Covid_19_India_1.ipynb

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  "metadata": {
    "colab": {
      "name": "Covid_19_India_1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMr48M7Jog63ILIUQaiSQMy",
      "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/ShrayankM/Covid-19_India_Analysis/blob/master/Covid_19_India_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJINNn3l49_a",
        "colab_type": "text"
      },
      "source": [
        "## Insight into Covid-19 Case Rise in India\n",
        "**Months February, March, April**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZ_-o_7GxW20",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FeP6ekV0zvpC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_covid = np.genfromtxt(\"covid_19_india.csv\",skip_header=1,delimiter=',')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUoN2eB30E9K",
        "colab_type": "code",
        "outputId": "91d4863a-efd7-41c1-ae33-122d9db4593d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "data_covid.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(739, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xp_4tiKK0Ipe",
        "colab_type": "code",
        "outputId": "b6655102-d0ef-48c4-f6c3-b65cc71a2ea5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "print(data_covid)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  1.  nan  nan ...   0.   0.   1.]\n",
            " [  2.  nan  nan ...   0.   0.   1.]\n",
            " [  3.  nan  nan ...   0.   0.   2.]\n",
            " ...\n",
            " [737.  nan  nan ...   5.   0.  31.]\n",
            " [738.  nan  nan ...  21.   3. 305.]\n",
            " [739.  nan  nan ...  13.   3.  91.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0LSesy350iAD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "total = np.sum(data_covid[709:,-1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PtPj3yaH0jTu",
        "colab_type": "code",
        "outputId": "1cbc51d2-52f2-479c-ecb0-8fd74beea7c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "total"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4155.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iAVtvh5R2YnW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-bXvQqel3Oos",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_covid = pd.read_csv(\"covid_19_india.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FQZGhHL3YBu",
        "colab_type": "code",
        "outputId": "da6862cd-77d3-4800-a2bf-c7e419021d08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "data_covid.head()"
      ],
      "execution_count": 0,
      "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",
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              "    }\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>Sno</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>ConfirmedIndianNational</th>\n",
              "      <th>ConfirmedForeignNational</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>30/01/20</td>\n",
              "      <td>6:00 PM</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>31/01/20</td>\n",
              "      <td>6:00 PM</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>01/02/20</td>\n",
              "      <td>6:00 PM</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>02/02/20</td>\n",
              "      <td>6:00 PM</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>03/02/20</td>\n",
              "      <td>6:00 PM</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Sno      Date     Time  ... Cured Deaths Confirmed\n",
              "0    1  30/01/20  6:00 PM  ...     0      0         1\n",
              "1    2  31/01/20  6:00 PM  ...     0      0         1\n",
              "2    3  01/02/20  6:00 PM  ...     0      0         2\n",
              "3    4  02/02/20  6:00 PM  ...     0      0         3\n",
              "4    5  03/02/20  6:00 PM  ...     0      0         3\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Vq-QVdO3aDl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "confirm_cases = data_covid['Confirmed']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VXrRRHsa3kmY",
        "colab_type": "code",
        "outputId": "2a4a08a7-80b6-4cc5-ef19-836d1447fa76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "type(confirm_cases)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IFx8EAAN4OXm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "date_data = data_covid['Date']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r8d43uP34V7I",
        "colab_type": "code",
        "outputId": "0c471ba2-4e29-4456-9eb0-550d6e81c4ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "date_data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(739,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1F45Lwz4fvW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_date_array = np.array(date_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yeyuHufc45Q1",
        "colab_type": "code",
        "outputId": "d888b126-19d5-488c-cec1-9e3bfc4f4e81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "new_date_array[new_date_array == '07/04/20']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['07/04/20', '07/04/20', '07/04/20', '07/04/20', '07/04/20',\n",
              "       '07/04/20', '07/04/20', '07/04/20', '07/04/20', '07/04/20',\n",
              "       '07/04/20', '07/04/20', '07/04/20', '07/04/20', '07/04/20',\n",
              "       '07/04/20', '07/04/20', '07/04/20', '07/04/20', '07/04/20',\n",
              "       '07/04/20', '07/04/20', '07/04/20', '07/04/20', '07/04/20',\n",
              "       '07/04/20', '07/04/20', '07/04/20', '07/04/20', '07/04/20',\n",
              "       '07/04/20'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cs7LoFvf5PeO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "confirm_array = np.array(confirm_cases)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLuzeCSu5wWe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "date_7_march = confirm_array[new_date_array == '07/04/20']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qgx4qnD5x1r",
        "colab_type": "code",
        "outputId": "b57a534c-8b5d-4532-e406-5d915d7b39c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "date_7_march"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([266,  10,   1,  26,  32,  18,  10, 523,   7, 144,  90,  13, 109,\n",
              "         4, 151, 327,  14, 165, 748,   2,   1,  21,   5,  76, 288, 621,\n",
              "       321,   1,  31, 305,  91])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9K1V5oFP6Czi",
        "colab_type": "code",
        "outputId": "5a71a08d-824d-47b9-ce37-a15685617cdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "np.sum(date_7_march)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4421"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DFMEmi86HUD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feb_month = []\n",
        "for i in range(1,30):\n",
        "    if i < 10:\n",
        "        date_str = '0'+str(i)+\"/02/20\"\n",
        "    else:\n",
        "        date_str =  str(i)+\"/02/20\"\n",
        "    feb_month.append(np.sum(confirm_array[new_date_array == date_str]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yRZxB7wU5MO5",
        "colab_type": "text"
      },
      "source": [
        "**February Case Rise**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k_8uhNqW7CHY",
        "colab_type": "code",
        "outputId": "d67037d0-9df3-4130-a70f-7bcb74c8825f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(feb_month)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wkOuQyb-7d3H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "march_month = []\n",
        "for i in range(1,32):\n",
        "    if i < 10:\n",
        "        date_str = '0'+str(i)+\"/03/20\"\n",
        "    else:\n",
        "        date_str =  str(i)+\"/03/20\"\n",
        "    march_month.append(np.sum(confirm_array[new_date_array == date_str]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FvWHr5lo5QcU",
        "colab_type": "text"
      },
      "source": [
        "**March Case Rise**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rMn7Qmh77qo_",
        "colab_type": "code",
        "outputId": "1cfa975b-ff53-4a06-a6a5-97c2edeb1213",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "print(march_month)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3, 5, 6, 28, 30, 31, 34, 39, 46, 58, 60, 74, 81, 84, 110, 114, 137, 151, 173, 223, 283, 360, 433, 519, 606, 694, 724, 909, 1024, 1251, 1397]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDocLI717sR9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "april_month = []\n",
        "for i in range(1,8):\n",
        "    if i < 10:\n",
        "        date_str = '0'+str(i)+\"/04/20\"\n",
        "    else:\n",
        "        date_str =  str(i)+\"/04/20\"\n",
        "    april_month.append(np.sum(confirm_array[new_date_array == date_str]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l2BtVRpS5T7U",
        "colab_type": "text"
      },
      "source": [
        "**April Case Rise**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VhlgKcun78bl",
        "colab_type": "code",
        "outputId": "339ff93f-4e25-4df0-c92f-65420f164a56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(april_month)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1834, 2069, 2547, 3072, 3577, 4281, 4421]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zV6kktP_1Z9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "date_feb_month = np.arange(1,30,1)\n",
        "dates_march_month = np.arange(1,32,1)\n",
        "date_april_month = np.arange(1,8,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WX2E7eHY-PMH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zuktPKtv5fEX",
        "colab_type": "text"
      },
      "source": [
        "**Plot for Month of February**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4r2nFbC_ktt",
        "colab_type": "code",
        "outputId": "7b41414e-11a9-4722-9931-c9fa30eb84bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plt.plot(date_feb_month,feb_month, color='green', linestyle='-', linewidth = 3, \n",
        "         marker='o', markerfacecolor='blue', markersize=4)\n",
        "plt.xlabel(\"Days\")\n",
        "plt.ylabel(\"Cases\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WD8elK7m5jdw",
        "colab_type": "text"
      },
      "source": [
        "**Plot for Month of March**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qT611RYp_pPz",
        "colab_type": "code",
        "outputId": "1350d262-87a3-4f0c-8c4a-a32c115d8a6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plt.plot(dates_march_month,march_month, color='green', linestyle='-', linewidth = 3, \n",
        "         marker='o', markerfacecolor='blue', markersize=4)\n",
        "plt.xlabel(\"Days\")\n",
        "plt.ylabel(\"Cases\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sMfAScJu77zCz6D53dzPzQ/k8dx8FjALIzs4+pPeKHA7m5czj6olXs2jTomhd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\nlCxERKRCShYiIlKh/w/0dAr5HyGpfgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ig2Fsn1l5nAU",
        "colab_type": "text"
      },
      "source": [
        "**Plot for month April**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vSJ4E6lrADw-",
        "colab_type": "code",
        "outputId": "a1ec6301-a1de-4890-da83-5903d51e275b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plt.plot(date_april_month,april_month, color='green', linestyle='-', linewidth = 3, \n",
        "         marker='o', markerfacecolor='blue', markersize=4)\n",
        "plt.xlabel(\"Days\")\n",
        "plt.ylabel(\"Cases\")\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7+yHgSzNbA7QM1q1x97UAZjY+2FZhEUd2HdzF/7zyP4xb/n0YJJZK5KE2D/Hb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\nT/8P5bNsUpPiJtgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBSEdrVyC5QM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "march_array = np.array(march_month)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "70Wj123VD4aT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "april_array = np.array(april_month)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rb0EJ1lqAHPB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "percentage_march = ((march_array[1:] - march_array[0:30]) / march_array[0:30]) * 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wD89V0hE5rVo",
        "colab_type": "text"
      },
      "source": [
        "**Percentage growth per day for month March**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bxr0pRERDYKj",
        "colab_type": "code",
        "outputId": "78051f49-b281-425e-c827-1a3b106af069",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "source": [
        "percentage_march"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 66.66666667,  20.        , 366.66666667,   7.14285714,\n",
              "         3.33333333,   9.67741935,  14.70588235,  17.94871795,\n",
              "        26.08695652,   3.44827586,  23.33333333,   9.45945946,\n",
              "         3.7037037 ,  30.95238095,   3.63636364,  20.1754386 ,\n",
              "        10.2189781 ,  14.56953642,  28.9017341 ,  26.9058296 ,\n",
              "        27.20848057,  20.27777778,  19.86143187,  16.76300578,\n",
              "        14.52145215,   4.32276657,  25.55248619,  12.65126513,\n",
              "        22.16796875,  11.67066347])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxvvA1DODmdQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "percentage_april = ((april_array[1:] - april_array[0:6]) / april_array[0:6]) * 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A22lFBUE5wsy",
        "colab_type": "text"
      },
      "source": [
        "**Percentage growth for month April**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFAyb71IEGOq",
        "colab_type": "code",
        "outputId": "22a67496-d05e-4ed8-e456-a16f96754415",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "percentage_april"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([12.81352236, 23.10294828, 20.61248528, 16.43880208, 19.68129718,\n",
              "        3.27026396])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tL7O6lSV12G4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "average_rise_march = (np.sum(percentage_march) - 366.666)/len(march_month) - 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9lmAFeb2MSr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "average_rise_april = np.mean(percentage_april)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRWbEqS82T2j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "average_rise_india = np.mean([average_rise_march,average_rise_april ] )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F4V2At-g50y_",
        "colab_type": "text"
      },
      "source": [
        "**Average percentage rise in India**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sW6CMfmk2e22",
        "colab_type": "code",
        "outputId": "d74106d9-ec83-4ccf-b539-b10959539d3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "average_rise_india"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15.813677110593158"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7GgxDc1cEIoL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "total_days = len(feb_month) + len(march_month) + len(april_month)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7OFnK4O6yJJg",
        "colab_type": "code",
        "outputId": "3bc8f511-820c-411a-f5c8-3befaede8b31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "total_days"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "67"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LOkO-AiGyK5f",
        "colab_type": "code",
        "outputId": "1efa9630-7f52-4dcf-b03a-30ac044b2671",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "type(feb_month)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r5QyjMYWyfwc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rise_in_cases = []\n",
        "for item in feb_month:\n",
        "    rise_in_cases.append(item)\n",
        "for item in march_month:\n",
        "    rise_in_cases.append(item)\n",
        "for item in april_month:\n",
        "    rise_in_cases.append(item)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBnMPasazmn4",
        "colab_type": "code",
        "outputId": "c437886f-db46-4e35-c334-e477a41c3dba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "print(rise_in_cases)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5, 6, 28, 30, 31, 34, 39, 46, 58, 60, 74, 81, 84, 110, 114, 137, 151, 173, 223, 283, 360, 433, 519, 606, 694, 724, 909, 1024, 1251, 1397, 1834, 2069, 2547, 3072, 3577, 4281, 4421]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ba61Z6PqzXsA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "days = np.arange(1,(total_days+1),1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZ0eqcL055Tw",
        "colab_type": "text"
      },
      "source": [
        "**Overall Case rise in India**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AeIipESZzCtd",
        "colab_type": "code",
        "outputId": "bed1ca14-eb99-4e5a-9054-1ffd827e198f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plt.plot(days, rise_in_cases, color='green', linestyle='dashed', linewidth = 3, \n",
        "         marker='o', markerfacecolor='blue', markersize=4)\n",
        "plt.xlabel(\"Days\")\n",
        "plt.ylabel(\"Cases\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jcl4JQfszHjEPaGdmJwFXArPcvcjdi4FZwNCa/4oiIuGbOG8iNCuBi+/BfpJJn+EzeHXk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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ppg4JR3841Uf",
        "colab_type": "text"
      },
      "source": [
        "## Future prediction of Covid-19 Cases in India"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYST7ogI2xgF",
        "colab_type": "code",
        "outputId": "4b26a448-2ace-4bca-c7f2-62e29f0587c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "average_rise_india"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15.813677110593158"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4sO40IV0dUV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "today_cases = 4421 #7 April 2020"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NDxl1Xbk21tM",
        "colab_type": "code",
        "outputId": "325b25f1-e054-4bf2-923b-8c6f4b9c7804",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "(4421 * 15)/100 #average increase per day"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "663.15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJNtCUC024G9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "next_30_days_project = []\n",
        "current_cases = today_cases\n",
        "for i in range(30):\n",
        "    cases = current_cases + ((current_cases * average_rise_india)/100)\n",
        "    next_30_days_project.append(cases)\n",
        "    current_cases = cases"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpBCxRD_3byp",
        "colab_type": "code",
        "outputId": "14c38302-b68d-4359-a8ed-825db4f69a9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "print(next_30_days_project)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[5120.1226650593235, 5929.802330978102, 6867.522124895406, 7953.529899224912, 9211.275436382824, 10667.916791659785, 12354.906707519613, 14308.67176156178, 16571.39891274978, 19191.946429520376, 22226.898869122742, 25741.788887983894, 29812.512265220215, 34526.96669339812, 39986.94972237414, 46310.35683784562, 53633.72713694602, 62115.19156875925, 71937.88740006922, 83313.91263369824, 96488.90576579297, 111747.34977113997, 129418.7148435932, 149884.57252963833, 173586.83486806814, 201037.29644060298, 232828.68537158592, 269647.46189708734, 312288.6408584025, 361672.9581768101]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-gXN_Jzq4hOM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "month_days = np.arange(1,31,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_ETCTTq59s7",
        "colab_type": "text"
      },
      "source": [
        "**Prediction Rise of Cases for next 30 days**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orq5vFfk3gUW",
        "colab_type": "code",
        "outputId": "9aebdb60-9a9f-4dd1-d29f-10374fd747c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plt.plot(month_days, next_30_days_project, color='green', linestyle='dashed', linewidth = 3, \n",
        "         marker='o', markerfacecolor='blue', markersize=4)\n",
        "plt.xlabel(\"Days\")\n",
        "plt.ylabel(\"Cases\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m5eQ8lMObq9+MOp2EUXEREWkgi9YuYmLeRADeWvMWn5Qm/3NbdkfFRUSkAbg7Vz5/JZVe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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BPbndjeG4bb3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}