matplotlib01.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "matplotlib01.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM51JNMUasu4ORrin+6REkD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LashenDEV/AIML/blob/main/matplotlib01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "eqd16FE2KMoh",
        "outputId": "a8b2bf51-805b-4e56-93dc-5b7ddc92d618"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib\n",
        "import math\n",
        "import numpy\n",
        "import matplotlib.pyplot as pyplot\n",
        "import matplotlib.gridspec as gridspec\n",
        "\n",
        "def rotation_curve(r):\n",
        "    v_rotation = numpy.sqrt((r*(1.33*(10**32)))/(1+r)**2)\n",
        "    return v_rotation\n",
        "\n",
        "curve_range = numpy.linspace(0, 100, 10000)\n",
        "\n",
        "fig = pyplot.figure(figsize=(16,6))\n",
        "\n",
        "gridspec_layout = gridspec.GridSpec(1,1)\n",
        "pyplot = fig.add_subplot(gridspec_layout[0])\n",
        "\n",
        "pyplot.plot(curve_range, rotation_curve(curve_range))\n",
        "\n",
        "matplotlib.pyplot.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd1\n",
        "url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports_us/04-12-2020.csv'\n",
        "pf = pd1.read_csv(url)\n",
        "pf.head(10)\n",
        "\n"
      ],
      "metadata": {
        "id": "3NpNGHD3NB5B",
        "outputId": "e2c70ab1-f5cb-4fad-e5a8-e4d8f099b47f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Province_State Country_Region          Last_Update      Lat     Long_  \\\n",
              "0           Alabama             US  2020-04-12 23:18:15  32.3182  -86.9023   \n",
              "1            Alaska             US  2020-04-12 23:18:15  61.3707 -152.4044   \n",
              "2    American Samoa             US                  NaN -14.2710 -170.1322   \n",
              "3           Arizona             US  2020-04-12 23:18:15  33.7298 -111.4312   \n",
              "4          Arkansas             US  2020-04-12 23:18:15  34.9697  -92.3731   \n",
              "5        California             US  2020-04-12 23:18:15  36.1162 -119.6816   \n",
              "6          Colorado             US  2020-04-12 23:18:15  39.0598 -105.3111   \n",
              "7       Connecticut             US  2020-04-12 23:18:15  41.5978  -72.7554   \n",
              "8          Delaware             US  2020-04-12 23:18:15  39.3185  -75.5071   \n",
              "9  Diamond Princess             US  2020-04-12 23:18:15      NaN       NaN   \n",
              "\n",
              "   Confirmed  Deaths  Recovered  Active   FIPS  ...  Total_Test_Results  \\\n",
              "0       3667      93        NaN     NaN    1.0  ...                 NaN   \n",
              "1        272       8       66.0   198.0    2.0  ...                 NaN   \n",
              "2          0       0        NaN     NaN   60.0  ...                 NaN   \n",
              "3       3542     115        NaN     NaN    4.0  ...                 NaN   \n",
              "4       1280      27      367.0   886.0    5.0  ...                 NaN   \n",
              "5      22201     632        NaN     NaN    6.0  ...                 NaN   \n",
              "6       7307     289        NaN     NaN    8.0  ...                 NaN   \n",
              "7      12035     554        NaN     NaN    9.0  ...                 NaN   \n",
              "8       1625      49      191.0  1385.0   10.0  ...             17870.0   \n",
              "9         49       0        0.0    49.0  888.0  ...                 NaN   \n",
              "\n",
              "   People_Hospitalized  Case_Fatality_Ratio         UID  ISO3  Testing_Rate  \\\n",
              "0                437.0                  NaN  84000001.0   USA  1.265076e+03   \n",
              "1                 31.0                  NaN  84000002.0   USA  1.344712e+03   \n",
              "2                  NaN                  NaN        16.0   ASM  5.391708e+00   \n",
              "3                  NaN                  NaN  84000004.0   USA  5.785223e+02   \n",
              "4                130.0                  NaN  84000005.0   USA  7.617534e+02   \n",
              "5               5234.0                  NaN  84000006.0   USA  4.854239e+02   \n",
              "6               1376.0                  NaN  84000008.0   USA  6.153900e+02   \n",
              "7               1654.0                  NaN  84000009.0   USA  1.156148e+03   \n",
              "8                190.0                  NaN  84000010.0   USA  1.835147e-07   \n",
              "9                  NaN                  NaN  84088888.0   USA           NaN   \n",
              "\n",
              "   Hospitalization_Rate        Date People_Tested  Mortality_Rate  \n",
              "0             12.264945  2020-04-12       62029.0        2.536133  \n",
              "1             11.397059  2020-04-12        8038.0        2.941176  \n",
              "2                   NaN  2020-04-12           3.0             NaN  \n",
              "3                   NaN  2020-04-12       52289.0        3.246753  \n",
              "4             10.156250  2020-04-12       19722.0        2.109375  \n",
              "5             22.961176  2020-04-12      190328.0        2.842214  \n",
              "6             18.831258  2020-04-12       34873.0        3.955112  \n",
              "7             13.743249  2020-04-12       41220.0        4.603241  \n",
              "8             11.692308  2020-04-12       11103.0        3.015385  \n",
              "9                   NaN  2020-04-12           NaN        0.000000  \n",
              "\n",
              "[10 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d4254b6b-4fa6-4063-97f0-3df3fb0ced38\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Province_State</th>\n",
              "      <th>Country_Region</th>\n",
              "      <th>Last_Update</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long_</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "      <th>Active</th>\n",
              "      <th>FIPS</th>\n",
              "      <th>...</th>\n",
              "      <th>Total_Test_Results</th>\n",
              "      <th>People_Hospitalized</th>\n",
              "      <th>Case_Fatality_Ratio</th>\n",
              "      <th>UID</th>\n",
              "      <th>ISO3</th>\n",
              "      <th>Testing_Rate</th>\n",
              "      <th>Hospitalization_Rate</th>\n",
              "      <th>Date</th>\n",
              "      <th>People_Tested</th>\n",
              "      <th>Mortality_Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-12 23:18:15</td>\n",
              "      <td>32.3182</td>\n",
              "      <td>-86.9023</td>\n",
              "      <td>3667</td>\n",
              "      <td>93</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>437.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84000001.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>1.265076e+03</td>\n",
              "      <td>12.264945</td>\n",
              "      <td>2020-04-12</td>\n",
              "      <td>62029.0</td>\n",
              "      <td>2.536133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alaska</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-12 23:18:15</td>\n",
              "      <td>61.3707</td>\n",
              "      <td>-152.4044</td>\n",
              "      <td>272</td>\n",
              "      <td>8</td>\n",
              "      <td>66.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84000002.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>1.344712e+03</td>\n",
              "      <td>11.397059</td>\n",
              "      <td>2020-04-12</td>\n",
              "      <td>8038.0</td>\n",
              "      <td>2.941176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>American Samoa</td>\n",
              "      <td>US</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-14.2710</td>\n",
              "      <td>-170.1322</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>60.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.0</td>\n",
              "      <td>ASM</td>\n",
              "      <td>5.391708e+00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-04-12</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Arizona</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-12 23:18:15</td>\n",
              "      <td>33.7298</td>\n",
              "      <td>-111.4312</td>\n",
              "      <td>3542</td>\n",
              "      <td>115</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84000004.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>5.785223e+02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-04-12</td>\n",
              "      <td>52289.0</td>\n",
              "      <td>3.246753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Arkansas</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-12 23:18:15</td>\n",
              "      <td>34.9697</td>\n",
              "      <td>-92.3731</td>\n",
              "      <td>1280</td>\n",
              "      <td>27</td>\n",
              "      <td>367.0</td>\n",
              "      <td>886.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>130.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84000005.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>7.617534e+02</td>\n",
              "      <td>10.156250</td>\n",
              "      <td>2020-04-12</td>\n",
              "      <td>19722.0</td>\n",
              "      <td>2.109375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>California</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-12 23:18:15</td>\n",
              "      <td>36.1162</td>\n",
              "      <td>-119.6816</td>\n",
              "      <td>22201</td>\n",
              "      <td>632</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5234.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84000006.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>4.854239e+02</td>\n",
              "      <td>22.961176</td>\n",
              "      <td>2020-04-12</td>\n",
              "      <td>190328.0</td>\n",
              "      <td>2.842214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Colorado</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-12 23:18:15</td>\n",
              "      <td>39.0598</td>\n",
              "      <td>-105.3111</td>\n",
              "      <td>7307</td>\n",
              "      <td>289</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1376.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84000008.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>6.153900e+02</td>\n",
              "      <td>18.831258</td>\n",
              "      <td>2020-04-12</td>\n",
              "      <td>34873.0</td>\n",
              "      <td>3.955112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Connecticut</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-12 23:18:15</td>\n",
              "      <td>41.5978</td>\n",
              "      <td>-72.7554</td>\n",
              "      <td>12035</td>\n",
              "      <td>554</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1654.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84000009.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>1.156148e+03</td>\n",
              "      <td>13.743249</td>\n",
              "      <td>2020-04-12</td>\n",
              "      <td>41220.0</td>\n",
              "      <td>4.603241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Delaware</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-12 23:18:15</td>\n",
              "      <td>39.3185</td>\n",
              "      <td>-75.5071</td>\n",
              "      <td>1625</td>\n",
              "      <td>49</td>\n",
              "      <td>191.0</td>\n",
              "      <td>1385.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>...</td>\n",
              "      <td>17870.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84000010.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>1.835147e-07</td>\n",
              "      <td>11.692308</td>\n",
              "      <td>2020-04-12</td>\n",
              "      <td>11103.0</td>\n",
              "      <td>3.015385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Diamond Princess</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-12 23:18:15</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>888.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84088888.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-04-12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d4254b6b-4fa6-4063-97f0-3df3fb0ced38')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d4254b6b-4fa6-4063-97f0-3df3fb0ced38 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d4254b6b-4fa6-4063-97f0-3df3fb0ced38');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    }
  ]
}