.008366e+05   10000   100000000\n",
              "BUSINESS                 0  1.455845e+05    1000   100000000\n",
              "COMICS                   5  3.832805e+05   50000    10000000\n",
              "COMMUNICATION            0  2.419759e+06   10000  1000000000\n",
              "DATING                   1  4.595702e+05   10000    10000000\n",
              "EDUCATION                0  7.466526e+04    5000   100000000\n",
              "ENTERTAINMENT            0  3.573559e+05   10000  1000000000\n",
              "EVENTS                   1  4.602683e+04    1000    10000000\n",
              "FINANCE                  0  2.236191e+05   10000   100000000\n",
              "FOOD_AND_DRINK           1  1.930530e+05    5000    50000000\n",
              "GAME_ACTION              0  4.063549e+06  100000   500000000\n",
              "GAME_ADVENTURE           1  1.155658e+06   10000   100000000\n",
              "GAME_ARCADE              1  2.960377e+06   10000  1000000000\n",
              "GAME_BOARD               5  1.159017e+06   10000   100000000\n",
              "GAME_CARD                1  8.000250e+05  100000   100000000\n",
              "GAME_CASINO              5  1.769368e+06  100000    50000000\n",
              "GAME_CASUAL              0  2.696059e+06  100000   500000000\n",
              "GAME_EDUCATIONAL         0  6.160472e+05   10000   100000000\n",
              "GAME_MUSIC               5  2.268880e+06  100000   100000000\n",
              "GAME_PUZZLE              0  1.184301e+06   10000   100000000\n",
              "GAME_RACING              1  5.540269e+06  500000   500000000\n",
              "GAME_ROLE_PLAYING       10  1.139481e+06  100000    50000000\n",
              "GAME_SIMULATION          5  1.807155e+06  100000   100000000\n",
              "GAME_SPORTS             10  3.580514e+06  100000   100000000\n",
              "GAME_STRATEGY            1  2.544039e+06  100000   500000000\n",
              "GAME_TRIVIA              1  6.068273e+05   10000   100000000\n",
              "GAME_WORD                0  1.046894e+06  100000    50000000\n",
              "HEALTH_AND_FITNESS       0  2.837032e+05   10000   500000000\n",
              "HOUSE_AND_HOME           1  3.353328e+05    5000   100000000\n",
              "LIBRARIES_AND_DEMO       1  1.630496e+05    5000    10000000\n",
              "LIFESTYLE                0  1.463108e+05   10000   100000000\n",
              "MAPS_AND_NAVIGATION      1  2.986137e+05   10000   100000000\n",
              "MEDICAL                  1  5.832896e+04    5000     5000000\n",
              "MUSIC_AND_AUDIO          0  3.264714e+05   10000  1000000000\n",
              "NEWS_AND_MAGAZINES       0  4.623790e+05   10000  1000000000\n",
              "PARENTING                5  1.447583e+05   10000    10000000\n",
              "PERSONALIZATION          0  3.970350e+05   10000   100000000\n",
              "PHOTOGRAPHY              0  1.292555e+06   50000  1000000000\n",
              "PRODUCTIVITY             0  1.391869e+06   10000  1000000000\n",
              "SHOPPING                 0  6.134706e+05   10000   100000000\n",
              "SOCIAL                   1  1.490510e+06   10000  1000000000\n",
              "SPORTS                   1  1.497417e+05   10000    50000000\n",
              "TOOLS                    0  1.104303e+06   10000  5000000000\n",
              "TRAVEL               10000  1.000000e+04   10000       10000\n",
              "TRAVEL_AND_LOCAL         0  1.222950e+06   10000  5000000000\n",
              "VIDEO_PLAYERS            1  3.554788e+06   10000  5000000000\n",
              "WEATHER                  5  5.188793e+05   10000   100000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJjrCUCZcNtC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "outputId": "0aaaba1b-b730-480c-edd2-851ea7506ee8"
      },
      "source": [
        "# Sorting (descending sorting) the dataframe by number of installs\n",
        "\n",
        "app.sort_values(by=\"Installs\", ascending= False)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>YouTube</td>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>4.368428</td>\n",
              "      <td>41919102.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2177</th>\n",
              "      <td>Maps - Navigate &amp; Explore</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>4.342798</td>\n",
              "      <td>10083666.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>821</th>\n",
              "      <td>Google</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.408893</td>\n",
              "      <td>10870728.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>842</th>\n",
              "      <td>Google Chrome: Fast &amp; Secure</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.335205</td>\n",
              "      <td>13636591.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 26, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>539</th>\n",
              "      <td>Subway Surfers</td>\n",
              "      <td>GAME_ARCADE</td>\n",
              "      <td>4.498131</td>\n",
              "      <td>29834812.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>85</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>March 7, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>1.100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253182</th>\n",
              "      <td>La Barbiera Sasso Marconi</td>\n",
              "      <td>BEAUTY</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>February 14, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>1.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249287</th>\n",
              "      <td>Rich &amp; Rich</td>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>369.99</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 5, 2017</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>1.0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24784</th>\n",
              "      <td>DUA KE QURAN AMHARIC</td>\n",
              "      <td>EDUCATION</td>\n",
              "      <td>4.730337</td>\n",
              "      <td>89.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>May 10, 2018</td>\n",
              "      <td>4.4 and up</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166479</th>\n",
              "      <td>??? LED ??????</td>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>1.941176</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>September 25, 2018</td>\n",
              "      <td>5.0 and up</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260161</th>\n",
              "      <td>Flugpreise Vergleichen &amp; G�nstige Fl�ge  Low Cost</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 25, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>267040 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 App Name  ...      Latest Version\n",
              "813                                               YouTube  ...  Varies with device\n",
              "2177                            Maps - Navigate & Explore  ...  Varies with device\n",
              "821                                                Google  ...  Varies with device\n",
              "842                          Google Chrome: Fast & Secure  ...  Varies with device\n",
              "539                                        Subway Surfers  ...             1.100.0\n",
              "...                                                   ...  ...                 ...\n",
              "253182                          La Barbiera Sasso Marconi  ...                1.12\n",
              "249287                                        Rich & Rich  ...               1.0.0\n",
              "24784                                DUA KE QURAN AMHARIC  ...                   1\n",
              "166479                                     ??? LED ??????  ...                   1\n",
              "260161  Flugpreise Vergleichen & G�nstige Fl�ge  Low Cost  ...                   2\n",
              "\n",
              "[267040 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FREMgQgZcNtE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "top_installed_apps=app.sort_values(by=\"Installs\", ascending= False)"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I7ZCCqi9cNtH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "outputId": "7c97a5cf-ef11-4dce-83cb-df0a699768b7"
      },
      "source": [
        "#*************************************************************\n",
        "\n",
        "# top 10 apps based on the number of installations\n",
        "\n",
        "#*************************************************************\n",
        "\n",
        "top_installed_apps.head(10)"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
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              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>YouTube</td>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>4.368428</td>\n",
              "      <td>41919102.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2177</th>\n",
              "      <td>Maps - Navigate &amp; Explore</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>4.342798</td>\n",
              "      <td>10083666.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>821</th>\n",
              "      <td>Google</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.408893</td>\n",
              "      <td>10870728.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>842</th>\n",
              "      <td>Google Chrome: Fast &amp; Secure</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.335205</td>\n",
              "      <td>13636591.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 26, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>539</th>\n",
              "      <td>Subway Surfers</td>\n",
              "      <td>GAME_ARCADE</td>\n",
              "      <td>4.498131</td>\n",
              "      <td>29834812.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>85</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>March 7, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>1.100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2147</th>\n",
              "      <td>Google Street View</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>4.215697</td>\n",
              "      <td>2171998.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 11, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28676</th>\n",
              "      <td>Samsung Print Service Plugin</td>\n",
              "      <td>PRODUCTIVITY</td>\n",
              "      <td>4.204499</td>\n",
              "      <td>322275.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>October 5, 2018</td>\n",
              "      <td>4.4 and up</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>704</th>\n",
              "      <td>Facebook</td>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>4.087946</td>\n",
              "      <td>85766433.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6411</th>\n",
              "      <td>Google Duo - High Quality Video Calls</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.596404</td>\n",
              "      <td>3641252.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>4.4 and up</td>\n",
              "      <td>50.1.240885383.DR50_RC06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6412</th>\n",
              "      <td>Google Play Movies &amp; TV</td>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>3.703356</td>\n",
              "      <td>1048972.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    App Name  ...            Latest Version\n",
              "813                                  YouTube  ...        Varies with device\n",
              "2177               Maps - Navigate & Explore  ...        Varies with device\n",
              "821                                   Google  ...        Varies with device\n",
              "842             Google Chrome: Fast & Secure  ...        Varies with device\n",
              "539                           Subway Surfers  ...                   1.100.0\n",
              "2147                      Google Street View  ...        Varies with device\n",
              "28676           Samsung Print Service Plugin  ...        Varies with device\n",
              "704                                 Facebook  ...        Varies with device\n",
              "6411   Google Duo - High Quality Video Calls  ...  50.1.240885383.DR50_RC06\n",
              "6412                 Google Play Movies & TV  ...        Varies with device\n",
              "\n",
              "[10 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8f71N-RvcNtN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "57abf549-6a4d-4a58-baa2-a7ce3f677ba2"
      },
      "source": [
        "# Apps with 5 billion installations (5b is the 1st greater class of installations in the dataset)\n",
        "\n",
        "len(app[app[\"Installs\"]>= 5000000000])"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZnr8aRlcNtP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2f91353f-855f-4da1-b3a5-0a161b8c3e3a"
      },
      "source": [
        "# Apps with more than 1 billion installations (1b is the 2nd greater class of installations in the dataset)\n",
        "\n",
        "len(app[app[\"Installs\"]>= 1000000000])"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyNVgiblcNtT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "outputId": "4294a09e-aa8e-4f3b-a24f-b51ec6facc6d"
      },
      "source": [
        "top_installed_and_rated_apps = app.sort_values(by=[\"Installs\", \"Rating\"], ascending=False)\n",
        "top_installed_and_rated_apps # main top apps"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>821</th>\n",
              "      <td>Google</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.408893</td>\n",
              "      <td>10870728.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>YouTube</td>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>4.368428</td>\n",
              "      <td>41919102.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2177</th>\n",
              "      <td>Maps - Navigate &amp; Explore</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>4.342798</td>\n",
              "      <td>10083666.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7064</th>\n",
              "      <td>Clean Master - Antivirus, Applock &amp; Cleaner</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.657038</td>\n",
              "      <td>44171776.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>7.0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6411</th>\n",
              "      <td>Google Duo - High Quality Video Calls</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.596404</td>\n",
              "      <td>3641252.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>4.4 and up</td>\n",
              "      <td>50.1.240885383.DR50_RC06</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>226376</th>\n",
              "      <td>Say My Text (Speech synthesizer)</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 6, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>1.0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39673</th>\n",
              "      <td>Vadaa Hunt</td>\n",
              "      <td>GAME_CASUAL</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>December 5, 2018</td>\n",
              "      <td>4.4 and up</td>\n",
              "      <td>1.0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225594</th>\n",
              "      <td>PIP Collage Maker Professional</td>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>2.130435</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 29, 2019</td>\n",
              "      <td>4.4 and up</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166479</th>\n",
              "      <td>??? LED ??????</td>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>1.941176</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>September 25, 2018</td>\n",
              "      <td>5.0 and up</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184817</th>\n",
              "      <td>Diabetes Ratgeber AR</td>\n",
              "      <td>HEALTH_AND_FITNESS</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>May 14, 2018</td>\n",
              "      <td>4.4 and up</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>267040 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           App Name  ...            Latest Version\n",
              "821                                          Google  ...        Varies with device\n",
              "813                                         YouTube  ...        Varies with device\n",
              "2177                      Maps - Navigate & Explore  ...        Varies with device\n",
              "7064    Clean Master - Antivirus, Applock & Cleaner  ...                     7.0.6\n",
              "6411          Google Duo - High Quality Video Calls  ...  50.1.240885383.DR50_RC06\n",
              "...                                             ...  ...                       ...\n",
              "226376             Say My Text (Speech synthesizer)  ...                     1.0.0\n",
              "39673                                    Vadaa Hunt  ...                     1.0.4\n",
              "225594               PIP Collage Maker Professional  ...                       2.5\n",
              "166479                               ??? LED ??????  ...                         1\n",
              "184817                         Diabetes Ratgeber AR  ...                         1\n",
              "\n",
              "[267040 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SCgCKBIBcNtZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "b823eb62-caef-4bc1-c5ce-4dc5c513be8e"
      },
      "source": [
        "#**************************************************************************\n",
        "# top 10 apps based on the number of installations and rating together (main top apps)\n",
        "#**************************************************************************\n",
        "\n",
        "top_installed_and_rated_apps.head(10)"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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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 Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>821</th>\n",
              "      <td>Google</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.408893</td>\n",
              "      <td>10870728.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>YouTube</td>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>4.368428</td>\n",
              "      <td>41919102.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2177</th>\n",
              "      <td>Maps - Navigate &amp; Explore</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>4.342798</td>\n",
              "      <td>10083666.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7064</th>\n",
              "      <td>Clean Master - Antivirus, Applock &amp; Cleaner</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.657038</td>\n",
              "      <td>44171776.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>7.0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6411</th>\n",
              "      <td>Google Duo - High Quality Video Calls</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.596404</td>\n",
              "      <td>3641252.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>4.4 and up</td>\n",
              "      <td>50.1.240885383.DR50_RC06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3269</th>\n",
              "      <td>SHAREit - Transfer &amp; Share</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.579340</td>\n",
              "      <td>10450444.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>4.7.38_ww</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1259</th>\n",
              "      <td>Google Photos</td>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>4.542380</td>\n",
              "      <td>16278468.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>653</th>\n",
              "      <td>Instagram</td>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>4.519560</td>\n",
              "      <td>79726403.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>539</th>\n",
              "      <td>Subway Surfers</td>\n",
              "      <td>GAME_ARCADE</td>\n",
              "      <td>4.498131</td>\n",
              "      <td>29834812.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>85</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>March 7, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>1.100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3267</th>\n",
              "      <td>Samsung Internet Browser</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.424015</td>\n",
              "      <td>832714.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>February 21, 2019</td>\n",
              "      <td>5.0 and up</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         App Name  ...            Latest Version\n",
              "821                                        Google  ...        Varies with device\n",
              "813                                       YouTube  ...        Varies with device\n",
              "2177                    Maps - Navigate & Explore  ...        Varies with device\n",
              "7064  Clean Master - Antivirus, Applock & Cleaner  ...                     7.0.6\n",
              "6411        Google Duo - High Quality Video Calls  ...  50.1.240885383.DR50_RC06\n",
              "3269                   SHAREit - Transfer & Share  ...                 4.7.38_ww\n",
              "1259                                Google Photos  ...        Varies with device\n",
              "653                                     Instagram  ...        Varies with device\n",
              "539                                Subway Surfers  ...                   1.100.0\n",
              "3267                     Samsung Internet Browser  ...        Varies with device\n",
              "\n",
              "[10 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pF0rGPhMcNtb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "outputId": "e5fb8f70-b49a-4dce-880c-bd1d8f91afc6"
      },
      "source": [
        "top_installed_and_reviewed_apps = app.sort_values(by=[\"Installs\", \"Reviews\"], ascending=False)\n",
        "top_installed_and_reviewed_apps"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>YouTube</td>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>4.368428</td>\n",
              "      <td>41919102.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>821</th>\n",
              "      <td>Google</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.408893</td>\n",
              "      <td>10870728.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2177</th>\n",
              "      <td>Maps - Navigate &amp; Explore</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>4.342798</td>\n",
              "      <td>10083666.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>671</th>\n",
              "      <td>WhatsApp Messenger</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.417610</td>\n",
              "      <td>86214292.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 27, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>704</th>\n",
              "      <td>Facebook</td>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>4.087946</td>\n",
              "      <td>85766433.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249287</th>\n",
              "      <td>Rich &amp; Rich</td>\n",
              "      <td>LIFESTYLE</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>369.99</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 5, 2017</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>1.0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251001</th>\n",
              "      <td>Tamil Dictionary Offline &amp; Multilingual Transl...</td>\n",
              "      <td>BOOKS_AND_REFERENCE</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 24, 2019</td>\n",
              "      <td>5.0 and up</td>\n",
              "      <td>1.0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260161</th>\n",
              "      <td>Flugpreise Vergleichen &amp; G�nstige Fl�ge  Low Cost</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 25, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>264007</th>\n",
              "      <td>Fm Resplandecer Misiones</td>\n",
              "      <td>MUSIC_AND_AUDIO</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 26, 2019</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>265585</th>\n",
              "      <td>DEUTSCH WITZE 2019</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4.3</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>March 25, 2019</td>\n",
              "      <td>4.0 and up</td>\n",
              "      <td>1.0.3.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>267040 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 App Name  ...      Latest Version\n",
              "813                                               YouTube  ...  Varies with device\n",
              "821                                                Google  ...  Varies with device\n",
              "2177                            Maps - Navigate & Explore  ...  Varies with device\n",
              "671                                    WhatsApp Messenger  ...  Varies with device\n",
              "704                                              Facebook  ...  Varies with device\n",
              "...                                                   ...  ...                 ...\n",
              "249287                                        Rich & Rich  ...               1.0.0\n",
              "251001  Tamil Dictionary Offline & Multilingual Transl...  ...               1.0.0\n",
              "260161  Flugpreise Vergleichen & G�nstige Fl�ge  Low Cost  ...                   2\n",
              "264007                           Fm Resplandecer Misiones  ...                 1.1\n",
              "265585                                 DEUTSCH WITZE 2019  ...             1.0.3.2\n",
              "\n",
              "[267040 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "flf5pOQgcNtd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "380ab9b4-8f29-48b0-a87d-844f73a38e26"
      },
      "source": [
        "#**************************************************************************\n",
        "# top 10 apps based on the number of installations and reviews together\n",
        "#**************************************************************************\n",
        "\n",
        "top_installed_and_reviewed_apps.head(10)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>YouTube</td>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>4.368428</td>\n",
              "      <td>41919102.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>821</th>\n",
              "      <td>Google</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.408893</td>\n",
              "      <td>10870728.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2177</th>\n",
              "      <td>Maps - Navigate &amp; Explore</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>4.342798</td>\n",
              "      <td>10083666.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>671</th>\n",
              "      <td>WhatsApp Messenger</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.417610</td>\n",
              "      <td>86214292.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 27, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>704</th>\n",
              "      <td>Facebook</td>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>4.087946</td>\n",
              "      <td>85766433.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>653</th>\n",
              "      <td>Instagram</td>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>4.519560</td>\n",
              "      <td>79726403.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>632</th>\n",
              "      <td>Messenger � Text and Video Chat for Free</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.085856</td>\n",
              "      <td>65469531.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7064</th>\n",
              "      <td>Clean Master - Antivirus, Applock &amp; Cleaner</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.657038</td>\n",
              "      <td>44171776.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>7.0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>539</th>\n",
              "      <td>Subway Surfers</td>\n",
              "      <td>GAME_ARCADE</td>\n",
              "      <td>4.498131</td>\n",
              "      <td>29834812.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>85</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>March 7, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>1.100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1259</th>\n",
              "      <td>Google Photos</td>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>4.542380</td>\n",
              "      <td>16278468.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         App Name  ...      Latest Version\n",
              "813                                       YouTube  ...  Varies with device\n",
              "821                                        Google  ...  Varies with device\n",
              "2177                    Maps - Navigate & Explore  ...  Varies with device\n",
              "671                            WhatsApp Messenger  ...  Varies with device\n",
              "704                                      Facebook  ...  Varies with device\n",
              "653                                     Instagram  ...  Varies with device\n",
              "632      Messenger � Text and Video Chat for Free  ...  Varies with device\n",
              "7064  Clean Master - Antivirus, Applock & Cleaner  ...               7.0.6\n",
              "539                                Subway Surfers  ...             1.100.0\n",
              "1259                                Google Photos  ...  Varies with device\n",
              "\n",
              "[10 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVCmJ-3bcNtf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "top_10_installed_and_rated_apps= top_installed_and_rated_apps.head(10)"
      ],
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PI3hNCqFcNth",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "46173684-c653-46f6-cc64-1714b8110b0f"
      },
      "source": [
        "top_10_installed_and_rated_apps.Category.sort_values(ascending=False)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "813        VIDEO_PLAYERS\n",
              "2177    TRAVEL_AND_LOCAL\n",
              "3269               TOOLS\n",
              "7064               TOOLS\n",
              "821                TOOLS\n",
              "653               SOCIAL\n",
              "1259         PHOTOGRAPHY\n",
              "539          GAME_ARCADE\n",
              "3267       COMMUNICATION\n",
              "6411       COMMUNICATION\n",
              "Name: Category, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t145Tr7wcNtj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2861b8a4-b6cf-4a41-a023-927f41b72ad2"
      },
      "source": [
        "# There are totally 244,396 apps\n",
        "app[\"App Name\"].nunique()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "244396"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NiboSUbjcNtl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "0cbb008f-9ad6-4caf-cfed-c9c4380a1ff5"
      },
      "source": [
        "# here i will see the number of apps which belong to the categories of the most installed and rated apps\n",
        "\n",
        "\n",
        "count_VIDEO_PLAYERS=0\n",
        "count_TRAVEL_AND_LOCAL=0\n",
        "count_TOOLS=0\n",
        "count_SOCIAL=0\n",
        "count_PHOTOGRAPHY=0\n",
        "count_GAME_ARCADE=0\n",
        "count_COMMUNICATION=0\n",
        "\n",
        "for x in app[\"Category\"]:\n",
        "    if x== \"VIDEO_PLAYERS\":\n",
        "        count_VIDEO_PLAYERS=count_VIDEO_PLAYERS+1\n",
        "    elif x== \"TRAVEL_AND_LOCAL\":\n",
        "        count_TRAVEL_AND_LOCAL= count_TRAVEL_AND_LOCAL+1\n",
        "    elif x== \"TOOLS\":\n",
        "        count_TOOLS= count_TOOLS+1\n",
        "    elif x== \"SOCIAL\":\n",
        "        count_SOCIAL= count_SOCIAL+1\n",
        "    elif x== \"PHOTOGRAPHY\":\n",
        "        count_PHOTOGRAPHY= count_PHOTOGRAPHY+1\n",
        "    elif x== \"GAME_ARCADE\":\n",
        "        count_GAME_ARCADE= count_GAME_ARCADE+1\n",
        "    elif x== \"COMMUNICATION\":\n",
        "        count_COMMUNICATION= count_COMMUNICATION+1\n",
        "print (\"*****************************************************************************************************\")\n",
        "print (\"*****************************************************************************************************\")\n",
        "print (\"Number of apps that belong in category: \\\"Video Players\\\" is: {}\".format(count_VIDEO_PLAYERS))\n",
        "print (\"*****************************************************************************************************\")\n",
        "print (\"Number of apps that belong in category: \\\"Travel and Local\\\" is: {}\".format(count_TRAVEL_AND_LOCAL))\n",
        "print (\"*****************************************************************************************************\")\n",
        "print (\"Number of apps that belong in category: \\\"Tools\\\" is: {}\".format(count_TOOLS))\n",
        "print (\"*****************************************************************************************************\")\n",
        "print (\"Number of apps that belong in category: \\\"Social\\\" is: {}\".format(count_SOCIAL))\n",
        "print (\"*****************************************************************************************************\")\n",
        "print (\"Number of apps that belong in category: \\\"Photography\\\" is: {}\".format(count_PHOTOGRAPHY))\n",
        "print (\"*****************************************************************************************************\")\n",
        "print (\"Number of apps that belong in category: \\\"Game Arcade\\\" is: {}\".format(count_GAME_ARCADE))\n",
        "print (\"*****************************************************************************************************\")\n",
        "print (\"Number of apps that belong in category: \\\"Communication\\\" is: {}\".format(count_COMMUNICATION))\n",
        "print (\"*****************************************************************************************************\")\n",
        "print (\"*****************************************************************************************************\")\n"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************************************\n",
            "*****************************************************************************************************\n",
            "Number of apps that belong in category: \"Video Players\" is: 2717\n",
            "*****************************************************************************************************\n",
            "Number of apps that belong in category: \"Travel and Local\" is: 6650\n",
            "*****************************************************************************************************\n",
            "Number of apps that belong in category: \"Tools\" is: 21591\n",
            "*****************************************************************************************************\n",
            "Number of apps that belong in category: \"Social\" is: 4745\n",
            "*****************************************************************************************************\n",
            "Number of apps that belong in category: \"Photography\" is: 7240\n",
            "*****************************************************************************************************\n",
            "Number of apps that belong in category: \"Game Arcade\" is: 2343\n",
            "*****************************************************************************************************\n",
            "Number of apps that belong in category: \"Communication\" is: 5486\n",
            "*****************************************************************************************************\n",
            "*****************************************************************************************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VC7oIYW-cNtn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "000c31f7-89a4-4ae9-8b34-899aac2d113e"
      },
      "source": [
        "top_10_installed_and_rated_apps[\"Content Rating\"].sort_values(ascending=False)"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "653             Teen\n",
              "813             Teen\n",
              "539     Everyone 10+\n",
              "3267        Everyone\n",
              "1259        Everyone\n",
              "3269        Everyone\n",
              "6411        Everyone\n",
              "7064        Everyone\n",
              "2177        Everyone\n",
              "821         Everyone\n",
              "Name: Content Rating, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Bp4Bzb8cNto",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "15fed55a-7ab7-4571-e33c-00abf5d08d06"
      },
      "source": [
        "app[\"Content Rating\"].nunique()"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKH1MJ6rcNtq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "94741e35-de6b-4afa-be03-8067f3421cc2"
      },
      "source": [
        "# There are totally 6 categories of content rating\n",
        "\n",
        "# In the top 10 installed and rated apps, there are 3 different content ratings\n",
        "\n",
        "# I will now see their performance in the whole dataset, along with the other 3 remaining content ratings in the whole dataset\n",
        "\n",
        "count_Teen=0\n",
        "count_Everyone_10 = 0\n",
        "count_Everyone=0\n",
        "\n",
        "count_Mature_17=0\n",
        "count_Adults_only=0\n",
        "count_Unrated=0\n",
        "\n",
        "\n",
        "for x in app[\"Content Rating\"]:\n",
        "    if x== \"Teen\":\n",
        "        count_Teen= count_Teen+1\n",
        "    elif x== \"Everyone 10+\":\n",
        "        count_Everyone_10= count_Everyone_10+1\n",
        "    elif x== \"Everyone\":\n",
        "        count_Everyone= count_Everyone+1\n",
        "    elif x== \"Mature 17+\":\n",
        "        count_Mature_17 = count_Mature_17+1\n",
        "    elif x== \"Adults only 18+\":\n",
        "        count_Adults_only= count_Adults_only+1\n",
        "    elif x== \"Unrated\":\n",
        "        count_Unrated= count_Unrated+1\n",
        "print (\"*****************************************************************************************************\")\n",
        "print (\"Number of apps of all the dataset, having the content rating which belong the top apps:\")\n",
        "print (\"*\")\n",
        "print (\"*\")\n",
        "print (\"Number of apps that belong to the content rating \\\"Teen\\\" is: {}\".format(count_Teen))\n",
        "print (\"*****************************************************************************************************\")\n",
        "print (\"Number of apps that belong to the content rating \\\"Everyone 10+\\\" is: {}\".format(count_Everyone_10))\n",
        "print (\"*****************************************************************************************************\")\n",
        "print (\"Number of apps that belong to the content rating \\\"Everyone\\\" is: {}\".format(count_Everyone))\n",
        "print (\"*****************************************************************************************************\")\n",
        "print (\"*****************************************************************************************************\")\n",
        "print (\"#####################################################################################################\")\n",
        "print (\"Number of apps having content rating not included in the top apps\")\n",
        "print (\"*\")\n",
        "print (\"*\")\n",
        "print (\"Number of apps that belong to the content rating \\\"Mature 17+\\\" is: {}\".format(count_Mature_17))\n",
        "print (\"Number of apps that belong to the content rating \\\"Adults only 18+\\\" is: {}\".format(count_Adults_only))\n",
        "print (\"Number of apps that belong to the content rating \\\"Unrated\\\" is: {}\".format(count_Unrated))\n",
        "\n"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************************************\n",
            "Number of apps of all the dataset, having the content rating which belong the top apps:\n",
            "*\n",
            "*\n",
            "Number of apps that belong to the content rating \"Teen\" is: 17263\n",
            "*****************************************************************************************************\n",
            "Number of apps that belong to the content rating \"Everyone 10+\" is: 4661\n",
            "*****************************************************************************************************\n",
            "Number of apps that belong to the content rating \"Everyone\" is: 241582\n",
            "*****************************************************************************************************\n",
            "*****************************************************************************************************\n",
            "#####################################################################################################\n",
            "Number of apps having content rating not included in the top apps\n",
            "*\n",
            "*\n",
            "Number of apps that belong to the content rating \"Mature 17+\" is: 3489\n",
            "Number of apps that belong to the content rating \"Adults only 18+\" is: 12\n",
            "Number of apps that belong to the content rating \"Unrated\" is: 33\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYjZLFVEcNts",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "2b8b2199-6409-4674-9f0b-bef88e2a4a26"
      },
      "source": [
        "# The aforementioned can be found more easily with the below command\n",
        "\n",
        "app[\"Content Rating\"].value_counts(ascending=False)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Everyone           241582\n",
              "Teen                17263\n",
              "Everyone 10+         4661\n",
              "Mature 17+           3489\n",
              "Unrated                33\n",
              "Adults only 18+        12\n",
              "Name: Content Rating, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OuSn3rvUcNtw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "b4b69234-3796-4e90-fcbb-4d5c460b0031"
      },
      "source": [
        "# In this and in the next 2 commands, i will try to see if there is any correlation between installations, Rating and Reviews\n",
        "\n",
        "top_10_installed_and_rated_apps"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>821</th>\n",
              "      <td>Google</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.408893</td>\n",
              "      <td>10870728.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>YouTube</td>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>4.368428</td>\n",
              "      <td>41919102.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2177</th>\n",
              "      <td>Maps - Navigate &amp; Explore</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>4.342798</td>\n",
              "      <td>10083666.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7064</th>\n",
              "      <td>Clean Master - Antivirus, Applock &amp; Cleaner</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.657038</td>\n",
              "      <td>44171776.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>7.0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6411</th>\n",
              "      <td>Google Duo - High Quality Video Calls</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.596404</td>\n",
              "      <td>3641252.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>4.4 and up</td>\n",
              "      <td>50.1.240885383.DR50_RC06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3269</th>\n",
              "      <td>SHAREit - Transfer &amp; Share</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.579340</td>\n",
              "      <td>10450444.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>4.7.38_ww</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1259</th>\n",
              "      <td>Google Photos</td>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>4.542380</td>\n",
              "      <td>16278468.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>653</th>\n",
              "      <td>Instagram</td>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>4.519560</td>\n",
              "      <td>79726403.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>539</th>\n",
              "      <td>Subway Surfers</td>\n",
              "      <td>GAME_ARCADE</td>\n",
              "      <td>4.498131</td>\n",
              "      <td>29834812.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>85</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>March 7, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>1.100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3267</th>\n",
              "      <td>Samsung Internet Browser</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.424015</td>\n",
              "      <td>832714.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>February 21, 2019</td>\n",
              "      <td>5.0 and up</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         App Name  ...            Latest Version\n",
              "821                                        Google  ...        Varies with device\n",
              "813                                       YouTube  ...        Varies with device\n",
              "2177                    Maps - Navigate & Explore  ...        Varies with device\n",
              "7064  Clean Master - Antivirus, Applock & Cleaner  ...                     7.0.6\n",
              "6411        Google Duo - High Quality Video Calls  ...  50.1.240885383.DR50_RC06\n",
              "3269                   SHAREit - Transfer & Share  ...                 4.7.38_ww\n",
              "1259                                Google Photos  ...        Varies with device\n",
              "653                                     Instagram  ...        Varies with device\n",
              "539                                Subway Surfers  ...                   1.100.0\n",
              "3267                     Samsung Internet Browser  ...        Varies with device\n",
              "\n",
              "[10 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tx1JvwobcNty",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "26db501a-b9ab-435a-e3d9-9cd15ec01fad"
      },
      "source": [
        "#****************************************************************************************************************\n",
        "# It seems that none of the best rated apps belong to the top installed (filtered by rating too) apps\n",
        "#****************************************************************************************************************\n",
        "app.sort_values(by=\"Rating\", ascending= False).head(10)"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>122315</th>\n",
              "      <td>Ocean 100</td>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>500</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>August 9, 2018</td>\n",
              "      <td>4.4 and up</td>\n",
              "      <td>9.6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166821</th>\n",
              "      <td>BuWizz Beta</td>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>500</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>January 11, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217017</th>\n",
              "      <td>Vitral: Loja Online</td>\n",
              "      <td>SHOPPING</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>7.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>January 14, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>1.0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113166</th>\n",
              "      <td>??????????????? ????? ?? ?????</td>\n",
              "      <td>BOOKS_AND_REFERENCE</td>\n",
              "      <td>5.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1000</td>\n",
              "      <td>3.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 24, 2019</td>\n",
              "      <td>4.0 and up</td>\n",
              "      <td>1.19.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166831</th>\n",
              "      <td>BoxMagic Business</td>\n",
              "      <td>SPORTS</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10</td>\n",
              "      <td>32</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 2, 2019</td>\n",
              "      <td>4.4 and up</td>\n",
              "      <td>0.1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52583</th>\n",
              "      <td>????? ??????</td>\n",
              "      <td>NEWS_AND_MAGAZINES</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1000</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>September 26, 2017</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>236288</th>\n",
              "      <td>Cocotte ??????</td>\n",
              "      <td>SHOPPING</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>100</td>\n",
              "      <td>9.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>September 21, 2018</td>\n",
              "      <td>4.3 and up</td>\n",
              "      <td>2.33.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195735</th>\n",
              "      <td>?????? ???? ?????</td>\n",
              "      <td>BOOKS_AND_REFERENCE</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>500</td>\n",
              "      <td>3.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>February 1, 2019</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "      <td>1.0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217012</th>\n",
              "      <td>Mobeely</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>100</td>\n",
              "      <td>5.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>February 17, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>3.9.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>236289</th>\n",
              "      <td>Meedori????????</td>\n",
              "      <td>SHOPPING</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>100</td>\n",
              "      <td>9.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>September 21, 2018</td>\n",
              "      <td>4.3 and up</td>\n",
              "      <td>2.33.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              App Name  ...      Latest Version\n",
              "122315                       Ocean 100  ...               9.6.0\n",
              "166821                     BuWizz Beta  ...  Varies with device\n",
              "217017             Vitral: Loja Online  ...               1.0.0\n",
              "113166  ??????????????? ????? ?? ?????  ...              1.19.2\n",
              "166831               BoxMagic Business  ...               0.1.1\n",
              "52583                     ????? ??????  ...                 1.3\n",
              "236288                  Cocotte ??????  ...              2.33.5\n",
              "195735               ?????? ???? ?????  ...               1.0.0\n",
              "217012                         Mobeely  ...               3.9.3\n",
              "236289                 Meedori????????  ...              2.33.5\n",
              "\n",
              "[10 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TCwin5KHcNt0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "outputId": "5e4d5f3a-7762-45ef-a755-c4ee91c2daef"
      },
      "source": [
        "#****************************************************************************************************************\n",
        "\n",
        "# Relationship between Reviews and main top apps\n",
        "\n",
        "# It seems that Instagram, Clean Master - Antivirus, Youtube and Subway Surfers\n",
        "\n",
        "# The above 4 apps belong to the top installed (filtered by rating too) and simultaneously to the top reviewed apps\n",
        "\n",
        "# So there is correlation of 4 out of 10 apps respecting top installed-rated apps and top reviewed apps\n",
        "#****************************************************************************************************************\n",
        "\n",
        "app.sort_values(by=\"Reviews\", ascending= False).head(10)"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>671</th>\n",
              "      <td>WhatsApp Messenger</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.417610</td>\n",
              "      <td>86214292.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 27, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>704</th>\n",
              "      <td>Facebook</td>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>4.087946</td>\n",
              "      <td>85766433.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>653</th>\n",
              "      <td>Instagram</td>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>4.519560</td>\n",
              "      <td>79726403.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>632</th>\n",
              "      <td>Messenger � Text and Video Chat for Free</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.085856</td>\n",
              "      <td>65469531.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>628</th>\n",
              "      <td>Clash of Clans</td>\n",
              "      <td>GAME_STRATEGY</td>\n",
              "      <td>4.606215</td>\n",
              "      <td>48401470.0</td>\n",
              "      <td>500000000</td>\n",
              "      <td>103</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>11.446.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7064</th>\n",
              "      <td>Clean Master - Antivirus, Applock &amp; Cleaner</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.657038</td>\n",
              "      <td>44171776.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>7.0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>YouTube</td>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>4.368428</td>\n",
              "      <td>41919102.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>539</th>\n",
              "      <td>Subway Surfers</td>\n",
              "      <td>GAME_ARCADE</td>\n",
              "      <td>4.498131</td>\n",
              "      <td>29834812.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>85</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>March 7, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>1.100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12638</th>\n",
              "      <td>Security Master - Antivirus, VPN, AppLock, Boo...</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.652842</td>\n",
              "      <td>25532160.0</td>\n",
              "      <td>500000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 29, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>4.9.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1542</th>\n",
              "      <td>Clash Royale</td>\n",
              "      <td>GAME_STRATEGY</td>\n",
              "      <td>4.545474</td>\n",
              "      <td>25449254.0</td>\n",
              "      <td>100000000</td>\n",
              "      <td>81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>February 13, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>2.6.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                App Name  ...      Latest Version\n",
              "671                                   WhatsApp Messenger  ...  Varies with device\n",
              "704                                             Facebook  ...  Varies with device\n",
              "653                                            Instagram  ...  Varies with device\n",
              "632             Messenger � Text and Video Chat for Free  ...  Varies with device\n",
              "628                                       Clash of Clans  ...           11.446.11\n",
              "7064         Clean Master - Antivirus, Applock & Cleaner  ...               7.0.6\n",
              "813                                              YouTube  ...  Varies with device\n",
              "539                                       Subway Surfers  ...             1.100.0\n",
              "12638  Security Master - Antivirus, VPN, AppLock, Boo...  ...               4.9.1\n",
              "1542                                        Clash Royale  ...               2.6.1\n",
              "\n",
              "[10 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4gy2bi0cNt2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "a4258078-625a-4ac8-d2a9-79bf0be4b561"
      },
      "source": [
        "top_10_installed_and_rated_apps"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>821</th>\n",
              "      <td>Google</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.408893</td>\n",
              "      <td>10870728.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>YouTube</td>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>4.368428</td>\n",
              "      <td>41919102.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2177</th>\n",
              "      <td>Maps - Navigate &amp; Explore</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>4.342798</td>\n",
              "      <td>10083666.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7064</th>\n",
              "      <td>Clean Master - Antivirus, Applock &amp; Cleaner</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.657038</td>\n",
              "      <td>44171776.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>7.0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6411</th>\n",
              "      <td>Google Duo - High Quality Video Calls</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.596404</td>\n",
              "      <td>3641252.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>4.4 and up</td>\n",
              "      <td>50.1.240885383.DR50_RC06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3269</th>\n",
              "      <td>SHAREit - Transfer &amp; Share</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.579340</td>\n",
              "      <td>10450444.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>4.7.38_ww</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1259</th>\n",
              "      <td>Google Photos</td>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>4.542380</td>\n",
              "      <td>16278468.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>653</th>\n",
              "      <td>Instagram</td>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>4.519560</td>\n",
              "      <td>79726403.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>539</th>\n",
              "      <td>Subway Surfers</td>\n",
              "      <td>GAME_ARCADE</td>\n",
              "      <td>4.498131</td>\n",
              "      <td>29834812.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>85</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>March 7, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>1.100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3267</th>\n",
              "      <td>Samsung Internet Browser</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.424015</td>\n",
              "      <td>832714.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>February 21, 2019</td>\n",
              "      <td>5.0 and up</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         App Name  ...            Latest Version\n",
              "821                                        Google  ...        Varies with device\n",
              "813                                       YouTube  ...        Varies with device\n",
              "2177                    Maps - Navigate & Explore  ...        Varies with device\n",
              "7064  Clean Master - Antivirus, Applock & Cleaner  ...                     7.0.6\n",
              "6411        Google Duo - High Quality Video Calls  ...  50.1.240885383.DR50_RC06\n",
              "3269                   SHAREit - Transfer & Share  ...                 4.7.38_ww\n",
              "1259                                Google Photos  ...        Varies with device\n",
              "653                                     Instagram  ...        Varies with device\n",
              "539                                Subway Surfers  ...                   1.100.0\n",
              "3267                     Samsung Internet Browser  ...        Varies with device\n",
              "\n",
              "[10 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jul-iugwcNt4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "71e0a7f6-fd01-4373-cf89-8645473aeddb"
      },
      "source": [
        "# Prices of the apps\n",
        "\n",
        "app[\"Price\"].value_counts().sort_values(ascending=False).head(10)"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00    255434\n",
              "0.99      2317\n",
              "1.99      1552\n",
              "2.99      1351\n",
              "4.99       883\n",
              "3.99       767\n",
              "1.49       761\n",
              "2.49       518\n",
              "3.49       339\n",
              "9.99       275\n",
              "Name: Price, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-XDPPmjcNt7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b92f19e7-89ec-4b1e-f703-e39f2fe0fd0b"
      },
      "source": [
        "app.Price.nunique()"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "488"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPigHvshcNuI",
        "colab_type": "text"
      },
      "source": [
        "# Visualising  Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rtm3GkUscNuJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "917acc95-2b41-4ab0-9c1e-e31442ed5c33"
      },
      "source": [
        "app.head(2)"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DoorDash - Food Delivery</td>\n",
              "      <td>FOOD_AND_DRINK</td>\n",
              "      <td>4.548562</td>\n",
              "      <td>305034.0</td>\n",
              "      <td>5000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 29, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TripAdvisor Hotels Flights Restaurants Attract...</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>4.400671</td>\n",
              "      <td>1207922.0</td>\n",
              "      <td>100000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>March 29, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            App Name  ...      Latest Version\n",
              "0                           DoorDash - Food Delivery  ...  Varies with device\n",
              "1  TripAdvisor Hotels Flights Restaurants Attract...  ...  Varies with device\n",
              "\n",
              "[2 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3fxapDrkcNuL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ziFbi0uKcNuN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "a99653d2-8acb-4859-88cf-8426996c8f9d"
      },
      "source": [
        "# Top 5 app Categories of all the dataset\n",
        "\n",
        "\n",
        "app[\"Category\"].value_counts().nlargest(5).sort_values(ascending=True).plot.barh()\n",
        "plt.ylabel(\"Categories\")\n",
        "plt.xlabel(\"Count\")\n",
        "plt.title(\"Google Playstore - Top 5 App Categories\")\n",
        "plt.show()"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMT-UGxfcNuP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "1489fc95-5fda-4ba5-d983-7bfa5bc9f1c9"
      },
      "source": [
        "app[\"Category\"].value_counts().nlargest(5).sort_values(ascending=False)"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EDUCATION              33394\n",
              "TOOLS                  21591\n",
              "BOOKS_AND_REFERENCE    21377\n",
              "ENTERTAINMENT          20603\n",
              "MUSIC_AND_AUDIO        17879\n",
              "Name: Category, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YY8RB4PrcNuR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "bae1426f-cfbc-4806-bd9e-f3f9271eff04"
      },
      "source": [
        "# In which category do main 100 top apps belong\n",
        "\n",
        "top_installed_and_rated_apps[\"Category\"].head(100).value_counts()"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PRODUCTIVITY           17\n",
              "TOOLS                  16\n",
              "COMMUNICATION          11\n",
              "VIDEO_PLAYERS           6\n",
              "SOCIAL                  6\n",
              "PHOTOGRAPHY             4\n",
              "GAME_ACTION             4\n",
              "GAME_CASUAL             4\n",
              "NEWS_AND_MAGAZINES      4\n",
              "TRAVEL_AND_LOCAL        3\n",
              "GAME_RACING             3\n",
              "PERSONALIZATION         3\n",
              "ENTERTAINMENT           3\n",
              "GAME_ARCADE             2\n",
              "GAME_SPORTS             2\n",
              "SHOPPING                2\n",
              "HEALTH_AND_FITNESS      2\n",
              "MUSIC_AND_AUDIO         2\n",
              "BOOKS_AND_REFERENCE     2\n",
              "GAME_STRATEGY           1\n",
              "EDUCATION               1\n",
              "LIFESTYLE               1\n",
              "GAME_SIMULATION         1\n",
              "Name: Category, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndM8jr0tcNuU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "70f7dd74-3306-401a-cb44-11e8b65323e2"
      },
      "source": [
        "status= (\"PRODUCTIVITY\", \"TOOLS\", \"COMMUNICATION\", \"SOCIAL\")\n",
        "y_pos= np.arange(len(status))\n",
        "numbers= [17,16,11,6]\n",
        "plt.bar(y_pos, numbers, align=\"center\", alpha=0.6)\n",
        "plt.xticks(y_pos, status)\n",
        "plt.ylabel(\"Count\")\n",
        "plt.title(\"Categories - (Main) Top 100 Apps\")\n",
        "plt.show()"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVQLhceTcNuW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x=top_installed_and_rated_apps.head(100)"
      ],
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwtSVQFwcNuY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "outputId": "0ccba27e-e719-4607-c4b2-e6eee2697e20"
      },
      "source": [
        "# Relationship betweeen: Classes and number of Installations\n",
        "\n",
        "\n",
        "app[\"Installs\"].value_counts().sort_values(ascending=False).plot.bar()\n",
        "plt.ylabel(\"Number of Installations\")\n",
        "plt.xlabel(\"Classes of Installations\")\n",
        "plt.title(\"Google Playstore - Grading in Number of Installations\")\n",
        "plt.show()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7bR85tdHcNuc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "c47c0db2-d81c-4639-f5f1-f59cc432bdd5"
      },
      "source": [
        "# Top 5 Gradings in the number of installations\n",
        "\n",
        "app[\"Installs\"].value_counts().nlargest(5)"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000     60533\n",
              "1000      48884\n",
              "100000    37499\n",
              "5000      26361\n",
              "50000     22794\n",
              "Name: Installs, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EVfUU0wQcNue",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "dd7f0d48-00cb-4898-fbd2-e910974cad3b"
      },
      "source": [
        "app[\"Price\"].value_counts().nlargest(5).sort_values(ascending=False).plot.bar()\n",
        "plt.ylabel(\"Number of Apps\")\n",
        "plt.xlabel(\"Prices in Dollars\")\n",
        "plt.title(\"Google Playstore - Prices\")\n",
        "plt.show()"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wSOxnTU9cNug",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "bbb2b617-f5d1-43aa-c4d6-2b4c2250ce0e"
      },
      "source": [
        "app[\"Price\"].value_counts().nlargest(5)"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00    255434\n",
              "0.99      2317\n",
              "1.99      1552\n",
              "2.99      1351\n",
              "4.99       883\n",
              "Name: Price, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kypRkDvlcNui",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "869ec711-2106-4a25-b6b1-d455514a1bb7"
      },
      "source": [
        "app[\"Content Rating\"].value_counts().sort_values(ascending=False).plot.bar()\n",
        "plt.ylabel(\"Number of Apps\")\n",
        "plt.xlabel(\"Content Rating\")\n",
        "plt.title(\"Google Playstore - Content Rating\")\n",
        "plt.show()"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "22oJe917cNul",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "2e5920fd-da9c-4625-d7e3-58065f5d38cf"
      },
      "source": [
        "app[\"Content Rating\"].value_counts()"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Everyone           241582\n",
              "Teen                17263\n",
              "Everyone 10+         4661\n",
              "Mature 17+           3489\n",
              "Unrated                33\n",
              "Adults only 18+        12\n",
              "Name: Content Rating, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FlOKebmzcNun",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "6b304571-7ca9-4c79-ab8a-71c8008d74d9"
      },
      "source": [
        "top_installed_and_rated_apps[\"Content Rating\"].head(100).value_counts()"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Everyone        69\n",
              "Teen            24\n",
              "Everyone 10+     5\n",
              "Mature 17+       2\n",
              "Name: Content Rating, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iTiLG8n8cNup",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#####################"
      ],
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gASv1bUBcNut",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "b2ba949a-6469-42b0-b40d-cafbf9e74b8a"
      },
      "source": [
        "top_installed_and_rated_apps.head(100).Installs.value_counts(ascending=False) "
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100000000     38\n",
              "500000000     35\n",
              "1000000000    24\n",
              "5000000000     3\n",
              "Name: Installs, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i04l27BucNuw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "5568d4cb-d6f9-462e-8d8b-fbf9bfc3269b"
      },
      "source": [
        "app_category= top_installed_and_rated_apps.head(100).Installs\n",
        "app_category.plot.density().set_yscale(\"log\")"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qnijynBTcNuy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "4395d242-dcd9-440f-ffb3-17613b1c0e1b"
      },
      "source": [
        "app_category= top_installed_and_rated_apps.head(100).Rating\n",
        "app_category.plot.density().set_yscale(\"log\")"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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e1V4FqChCRnuXj399YRsTMkfx2YsLvY4jEtRykuP5zNwCnl1fyf567VWoKELEz9/aS0X9Mf75umnEROl/q8i5uu+KCUREmOYqUFGEhJqmNn70ajlXF2dz2eRMr+OIhITspDjtVfRSUYSA767cga/b8Q/XFnsdRSSkaK+ih4oiyO081MIz6yq5ff44CtK1TIfIcNJeRQ8VRZD7z5d2MComivuvnOh1FJGQdGKv4qE3dnsdxTMqiiBWWnGEV8oOce8VE3QGtoifZCfF8cnZ+Ty7vpLalvC8uJGKIkg513OhlazEWD63oNDrOCIh7Z7LxtPp6+axdyq8juKJgC8KM5tqZj81s2fM7D6v8wSKVWW1lO5r4EtXTSYhJsrrOCIhrShjFIumjeHxd/dxtL3L6zgjzq9FYWa/NLNaM9vSb/siM9thZuVm9tWB7sM5V+acuxf4FLDAn3mDhXOOH6zaRUFaAjeVaAlxkZFw7+UTaGnr4on39nsdZcT5e4/iMWBR3w1mFgn8GLgGKAZuMbNiM5tuZsv7fWX1/s4S4I/ACj/nDQqv7ahlc1UTD1w5kWhdtU5kRMwcm8JF49P4xdt76ejq9jrOiPLru4xz7k2g/0Vo5wLlzrk9zrkO4ElgqXNus3Nucb+v2t77Weacuwa41Z95g0HP3kQ5+anxfGJ2ntdxRMLKvZdPoKa5jT9srPI6yojy4uNoHtD3WoOVvdtOycyuMLMfmtnDDLBHYWb3mFmpmZXW1dUNX9oA88bOOj440Mj92psQGXGXT85kSnYij75TgXPhc23tgH+ncc697pz7onPur51zPx7gdo8450qccyWZmaG5jMWJuYm8lHg+OVtzEyIjzcy4c0Eh2w42s7aiwes4I8aLoqgCxvb5Pr93mwzinfJ6Nuxv5L4rJmjhPxGPXD8rj+T4aB4Lo8ulevFusxaYZGZFZhYD3Aws8yBH0PnpG7vJSozVkU4iHoqPieTmuWN5eeshqhqPex1nRPj78NgngDXAFDOrNLO7nXNdwAPAy0AZ8LRzbqs/c4SCLVVNvF1+mM8tKCI2KtLrOCJh7faLxuGc4/E1+7yOMiL8eqaWc+6W02xfgQ51PSM/e2sPo2Ii+cy8Aq+jiIS9/NQEri4ew5Nr9/M3H51EfExof3jTQHcQqGw4xvJNB7llbgHJ8dFexxER4M4FhTQe6wyLQ2VVFEHg0XcqMOCuS4q8jiIiveYVpTE1J4nHVof+obIqigDXdLyTJ9/fz3Uzc8lNifc6joj0MjNuv2gc22ta2HCg0es4fqWiCHBPvL+f1g4ff3XpeK+jiEg/S2blMiomkt+G+PpPKooA5uvuOapi/vh0inOTvI4jIv2Mjo1iyaw8lm+qpul4p9dx/EZFEcBe3V5LVeNxPnvxOK+jiMhpfGZuAW2d3SE9qa2iCGC/Wl1BbnIcV03N9jqKiJzG9Pxkzs9L4rfv7Q/ZSW0VRYAqr23h7fLD3HrROKK0+J9IQLtlbkFIT2rrHShAPb5mHzGREdx84djBbywinloyM5eEmMiQvaiRiiIAtbR18sy6ShbPzCF9dKzXcURkEIlx0SyZmcsLITqpraIIQM9vqKK1w8dn5xd6HUVEhuiW3knt5ZuqvY4y7FQUAebEQmMz85OZOTbF6zgiMkQz8pOZlDWaZ9dVeh1l2A2pKMzsOTO71sxULH62fn8Du2qPcus8HRIrEkzMjE/OyWf9/kb21B31Os6wGuob/0+AzwC7zOzbZjbFj5nC2pPvH2BUTCTXzsjxOoqInKFPXJBHhMFz60PrnIohFYVz7hXn3K3AbKACeMXMVpvZ58xMy5kOk5a2TpZvOsiSWXmMivXrCvAi4gfZSXFcOimT59ZX0t0dOudUDHkoyczSgTuBzwMbgB/QUxwr/ZIsDC37oJrjnT4dEisSxD45J5/qpjbW7Kn3OsqwGeocxfPAW0ACcJ1zbolz7inn3P8CRvszYDh5au0BzhuTyIz8ZK+jiMhZuro4m8S4qJCa1B7qHsXPnHPFzrkHnXMHAcwsFsA5V+K3dGFka3UTmyqbuPnCsZiZ13FE5CzFRUeyeEYuL26p4Wh7l9dxhsVQi+Jbp9i2ZjiDhLun1x4gJiqC6y/I8zqKiJyjG+fkcbzTx4rNB72OMiwGnDE1szFAHhBvZhcAJz7qJtEzDCXDoK3Tx/Mbqvj4+WNISYjxOo6InKPZBakUpifwh41VfKok+OccBzu05mP0TGDnA9/ts70F+LqfMoWdP207RHNbV0i8oESk55yKJbPy+J9Xd1Hb3EZWUpzXkc7JgENPzrlfOeeuBO50zl3Z52uJc+65EcoY8p5fX0luchwXjU/3OoqIDJMlM3NxDpZvCv7hp8GGnm5zzv0GKDSzv+3/c+fcd0/xa3IG6lraeXPXYf76svFERGgSWyRUTMwaTXFOEss+qOauS4q8jnNOBpvMHtX752gg8RRfco6WfVCNr9txw2xNYouEmiWzctl4oJH99ce8jnJOBtyjcM493PvnN0YmTvh5bn0l0/OSmZil3hUJNdfNzOXbL27nhU3V3H/lRK/jnLWhnnD3n2aWZGbRZrbKzOrM7DZ/hwt1O2pa2FrdrL0JkRCVlxLPhYWpQX897aGeR3G1c64ZWEzPWk8Tgf/rr1Dh4rkNlURGGNfNzPU6ioj4yZKZuew8dJTtNc1eRzlrQy2KE0NU1wK/c841+SlP2PB1O/6woZrLJ2eSoavYiYSsj0/PITLCWLYxeC9oNNSiWG5m24E5wCozywTa/Bcr9K3ZXU9Nc5uGnURCXProWC6ZmMGyD6pxLjhXlB3qMuNfBS4GSpxznUArsNSfwU4wsyvM7C0z+6mZXTESjzkSfr+xisTYKK6amu11FBHxs2tn5FDZcJwtVcE5/HQmV6w7D/i0md0B3AhcPdgvmNkvzazWzLb0277IzHaYWbmZfXWQu3HAUSAOCInlGNu7fLy8tYaF07KJi470Oo6I+NnVxdlERRgrtgTnyXdDujqOmT0OTAA2Ar7ezQ749SC/+hjwo763M7NI4MfAQnre+Nea2TIgEniw3+/fBbzlnHvDzLLpWUbk1qFkDmRv7TxMS1sX183QJLZIOEhJiGH+hHRe3HyQr3xsStCtED3Uy6iVAMXuDAfYnHNvmllhv81zgXLn3B4AM3sSWOqce5Ceo6pOpwEIiVnf5ZuqSY6PZsHEDK+jiMgIueb8HL7+/Ga217QwNSfJ6zhnZKhDT1uAMcP0mHnAgT7fV/ZuOyUzu8HMHgYep2fv5HS3u8fMSs2stK6ubpiiDr+2Th8rtx1i0bQxxESdycifiASzq6dlE2Hw4pYar6OcsaHuUWQA28zsfaD9xEbn3BK/pOqjd/HBQRcgdM49AjwCUFJSErCHFry2vZbWDp/OnRAJMxmjY5lblMaLmw/ytwsnex3njAy1KP5lGB+zCui7nnZ+77awsHzTQdJHxXDR+DSvo4jICPv49Bz+6Q9bKa9tCaple4Z6eOwb9JyRHd3797XA+rN8zLXAJDMrMrMY4GZg2VneV1Bpbe9i1fZDXDN9DFGRGnYSCTcfm9Yzgv/i5uAafhrqWk9/BTwDPNy7KQ/4/RB+7wl6Lpk6xcwqzexu51wX8ADwMlAGPO2c23o24YPNqu21tHV2s1hHO4mEpeykOErGpbIiyOYphjr0dD89Ryu9B+Cc22VmWYP9knPultNsXwGsGGrIULH8g2qyEmO5sFDDTiLhatH5Y/jWH8uoONxKYcaowX8hAAx1/KPdOddx4hszi6LnPAoZopa2Tl7fWce1M3rWfRGR8HTN9BwguI5+GmpRvGFmXwfizWwh8DvgBf/FCj2vbq+lo6uba3tfJCISnvJS4pmRn8yftoVeUXwVqAM2A39Nz7DRP/grVCh6eWsNmYmxzC5I9TqKiHjsqqnZbDzQSG1LcKytOtSjnrrpmbz+gnPuRufcz870LO1w1tbp47XtdVxdnK3rYosIC4uzcQ5eLav1OsqQDFgU1uNfzOwwsAPY0Xt1u38amXih4a1dhzne6Tt5aJyIhLfzxiSSlxLPK2WHvI4yJIPtUXwZWABc6JxLc86lAfOABWb2Zb+nCxEvbakhKS6Ki8anex1FRAKAmbGwOLvnQ2SHb/Bf8NhgRXE7cItzbu+JDb2L+d0G3OHPYKGiy9fNqu2H+OjUbK3tJCInLSzOpr2rm7d2Be7adCcM9s4V7Zw73H+jc64OiPZPpNDy/t4jNB7r1LCTiHzI3KI0EuOigmL4abCi6DjLn0mvl7bWEBcdweWTM72OIiIBJDoygiunZLGqrBZfd2AfGzRYUcw0s+ZTfLUA00ciYDDr7nb8aeshLp+cSXyMrmQnIh92VXE29a0dbDzQ4HWUAQ1YFM65SOdc0im+Ep1zGnoaxAeVjdQ0t2nYSURO6YopmURFGCu3BfZhsppd9aOXtx4iKsL46HnZXkcRkQCUFBfNRePTWRngZ2mrKPzoT1trmD8hneQE7XyJyKktLM5md10re+qOeh3ltFQUfrKn7ih7Drdy1VTtTYjI6X3kvJ6FuF/bEbiHyaoo/OTV7T1jjideBCIipzI2LYGJWaN5fUfgzlOoKPxkVVktU7ITGZuW4HUUEQlwV07J5L09R2ht7/I6yimpKPyg6XgnayuO8JGp2psQkcFdOSWLDl83q3fXex3llFQUfvDmzjq6uh1XqShEZAhKCtMYFRMZsMNPKgo/WFV2iLRRMcwaq2tPiMjgYqIiWDAxg9d31BGIV3BQUQyzLl83r++s44opmbrkqYgM2ZXnZVHVeJxdtYF3mKyKYpit399I47FOnWQnImfkiik968G9tj3whp9UFMNs1faes7Evm5zhdRQRCSI5yfGcNyaR1wJwnkJFMcxWldUyb3waiXE6G1tEzswVU7IorWigpa3T6ygfoqIYRvvqWymvPaphJxE5K1dOyaSr2/FO+V9cBshTKophdOJs7I/qsFgROQuzx6WSGBfFa9sDazkPFcUwen1HHeMzRjEufZTXUUQkCEVHRnDppAxe31kbUIfJqiiGSVunj3f31HP5FF3JTkTO3mWTMjnU3B5Qh8mqKIbJe3uP0N7VzWW65KmInINLJvUcMfnmzsAZflJRDJM3d9YRExXBRUXpXkcRkSCWn5rA+MxRvB1AE9oqimHyxs465hWl6drYInLOLp2Ywbt76mnv8nkdBQiCojCzS83sp2b2czNb7XWeU6lsOEZ57VEu17CTiAyDSydl0tbZzbp9DV5HAfxcFGb2SzOrNbMt/bYvMrMdZlZuZl8d6D6cc2855+4FlgO/8mfes/Xmzp5dxCs0kS0iw+CiCelERRhv7QqM4Sd/71E8Bizqu8HMIoEfA9cAxcAtZlZsZtPNbHm/r74nJHwG+K2f856VN3bWkpcSz4TM0V5HEZEQMDo2itkFqbwdDkXhnHsTONJv81yg3Dm3xznXATwJLHXObXbOLe73VQtgZgVAk3Ou5XSPZWb3mFmpmZXW1Y3c0QKdvm7eKa/nssmZmGm1WBEZHpdMymBLdRNHWju8juLJHEUecKDP95W92wZyN/DoQDdwzj3inCtxzpVkZo7cEND6fQ0cbe/ici0CKCLD6NJJGThHQCznEfCT2QDOuX92zgXkRPabu+qIjDAunqiiEJHhMyM/haS4KN7a5f35FF4URRUwts/3+b3bgtIbO+uYU5BKklaLFZFhFBlhLJiYwdu7Dnu+nIcXRbEWmGRmRWYWA9wMLPMgxzmra2lnS1Wzlu0QEb+4dFIm1U1t7K5r9TSHvw+PfQJYA0wxs0ozu9s51wU8ALwMlAFPO+e2+jOHv5zYJdT5EyLiD5f2LufxtsfDT1H+vHPn3C2n2b4CWOHPxx4Jb+86TNqoGIpzkryOIiIhaGxaAoXpCby16zB3LijyLEdQTGYHIucc7+w+zPwJ6URE6LBYEfGPSyb1LOfR6ev2LIOK4iztrjvKoeZ2LtHRTiLiR/PHZ9Da4WNzVZNnGVQUZ+md8noAFYWI+NVF49MAWLO73rMMKoqz9Hb5YcamxTM2LcHrKCISwtJHx3LemEQVRbDp8nXz7u567U2IyIiYPyGd0n1HPFt2XEVxFjZXNdHS3sXFE1QUIuJ/88en09bZzcb9jZ4ugIIAAAi3SURBVJ48voriLJxYe+XiCbqanYj437zx6UQYrNnjzfCTiuIsvFNeT3FOEumjY72OIiJhIDk+mvPzklnt0TyFiuIMHe/wsW5fAwsmam9CREbO/PHpbNzfyPGOkZ+nUFGcodJ9R+jwdbNAE9kiMoLmT0inw+fN5VFVFGfo7fLDREcac4vSvI4iImHkwsI0oiKMNXtG/voUKooztLq8ngsKUkmI8esyWSIiHzIqNoqZY1M8madQUZyBhtYOtlQ36fwJEfHE/PHpbKps4mh714g+roriDKzZU49zaCJbRDxx8YR0fN2OtXuPjOjjqijOwLt76kmIiWRGforXUUQkDM0el0pMZMSIn0+hojgD7+6pp6QwjehIPW0iMvLioiOZPS6F1btHdkJb73hDdPhoOzsPHT25kqOIiBfmFaWzrbqZ5rbOEXtMFcUQvd87JnjReM1PiIh35hWl0e0Y0fMpVBRDdGJ+YnpestdRRCSMXVCQSlSEnfzwOhJUFEOk+QkRCQTxMZHMyE9WUQSaes1PiEgAmVuUzqbKkVv3SUUxBCeae16R5idExHvzitLo9Dk2HBiZeQoVxRC8u6ee+Oie3T0REa/NKUzFjBEbflJRDMG7e45QUpiq+QkRCQhJcdEU5ySpKAJF/dF2dhxq0WGxIhJQ5halsX5/Ax1d3X5/LBXFIHT+hIgEonlFabR1drO5qsnvj6WiGITmJ0QkEF1Y2HMU5kgMP6koBvHeXs1PiEjgSR8dy8Ss0by/1/8LBOrdbwBHWjvYXqP5CREJTHOL0iitaMDX7fz6OAFfFGZWbGZPm9lDZnbjSD72iaaep8ueikgAmleURkt7F2UHm/36OH4tCjP7pZnVmtmWftsXmdkOMys3s68OcjfXAP/jnLsPuMNvYU/h/b0NxEZF6PoTIhKQRmqewt97FI8Bi/puMLNI4Mf0FEAxcEvvXsN0M1ve7ysLeBy42cz+CxjRMaDSfUeYNTaFmKiA3/ESkTCUmxJPfmo87/l5niLKn3funHvTzAr7bZ4LlDvn9gCY2ZPAUufcg8Di09zV/b0F89zpHsvM7gHuASgoKDjH5NDa3sXW6ma+cMWEc74vERF/mVuYxhs763DOYWZ+eQwvPirnAQf6fF/Zu+2UzKzQzB4Bfg381+lu55x7xDlX4pwryczMPOeQG/Y34ut2lBRqfkJEAldJYRr1rR1U1B/z22P4dY9iODjnKujdUxhJayuOEGEwu0DzEyISuEoKUwEorThCUcYovzyGF3sUVcDYPt/n924LKKX7jjA1J4nEuGivo4iInNbEzNEkx0dTWuG/lWS9KIq1wCQzKzKzGOBmYJkHOU6r09fN+n2NJ48oEBEJVBERxpxxqZTu89+RT/4+PPYJYA0wxcwqzexu51wX8ADwMlAGPO2c2+rPHGdqW3Uzxzt9J3fpREQC2Zxxqeyua+VIa4df7t/fRz3dcprtK4AV/nzsc7G2oqeZtUchIsHgxHvVun0NLCzOHvb71wkCp7C24ggFaQlkJ8V5HUVEZFAz8pOJjjS/DT+pKPpxzlFa0aBhJxEJGnHRkZyfl8w6P01oqyj62Xu4lfrWDg07iUhQubAwjU2VTbR1+ob9vlUU/fx5fkJ7FCISPOaMS6XD180WP1zISEXRz9qKBlITopmQOdrrKCIiQ3ZRUToP3TqbSdmJw37fAX9m9kgrrThCSWGa39ZMERHxh+SEaK6ZnuOX+9YeRR+1LW1U1B/TsJOISB8qij5OnAKviWwRkT9TUfSxtuIIcdERTMtN9jqKiEjAUFH0UZg+ik+VjNWFikRE+tBkdh+fvbjQ6wgiIgFHH51FRGRAKgoRERmQikJERAakohARkQGpKEREZEAqChERGZCKQkREBqSiEBGRAZlzzusMw87M6oB9XufwSAZw2OsQAUbPyYfp+fgwPR9/Ns45l9l/Y0gWRTgzs1LnXInXOQKJnpMP0/PxYXo+BqehJxERGZCKQkREBqSiCD2PeB0gAOk5+TA9Hx+m52MQmqMQEZEBaY9CREQGpKIQEZEBqSiCkJnFmdn7ZvaBmW01s2+c4jZ3mlmdmW3s/fq8F1lHkplFmtkGM1t+ip/FmtlTZlZuZu+ZWeHIJxxZgzwf4fj6qDCzzb3/vaWn+LmZ2Q97XyObzGy2FzkDka5wF5zagY84546aWTTwtpm96Jx7t9/tnnLOPeBBPq/8DVAGJJ3iZ3cDDc65iWZ2M/AfwKdHMpwHBno+IPxeHwBXOudOd3LdNcCk3q95wEO9f4Y97VEEIdfjaO+30b1fYX1UgpnlA9cCPz/NTZYCv+r9+zPAR83MRiKbF4bwfMhfWgr8uvff17tAipnleB0qEKgoglTvsMJGoBZY6Zx77xQ3+2TvLvQzZjZ2hCOOtO8DXwG6T/PzPOAAgHOuC2gC0kcmmicGez4gvF4f0PNh6k9mts7M7jnFz0++RnpV9m4LeyqKIOWc8znnZgH5wFwzO7/fTV4ACp1zM4CV/PnTdMgxs8VArXNunddZAsEQn4+weX30cYlzbjY9Q0z3m9llXgcKFiqKIOecawReAxb1217vnGvv/fbnwJyRzjaCFgBLzKwCeBL4iJn9pt9tqoCxAGYWBSQD9SMZcgQN+nyE2esDAOdcVe+ftcDzwNx+Nzn5GumV37st7KkogpCZZZpZSu/f44GFwPZ+t+k7trqEnknNkOSc+5pzLt85VwjcDLzqnLut382WAZ/t/fuNvbcJyXmdoTwf4fT6ADCzUWaWeOLvwNXAln43Wwbc0Xv000VAk3Pu4AhHDUg66ik45QC/MrNIesr+aefccjP7V6DUObcM+KKZLQG6gCPAnZ6l9Ui/5+MXwONmVk7P83Gzp+E8EOavj2zg+d7jF6KA3zrnXjKzewGccz8FVgAfB8qBY8DnPMoacLSEh4iIDEhDTyIiMiAVhYiIDEhFISIiA1JRiIjIgFQUIiIyIBWFiIgMSEUhIiID+v8BwHPmkXA0wQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eW7nYehOcNu3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "eb6486c9-c394-4df3-a186-dc890b7cd38b"
      },
      "source": [
        "top_installed_and_rated_apps.head(100).Rating.value_counts(ascending=False) "
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.582568    1\n",
              "4.703391    1\n",
              "4.666019    1\n",
              "4.657038    1\n",
              "4.463741    1\n",
              "           ..\n",
              "4.351907    1\n",
              "4.491666    1\n",
              "4.355916    1\n",
              "4.626627    1\n",
              "4.652842    1\n",
              "Name: Rating, Length: 100, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "39tqr_UicNu4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "8418c916-715d-4887-895c-26dc84c45ebc"
      },
      "source": [
        "top_installed_and_rated_apps.head(100).Reviews.value_counts()"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1745403.0     1\n",
              "19573637.0    1\n",
              "1888392.0     1\n",
              "2608408.0     1\n",
              "966728.0      1\n",
              "             ..\n",
              "2920141.0     1\n",
              "1976168.0     1\n",
              "58506.0       1\n",
              "99127.0       1\n",
              "24657922.0    1\n",
              "Name: Reviews, Length: 100, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FXItN__cNu6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "9a694917-3b07-462e-de2e-732941892b0c"
      },
      "source": [
        "app_category= top_installed_and_rated_apps.head(100).Reviews\n",
        "app_category.plot.density().set_yscale(\"log\")"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZoFpACzNcNu8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "####################"
      ],
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HvvD6DbncNvB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "cd341111-640e-42a0-b382-be7e3de9797e"
      },
      "source": [
        "app[\"Rating\"].value_counts()"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.000000    23805\n",
              "4.000000     5469\n",
              "4.500000     3519\n",
              "3.000000     2581\n",
              "4.333333     2145\n",
              "            ...  \n",
              "4.286852        1\n",
              "4.396635        1\n",
              "4.449430        1\n",
              "4.363723        1\n",
              "3.945443        1\n",
              "Name: Rating, Length: 100205, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2OY3tHLicNvD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "2c686bb9-bc4c-4530-e6af-d5cb7b14ad9a"
      },
      "source": [
        "app_rating= app[\"Rating\"]\n",
        "num_bins=7\n",
        "plt.hist(app_rating, num_bins, facecolor=\"green\", alpha = 1)\n",
        "plt.xlabel(\"Google Playstore - App Ratings\")\n",
        "plt.ylabel(\"Number of Apps\")\n",
        "plt.show"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tD8LyjcOcNvE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "###############################"
      ],
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h3zZDG1qcNvG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "outputId": "d2fc0e0f-bc71-4bb6-aa27-3fa5623c9db8"
      },
      "source": [
        "app1=top_installed_and_rated_apps.head(100)\n",
        "app1[\"Content Rating\"].value_counts().plot.pie()\n",
        "plt.title(\"Content Rating - Top 100 (Main) Apps\")\n",
        "plt.show()"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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di9nIrcBZyXi0IXQgJrssWXWDv4R+C64TO1N4PgROTMajL4QOxGSPJasu8D1wXoZrgV4qPSGUqmbgR8l49KrQgZjssGSVoUgsMQyYChwSOhbTJX8BzrZeHYqfJasMRGKJ7YF/4foyN8XnSeBY63K5uFmy6kQklpgE3AsMDh2L6ZFZwFHJePTN0IGY7inEewMLRiSWOAZ3j6AlquL3OeDpSCxhXfIUKUtWHYjEEmcBd+G6iTGloQZ4OBJLHBw6ENN1lqzaEYklfgZcg70/pag/cH8kljg6dCCma+zL2EYklrgAuDR0HCanqoG7I7HEiaEDMZmzCvYUkVjiXOC3oeMwedMCnJCMR+8OHYjpnCUrLxJLnI3rIM+Ul3VANBmPPhI6EJOeJSsgEkv8D/BXrFV6uWoEDkrGoy+GDsR0rOyTVSSW+BLwAIXZH73Jn6XApGQ8+nboQEz7yjpZRWKJbXBdJNsNyQbc6EkTk/HowtCBmM8q26uBkVhiOK6Pd0tUptVYYJrv9dUUmLJMVn78vn8Cm4WOxRScfXEjcZsCU5bJCrgK2C90EKZgfTsSS5wROgizsbKrs4rEEscBNsCA6cw64AvJePTZ0IEYp6ySVSSW+BzwKlZPZTLzEbBjMh6tDx2IKaPTQF9pegeWqEzmxuPuETUFoGySFfBLYI/QQZiic1IkljghdBCmTE4DfR9GT1Feydlkz3Jgh2Q8+nHoQMpZyX95fTOFv1AGr9XkzBDgptBBlLty+ALHgG1DB2GK3sGRWGJy6CDKWUmfBkZiia2B13D9FxnTU4uBrZPx6LLQgZSjki1ZRWIJwZ3+WaIy2TICd6HGBFCyyQo4AWulbrLv9EgssUvoIMpRSSYrX6l+eeg4TEmqwO4dDKIkkxVwFnaTssmdfSOxxJGhgyg3JVfBHoklaoAPgGGhYzEl7TVg12Q8WlpfoAJWiiWrGJaoTO7tDBwbOohyUlIlK9+h3mygX+hYTFl4G9eyvSV0IOWg1EpW38cSlcmfbYGvhw6iXJRMsorEEgOB74aOw5SdC0IHUC5KJlkBpwGDQwdhys72kVjii6GDKAedJisRaRCRlW0ec0TknyJSEM0DIrFEJfC90HGYsmXHXh5kUrL6A/BjYAxu9I/zgKm4juxuzF1oXXI0EAkdhClbR0ZiiUjoIEpdJsnqKFX9s6o2qOpKVb0BOFRV78R1nVEITg0dgClrFVh9ac5lkqxWi8jxIlLhH8cDTX5e8HYPkVhiNHBo6DhM2Ts1Ekv0CR1EKcskWZ0EfAtYBCz0/39TRPpSGL8mJwOVoYMwZW8wcEToIEpZVWcLqOqHQEf3QT2d3XC65ZTQARjjnQjcHTqIUtVpC3YRGQGcjqvA/iS5qer/5DSyDERiiX0pjIRpDMBaYKQN3ZUbnZasgHtxgy08AmzIbThddnzoAIxJUY27X7BQrpKXlEySVT9VPT/nkXTPUaEDMKaNE7FklROZVLDfLyKH5zySLorEEjtibatM4TkwEkvYnRQ5kEmy+j4uYa3xrdcbRGRlrgPLgJWqTCGqBA4KHUQp6jRZqepAVa1Q1b6qOsg/H5SP4DphycoUKmv3lwMd1lmJyNaq+q6I7NrefFWdkbuw0ovEEiOBiaH2b0wnLFnlQLoK9nOBM4DftjNPgZB3mu8HSMD9G5PO+EgssXUyHn03dCClpMNkpapn+H8PU9Wm1HkiEvq2AhtiyxS6QwBLVlmUSQX7sxlOy6d9A+/fmM7sFTqAUpOuzmpTXLcwfUVkFz497RpEwK6DI7FEP8AGmTSFbvfQAZSadHVWhwJTcH1Y/S5legNwYQ5j6syeZNaY1ZiQtojEEkOS8ejy0IGUinR1VjcDN4vIsao6LY8xdWaP0AEYk6GJwH9CB1EqMul1YZqIRIHtgD4p0y/NZWBpbBdov8Z01e5YssqaTPpgvx44ATgHV291HDAhx3Gls03AfRvTFe22UTTdk8nVwH1U9WRguapeAuwNfD63YbUvEksIsHWIfRvTDVuEDqCUZJKs1vi/q0VkNLAeGJW7kNIaBwwItG9jumrz0AGUkkx7XRgM/AaYASSB23MZVBrbBtqvMd0xIBJLbBI6iFKRSQX7Zf7faSJyP66SvTmnUXXsc4H2a0x3bY4bv8D0UNqSlYiMEZGJItLbT6oBzgfez3lk7Qt1+mlMd9mpYJZ0mKxE5AfAa8DVwPMichrwDtAX2C0/4X3GpoH2a0x3jQ8dQKlIdxp4BrCVqi4TkfHATGBfVX0lP6G1y5KVKTZDQwdQKtKdBjap6jIAVf0IeC9wogJLVqb4FMqo5UUvXclqrIhclfJ8VOpzVf1e7sLq0MgA+zSmJyxZZUm6ZPXjNs9Dl6rA9fhgTDGx08As6exG5kJTHToAY7rISlZZkkmj0ELSu/NFjCkodsdFlhRNsorEEpW4YY6MKSZ2zGZJJr0ufKYL4fam5YGdAppiZMkqSzLpcfNqPtvVRXvTcq1XnvdXNr5Q8errU3pdXz9t/ZDV1Sukz4TFNI9dqpXD67X/wCYdWrWhZQg2mlC3qFTUE4+GDqMkpOuDfW9gH2CEiJybMmsQYX4t1gfYZ1m4oGpq4+elYdIBvRu4b7N+L/164pDqlZWVO7r8JPReL2tGLWP++MW6bMJCXT1uCbrpcu09eBU11esYWQHDQr+GgqUtjaFDKBXpSla9cZWDVcDAlOkrga/lMqgOrMGNV2i/8Fk0mIblW8rHn9w+dVTj6t2PalzN0337vFE7fOjahZWVE9f1kr6zR7LZ7JGy2VPbf3YbfdZp4+ilLBi3WJdHFunqcYth5AqtrllFTfV6RgkMzudrKjAbMllIRBS4TVW/6Z9XAfOBF1T1iDTr7QyMVtUHshFsO9u/HDgZGKKqA1Km/x74gn/aD9hEVXP6OYuqpl9AZIKqzs5lEJmKxBKrCDiyTim6pOqmJyZXPXxAR/Pf7N37/YtGDFv0Qa+qvRDpVom6b5OuHL2MBRMW6YoJi7Rp3BJ0kxXad9BqBlevZ6S4G+RL1VvbvPtOOyl+YyLSCPwX2FtV14jIYcCvgLmdJKspwERV/W6mAYmI4L77LRksuxcwG3g/NVm1WeYcYBdV/Z92Youoam2msaWTSZ1VtYjcAERSl1fVECMyW7LKshMqHx+Tbv7269Ztec/H87ecXVU156IRw2a9Vt17D7o4yO2aPjLog9EM+mB0+4XiAWu0fvRSFoxfpMsji3Tt2CXKJivoN3ANg3s3M0qK+/L/yi4s+wAQBe4GvoHrN25/ABHZA/gjroumNcApwCzgUtxwefvhkts2QKOqXunXexNoTXYPAS/gOiI4XESOB47HXbz6p6pe3DYgVX3ebydd3N8APrNutmWSrO4Crgf+SoZF2hxaBYwIHEPJ2L/ijTf7yPpOf/UBJjQ3j/v7/IXjllRWLK4dPvT5J/r23QWRrJSIGvtKzcyx1Mwc2/4XYtAqXTZmKQvHL9IVkUW6bswSlRH19B24hqG9NjBKCvsHrCvJ6g7g577fuB2BG/HJCje68/6q2iwiBwO/VNVjReTnpJSsRKQ2zfa3BCar6vMicoh/vgeuauU+EZmkqk925cWJyARcP3OPdWW97sgkWTWr6nW5DiRDVlmZRRdUTV3R1XWGb2gZ8aeFSw5sEFl5xbAh0+8d0H87FcnpD8jK/jJ0ZX+GvjO+/WRW06iLxy7RRRMWsXLCIl3rrmTSb0ATQ6tcMutSSTDLlmW6oKq+ISIRXEmlbR1UDW5ovC1xdbfduTo+u7WkhBve/hDgVf98AC55dSlZAV8H7lbVDQAiMgx41M8bCvQWka/4599S1bpuxA1klqz+JSJnAf8E1rZObO2RIc+sx8UsGciq+m3ko243PxmoOuiyJcsOvGjpsqZrBg9+8u81AyPNriuhvKsfICPqB8iItyLtzFTVoY0sGrNEF0YW0jB+sa4fs1QrhtfTv38Tw6paGCW5vTNiaReXvw+4EjiQja+yXgY8rqpf9QltegfrN7Nx+8nURL0q5X8BfqWqf+5ifG19HTi79YmqLgV2hjB1VpP939QbmxXYLBsBdNH8APssSedU3fO6CJN6up1qpc+5y1dM+v7yFRv+Pmjgs9cMqRneVFERZPSjdonIsoFssmygbFLXTqfYotoybCULxi7Rxb5k1jx6qVYOa6B//yaGV7pk1pMRwBd3cfkbgRWqWiciB6ZMrwE+9v9PSZnewMZX65P4OioR2ZWOuwJ/CLhMRG5T1UYRGQOsV9WMCwQisjXu3sfnMl2nJzLpg72Q+j2fFzqAUvHNykey2t1OJVROWdmwz5SVDdw3oN9Lvx7a2larsKlIxZIaNl1SI5u+1k4HxBUtumFEPR+PXaJLJiyiYcIi3TBqmVYNa6B/vyZGVCibSvp2h3O6FI/qXOCqdmZdgTsNvAhIpEx/HIiJyGu4CvZpwMki8hauMn1mB/t5WES2AZ7zleeNwDdpc/YiIlcAJwL9RGQu8NeUktLXgTu0syYFWZJJ04V+wLnAeFU9w58zb6Wq9+cjwFSRWOIc2v8gTRfsKW+/fWf1L3I+UlBqW63OLicVq4oWbd5kBfPHL9alExZp4/hFNI9apr2GNjKw71pGCJy03TvvPB46zlKQSfH2b7i+rPbxzz/GXSHMe7LCFXFND13Ya2pX61G6Zb81TTs+MmdeVtpqFaqWCqlaMJRxC4bKuBe3aneRj7pdo2w2kkmvC5ur6hX4211UdTXhWpHPCrTfktGfNY07yoe75HOfvq3WvvfPnT9v56a1T6LalM/9B7Qe+4HNmkyS1ToR6YurVEdENiflqmCezcTuEeyR71Td96pImEaWvq3WpMfnfNxwwOrV01GtDxFHHs2um1wXum1iycgkWdUCDwLjROQ2XBuK83MZVEeS8eg64L0Q+y4VUyofCn7TcWtbrWdnz5WvNDROF9WuXjErFnasZlGnyUpVHwaOwV0uvR3XWjZkhaFVAXTTzvLf9wZIU84r1jPV2lbrpdlzBp6yYuWTVW4UpVLycugASkkmne89qqpLVTWhqver6hIRebSz9XLojYD7LmoX9bp1YegY2tPaVuvl5JwxP1q6/Nk+LS3tXm4vQi+FDqCUpOvPqg/unqvhIpLa+dogIO3Nrzlmyaob+rJ29W4yc6fQcaSzcVut/i/9eujgomirlYYlqyxK13Th28APgNG4pgutyWol8Kccx5XOjID7LlqnVj4wQ4T9QseRqaMaV+1+VOMqnurbp+6S4UObFlZV7R46pi6aXTe5zm4Py6JMGoWeo6pX5ymejERiifeAwrmlowi8Xn1aXY2s3iF0HN31Zu/e7/90xLDFH/aq2rNI2mrdXTe57rjQQZSSTG63uVpE9uGz/VndksO4OjMdS1YZ21aSHxRzogLXVute36/WT0cMm/V6N/rVyjM7BcyyTCrY/467C3w/YHf/mJjjuDozPfD+i8pFVbfODR1Dtkxobh536/yFkx6b83HDpNVrnijgtlpPhQ6g1GRyu81EYNt83ayYIbvXKkO9Wb92r4q3i7mSul0jNrSMuGbh4gNWVsjKK4YOeeK+Af23zXW/Wl2wAngxdBClJpNGoW8Cm+Y6kK5IxqMLsAZ3GZlS+dDLFVK6Q5gPatFBv1iy7ICXZs8ZOGXFyqcKpK3Wo5m0XBeRDSLyWsojlo/gsk1EHhSRFb6H09TpnxORF0TkvyJyp4j0qN+wTJLVcOBtEXlIRO5rffRkp1ny79ABFIMzq+4r5v7LM1at9PnR8hX7v5ycM+bcZcHbaj2Y4XJrVHXnlEe8pzuWMBcffgN8q53pvwZ+r6pbAMuBU9suICLTfWeCncr0dpuvAL8EfpvyCO0foQModJ+XObOGSmNBt63KtkqoPKW+YZ+XZs/9/OWLl740aMOGfLfLU+Bf3V1ZRL4sInelPD+wtcQiIoeIyHMiMkNE7hKRAX56UkR+LSIzcH1bzUhZf8vW5yJykIi8KiJ1InKjiFSnrH+J326d71QPEenvl3vRr3d0uy9Y9VFcJ4Cpr0OAL+IGvwC4GZdHui2T222ewHVWP9A/3vHTQnsGKMgW2YXip1W3FcQQaqEc1bhq92c++njHaxcsqhvZ3Jyvq3Mv1k2uy/S47NvmNPAE4BFgTxHp78EenYsAAAwMSURBVJc5AbhDRIYDFwEHq+quuFt5UgcfXqqqu6rq5UC9H08Q3Cg4f/ONvG8CTlDVHXD11d9JWX+J3+51wHl+2k+Bx1R1D9wYgb9Jiaszw3A9njb753PpYWPyTK4GHo+rLDwON2zPCyISYpDTjSTj0RZcv/CmHb1oXrd/RV1GI9eUuv3XNO3wyJx5u9/+8YL3N1u3/ln84AY5cm8Xlm17Gnin/3I/CBzpBzqN+m3uBWwLPON7BZ0MTEjZ1p0p//8VOMWfEp4ATAW2Amapauvp8c2wUbfWrWcqr+CaKYEbUKK1F9LpuP7ce9zPvoic0pqgcRfwHvDP036fM7ka+FNg99a+mcVdcXmET4t3IU0DzgwdRCE6sfKRVypE9w4dRyHJQ1utFuDWLGznDuC7uJFxXlbVBn9a9R9V/UYH66QOBjENN47fY8ArqrpURMZ2ss/Wbp828GleEOBYVe3OxaylwGARqfIJeCy+D3lV/RuuU09EZDowRVWTnW0wkzqrijadyC/NcL18mA4sCR1EIfpu1b25HLGlqOWwrdZjdZPrutTnegeeAHYFTsclLoDngX1FZAv4pD6p3YbR6jo3fAh3Svc3P/k9INK6Pq5CvLPqnIeAc3yiREQy7rTRN3V6HGg9C5tM10qdn5FJ0nnQXwmc4ofWSVAgV+KS8Wgz8PfQcRSaiMyfM5z6bg+zVS5a22o989FcObqh8Yks9Kv1t84X2UjbOqs4gB+D737gMP8XdbFNAW4XkTdwI8psnWbbt+FKeg/79Ztw9Vd3iUidn3d9J/Fdhhuf8A0/AMVl7S0kIk/hujo/SETmisihftb5wLki8l9cHdb/drK/tDq9N9AHcwx8chPsU6paMHVFkVhia+Cd0HEUkr/0unL6lypnHBg6jmKzVmj60+DBL91aM3BCN8ZArAdG1U2uW5OL2LpKRM4DalT1Z6FjyZYOk5UvLo5U1WfaTN8PmK+qH+QhvoxEYoknoOdj4JWCSjY0z6w+eWmlaFaH2ionG2DDLTUDX7h2cJfGQLyhbnLdt3MaWIZ8RfXmwBdVtWSqSdKdBv4B1x1MW/V+XiHp6aiyJeP4yukzLFH1TDfbanX1FDBnVPWrqrpjKSUqSJ+sRrY3Lr2fFslZRN0zja4P012Svl/1j5Icny+UDNtqzaibXPd8XgMrQ+mS1eA08/pmO5CeSMaja7HSFWNl8byRLN8tdBylqJO2WlcGC6yMpEtWL4vI6W0nishpuIZjheb3wOrQQYR0QdXU90UKpllJSfJttfa5f+78eTs1rX1SXCPLuzpd0fRYugr2kbgW4uv4NDlNBHoDX1XVBXmJsAsiscTvcV0xlx2hpeX96pMXVEnL6NCxlJMmkbP7XLzi2tBxlIMOW7Cr6kJgHxH5AtB620ZCVR/LS2TdcyVwFi6hlpWvVjz9SpW0FFs/5cVuQR/VG0MHUS4yamdVTCKxxJ+BM0LHkW9PV3/vhbGyZM/QcZSZH1Fb/7vQQZSLUqzfiONOXcvGpixbOIYlVrGeX4vpvAW4yaKSS1bJeHQWcFXoOPLp/F63vyuS0U3pJnsuora+rC/o5FvJJSvvF7hfvjKgekTF85uFjqLMvILrhsXkUUkmq2Q8Wo/rIqPkRStemNFLNowLHUcZUeC71Na3hA6k3JRksvJuwA12UdJ+XHXn+tAxlJmbqa231uoBlGyySsajG4Afho4jl4azYvEEWWgV6/lTj+v2xARQsskKIBmPPkIB3WCabedV/d9bIvQKHUcZuZja+kWdL2ZyoaSTlXcuMC90ELlwTOXTkdAxlJE3gWtCB1HOSj5ZJePRFUBB9DOUTV+qePm13tIcCR1HmVgHTKa2vrnTJU3OlHyyAkjGo/eTnY78C8b5VXdYG5/8iVFbP6PzxUwulUWy8r6HH12j2A1h5bLNZZ5VrOfHAxReZ5NlqWySVTIeXQ58AzfUUFH7YdW0OhGqQ8dRBuYDU6itL60baItU2SQrgGQ8+hQl0Fj0+MrpnY0BZ3quBfgWtfVlcidE4SurZOX9EjecWFGaVPH6G31k/eah4ygDv6G2/tHQQZhPlV2ySsajihvg8cPQsXTHBVW3tzeIh8muZ4CLQgdhNlZ2yQo+qb86GtciuWgMorF+a/nIKtZzayZwtDVTKDxlmawAkvHom8CxQNHcW/e9qnteFymswTpKzELgy9TW20hJBahskxVAMh59FDgtdByZOrHyERsPMHdWAUdQWz8rdCCmfWWdrACS8egtwM9Dx9GZvSreerufrNsqdBwlagNwPLX1L4cOxHSs7JMVQDIevQzXpUzBurBqqp2a5M53qK1/IHQQJj1LVp86EyjIkUr6s6ZxB5m1S+g4StQl1Nb/JXQQpnOWrDzfpOE04H9Dx9LWWVX3vSrCgNBxlKALqa2vDR2EyYwlqxQ+YZ0OFNQv7eTKh4aFjqHEKHAWtfW/Ch2IyZwlqzZ8wvo28OfQsQDsKjPfHSBN24aOo4Q0426juS50IKZrSm6Q02yKxBIXA7UhY5jW++Ind6t4f1LIGErIWuAEauvvDR2I6TpLVp2IxBIn44Zdynv3wX1Zu/rt6lOaRRiU732XoEZcy/THQgdiusdOAzvh22F9mQC35pxeef8MS1RZ8RFwoCWq4mbJKgPJePQxYF/yfPPzaVX/rsnn/krUw8Cu1Na/EjoQ0zOWrDKUjEffAnYD7snH/raTWf8dJKt3yMe+SpQClwGH2b1+pcHqrLohEkucC8TJYT3WHb0ve3KvinesYr17lgPftFbppcWSVTdFYom9gTuBrA/dXs26pneqpzRVCIOzve0y8CpwrN2QXHrsNLCbkvHoc8DOwNRsb3tK5UOvWKLqsg3Ab4F9LFGVJitZZUEkljgSuB4YnY3tvVp9xutDpHGnbGyrTMwATrfhskqblayyIBmP/gvYlizcV7iVfDTLElXGVgPnAXtYoip9VrLKskgscRDwR2C77qx/S69fPTGpsu6A7EZVkh4GzrRTvvJhySoHIrFEJXAGcAkwItP1etG87r3qySsrRIfnLLji9zFuhOSSGmHbdM5OA3MgGY9uSMaj1wFbAFfg7knr1Dcr//OKJaoOLQC+D2xuiao8WckqDyKxRAS4EJgM9O5ouZerz3x1uKy0TvY2thj4NXAttfVrQgdjwrFklUeRWGIMrkL4DKBf6rzPybyPHut93jgRJEhwhWcZcCVwNbX1jaGDMeFZsgogEksMx53SnA0MAfhrr99MP7jy1QNDxlUgkrhmINdRW28DuppPWLIKKBJL9AVOqGTD6TOrT96iUnST0DEFosBDwDXAA9TWtwSOxxQgS1aForZme+AU4CSgXMYH/AC4GbiZ2vqPQgdjCpslq0JTW1MFHAZ81f/dNGxAWfcB8ABwN/AUtfV2AJqMWLIqZLU1AuwCRIHDgT0ovuYm64AngQTuFG9m4HhMkbJkVUxqa4bhei09DJe4toCCu3rYDLwDPIcrQT1Cbf2qsCGZUmDJqpjV1gwAdsKVvnbB9QKxPWnacmXZKuB14DVc1yyvAm9SW59RI9iOiMgw4FH/dFNcjwqL/fM9VHVdT7ZvipMlq1JTW9ML2AYYj/uij/J/2z76ptmK4gZYWNDBYz4wC3g/11fuRKQWaFTVK3O5H1P4LFmZgtaarIDHgd8BA4AlwBRVnS8im+OaPIzA9cJwuqq+KyI3ASuBibjk/BNVvTv/r8BkS7FV1pryJMDVwNdUdTfgRuByP+8G4Bw//Tzg2pT1RgH7AUfguqE2RawqdADGZKAaVxf3HxEBqATmi8gAYB/gLj+9ddlW96hqC/C2iJRL27WSZcnKFAMB3lLVvTeaKDIIWKGqO3ewXmpFf6FdNTVdZKeBphisBUaIyN4AItJLRLZT1ZXALBE5zk8XEbFeVkuUJStTDFqArwG/FpHWphL7+HknAaf66W8BR4cJ0eSaXQ00xhQFK1kZY4qCJStjTFGwZGWMKQqWrIwxRcGSlTGmKFiyMsYUBUtWxpiiYMnKGFMULFkZY4qCJStjTFGwZGWMKQqWrIwxRcGSlTGmKFiyMsYUBUtWxpiiYMnKGFMULFkZY4qCJStjTFGwZGWMKQqWrIwxRcGSlTGmKFiyMsYUhf8HkddHnuxHadwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVkU9wrEcNvI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "28e10968-91ba-460d-d815-bff1cc842bb2"
      },
      "source": [
        "app1[\"Content Rating\"].value_counts()"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Everyone        69\n",
              "Teen            24\n",
              "Everyone 10+     5\n",
              "Mature 17+       2\n",
              "Name: Content Rating, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MDL92yjcNvJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "outputId": "747893fc-16a6-4ba1-ce85-46cc8301b130"
      },
      "source": [
        "app2= top_installed_and_rated_apps.head(100)\n",
        "app2[\"Installs\"].value_counts().plot.pie()\n",
        "plt.title(\"Gradation of Installations - Main Top 100 Apps\")\n",
        "plt.show()"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8BRjbPl7cNvL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "c4a0c188-6ede-469f-9f4b-d3025aecc93d"
      },
      "source": [
        "app2[\"Installs\"].value_counts()"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100000000     38\n",
              "500000000     35\n",
              "1000000000    24\n",
              "5000000000     3\n",
              "Name: Installs, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MD_09PNccNvO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "c3a284ed-fb28-4505-b7ea-ce241e044e48"
      },
      "source": [
        "top_10_installed_and_rated_apps"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Size</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Minimum Version</th>\n",
              "      <th>Latest Version</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>821</th>\n",
              "      <td>Google</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.408893</td>\n",
              "      <td>10870728.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>YouTube</td>\n",
              "      <td>VIDEO_PLAYERS</td>\n",
              "      <td>4.368428</td>\n",
              "      <td>41919102.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2177</th>\n",
              "      <td>Maps - Navigate &amp; Explore</td>\n",
              "      <td>TRAVEL_AND_LOCAL</td>\n",
              "      <td>4.342798</td>\n",
              "      <td>10083666.0</td>\n",
              "      <td>5000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7064</th>\n",
              "      <td>Clean Master - Antivirus, Applock &amp; Cleaner</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.657038</td>\n",
              "      <td>44171776.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>7.0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6411</th>\n",
              "      <td>Google Duo - High Quality Video Calls</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.596404</td>\n",
              "      <td>3641252.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>4.4 and up</td>\n",
              "      <td>50.1.240885383.DR50_RC06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3269</th>\n",
              "      <td>SHAREit - Transfer &amp; Share</td>\n",
              "      <td>TOOLS</td>\n",
              "      <td>4.579340</td>\n",
              "      <td>10450444.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>4.7.38_ww</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1259</th>\n",
              "      <td>Google Photos</td>\n",
              "      <td>PHOTOGRAPHY</td>\n",
              "      <td>4.542380</td>\n",
              "      <td>16278468.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>653</th>\n",
              "      <td>Instagram</td>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>4.519560</td>\n",
              "      <td>79726403.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>April 2, 2019</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>539</th>\n",
              "      <td>Subway Surfers</td>\n",
              "      <td>GAME_ARCADE</td>\n",
              "      <td>4.498131</td>\n",
              "      <td>29834812.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>85</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone 10+</td>\n",
              "      <td>March 7, 2019</td>\n",
              "      <td>4.1 and up</td>\n",
              "      <td>1.100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3267</th>\n",
              "      <td>Samsung Internet Browser</td>\n",
              "      <td>COMMUNICATION</td>\n",
              "      <td>4.424015</td>\n",
              "      <td>832714.0</td>\n",
              "      <td>1000000000</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>February 21, 2019</td>\n",
              "      <td>5.0 and up</td>\n",
              "      <td>Varies with device</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         App Name  ...            Latest Version\n",
              "821                                        Google  ...        Varies with device\n",
              "813                                       YouTube  ...        Varies with device\n",
              "2177                    Maps - Navigate & Explore  ...        Varies with device\n",
              "7064  Clean Master - Antivirus, Applock & Cleaner  ...                     7.0.6\n",
              "6411        Google Duo - High Quality Video Calls  ...  50.1.240885383.DR50_RC06\n",
              "3269                   SHAREit - Transfer & Share  ...                 4.7.38_ww\n",
              "1259                                Google Photos  ...        Varies with device\n",
              "653                                     Instagram  ...        Varies with device\n",
              "539                                Subway Surfers  ...                   1.100.0\n",
              "3267                     Samsung Internet Browser  ...        Varies with device\n",
              "\n",
              "[10 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pizz_eNTcNvQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "5c7968ad-8bcb-477e-a43f-cce210e3d1bc"
      },
      "source": [
        "\n",
        "# top 10 main apps \n",
        "\n",
        "app4= top_10_installed_and_rated_apps\n",
        "top_apps=app4.groupby([\"Installs\", \"App Name\"]).size().unstack()\n",
        "top_apps.plot(kind=\"bar\", stacked=True)\n",
        "ax=plt.gca()\n",
        "plt.show()"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYDwEjEocNvT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "f7f789fb-077b-47ed-d53f-aec3ebbbd5a6"
      },
      "source": [
        "top_installed_and_rated_apps.head(100)[\"Content Rating\"].value_counts(ascending= False) "
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Everyone        69\n",
              "Teen            24\n",
              "Everyone 10+     5\n",
              "Mature 17+       2\n",
              "Name: Content Rating, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S6-z6MMGcNvU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "outputId": "49012068-6ce8-4796-8286-b9460c67de68"
      },
      "source": [
        "# violin plot - main top 100 apps - content rating vs installs\n",
        "app5=top_installed_and_rated_apps.head(100)\n",
        "plt.figure(figsize=(15,10))\n",
        "sns.violinplot(x= \"Content Rating\", y=\"Installs\", data= app5)\n",
        "plt.show()"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlJ428BncNvY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "aa64e9b8-3b34-42eb-e698-33b1c692f716"
      },
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "corr= app.corr()\n",
        "sns.heatmap(corr)\n",
        "plt.show()"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6diIChfAcNvh",
        "colab_type": "text"
      },
      "source": [
        "# Unsupervised Methods"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RdCQ1Kn9cNvh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_app = app.head(1000) # taking a part(sample) from the data set to apply supervided and unsupervised\n",
        "# i did not take a bigger sample because of memory crashes"
      ],
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4QS6OwT9cNvp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "85b17604-28a7-4877-848f-8a11fcfdc6ed"
      },
      "source": [
        "new_app.shape"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dS2FNR_AcNvr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "6cae5e69-c792-40dc-83af-27a834899299"
      },
      "source": [
        "new_app[\"Installs\"].value_counts().sort_values(ascending=False)"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000000       262\n",
              "10000000      229\n",
              "100000        113\n",
              "5000000       112\n",
              "500000         62\n",
              "50000000       54\n",
              "100000000      52\n",
              "10000          46\n",
              "50000          37\n",
              "500000000      12\n",
              "1000000000      8\n",
              "5000            8\n",
              "1000            3\n",
              "5000000000      2\n",
              "Name: Installs, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fkOHxd_OcNvt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "outputId": "3198eec3-83bf-4699-a0fd-e973d70bc343"
      },
      "source": [
        "# I want to see the gradation of the number of installations of the new dataframe(sample), so as to compare later \n",
        "# so as to compare the unsupervised and supervised methods results\n",
        "\n",
        "new_app[\"Installs\"].value_counts().sort_values(ascending=False).plot.bar()\n",
        "plt.ylabel(\"Number of Installations in the Sample\")\n",
        "plt.xlabel(\"Classes of Installations\")\n",
        "plt.title(\"Gradation in Number of Installations - Sample (first 1000 Lines of the Dataset)\")\n",
        "plt.show()"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iV6DTfK-cNvv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#**********************************\n",
        "# Import packages from Scikit Learn\n",
        "\n",
        "from sklearn import cluster # in unsupervised method we have clusters/ data are grouped into clusters\n",
        "from sklearn import metrics # for the distances between the data \n",
        "from sklearn.preprocessing import scale # for scaling\n",
        "from sklearn.preprocessing import LabelEncoder # for converting strings to floats\n",
        "from sklearn.preprocessing import OrdinalEncoder # for converting strings to floats when x(attributes) are strings"
      ],
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kSAvhGETcNvy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#********************************************************************************\n",
        "\n",
        "# Segmenting the data i have chosen into attributes (features)=x, and target=(y)\n",
        "\n",
        "# y will be the number of installations\n",
        "\n",
        "# x will be: Category, Rating, Reviews and Content Rating\n",
        "\n",
        "\n",
        "x= new_app[['Category', 'Rating', 'Reviews', 'Content Rating']] # attributes\n",
        "\n",
        "y= new_app[\"Installs\"] # y included the classes of installations. e.g. 100,000 in the dataset means more than 100,000 installations"
      ],
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCdouT-scNv3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# x has strings. This command is for converting strings to floats\n",
        "\n",
        "x_transformed= OrdinalEncoder().fit_transform(x) \n"
      ],
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZaia-kucNv9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Preparing the data- Scaling/ Handling the data in such way they can belong in a spesific range\n",
        "# and represent the same degree of difference\n",
        "#***********************************************************************************************************\n",
        "\n",
        "scaled_data= scale(x_transformed)"
      ],
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jaN6A6UgcNwC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "290174fe-9586-4100-b870-a784222ae1ca"
      },
      "source": [
        "scaled_data"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.77036381,  1.06735713,  0.86717825, -0.57690942],\n",
              "       [ 2.36819293,  0.28702897,  1.37371577, -0.57690942],\n",
              "       [ 1.96321786, -1.66552549, -1.2428125 , -0.57690942],\n",
              "       ...,\n",
              "       [ 2.1657054 , -1.68980237,  0.0043178 , -0.57690942],\n",
              "       [ 2.1657054 , -1.72448362,  0.30125358, -0.57690942],\n",
              "       [ 2.1657054 ,  1.16099651, -0.64894093, -0.57690942]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVW1VWeHcNwG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import python libraries for creating clusters, for converting and for scaling\n",
        "\n",
        "from sklearn import cluster\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import scale "
      ],
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkbwQjoQcNwI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6bb397c8-1ca6-4c8c-bb6a-4e88b4b05092"
      },
      "source": [
        "# i have taken a sample, so now the clusters of installations are 14 from 21 that normally are for the whole dataset\n",
        "# creating  clusters using Agglomerative Clustering\n",
        "len(np.unique(y))"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cb_Fdrz1cNwQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "ee5745b6-2379-4ecc-c160-c63fef21428b"
      },
      "source": [
        "y.unique()"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([   5000000,  100000000,     100000,   10000000,      10000,\n",
              "          1000000,   50000000,     500000,      50000,       5000,\n",
              "             1000,  500000000, 1000000000, 5000000000])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGa4-4qQcNwT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "e7698bbc-02bd-4e2d-f7b4-20468e5e5572"
      },
      "source": [
        "#******************************************************************************************************\n",
        "# Hierarchical agglomerative clustering - bottom-up approach\n",
        "#******************************************************************************************************\n",
        "\n",
        "# Using average in linkage means that i use the average of the distances of each observation \n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "n_samples, n_features = scaled_data.shape \n",
        "n_digits = len(np.unique(y)) \n",
        "model = cluster.AgglomerativeClustering(n_clusters=n_digits, linkage=\"average\", affinity=\"cosine\") \n",
        "model.fit(scaled_data) \n",
        "# this is the model created"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AgglomerativeClustering(affinity='cosine', compute_full_tree='auto',\n",
              "                        connectivity=None, distance_threshold=None,\n",
              "                        linkage='average', memory=None, n_clusters=14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xqkscn7acNwW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 722
        },
        "outputId": "91f4e847-ab35-4578-a8a9-62dca9d974fb"
      },
      "source": [
        "print (model.labels_)"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 0  8  4  5  9  6  3  7  4  7  7  6  4  6  0  0  6  7  7  6  7  5  4  4\n",
            " 10  7  8  7  5  7  5  9  6  5  5  8  6  7  3  6  6  7  9  3  5  4  2  9\n",
            "  1  9 10  9  6 10  2 10  6  9  1  6  9  1  6  3  9  9 10  1 10  9  1  7\n",
            "  9  3  6  3  2  2 10  9  1  6  7  2  1  6  6  6  9  9  9 10  1  6  9  6\n",
            " 10  6  1  6  6  6  1  6 10  7  3  7  6  6  7  6  5  5  6  7  9  6  7  7\n",
            "  7  9  7  0  6  5  9  5  9  9  6  7  6  6  6  6  6  6  5  5  5  5  6  9\n",
            "  6  6  6  7  6  7  5  6  7  6  6  6  3  7  6  6  5  6  7  0  8  7  7  6\n",
            "  0  6  7  9  6  6  9  7  6  6  6  6  6  6  6  6  6  6  6  6  7  6  6  6\n",
            "  6  9  9  6  6  9  7  6  4  6  9  6  6  6  6  6  6  6  6  6  6  6  9  5\n",
            "  6  6  6  9  6  9  6  2  6  6  6  6  9  6  6  6  6  9  6  6  6  6  6  6\n",
            "  6  9  6  6  0  6  6  6  4  6  6  6  6  9  9  7  5  5  3  3  3  3  7  6\n",
            "  6  2 11  6  3  0  2  2  9  3  3  4  6  2  2  2  2  1  6  2  3  3  2  7\n",
            "  6  2  2  9  3  5  3  2  1 13  3  0  5  7  9  5  3  3  2  6  2  2  3 11\n",
            "  4  3  9  0  9 12  0  0 11  9  8  4  0  0  0  6  7  0  4 11  4 11 11  5\n",
            "  7  5  5 12 11  5 11  0 11  6 12  6  8  5 12  9  6 12 11  9  8 11 11  6\n",
            "  0 11  6  8  4  8  6  7 11  5  8  6  6  5  9  8  8  7  6  4  6  4  8  4\n",
            "  6  8  4  2  7  7  5  8  9  6  6  9  6  5  6  7  8  8  7  6  7  5  5  4\n",
            "  8  9  6  7  6  8  6  7  5  7  6  7  8  7  5 11  8  6  5  6  6  8  6  6\n",
            "  5  8  6  6  9 11  7  6  8  8  6  4  9  6  7  8  4 11  2  1  8 11  9  4\n",
            "  8  9  8  4  4  8  8  3 10  8 11  8  8  9  8  4  8  7  1  8  8  4  8  8\n",
            "  4 10  4 11  8 10  7  8  4  8  1  1  5 10  2  8  4  4  8  8 11  8  8  4\n",
            "  4 10  8  3  8  7  3  4 10  0  4  4  8 10  8  4  3  8  9  3  8  8  8  8\n",
            "  7  4  4 11  8  8  4  9  0  4  8  0  2  3  5  5  5  3  6  7  6  2  6  2\n",
            "  2  0  5  3  3  5  7  6  3  3  7  6  0  9  9  2  6  3  2  7  3  5  0  9\n",
            "  6  0  8  2  9  6  5  2  5  3  0  1  6  5  3  6  9  3  5  5  5  3  3  2\n",
            "  0  2  3  0  3  5  5  2  5  5  5  5  3  9  0  9  5  2  7  5  3  3  0  5\n",
            " 10  8  0  0  0 12  8 10  5  3  0 10  3  0  3 11  8  0  3  7 11  4  3  6\n",
            "  0 12  1  3  5  3  9  4  5  1  3  4  3  0  6  1  3 11  5  3 12  3  5  5\n",
            "  1  9  5  3  3  3  3  8  0  3  5  3  6  5  5  6 11  8  0  3  7  0  3  7\n",
            "  4  8  8 13  0  3  3  0  3  3  3  6  5  6  9  2  6  6  6  6  7  9 11  0\n",
            "  6 12  9  4  3  0  3  0  3  0  3 11  3  0  3  3  0  3  0  5  0  3  0  0\n",
            "  0  3  3  0  1  0  3  3  3  3  0  3  0  3  3  3  3  6  6  7  6  6  3  1\n",
            "  3  9  9 12  7  7  5  6  6  6  6  6  9  2  2  5  6  5  6  6  3  3  6  0\n",
            "  0  9  7  5  6  6  6  6  2  3  2  0  6  7  3  9  0  3 13  9  7  3  3  5\n",
            "  0  0  8 12 12  8 11  7  8  3  8  8 11  7  0  3  5  3  2  8  9  6  1  3\n",
            "  5  0  5  8  8  3  0  4  7  1 13  0  5  6 11  1  3  3  7  5  9  2  3  1\n",
            "  0  0  1  2  1  3  1  7  6  5  0  4  3  3 11  9  7  7 11  3  5  3 12 11\n",
            "  2  5  3  7  3  6  3  2  5  6  6  7  6 11  9 11  0  4  2  1  4  3  3  0\n",
            " 12  3  0  3  3 12 11  0 11  0  6 11  0 11  3 11  0  3 12  0  6  0 11 11\n",
            "  3  1 12 12  0 11  1 11 12  4 11 10  3  1  0 11 11  6 10  0 10  9 11 12\n",
            "  1 11 11 12  8 10  1  0 12 10  3  0  0  8 11  3  4  4  8  8 11  6  8  4\n",
            "  8  8  8  4  4  8  4  4  4  8  8  4  4  4  4  8]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKTrtQNEcNwc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "abf44a54-daf6-48d2-c971-5d2532d005b9"
      },
      "source": [
        "\n",
        "# Silhouette score: comprares the similarity of an object to its own cluster with that to other clusters \n",
        "\n",
        "# models labels= models assigned to the model\n",
        "#\n",
        "#\n",
        "print (metrics.silhouette_score(scaled_data,model.labels_))\n",
        "print (metrics.completeness_score(y, model.labels_))\n",
        "print (metrics.homogeneity_score(y, model.labels_))"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.2046255768331342\n",
            "0.18939644179827697\n",
            "0.21934387424949217\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w0Ocy8U_cNwe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5e9553ec-ebec-4127-a0b4-9e77c44c6174"
      },
      "source": [
        "len(np.unique(y))"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQE3AkNHcNwk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from scipy.cluster.hierarchy import dendrogram, linkage"
      ],
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVsSMBJBcNwl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "5307d085-2d8e-4418-fc56-28317b171ecf"
      },
      "source": [
        "# Creating Hierarchical Clustering Dendrogram\n",
        "\n",
        "model= linkage(scaled_data, \"ward\")\n",
        "plt.figure()\n",
        "plt.title(\"Hierarchical Clustering Dendrogram\")\n",
        "plt.xlabel(\"sample index\")\n",
        "plt.ylabel(\"distance\")\n",
        "dendrogram(model, leaf_rotation=90.,  leaf_font_size=8.)\n",
        "plt.show()"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xpAfab24cNwm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e1b79a50-be28-4c7d-a8b1-9cda820b9b91"
      },
      "source": [
        "len(np.unique(y))"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kCuWOVvbcNwo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        },
        "outputId": "7049d002-1f95-4794-a85f-1791430b9783"
      },
      "source": [
        "#******************************************************************************************************\n",
        "# Clustering using K-means\n",
        "# need for spesification of numbers of clusters\n",
        "# clusters in this sample are 14\n",
        "#******************************************************************************************************\n",
        "\n",
        "from sklearn import cluster \n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "n_samples, n_features = scaled_data.shape\n",
        "n_digits = len(np.unique(y))\n",
        "for k in range(2, 15): \n",
        "    kmeans = cluster.KMeans(n_clusters=k)   \n",
        "    kmeans.fit(scaled_data)  \n",
        "    print(k)    \n",
        "    print(metrics.silhouette_score(scaled_data, kmeans.labels_))\n",
        "    print(metrics.completeness_score(y, kmeans.labels_))    \n",
        "    print(metrics.homogeneity_score(y, kmeans.labels_)) \n",
        "    \n",
        "# different results on every iteration because we are using random starting points# best score seems to be when k=13 (sometimes when k=14)"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "0.3160804206956437\n",
            "0.01748956052802014\n",
            "0.004283511685507621\n",
            "3\n",
            "0.2712690890288309\n",
            "0.09333830946090167\n",
            "0.047178895119585326\n",
            "4\n",
            "0.27141363588810574\n",
            "0.08157230707935778\n",
            "0.05349742834246447\n",
            "5\n",
            "0.264274824043344\n",
            "0.1555394783970132\n",
            "0.11921920342643202\n",
            "6\n",
            "0.27390111430441044\n",
            "0.17666325277367081\n",
            "0.14712351125455048\n",
            "7\n",
            "0.27813375062386736\n",
            "0.16437907214944464\n",
            "0.1502193016427626\n",
            "8\n",
            "0.26963860947668\n",
            "0.18486261126468265\n",
            "0.18111787282636088\n",
            "9\n",
            "0.27557725044965187\n",
            "0.16845281046606472\n",
            "0.1715412241273527\n",
            "10\n",
            "0.27125138991156866\n",
            "0.17272048690164382\n",
            "0.1850531102066018\n",
            "11\n",
            "0.27485254037394674\n",
            "0.18967960130628694\n",
            "0.2083048848209933\n",
            "12\n",
            "0.25708577797292004\n",
            "0.17887293085635503\n",
            "0.20730843412470693\n",
            "13\n",
            "0.2757428555657989\n",
            "0.20643555746638143\n",
            "0.24790462543217665\n",
            "14\n",
            "0.27523865617707916\n",
            "0.2032036233986396\n",
            "0.25283399185714245\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LvJfle14cNwp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "75e5d208-8339-407b-be38-1d927dc30ec1"
      },
      "source": [
        "# same command with above, but now creating a list for every score in order to show it to a graph\n",
        "\n",
        "n_samples, n_features = scaled_data.shape\n",
        "n_digits = len(np.unique(y))\n",
        "\n",
        "y_silhouette=[]\n",
        "y_completeness=[]\n",
        "y_homogeneity=[]\n",
        "\n",
        "\n",
        "for k in range(2, 15): \n",
        "    kmeans = cluster.KMeans(n_clusters=k)   \n",
        "    kmeans.fit(scaled_data)  \n",
        "    print(k)    \n",
        "    print(metrics.silhouette_score(scaled_data, kmeans.labels_))\n",
        "    y_silhouette.append(metrics.silhouette_score(scaled_data, kmeans.labels_))\n",
        "    \n",
        "    print(metrics.completeness_score(y, kmeans.labels_))    \n",
        "    y_completeness.append(metrics.completeness_score(y, kmeans.labels_))\n",
        "    \n",
        "    \n",
        "    print(metrics.homogeneity_score(y, kmeans.labels_)) \n",
        "    y_homogeneity.append(metrics.homogeneity_score(y, kmeans.labels_))\n",
        "    \n",
        "\n",
        "   \n",
        "print(\"*********************************************************************************************************************\")\n",
        "print(\"*********************************************************************************************************************\")\n",
        "print (\"silhouette scores are:\\n{}\".format(y_silhouette))\n",
        "print(\"*********************************************************************************************************************\")\n",
        "print (\"completeness scores are:\\n{}\".format(y_completeness))\n",
        "print(\"*********************************************************************************************************************\")\n",
        "print (\"homogeneity scores are:\\n{}\".format(y_homogeneity))\n",
        "print(\"*********************************************************************************************************************\")\n"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "0.3160804206956437\n",
            "0.017489560528020146\n",
            "0.004283511685507623\n",
            "3\n",
            "0.27121160128133726\n",
            "0.09437778328291656\n",
            "0.04771021643296381\n",
            "4\n",
            "0.27141363588810574\n",
            "0.08157230707935778\n",
            "0.05349742834246447\n",
            "5\n",
            "0.264274824043344\n",
            "0.1555394783970132\n",
            "0.11921920342643202\n",
            "6\n",
            "0.27390111430441044\n",
            "0.17666325277367084\n",
            "0.14712351125455048\n",
            "7\n",
            "0.27879443776719676\n",
            "0.1621441545679201\n",
            "0.1480581117908886\n",
            "8\n",
            "0.27277709687098467\n",
            "0.1909480825558646\n",
            "0.18544872213772343\n",
            "9\n",
            "0.27449942414182965\n",
            "0.16593232145761067\n",
            "0.16878332701444224\n",
            "10\n",
            "0.26984565320705517\n",
            "0.16791382003207297\n",
            "0.1805603329799097\n",
            "11\n",
            "0.2580221299998529\n",
            "0.17603168487257703\n",
            "0.19910609203323187\n",
            "12\n",
            "0.2736863444143874\n",
            "0.2011843570744462\n",
            "0.23647214321269086\n",
            "13\n",
            "0.27396922457974016\n",
            "0.20401084094015792\n",
            "0.24543278613509792\n",
            "14\n",
            "0.2717781151713433\n",
            "0.20021922646377197\n",
            "0.24921592976129558\n",
            "*********************************************************************************************************************\n",
            "*********************************************************************************************************************\n",
            "silhouette scores are:\n",
            "[0.3160804206956437, 0.27121160128133726, 0.27141363588810574, 0.264274824043344, 0.27390111430441044, 0.27879443776719676, 0.27277709687098467, 0.27449942414182965, 0.26984565320705517, 0.2580221299998529, 0.2736863444143874, 0.27396922457974016, 0.2717781151713433]\n",
            "*********************************************************************************************************************\n",
            "completeness scores are:\n",
            "[0.017489560528020146, 0.09437778328291656, 0.08157230707935778, 0.1555394783970132, 0.17666325277367084, 0.1621441545679201, 0.1909480825558646, 0.16593232145761067, 0.16791382003207297, 0.17603168487257703, 0.2011843570744462, 0.20401084094015792, 0.20021922646377197]\n",
            "*********************************************************************************************************************\n",
            "homogeneity scores are:\n",
            "[0.004283511685507623, 0.04771021643296381, 0.05349742834246447, 0.11921920342643202, 0.14712351125455048, 0.1480581117908886, 0.18544872213772343, 0.16878332701444224, 0.1805603329799097, 0.19910609203323187, 0.23647214321269086, 0.24543278613509792, 0.24921592976129558]\n",
            "*********************************************************************************************************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hualPI25cNwt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "20a8173a-6247-473f-ab75-266874b614b0"
      },
      "source": [
        "plt.plot(y_silhouette)\n",
        "plt.plot(y_completeness)\n",
        "plt.plot(y_homogeneity)\n",
        "plt.legend([\"Silhouette\", \"Completeness\", \"Homogeneity\"])\n",
        "plt.title(\"K-means Scores\")\n",
        "plt.show()"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JKy6h2GQm8BJ/D5LoHUBWfjH7ThhJf09SNnuSc9ibnFOu3Ts8yIsrGvjSpoEvrRsaJ4DIet44X2T7YHpuEdGH04k+lM7mw+nsOp5NiVnj7KRo18iP7pbEHhUZVOOSgzi/0xfEj2Xmcyg1l7X7Uli19yQpOYUoBVc2CWBAm1D6XVGfNg197VZLTskp5Pt/EvkqOoGDqbn4urtwS+dG3N09nPZh/naJqTaQRO+gtNYkZuRbkn42cZaTwKHU3NKLex6uTrQK9eWKUCP5t7HU/uv5uJfuJykrv7QZJvpwOvtOnAKMWxM7NwkwmmEig+gSEVhl3xqEfZjNml3Hs1m55wSr9pxke2IWAA39PejXuj4DWtfnmubBeLpVbVt/iVmzdn8KX21OYEXcCUxmTVREICO6h3NjhwZ4ucnfXWUk0dcxBcUlHDh5irikbPYmW74FJGeTeqqotEyIrzst6/uQkJFHQrrRLOTj7kLXiMDSZpiOjf1rxcVgYTsnswtYszeFlXtOsH5/KrlFJbi7OHFN83r0bxNK/9b1CQuwXfNcYkYe38Qk8k1MAsezCgjyduOOLmEM79ZE7m67SJLoBWB8Jd5rSfp7knPYfyKHBv4edG9qXDxt3cAXl1pycU5UvUJTCZsPpbMy7iSr9pzkaHoeAK0b+NK/dX0GtKlP5yaBF900WGQysyLuBF9GJ7BufwoAvVoEM6JbOAPbhuLmIn+Dl0ISvRDishjPRuSyytLEE304gxKzJtDLlb5X1Kdf6/r0aRmCv9f5L5AeOHmKr2MS+G5LImm5RTT09+CuqCbc1bUxTYK8qvHTOCZJ9EIIm8rKL2btvhRW7znJ6r0nycgrxtlJERURWFrbbx7iQ0GxmV92JPFV9FGiD2fg4qQY0KY+I7qF07tVyEV/GxDnJ4leCFFlSsya2IRMVu05wcq4k+xJzgGMZyoyc4vJKTTRNNib4d2acHuXMOr7etg5YsckiV4IUW2OZeazes9J1uxNwc/DhWHdmtCjaVCtfnCuNrhQopd7loQQNhUW4Ml9V0Vw31UR9g5FWFh1eVspNVgptVcpdUApNfEc659SSu1WSm1XSq1USkWUWVeilIq1TEttGbwQQojKVVqjV0o5A3OAgUAiEK2UWqq13l2m2FYgSmudp5QaB7wGDLesy9dad7Zx3EIIIaxkTY2+O3BAa31Qa10EfAkMLVtAa71aa51nmf0baGzbMIUQwnFprckqzCI5N7lK9m9NG30YkFBmPhHocYHyDwG/lZn3UErFACZghtb6x4obKKVGA6MBwsPDrQhJCCFqvgJTAWkFaaTkpZCWn0ZqfiqpBanGa15quXmT2UTnkM58euOnNo/DphdjlVL3AVFAnzKLI7TWx5RSzYBVSqkdWuv4sttprecCc8G468aWMQkhhC2ZtZmMggxS81NJy08jJT/FSNgV5tPy08gpzjlre4UiyCOIYM9ggj2DaRbQjBDPEII9gwn3q5qKrjWJ/hjQpMx8Y8uycpRS1wHPAX201qUdY2utj1leDyql1gBXAvEVtxdCiJokrziPnak72ZayjR2pO0jOTSY1P5X0gnRK9NljUni5eBHiFUI9j3q0CmxFcKPg0mRedgr0CMTFqXpveLTmaNFAS6VUU4wEPwK4p2wBpdSVwIfAYK31yTLLA4E8rXWhUioY6IlxoVYIIWoMrTWJpxKJPRnLtpRtbE/Zzt6MvZi1Me5CpF8k4X7htKnXhnoe9Qj2DCbEy6iFB3sEU8+zHl6uNbcbh0oTvdbapJR6DPgDcAbma613KaWmAjFa66XA64AP8I3loYijWutbgDbAh0opM8aF3xkV7tYRQohqV2AqYFfaLralbCtN7ukF6YBRM+8Q0oFHOjxCp5BOdAzpiL977e4HX56MFUI4NK01ybnJxKYYCX3byW3sSd+DSRuD9kT4RdAppFPp1CKgBc5Ota97bnkyVghRZxSVFLE7bbeR1C2J/WS+0aLs6eJJu3rtGNluJJ3rd6ZjSEeCPILsHHHVk0QvhKjVTuadLG1+iU2JJS4tjmJzMQBhPmFENYgyauv1O9EqsBWuTo4z1qy1JNELIWolszbzevTrfBb3GQBuTm60C27HvW3upXNIZzrV70SwZ7Cdo6wZJNELIWodk9nElA1TWBq/lDtb3cltLW6jTVAbXJ3rXm3dGpLohRC1SmFJIc/++SyrElYxvvN4xnYcK10gV0ISvRCi1sgtzuWJVU+wKXkTE7tP5N4299o7pFpBEr0QolbIKsxi3Ipx7E7bzfRe07ml+S32DqnWkEQvhKjxTuadZMzyMRzJPsKbfd+kf3h/e4dUq0iiF0LUaAk5CYxeNpq0gjTev+59ejS8UOe54lwk0QshaqwDGQcYvXw0ReYiPh70MR1COtg7pFrJqqEEhRCiuu1I2cGoP0YBsOD6BZLkL4MkeiFEjbMpaRMPL3sYH1cfFt2wiJaBLe0dUq0miV4IUaOsOrqK8SvG08inEZ/c8AlNfJtUvpG4IEn0QogaY2n8Up5a8xStg1qzcPBC6nvVt3dIDkEuxgohaoTP4z5nxuYZ9GjYg3f6vVOjB/KobSTRCyHsSmvNB9s/4L3Y9+jfpD+v9XkNd2d3e4flUCTRCyHspmwPlLc0v4WXrnmp2sdTrQvkJyqEsAuT2cSLG15kSfwS7m1zL892exYnJZcNq4IkeiFEtSsqKWLC2gmsOLqCcZ3GMa7TOOmBsgpJohdCVKu84jyeXP0kG5M2MqHbBO5re5+9Q3J4kuiFENUmqzCL8SvHszN1J9N6TuPWFrfaO6Q6QRK9EKJapOanMnr5aA5nHebNPm8yIGKAvUOqMyTRCyGq3LFTx3hk2SOk5qcyZ8Acrm50tb1DOjdTIeRnQn5G+akgC9x9wScUfOqDbwPwCgbn2pFCrYpSKTUYeBtwBj7SWs+osP4p4GHABKQAD2qtj1jWjQQmW4q+rLVeZKPYhRC1QHxmPKOXjaagpIB5g+bRKaRT1R+0OP9Mks5LPztxl5vKJPbi3Is4iALvYEvyDz1zEvAJBd8Ky9z9wI4XmytN9EopZ2AOMBBIBKKVUku11rvLFNsKRGmt85RS44DXgOFKqSBgChAFaGCLZdsMW38QIUTNszN1J+NWjMPFyYUFgxfQKrCVbQ+w93eI/ezsZG4qOP82Tq7gFQSegcYU0AQadrTMB5xZXnby8IeCbDh1Ek4lw6kTlvcnIOeE8Zqy13g1F599TBfPMyeB0pNBgwrLGoB3CLi42fZnhHU1+u7AAa31QQCl1JfAUKA00WutV5cp/zdw+jL69cByrXW6ZdvlwGBg8eWHLoSoyf469hdPrXmKQI9A5g2cRxM/G3ZOlp0Evz0LcUvBLwwCm0JQs3Mn6bKTVxC4el1a7dozEAIjLlxGa+NEc+rEmZNBTtkTQzKkHYAjfxnlKmrYCcasvfjYKmFNog8DEsrMJwIXGuLlIeC3C2wbVnEDpdRoYDRAeHi4FSEJIWoiszazLnEd83fO55+T/9DcvzkfDvyQUO9QGx3ADDEfw8qpUFIEA16Aqx+vklrwJVHKOJl4BUH9Nhcuayq0JP+TZ04Mbt5VEpZNryQope7DaKbpczHbaa3nAnMBoqKitC1jEkJUveKSYn499CsLdy3kQOYBGno3ZEK3Cdze8nbbdU52Yhf89AQkRkOzvnDTm1CvuW32bQ8u7kazUUDVd8NsTaI/BpSNpLFlWTlKqeuA54A+WuvCMtv2rbDtmksJVAhR8+QV5/Htvm/5ZPcnnMg7QcvAlrzS6xUGNx2Mq5OrbQ5SnA9/zoQNs4228tvmQsdhdr24WdtYk+ijgZZKqaYYiXsEcE/ZAkqpK4EPgcFa65NlVv0BvKKUCrTMDwImXXbUQgi7SstP4/O4z/lq71dkF2UTFRrFlKun0Cusl227MohfBT//BzIOQ+f7YNA0o1lEXJRKE73W2qSUegwjaTsD87XWu5RSU4EYrfVS4HXAB/jG8ks+qrW+RWudrpSahnGyAJh6+sKsELVOXrrRhupSd7vQTchOYOGuhSyJX0JRSREDwgfwQPsH6BjS0bYHOpUCf0yCHd9AUHMY+RM07W3bY9QhSuua1SQeFRWlY2Ji7B2GEGdoDVs/M+7y8A4x2oZbXmfvqKrVrrRdLNi5gOVHluOsnLml+S2MbDeSpv5NbXug0z/rZZOhKBd6/Qeu/T9w9bDtcRyQUmqL1jrqXOtqx2NdQthLfobRdLDrB4joBbkn4fM7oN3tMHiG8WCMg9JaszFpI/N3zmdT0iZ8XH0Y1W4U97W5jxCvENsfMHU//PQkHFkP4dfAzbMg5ArbH6cOkkQvxPkc2QjfPwI5STBgCvR8Aswm+OttWPs6HFgJA1+ELqPAyXH6UTeZTSw/spwFOxcQlx5HiGcIT3V9irta3YWPm08VHLAQ1r8F6/4Hrp5w8ztw5b8c6mdqb5LohaioxARrXzOSeUAEPLiM4kYdeS/2XTxdPLm9+4MEt7sdfvmPUduPXWzUPkPb2Tvyy5JvymfJgSUs2rWIxFOJRPpF8tI1LzGk2RDcnKvoPvXD641afNp+aH8nDH7VeEpU2JS00QtRVsYRoxafsAk63Q03vk6hixtPr3maNYlrAHBxcmFgxEBGtBrOlUl7Ucueg8JsuOZx6P0suNWuQa2zCrNYvGcxX8R9QUZhBh1DOvJg+wfp16Rf1Y34lJcOy5832uMDIurkdQ9bkzZ6Iayx41ujhg5w+0fQ8S5yi3N5fMU4opOjea7Hc/Ro2IOv937NkgNL+O3Qb7QKbMXwG19gyIFNeK1/C3Z+X2uSVtKpJD7Z/Qnf7f+OfFM+vRv35oF2D9A1tGvVjfaktXEnze+TjOsfPZ+APhNr3cmxtpEavRCFOfDbBIj9HBp3hzvmQWAkGQUZjFsxjj3pe5jWcxo3N7+5dJO84jx+PfQrX+75kr0Ze/Fx9WFoSBTD9v1Fs5R4aH8HXP9qjbtYm1ecx99Jf7PsyDL+OPQHADc2u5GR7UbavsOxitIPwi//Z9wbH9YVbn4bGnSo2mPWIReq0UuiF3XbsS3w3cPGAznXPg19JoCzCydyTzBm+RgSchJ4o88b9Avvd87NtdbEpsTy5Z4vWXZkGSaziR4eDRiREEffYidcasDF2oScBNYmrmVd4jqik6MpMhfh4+rDrS1u5f6299PQp2HVBlBSbDzV+udMo+fIAS9At4fAyblqj1vHSKIX9mMuMf7Ra9p90GYzbHgHVk0zuoe9fS5E9gSMh4IeWf4IGQUZzO4/m+4Nu1u1y9T8VL7f/z3f7PuG5NxkQrUzd2WmcYdvK4KHzIbQtlX5iUoVm4uJPRnL2sS1rE1cy8GsgwBE+kXSu3FvejfuTZf6XXB1tlEXBReSEG30T3NyF7QeAje8Bv5n9WsobEASvah+5hKjLXb1K8btia0GQ6cR0GKg/XsazE6CH8bAoT+h7VCjCcHT6KVjX8Y+xiwfQ7G5mA+u+4D2we0vevcms4k/E//kqz1fsTFpIy5aMzAvnxERN3DldTNR7rbvoTCjIIP1x9bzZ+KfbDi2gZziHFycXIgKjaJP4z70btybcL9q6Bm2MAeStsPxrcYF7bifwK8R3Pg6tL6p6o9fh0miF9VHa9j7K6ycBilx0KAjNOkOu36EvFTwDIL2t0PHEdA4qvo7ptrzKyx51BiY4oaZxv3alhi2p2xn3IpxeDh78OHAD2kR2OKyD3co6xBf71zEkgM/kkMJrUpgeKu7GNLj6cvq1VFrzd6MvaW19u0p29Fogj2DuTbsWno37s3Vja7G27Vqur0FoCgPkncYSf30lLoPY4whwK+xcSLtN8kYhk9UKUn0onocWgcrXzK6ka3XAvpPhjZDjfbpkmLjIty2L40TganA6MOk43CjJ8IgGz9KX1FxvvFYffRHxsnnjo8h5MzFx7+T/ubfq/5NPY96zBs0j8a+jW16+LziPH6Nfpsv9yxmr7PGB2duaX4zwzs8QDP/ZlbvY1PSJtYeM9rbT+SdAKB9vfalTTJt6rWpmlsiiwuMboKP/wPHY42knhIH2mys9wmFRl2g0ZWWqbPcD1/NJNGLqnV8qzEQRPwqY7SfPhOg873nHzi5IAt2L4XtX8HhdcayJldBp+HQ7gDItaIAACAASURBVLbSZhSbObELvn3ISExXP2ZcDCzTMdnKIyt5Zu0zRPhFMHfg3Kp5vN9CFxewbeVzLD64hGVeHpiUokeDHoxoPYK+Tfri4lT+Z5aYk2jU2o+tJTrJuJDq5eLFNY2uoXfj3lzb+FqCPYNtG6SpCE7uLl9TP7nbeCoYjEGxSxO6ZfKr4gu6olKS6EXVSNkHq1+G3UuMJplr/w+6PXxxF14zE4y2/O1fQcoecHaDloOM9vyWgy6vp0itYfM8oybv4Q+3vQ8tyt/fvuTAEl7Y8ALt67Xnvevew9/d/9KPdzFS95P687/5IWMnXwcGkazMhHqFcleru+gY0pGNSRtZm7CW+Kx4ACL8Irg27Fr6NOlD1/pdbXchtcRk/NzLJvUTO43Rm8A46TbsXD6p+zeWvuBrIEn0wrYyE+DPGRD7hTH+5tWPGjVlD79L36fWkLTNSPg7voHcFPAIONOe36T7xSWX3FSjLX7f78YJY+h74FO+pv553OfM2DyDHg178E6/d2w3EpK1tIZtizH98RxrnYr4sklrNhYawzm4KBe6NuhK7zCjSSbSP7Ly/ZkKofAUFFmmsu+Lci3zOcb7/AxI3gnJ288MpO3uZzS5lE3qARGS1GsJSfTCNnJTYd2bED3PmO/2sFGL97Zx00GJCQ6uNtrz9/wCpnxj8OfT7fmVDR8Xvwp+GGsks4HToMeYcslKa80H2z7gvW3vMSB8AK/1fq3q+nKxRm6a0R1A7OccrhfBkfa30tWnCT4lpvLJ+awknmt5b1lvLrbueE4uxsXR+m3LJ/XAptKRWC0miV5cnoJs2DgHNr4LxXnQ+R7jsfVqGOuSgmzjFr3tXxoXe9HG06udhhtdBZcdbchUBKumGg/nBF8Bd86HBuVvjzRrM69Hv85ncZ9xS/NbeOmal85qF7ebQ2uNLhjSDpRf7uwGbj7g7mO8lr73BjffMu99jARe+t7HWO/mXf69i7vU0h2QJHpxaYoLjLtU1v0P8tOhzS3GnTT26iM86xjs+Bq2fWVcWHVytbTnD4d6LeHHcZAUC1EPwqDpZ/WfYjKbeHHDiyyJX8J9be7jmW7PVF2nXZfKVGR0FeDmdSap2/u5A1ErSKIXF6fEZPT78udMyD4GzfoZd6qEdbF3ZAatjbbl7V8b7fmnjNsM8QyEW96FNkPO2qSopIgJayew4ugKxncaz9hOY6uu4y4h7EB6rxTWMZshbgmsmm70Dx7WFW59H5r1sXdk5SkFDTsZ03UvwaE1kLgFrrzvnI/X5xXn8eTqJ9mYtJEJ3SZwX9v7qj9mIexIEr0wasjxK4174ZO2QUhrGP658ch6Ta/1OrsYt0y2OHe3wFmFWYxfOZ6dqTuZ1nMat7a4tZoDFML+JNHXdQnRxtOsh9dBQDjc+oFxZ4sD9CyYmp/K6OWjOZx1mP/1+R/XRdT8PuKFqAqS6Osqs9kYSWnnt+AdAje8Dl1HXt4DSjXIsVPHeGTZI6TmpzJnwByubnS1vUMSwm6suuVAKTVYKbVXKXVAKTXxHOt7K6X+UUqZlFJ3VlhXopSKtUxLbRW4uEw7vzWma/4N/46FHqMdJskfzDzI/b/dT2ZhJvMGzZMkL+q8Smv0SilnYA4wEEgEopVSS7XWu8sUOwqMAp4+xy7ytdadbRCrsBVTodEPe4OOxsVMB3pIZlfqLsauGIuLkwsLBy+s+lGThKgFrPkP7w4c0Fof1FoXAV8CQ8sW0Fof1lpvB8xVEKOwteiPIfMoDHSsJB+dHM1Dyx7C29WbRYMXSZIXwsKa//IwIKHMfKJlmbU8lFIxSqm/lVJyy4O9FWTB2tehWV9o3t/e0djMnwl/Mm7FOEK9Qlk0eFH1DLIhRC1RHRdjI7TWx5RSzYBVSqkdWuv4sgWUUqOB0QDh4fIPWqX+ett4yvW6l6rlcEeyj7ApaRPF5mJMZhPF5mKKS4qN19NTZfNllpXu4/R7y/KCkgLa1WvH+9e9T6CHjbs5FqKWsybRHwPKdmrS2LLMKlrrY5bXg0qpNcCVQHyFMnOBuWA8GWvtvsVFyk6Cje9B+zuNXgqrkFmbWbxnMW9teYvCksKz1rsoF1ydXXFxcsHVyfXM5Ox61ryPqw+u7meXKbutn7sfI64YgY+bT5V+LiFqI2sSfTTQUinVFCPBjwDusWbnSqlAIE9rXaiUCgZ6Aq9darDiMq151Rg8ov/kKj3MidwTPP/X82xM2kjvxr15ttuz+Lv5l0vQNa6PGSEcWKWJXmttUko9BvwBOAPztda7lFJTgRit9VKlVDfgByAQuFkp9ZLWuh3QBvhQKWXGuB4wo8LdOqK6pOyDrZ9C99FVOmzf74d/Z9rGaRSbi3n+que5q9Vd0qeMEHZmVRu91vpX4NcKy14o8z4ao0mn4nYbgA6XGaOwhZUvgas39H6mSnafXZTNq5te5eeDP9MhuAOv9HrFusEyhBBVTp6MrQuOboI9P0O/ybYfJATjtsb/rv8vKXkpjO80nkc6PlJz+ngXQkiid3haw/IXwCcUrh5v010XlhQy+5/ZfLL7E8L9wvn0hk/pECJf4ISoaSTRO7q9v0HC3zDkLWN0IVvtNn0vk9ZPYn/GfoZfMZynuj5V/WOuCiGsIonekZWYYMWLUK8FXPkv2+zSXMInuz9h9tbZ+Ln5MWfAHHo37m2TfQshqoYkeke27QtI3QvDPgVn18ve3fFTx3lu/XPEnIihf5P+TLlmCkEeQZVvKISwK0n0jqooD1a/Co27QZubL2tXWmt+Pvgzr2x6BbM2M/Waqdza4la5bVKIWkISvaPa9AHkHIc7PrqsUaIyCzKZ9vc0lh1ZRpf6XZjeazqNfc+6k1YIUYNJondEeemwfha0GgyRPS95NxuObWDyX5PJKMzgiS5P8EC7B3B2gJGnhKhrJNE7onX/g6IcGDDlkjbPN+Xz1pa3WLxnMc39mzNnwBza1Gtj4yCFENVFEr2jyTwKm+dCp3sgtO1Fb74rbReT1k3iUNYh7mtzH090eQIPF48qCFQIUV0k0TuaVdNBOUG/SRe1mclsYv7O+bwf+z5BnkHMHThXhuATwkFIonckyTtg+1fQ89/gb/0F04TsBP67/r/EpsQyOHIwk6+ajL+7fxUGKoSoTpLoHcmKF8HDH3r9x6riWmt+OPADMzfPxFk5M+PaGdzU7KaqjVEIUe0k0TuKg3/CgRUwcBp4Vj7CklmbeebPZ1h2ZBndG3Rneq/pNPBuUA2BCiGqmyR6R2A2w4op4NfY6G/eCt/v/55lR5YxvvN4xnQcIwOBCOHAJNE7gt0/wvGtcOv74Fr5HTLpBem8teUtokKjGNtxrDzhKoSDk2pcbWcqgpVToX476Djcqk1mbZlFXnEek6+aLEleiDpAavS13T+LIOMQ3PMNWPHU6taTW/nhwA880P4Bmgc0r4YAhRD2JjX62qwwB9bMgIhe0HJgpcVNZhPT/p5GA+8GjO04thoCFELUBFKjr802vAt5qTBwqlUdl30R9wX7M/Yzq+8sGSREiDpEavS11amTsGE2tB0KjbtWWvxE7gnmxM7h2rBr6R/evxoCFELUFJLoa6s/Z4KpwOqOy96IeYMSXcKkHpPkAqwQdYwk+tooLR62LISuo6Be5RdUNxzfwO+Hf+fhDg/TxLdJlYcnhKhZrEr0SqnBSqm9SqkDSqmJ51jfWyn1j1LKpJS6s8K6kUqp/ZZppK0Cr9NWTgVnd+gzodKiRSVFvLLpFcJ9w3mg/QPVEJwQoqap9GKsUsoZmAMMBBKBaKXUUq317jLFjgKjgKcrbBsETAGiAA1ssWybYZvw66DELcYDUn0mgG9opcUX7FzAkewjfHjdh7g7u1dDgEKImsaaGn134IDW+qDWugj4EhhatoDW+rDWejtgrrDt9cByrXW6JbkvBwbbIO66SWujqwOvYLjm8UqLJ+QkMG/HPAZFDOKasGuqIUAhRE1kTaIPAxLKzCdallnDqm2VUqOVUjFKqZiUlBQrd10HHVgBh9cZtXl33wsW1VozY/MMnJUzz3Z7tpoCFELURDXiYqzWeq7WOkprHRUSEmLvcGomcwksnwKBTY2LsJVYnbCatYlrGd95PKHelTfxCCEclzWJ/hhQ9laNxpZl1ricbUVZ27+Gk7tgwPPg4nbBonnFeczYPIOWgS25p8091RSgEKKmsibRRwMtlVJNlVJuwAhgqZX7/wMYpJQKVEoFAoMsy2qvX/4P5lxl3PlyfKvRbl7Vigtg9XRo2Bna3lZp8bnb55KUm8TkHpNxdXKt+viEEDVapYlea20CHsNI0HHA11rrXUqpqUqpWwCUUt2UUonAXcCHSqldlm3TgWkYJ4toYKplWe2UtB2iPzIeVFo/C+b2hVkd4PdJcGSD0bxSFaLnQVaC0dWB04V/ZfGZ8SzatYihzYfSJbRL1cQjhKhVlK6OGulFiIqK0jExMfYO49w+HwYJf8MT20GbYe9vEPcTxK+CkkLwDoHWN0Hrm6Fp70qbWKySnwFvd4awrvCv7y9YVGvNQ8seYm/6Xn667SeCPIIu//hCiFpBKbVFax11rnXSqZm1EjbD/j9gwAvgGWAsu/JeYyrMgf3LjaS/41vjqVV3f2h1PbS5GVoMADfvSzvu+llQkAUDX6q06C+HfiE6OZrnr3pekrwQopQkemutnGrU2Huco3tfd19of7sxFRfAwTVG0t/7C+z4Glw8jWTf5hYj+Z8+UVQm6xhs+gA6DoMGHS5YNKcohzei36BDcAfuaHnHxX8+IYTDkkRvjYNrjPvXB8+ovGbu6gFXDDamkrfhyF9G0t/zszE5uUDTPkZNv/VN4FP//Pta84rRRNTvuUpDfHfru2QUZjDnujk4WzEAiRCi7pBEXxmtYeU0Y+DtrhfZV4yzCzTrY0w3vAbHtsCen2D3Uvj5Sfj5PxB+NbQZAq2HQGDEmW1PxkHsF9BjXPnl57A7bTdf7v2SYa2G0a5eu0v4kEIIRyaJvjJ7f4NjMXDzO1YNvH1eTk7QpJsxXfcSnNxt1PTjfoI//mtMDTsZNf02t8CKl8DNB3o/fcHdmrWZl/9+mQD3AB7vUnm3CEKIukcS/YWYzcb960HNoLMNHzxSCkLbGVPfiUa3w3t+NpL+qpeNCYwLv14Xvqj63f7v2JG6g1d6vYKfm5/tYhRCOAxJ9Bey63s4sRNu/wicq/DBo3rNoecTxpR9HPb8Aqn7jWabC0gvSGfWlllEhUYxpNmQqotPCFGrSaI/nxITrHkV6reF9tV4F4tfI+j+iFVFZ22ZRV5xHpOvmiyjRgkhzksS/flsWwxpB2DEF5U+jWoPW09u5YcDP/Bg+wdpHlD5KFNCiLqr5mWwmsBUaIzJGtYVrrjR3tGcxWQ2Me3vaTTwbsCYjmPsHY4QooaTGv25bFlo9C1zyzvGhdMa5ou4L9ifsZ9ZfWfh5epl73CEEDWc1OgrKsqFtW9ARC9o1s/e0ZzlRO4J5sTO4dqwa+kf3t/e4QghagGp0Ve0eS7knoThn9bI2vzrMa9TokuY1GOSXIAVNldcXExiYiIFBQX2DkWch4eHB40bN8bV1fo7ASXRl5WfaXQi1nIQhF9l72jOsuH4Bv44/AePdn6UJr5NKt9AiIuUmJiIr68vkZGRUpGogbTWpKWlkZiYSNOmTa3eTppuyto4Bwoyof9ke0dylqKSIl7Z9AoRfhE80P4iu2IQwkoFBQXUq1dPknwNpZSiXr16F/2NS2r0p+Wmwt/vQduhRlcENcyCnQs4kn2ED6/7EHdnd3uHIxyYJPma7VJ+P1KjP239W1CcZ1VPkdUtISeBeTvmMShiENeEXWPvcIQQtYwkejC6HYj+CDqOgJAr7B1NOVprXt30Ks7KmWe7PWvvcISoctOnT6ddu3Z07NiRzp07s2nTJh5++GF2794NQGRkJKmpqRw+fJj27dtXaSyHDx/miy++KJ2PjY3l119/rdJjVgVpugFY+7ox3mvfCfaO5CyrElax7tg6no56mlDvUHuHI0SV2rhxIz///DP//PMP7u7upKamUlRUxEcffWSXeE4n+nvuMTo1jI2NJSYmhhtvrHkPUl6IJPr0Q/DPJ9BlJARG2juacvKK85i5eSYtA1tyTxsb9p4phBVe+mkXu49n23SfbRv5MeXm84+ZkJSURHBwMO7uxnWo4OBgAPr27csbb7xBVFT5IVFLSkp45JFH2LBhA2FhYSxZsgRPT09iY2MZO3YseXl5NG/enPnz5xMYGFhuP6mpqURFRXH48GFKSkqYOHEia9asobCwkEcffZQxY8YwceJE4uLi6Ny5M3fffTdz5swhPz+f9evXM2nSJIYMGcLjjz/Ozp07KS4u5sUXX2To0KE2/ZnZgjTd/DnTGPWp9zP2juQsc7fPJSk3ick9JuPqVIW9ZwpRQwwaNIiEhARatWrF+PHj+fPPPy9Yfv/+/Tz66KPs2rWLgIAAvvvuOwDuv/9+Zs6cyfbt2+nQoQMvvXThMZc//vhj/P39iY6OJjo6mnnz5nHo0CFmzJjBtddeS2xsLBMmTGDq1KkMHz6c2NhYhg8fzvTp0+nfvz+bN29m9erVPPPMM+Tm5trs52ErdbtGn7IXtn8FV40Hv4b2jqac+Mx4Fu1axNDmQ+kS2sXe4Yg66EI176ri4+PDli1bWLduHatXr2b48OHMmDHjvOWbNm1K586dAejatSuHDx8mKyuLzMxM+vTpA8DIkSO56667LnjcZcuWsX37dr799lsAsrKy2L9/P25ubpVut3TpUt544w3AuD316NGjtGnTxurPXB3qdqJfPR1cvaDXU/aOpBytNdM3TcfL1YunompWbEJUNWdnZ/r27Uvfvn3p0KEDixYtOm/Z0008p7fLz8+/4L5dXFwwm80A5e5F11oze/Zsrr/++nLl16xZc8H9aa357rvvuOKKmnUTR0VWNd0opQYrpfYqpQ4opSaeY727Uuory/pNSqlIy/JIpVS+UirWMn1g2/Avw/FY2L0Ern4UvOvZJYTc4lx2p+3m90O/8+G2D3lu/XPc9+t99PmqD9HJ0TzR5QmCPC48wpQQjmTv3r3s37+/dD42NpaIiAuPmVyRv78/gYGBrFu3DoBPP/20tHYfGRnJli1bAEpr7wDXX38977//PsXFxQDs27eP3NxcfH19ycnJKS1Xcf76669n9uzZaK0B2Lp160XFWl0qrdErpZyBOcBAIBGIVkot1VrvLlPsISBDa91CKTUCmAkMt6yL11p3tnHcl2/Vy+ARYCT6KpRXnEdCTgJHso9wNOeo8ZptvKYVpJUrW9+rPhF+EfQP70/74Pbc3vL2Ko1NiJrm1KlTPP7442RmZuLi4kKLFi2YO3cud95550XtZ9GiRaUXY5s1a8aCBQsAePrppxk2bBhz587lpptuKi3/8MMPc/jwYbp06YLWmpCQEH788Uc6duyIs7MznTp1YtSoUYwcOZIZM2bQuXNnJk2axPPPP8+TTz5Jx44dMZvNNG3alJ9//tmmPxNbUKfPROctoNTVwIta6+st85MAtNavlinzh6XMRqWUC5AMhAARwM9aa6tvdo2KitIxMTEX/UEuytG/Yf71cN2L0Os/l727wpJCErITOJJzJomfTuon806WK1vPox4RfhFE+EUQ7hduvPqG08S3iXQ5LOwuLi6uxrUvi7Od6/eklNqitY46V3lr2ujDgIQy84lAj/OV0VqblFJZwOn2kKZKqa1ANjBZa72u4gGUUqOB0QDh4eFWhHQZtIaV08C7PnQffdGbH8k+wpqENUZCtyT25NxkNGdOmIHugYT7hXNVw6sI9w0vTerhvuH4uPnY8tMIIUSlqvpibBIQrrVOU0p1BX5USrXTWpe7OVdrPReYC0aNvkojOrgajqyHG14HN++L2nR/xn5G/T6K7KJs/Nz8iPCLoEtoFyJ8y9TO/cLxc/OrouCFEOLiWZPojwFl+8RtbFl2rjKJlqYbfyBNG+1ChQBa6y1KqXigFVDFbTPnoTWsnAr+TaDryIvaNDEnkTHLx+Du7M7SW5fS1N/6LkKFEMKerLnrJhpoqZRqqpRyA0YASyuUWQqczpx3Aqu01lopFWK5mItSqhnQEjhom9AvwZ5f4PhW6DMBXKzvATI1P5Uxy8dQUFLAhwM/lCQvhKhVKq3RW9rcHwP+AJyB+VrrXUqpqUCM1nop8DHwqVLqAJCOcTIA6A1MVUoVA2ZgrNY6vSo+SKXMJcZ98/VaQKe7rd4spyiH8SvGk5KfwtyBc2kZ2LIKgxRCCNuzqo1ea/0r8GuFZS+UeV8AnPXomdb6O+C7y4zRNnZ+Dyd3wx0fg7N1lyYKTAU8vupx9mfsZ/aA2XSuX/PuEhVCiMrUjb5uSoqN2nxoe2hn3b3pJrOJZ9Y+wz8n/mF6r+n0CutVxUEKIQCSk5MZMWIEzZs3p2vXrtx4443s27evSo61Zs0ahgwZcsEytbVr4rLqRqKP/RwyDhlDBDpV/pHN2syUDVNYk7CGST0mcWOz2tUlqRC1ldaa2267jb59+xIfH8+WLVt49dVXOXHihN1icoRE7/h93RQXwJ+vQVgUtBpcaXGtNW/GvMnS+KWM7zSeu1tb354vhEP5bSIk77DtPht0gBvO30nZ6tWrcXV1ZezYsaXLOnXqhNaaZ555ht9++w2lFJMnT2b48OGsWbOGKVOmEBAQwI4dOxg2bBgdOnTg7bffJj8/nx9//JHmzZszatQoPDw8iImJITs7mzfffPOsmnxubu5ZXQ7fcMMNvPDCC1Z1Tbxw4UKWLl1KXl4e8fHx3Hbbbbz22muA0fnZlClTKCwspHnz5ixYsAAfHx8mTpzI0qVLcXFxYdCgQbzxxht88803vPTSSzg7O+Pv78/atWsv+8fu+Il+ywLIPga3vgdWjLU4f+d8Fu1exN2t72Zsp7GVlhdC2M7OnTvp2rXrWcu///57YmNj2bZtG6mpqXTr1o3evXsDsG3bNuLi4ggKCqJZs2Y8/PDDbN68mbfffpvZs2cza9YswBhEZPPmzcTHx9OvXz8OHDhQ7hinuxyeP38+mZmZdO/eneuuu46pU6cSExPDu+++C8B///vfc5YDo/a/detW3N3dueKKK3j88cfx9PTk5ZdfZsWKFXh7ezNz5kzefPNNHn30UX744Qf27NmDUorMzEwApk6dyh9//EFYWFjpssvl2Im+8BSs+x807Q3N+lZa/Nt93zLrn1nc0PQGJnafKIMki7rtAjXv6rZ+/XruvvtunJ2dCQ0NpU+fPkRHR+Pn50e3bt1o2NDoZrx58+YMGjQIgA4dOrB69erSfQwbNgwnJydatmxJs2bN2LNnT7ljnK/L4YouVG7AgAH4+/sD0LZtW44cOUJmZia7d++mZ8+eABQVFXH11Vfj7++Ph4cHDz30EEOGDCn9htGzZ09GjRrFsGHDuP122/R35diJftMHkJsC/RdXWnT5keVM+3savcJ6Mb3ndJxU3bh8IURN0q5du3K9SlqjbFfFTk5OpfNOTk6YTKbSdRUrbhXnz9fl8KZNm6wuV7HbZJPJhNaagQMHsnjx2Xlo8+bNrFy5km+//ZZ3332XVatW8cEHH7Bp0yZ++eUXunbtypYtW6hX7/J62HXcbJafCRveMdrlm3S7YNG/k/5mwtoJdAzuyJt938TVWUZzEsIe+vfvT2FhIXPnzi1dtn37dgICAvjqq68oKSkhJSWFtWvX0r1794va9zfffIPZbCY+Pp6DBw+elajP1+Xw5XZNfNVVV/HXX3+VNhXl5uayb98+Tp06RVZWFjfeeCNvvfUW27ZtAyA+Pp4ePXowdepUQkJCSEhIuNDureK4NfoNs6EgC/o9d8FiO1N38sSqJ4jwi+DdAe/i6eJZTQEKISpSSvHDDz/w5JNPMnPmTDw8PIiMjGTWrFmcOnWKTp06oZTitddeo0GDBmc1v1xIeHg43bt3Jzs7mw8++AAPD49y68/X5XC/fv0uq2vikJAQFi5cyN13301hYSEAL7/8Mr6+vgwdOpSCggLjJpA33wTgmWeeYf/+/WitGTBgAJ06dbqEn2R5lXZTXN1s0k3xqRR4uxO0GgR3LTxvsYNZBxn520i8Xb355IZPqO9V//KOK0Qt56jdFI8aNYohQ4ZcdL/2NdXFdlPsmE03698CU/4Fa/PJucmMWT4GJ+XE3IFzJckLIRyW4zXdZB2D6I+g0z0QfO5+aTIKMhi9fDSnik6xYPACwv2quA98IYRdLVy40N4h2JXjJfq1r4E2Q59nz7k6tziX8SvGc/zUcT647gNaB7Wu5gCFEKJ6OVaiTz8IWz+Drg9A4NkDCheVFPHE6ieIS49jVr9ZRDU4Z3OWEEI4FMdK9GtmgJMr9H76rFUl5hImrpvIpqRNTO81nb5N+lZ/fEIIYQeOczE2LR62fw09RoNvg3KrtNa8vOlllh9ZzjNRz3BL81vsFKQQQlQ/x0n0Qc3g7sXQ88mzVs3eOptv933LIx0e4f5299shOCGENXx8fMrNL1y4kMcee8xO0djOjTfeSGZmJpmZmbz33nvVfnzHSfRKwRU3gFdQucWLdi1i3o553NnqTh6/8nE7BSeEqMt+/fVXAgIC7JboHauNvoIlB5bwRswbDIwYyOQek6WTMiEuwszNM9mTbv2Tp9ZoHdSaCd0nXNK2hw8f5sEHHyQ1NZWQkBAWLFhAeHg4o0aNwtPTk61bt3Ly5Enmz5/PJ598wsaNG+nRo0fprZWLFy/mlVdeQWvNTTfdxMyZMwH4+OOPmTlzJgEBAXTq1Al3d3feffddUlJSGDt2bGmHZbNmzaJnz568+OKLHD16lIMHD3L06FGefPJJ/v3vfwPw2Wef8c4771BUVESPHj147733cHZ2JjIykpiYGCZOnEh8fDydO3dm4MCBnDhxgttvv51bb70VgHvvvZdhw4YxdOjQy/xJl+c4NfoK1iSsYcqGKVzV8CpmXDsDmdyd5wAACUNJREFUZydne4ckhKhE/v+3d/cxVd1nAMe/TxDBl6YvVqlKM7FpOm8R6VTWSRx1qOuG1T86EzdnmdPMRMccWd90SVPXZiVxaWetqTVdsVEmNsCcWbJZ0YUlFirY3fkCdnWrdBRfEF+w1rSgz/44hzugaC/3Xjjcs+eT3Nx7zz0vz497ee45v3Pu77l6lczMzNDtmWdCFUspKCggPz+fw4cPs3jx4lByBbhw4QLV1dW89NJLzJ8/n8LCQo4dO8aRI0cIBoM0Nzfz1FNPsX//foLBILW1tezatYvm5maee+45ampqOHDgQLchFVavXk1hYSG1tbWUl5ezfPny0GvHjx9nz549HDx4kHXr1tHe3k5DQwM7d+7kwIEDBINBEhISKCkp6da+oqIi7rnnHoLBIOvXr2fZsmWhL6JLly7xzjvvkJeXF/O/qy/36OtO1/F41eMERgXYMGsDQxOGeh2SMXEn0j3vaAwbNoxgMBh6vnXrVjqHRKmurqaiogKAJUuW8OST//utzCOPPIKIMHnyZFJSUpg8eTLgjIZ58uRJGhsbeeihhxg9ejTg7Dl3FvTIycnhjjucLt+FCxeGyhZWVlZSX18f2kZbWxuffPIJAHl5eSQlJZGUlMSYMWM4c+YM+/bt49ChQ0yf7gyiePXqVcaMufkv7nNycli5ciUtLS2Ul5fz6KOPMmRI7NOy7xL98fPHKdhfwLiR49iUu4nhicO9DskY08+6Dk3cc9jijo4OEhP7PiLt9evXqamp+cLgZ123B92HI87Pz+eFF17o03Yee+wxtm/fTmlpKcXFxX2OMxy+6rppbGtkxd4VjBw6ki1ztnB78u1eh2SMiZEZM2ZQWloKQElJCTNnzgx72aysLKqqqjh37hzXrl1jx44d5OTkMH36dKqqqrhw4QIdHR2Ul5eHlpk7dy4bN24MPe96pNGb3NxcysrKOHv2LADnz5+nsbGx2zw9hzwGZ8C1zipYgUAg7Db1RViJXkQeFpH3ReSEiDzdy+tJIrLTff1dEZnQ5bU17vT3ReTbsQu9u5ZPW1ixdwWqymtzXuOuEXd9+ULGmLixceNGiouLycjIYNu2bWzYsCHsZceOHUtRURGzZs1iypQpTJ06lQULFjB+/HjWrl1LVlYW2dnZTJgwIVQh6uWXX6auro6MjAwCgQCbN2++6TYCgQDPP/88c+fOJSMjgzlz5nDq1Klu84waNYrs7GzS09N54oknAEhJSWHSpEksXbq0j3+RPlDVm96ABOBfwERgKPAPINBjnpXAZvfxImCn+zjgzp8EpLnrSbjZ9qZOnaqRaPusTVdVrtKjLUcjWt4Yo1pfX+91CAPu8uXLqqra3t6u8+bN04qKigHd/pUrV3TixIl68eLFsJfp7X0C6vQGeTWcPfos4ISq/ltVPwdKgZ7X/iwA3nQflwG54lzLuAAoVdXPVPVD4IS7vpi7ZegtvJL7CvffeX9/rN4Y41PPPvssmZmZpKenk5aWFrrUcSBUVlYyadIkCgoKQkcS/SGck7Hjga61rJqAr99oHlXtEJFLwCh3ek2PZcdHHK0xxsRYZ5FvL8yePfsL/fj9YVCcjBWRn4hInYjUtbS0eB2OMf/XdJBVnTPdRfL+hJPoPwbu7vI81Z3W6zwiMgS4FWgNc1lUdYuqTlPVaZ3XuRpjBl5ycjKtra2W7AcpVaW1tbXXSz5vJpyum1rgXhFJw0nSi4Af9JhnN5APVAPfA/arqorIbuD3IvIiMA64FzjYpwiNMQMmNTWVpqYm7Mh68EpOTiY1NbVPy3xponf73H8K7MG5AucNVT0mIr/COcu7G/gdsE1ETgDncb4McOd7C6gHOoBVqnqtTxEaYwZMYmIiaWlpXodhYkwG2yHatGnTtPMnz8YYY8IjIodUtdeyeYPiZKwxxpj+Y4neGGN8btB13YhICxDNhaV3AudiFI6X/NIOsLYMVn5pi1/aAdG15Suq2utli4Mu0UdLROpu1E8VT/zSDrC2DFZ+aYtf2gH91xbrujHGGJ+zRG+MMT7nx0S/xesAYsQv7QBry2Dll7b4pR3QT23xXR+9McaY7vy4R2+MMaYLS/TGGONzvkn0X1buMF6IyN0i8lcRqReRYyKy2uuYoiEiCSLydxH5k9exRENEbhORMhE5LiINIvINr2OKlIgUup+toyKyQ0T6NhSih0TkDRE5KyJHu0y7Q0T2isgH7n1cFIu+QVvWu5+xwyLyBxG5LRbb8kWiF5EEYBPwHZzyhd8Xkf6pstv/OoBfqGoAeBBYFcdtAVgNNHgdRAxsAP6iql8FphCnbRKR8cDPgGmqmo4zUOEib6Pqk63Awz2mPQ3sU9V7gX3u83iwlS+2ZS+QrqoZwD+BNbHYkC8SPeGVO4wLqnpKVd9zH1/GSShxWZVLRFKBPOB1r2OJhojcCnwTZ5RWVPVzVb3obVRRGQIMc2tHDAeaPY4nbKr6N5wRcrvqWsr0TWDgagFGobe2qOrbqtrhPq3BqeERNb8k+t7KHcZlcuxKRCYADwDvehtJxH4LPAlc9zqQKKUBLUCx2w31uoiM8DqoSKjqx8BvgI+AU8AlVX3b26iilqKqp9zHp4EUL4OJoR8Df47FivyS6H1HREYC5cDPVbXN63j6SkTmAWdV9ZDXscTAEOBrwKuq+gBwhfjpHujG7b9egPPlNQ4YISI/9Daq2FHnevG4v2ZcRH6J041bEov1+SXRh1WyMF6ISCJOki9R1Qqv44lQNjBfRE7idKV9S0S2extSxJqAJlXtPLIqw0n88Wg28KGqtqhqO1ABzPA4pmidEZGxAO79WY/jiYqI/AiYByzWGP3QyS+JPlTuUESG4pxc2u1xTBEREcHpC25Q1Re9jidSqrpGVVNVdQLO+7FfVeNyz1FVTwP/EZH73Em5OFXT4tFHwIMiMtz9rOUSpyeWu+gsZYp7/0cPY4mKiDyM0905X1U/jdV6fZHo3ZMXneUOG4C3VPWYt1FFLBtYgrMHHHRv3/U6KEMBUCIih4FM4NcexxMR96ikDHgPOIKTA+JmCAER2YFTm/o+EWkSkWVAETBHRD7AOWIp8jLGcN2gLa8AtwB73f/9zTHZlg2BYIwx/uaLPXpjjDE3ZoneGGN8zhK9Mcb4nCV6Y4zxOUv0xhjjc5bojTHG5yzRG2OMz/0X1RxmALTCTjgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h_hM90-VcNw3",
        "colab_type": "text"
      },
      "source": [
        "# Supervised Methods"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lV9ZnPZ4cNw3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e2261c3f-58bb-40de-9c8c-de78d1954eea"
      },
      "source": [
        "new_app.shape"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJLd-FsycNw6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "supervised_app_x= new_app[['Category', 'Rating', 'Reviews', 'Content Rating']] \n",
        "supervised_app_y= new_app[\"Installs\"]"
      ],
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zsdWoFVXcNw7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "supervised_x=supervised_app_x.values # attributes\n",
        "supervised_y= supervised_app_y.values #target"
      ],
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_HNXz0i5cNw8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "supervised_x_transformed= OrdinalEncoder().fit_transform(supervised_x) # conversting the string values to floats for applying distance metrics\n"
      ],
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wwer17pycNw-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# segmenting the data in a training and test set of a 60/40 split"
      ],
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCsqoHGUcNw_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 178,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IuxxXByncNxB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "supervised_x_transformed_train, supervised_x_transformed_test, supervised_y_train, supervised_y_test= train_test_split(supervised_x_transformed, supervised_y, test_size=0.4)"
      ],
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R9keL_vVcNxC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Creating classifiers to predict the class of installations, using:\n",
        "# i. Logistic regression \n",
        "# ii. KNN\n"
      ],
      "execution_count": 180,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUKy90CfcNxE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "ab2b4b8c-c806-4755-b990-af7432fd4f0b"
      },
      "source": [
        "\n",
        "print(\"LOGISTIC REGRESSION\") \n",
        "print(\"**************************************\") \n",
        "from sklearn.linear_model import LogisticRegression\n",
        "lm = LogisticRegression()\n",
        "lm.fit(supervised_x_transformed_train, supervised_y_train)\n",
        "lm.predict_proba(supervised_x_transformed_test)"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LOGISTIC REGRESSION\n",
            "**************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.98684662e-24, 7.19580082e-23, 7.04087936e-08, ...,\n",
              "        1.57494360e-02, 9.67128347e-03, 6.19564180e-04],\n",
              "       [1.76471696e-07, 7.04736320e-07, 3.88585840e-02, ...,\n",
              "        2.34953592e-03, 1.34127552e-03, 1.71211847e-04],\n",
              "       [1.26822321e-14, 1.30184713e-13, 4.57592990e-04, ...,\n",
              "        3.19867877e-03, 2.35914343e-03, 6.77778932e-05],\n",
              "       ...,\n",
              "       [3.29333857e-23, 1.00059824e-21, 5.03510836e-07, ...,\n",
              "        4.05554844e-03, 3.31991279e-03, 5.06400014e-05],\n",
              "       [2.66663570e-05, 7.79106886e-05, 2.15307177e-01, ...,\n",
              "        3.97721320e-04, 2.48304561e-04, 1.77841008e-05],\n",
              "       [2.70905724e-07, 1.28432683e-06, 6.02953854e-02, ...,\n",
              "        2.69928737e-04, 1.08981887e-04, 1.33508531e-05]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sxmNF0dicNxF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "d51bc5a4-401e-49ac-f572-194098396791"
      },
      "source": [
        "print(lm.intercept_)"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-4.42569650e-04 -1.63478135e-05  2.03110744e-03  1.36074287e-03\n",
            "  5.27869880e-03  2.27676398e-03  8.03947674e-03 -3.65258424e-04\n",
            " -3.45955718e-03 -4.22098547e-03 -5.06379976e-03 -2.44579786e-03\n",
            " -2.14437478e-03 -8.28098893e-04]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zg9NJrpOcNxH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "e7610d93-9aa4-4f40-9303-af2790761f43"
      },
      "source": [
        "print(lm.coef_)"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-0.00982919  0.006927   -0.0584481  -0.00070597]\n",
            " [ 0.00341712  0.00722684 -0.05475076  0.00063091]\n",
            " [ 0.02922758  0.00667063 -0.01192129  0.00421362]\n",
            " [ 0.04663884  0.00520035 -0.00572545  0.00301637]\n",
            " [ 0.08031863  0.00365358  0.00184378  0.00830331]\n",
            " [ 0.01183577  0.00254823  0.0069414   0.00404423]\n",
            " [ 0.03574871  0.00097535  0.01169264  0.00950203]\n",
            " [ 0.00315365 -0.00168526  0.01462466 -0.00188396]\n",
            " [-0.04588656 -0.00279878  0.01788788 -0.00874682]\n",
            " [-0.05772025 -0.00427256  0.01723193 -0.00607613]\n",
            " [-0.0450755  -0.00422402  0.01711146 -0.00537082]\n",
            " [-0.0117074  -0.00555455  0.01490854 -0.00374307]\n",
            " [-0.03310277 -0.00609507  0.01567918 -0.00262406]\n",
            " [-0.00701864 -0.00857175  0.01292412 -0.00055966]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YR6KW3SQcNxJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "outputId": "39182833-e50c-47fd-bfaf-f32d84b40165"
      },
      "source": [
        "predicted = lm.predict(supervised_x_transformed_test)\n",
        "print(metrics.classification_report(supervised_y_test, predicted)) \n",
        "print(metrics.confusion_matrix(supervised_y_test, predicted))"
      ],
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        1000       0.00      0.00      0.00         2\n",
            "        5000       0.00      0.00      0.00         4\n",
            "       10000       0.12      0.11      0.11        18\n",
            "       50000       0.00      0.00      0.00        13\n",
            "      100000       0.36      0.49      0.41        41\n",
            "      500000       0.00      0.00      0.00        19\n",
            "     1000000       0.44      0.53      0.48       107\n",
            "     5000000       0.00      0.00      0.00        41\n",
            "    10000000       0.42      0.80      0.55       100\n",
            "    50000000       0.00      0.00      0.00        26\n",
            "   100000000       0.00      0.00      0.00        22\n",
            "   500000000       0.00      0.00      0.00         4\n",
            "  1000000000       0.00      0.00      0.00         2\n",
            "  5000000000       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.40       400\n",
            "   macro avg       0.10      0.14      0.11       400\n",
            "weighted avg       0.26      0.40      0.31       400\n",
            "\n",
            "[[ 0  0  0  0  2  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  3  0  1  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  2  0 13  0  3  0  0  0  0  0  0  0]\n",
            " [ 0  0  6  0  6  0  1  0  0  0  0  0  0  0]\n",
            " [ 0  0  6  0 20  1  9  1  4  0  0  0  0  0]\n",
            " [ 0  0  0  0  5  0 13  0  1  0  0  0  0  0]\n",
            " [ 0  0  0  0  9  3 57  2 36  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0 19  0 22  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0 20  0 80  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  2  0 24  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  5  0 17  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  4  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  2  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  1  0  0  0  0  0]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4j1zKqR9cNxK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "a86379b4-67a9-405f-ae37-91cb5c2d157b"
      },
      "source": [
        "#K nearest neighbours\n",
        "\n",
        "print(\"KNN\") \n",
        "print(\"**************************************\") \n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "model = KNeighborsClassifier()\n",
        "model.fit(supervised_x_transformed_train, supervised_y_train) \n",
        "print(model)\n"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KNN\n",
            "**************************************\n",
            "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
            "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
            "                     weights='uniform')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWMxSTQ2cNxL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "outputId": "75559cf8-04db-4e51-ea67-bed0e1f94c98"
      },
      "source": [
        "predicted= model.predict(supervised_x_transformed_test)\n",
        "print (metrics.classification_report(supervised_y_test, predicted))\n",
        "print (metrics.confusion_matrix(supervised_y_test, predicted))"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        1000       0.00      0.00      0.00         2\n",
            "        5000       0.33      0.25      0.29         4\n",
            "       10000       0.19      0.22      0.21        18\n",
            "       50000       0.20      0.15      0.17        13\n",
            "      100000       0.43      0.56      0.48        41\n",
            "      500000       0.19      0.16      0.17        19\n",
            "     1000000       0.61      0.68      0.64       107\n",
            "     5000000       0.24      0.20      0.22        41\n",
            "    10000000       0.57      0.59      0.58       100\n",
            "    50000000       0.14      0.08      0.10        26\n",
            "   100000000       0.48      0.50      0.49        22\n",
            "   500000000       0.00      0.00      0.00         4\n",
            "  1000000000       0.00      0.00      0.00         2\n",
            "  5000000000       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.47       400\n",
            "   macro avg       0.24      0.24      0.24       400\n",
            "weighted avg       0.44      0.47      0.45       400\n",
            "\n",
            "[[ 0  0  1  1  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  1  1  1  1  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  1  4  3  8  2  0  0  0  0  0  0  0  0]\n",
            " [ 0  1  6  2  3  1  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  9  3 23  3  3  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  9  3  7  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0 10  7 73  8  9  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0 23  8 10  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0 14 16 59  8  3  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  1 18  2  5  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  7  3 11  1  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  1  1  2  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  1  1  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  1  0  0  0]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWJ3cy78cNxN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "33700b1b-cfdc-44f7-af56-e5525f3e8356"
      },
      "source": [
        "print (metrics.accuracy_score(supervised_y_test, predicted))"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.465\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}