client_task.ipynb

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  "metadata": {
    "colab": {
      "name": "client task.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNb7Oe+Sg0eTTCGVEuXBdwg",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/usmankhawar22/IBM-DATA-VISUALISATION/blob/master/client_task.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "NJqgUKnBXOle",
        "colab_type": "text"
      },
      "source": [
        "# Downloading and Prepping Data <a id=\"2\"></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "BRPPsDBOXOlg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np  # useful for many scientific computing in Python\n",
        "import pandas as pd # primary data structure library"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "SKrfQUYoXOln",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "43e857ec-84e9-4395-f8cd-c84d4535f733"
      },
      "source": [
        "df = pd.read_excel('schools-ge-by-zone-stage-2012-2013.xlsx',\n",
        "                       sheet_name='Sheet1',\n",
        "                      #  skiprows=range(20),\n",
        "                      #  skipfooter=2\n",
        "                      )\n",
        "\n",
        "print('Data downloaded and read into a dataframe!')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data downloaded and read into a dataframe!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "6PrnZpROXOlt",
        "colab_type": "text"
      },
      "source": [
        "Let's take a look at the first five items in our dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "NhCRM4flXOlt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f71e8bdc-fff6-4429-e4a3-1291d9bf8155"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 6,
      "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>Year</th>\n",
              "      <th>Category_EN</th>\n",
              "      <th>Category_AR</th>\n",
              "      <th>Zone_EN</th>\n",
              "      <th>Zone_AR</th>\n",
              "      <th>Stage_EN</th>\n",
              "      <th>Stage_AR</th>\n",
              "      <th>School_Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2012/2013</td>\n",
              "      <td>Government Education</td>\n",
              "      <td>تعليم حكومي</td>\n",
              "      <td>Abu Dhabi Edu. Zone</td>\n",
              "      <td>منطقة أبوظبي التعليمية</td>\n",
              "      <td>kindergarten</td>\n",
              "      <td>رياض الاطفال</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012/2013</td>\n",
              "      <td>Government Education</td>\n",
              "      <td>تعليم حكومي</td>\n",
              "      <td>Al Ain Edu. Zone</td>\n",
              "      <td>منطقة العين التعليمية</td>\n",
              "      <td>kindergarten</td>\n",
              "      <td>رياض الاطفال</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2012/2013</td>\n",
              "      <td>Government Education</td>\n",
              "      <td>تعليم حكومي</td>\n",
              "      <td>Al Garbia Edu. Zone</td>\n",
              "      <td>المنطقة الغربية التعليمية</td>\n",
              "      <td>kindergarten</td>\n",
              "      <td>رياض الاطفال</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012/2013</td>\n",
              "      <td>Government Education</td>\n",
              "      <td>تعليم حكومي</td>\n",
              "      <td>Dubai Edu. Zone</td>\n",
              "      <td>منطقة دبي التعليمية</td>\n",
              "      <td>kindergarten</td>\n",
              "      <td>رياض الاطفال</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012/2013</td>\n",
              "      <td>Government Education</td>\n",
              "      <td>تعليم حكومي</td>\n",
              "      <td>Sharjah Edu. Zone</td>\n",
              "      <td>منطقة الشارقة التعليمية</td>\n",
              "      <td>kindergarten</td>\n",
              "      <td>رياض الاطفال</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Year           Category_EN  ...       Stage_AR School_Count\n",
              "0  2012/2013  Government Education  ...  رياض الاطفال            26\n",
              "1  2012/2013  Government Education  ...  رياض الاطفال            19\n",
              "2  2012/2013  Government Education  ...  رياض الاطفال             6\n",
              "3  2012/2013  Government Education  ...  رياض الاطفال            12\n",
              "4  2012/2013  Government Education  ...  رياض الاطفال            23\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "GmrrIwvzXOly",
        "colab_type": "text"
      },
      "source": [
        "Let's find out how many entries there are in our dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "vC9VLdpMXOl9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "4bc44ff5-c6e7-4fda-9b68-0f5c640345d9"
      },
      "source": [
        "df.drop(['Year', 'Category_EN', 'Category_AR', 'Zone_AR', 'Stage_AR'], axis=1, inplace=True)\n",
        "\n",
        "# let's view the first five elements and see how the dataframe was changed\n",
        "df.head()"
      ],
      "execution_count": 8,
      "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>Zone_EN</th>\n",
              "      <th>Stage_EN</th>\n",
              "      <th>School_Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Abu Dhabi Edu. Zone</td>\n",
              "      <td>kindergarten</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Al Ain Edu. Zone</td>\n",
              "      <td>kindergarten</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Al Garbia Edu. Zone</td>\n",
              "      <td>kindergarten</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Dubai Edu. Zone</td>\n",
              "      <td>kindergarten</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sharjah Edu. Zone</td>\n",
              "      <td>kindergarten</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Zone_EN      Stage_EN  School_Count\n",
              "0  Abu Dhabi Edu. Zone  kindergarten            26\n",
              "1     Al Ain Edu. Zone  kindergarten            19\n",
              "2  Al Garbia Edu. Zone  kindergarten             6\n",
              "3      Dubai Edu. Zone  kindergarten            12\n",
              "4    Sharjah Edu. Zone  kindergarten            23"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "tDZaBgN5XOmG",
        "colab_type": "text"
      },
      "source": [
        "Notice how the columns Type, Coverage, AREA, REG, and DEV got removed from the dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "9e_ga199XOmV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "caefc081-6968-4ffe-ea7c-ea832c21d21c"
      },
      "source": [
        "df.columns = list(map(str, df.columns))\n",
        "\n",
        "# let's check the column labels types now\n",
        "all(isinstance(column, str) for column in df.columns)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C1-pGbiTy7MN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748
        },
        "outputId": "2af2d0f0-f00e-40f3-febe-2ca006b90fe8"
      },
      "source": [
        "pd.pivot_table(df, values = 'School_Count', index = 'Zone_EN', columns = 'Stage_EN').plot.bar(figsize=(20,10))\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f864111e7f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzOZcVXtAalN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "63b0dd13-6abe-4c35-bf8d-77439e15459e"
      },
      "source": [
        "df_continents = df.groupby('Stage_EN', axis=0).sum()\n",
        "df_continents"
      ],
      "execution_count": 21,
      "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>School_Count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stage_EN</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cycle 1</th>\n",
              "      <td>185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cycle 2</th>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Religious</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Secondary</th>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>kindergarten</th>\n",
              "      <td>124</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              School_Count\n",
              "Stage_EN                  \n",
              "Cycle 1                185\n",
              "Cycle 2                118\n",
              "Religious                2\n",
              "Secondary               96\n",
              "kindergarten           124"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0CF4kdBBEu3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a75e1c45-5633-4ec1-869f-99f1b0d3fc9b"
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "mpl.style.use('ggplot') # optional: for ggplot-like style\n",
        "\n",
        "# check for latest version of Matplotlib\n",
        "print('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Matplotlib version:  3.2.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4EO83kgf_TSf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "3c9d16fe-62d6-4ca4-989b-21922f43eafa"
      },
      "source": [
        "# autopct create %, start angle represent starting point\n",
        "df_continents['School_Count'].plot(kind='pie',\n",
        "                            figsize=(5, 6),\n",
        "                            autopct='%1.1f%%', # add in percentages\n",
        "                            startangle=90,     # start angle 90° (Africa)\n",
        "                            shadow=True,       # add shadow      \n",
        "                            )\n",
        "\n",
        "plt.title('Immigration to Canada by Continent [1980 - 2013]')\n",
        "plt.axis('equal') # Sets the pie chart to look like a circle.\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qgjqld5zBHzC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "f2b858cc-717e-49f1-b975-282fc69c1726"
      },
      "source": [
        "dfc = df.groupby('Zone_EN', axis=0).sum()\n",
        "dfc"
      ],
      "execution_count": 26,
      "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>School_Count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zone_EN</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Abu Dhabi Edu. Zone</th>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ajman Edu. Zone</th>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Al Ain Edu. Zone</th>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Al Fujairah Edu. Zone</th>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Al Garbia Edu. Zone</th>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dubai Edu. Zone</th>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ras-Al-Khaimah Edu. Zone</th>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharjah Edu. Zone</th>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Umm Al Quwain Edu. Zone</th>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          School_Count\n",
              "Zone_EN                               \n",
              "Abu Dhabi Edu. Zone                111\n",
              "Ajman Edu. Zone                     33\n",
              "Al Ain Edu. Zone                    79\n",
              "Al Fujairah Edu. Zone               43\n",
              "Al Garbia Edu. Zone                 17\n",
              "Dubai Edu. Zone                     62\n",
              "Ras-Al-Khaimah Edu. Zone            64\n",
              "Sharjah Edu. Zone                   99\n",
              "Umm Al Quwain Edu. Zone             17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Au1kyRX1BU-D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "52e5722c-6e4a-4d83-b006-97f50a078b2f"
      },
      "source": [
        "\n",
        "dfc['School_Count'].plot(kind='pie',\n",
        "                            figsize=(5, 6),\n",
        "                            autopct='%1.1f%%', # add in percentages\n",
        "                            startangle=90,     # start angle 90° (Africa)\n",
        "                            shadow=True,       # add shadow      \n",
        "                            )\n",
        "\n",
        "plt.title('Immigration to Canada by Continent [1980 - 2013]')\n",
        "plt.axis('equal') # Sets the pie chart to look like a circle.\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}