data-graph-explorer/Data Graph Explorer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3eda4984-69fb-4455-8fef-20790aca32ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#interactive matplotlib graphs\n",
    "%matplotlib widget\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from ipywidgets import FileUpload\n",
    "import pandas as pd\n",
    "import io\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b170f8a-9538-46f1-bd10-f2fe9e1a2699",
   "metadata": {},
   "source": [
    "## Upload File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2679573-277b-4463-af0a-bf81d3e97248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9c9f575fc90e44499c05fd28f181f837",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FileUpload(value=(), accept='.csv', description='Upload')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uploaded = FileUpload(accept=\".csv\")\n",
    "display(uploaded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "55ee739e-f545-4f5d-ad89-5f0d996b7e0b",
   "metadata": {},
   "outputs": [
    {
     "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>CSIRO Adjusted Sea Level</th>\n",
       "      <th>Lower Error Bound</th>\n",
       "      <th>Upper Error Bound</th>\n",
       "      <th>NOAA Adjusted Sea Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.952756</td>\n",
       "      <td>0.952756</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1881</td>\n",
       "      <td>0.220472</td>\n",
       "      <td>-0.732283</td>\n",
       "      <td>1.173228</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1882</td>\n",
       "      <td>-0.440945</td>\n",
       "      <td>-1.346457</td>\n",
       "      <td>0.464567</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1883</td>\n",
       "      <td>-0.232283</td>\n",
       "      <td>-1.129921</td>\n",
       "      <td>0.665354</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1884</td>\n",
       "      <td>0.590551</td>\n",
       "      <td>-0.283465</td>\n",
       "      <td>1.464567</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2009</td>\n",
       "      <td>8.586614</td>\n",
       "      <td>8.311024</td>\n",
       "      <td>8.862205</td>\n",
       "      <td>8.046354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2010</td>\n",
       "      <td>8.901575</td>\n",
       "      <td>8.618110</td>\n",
       "      <td>9.185039</td>\n",
       "      <td>8.122973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2011</td>\n",
       "      <td>8.964567</td>\n",
       "      <td>8.661417</td>\n",
       "      <td>9.267717</td>\n",
       "      <td>8.053065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2012</td>\n",
       "      <td>9.326772</td>\n",
       "      <td>8.992126</td>\n",
       "      <td>9.661417</td>\n",
       "      <td>8.457058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2013</td>\n",
       "      <td>8.980315</td>\n",
       "      <td>8.622047</td>\n",
       "      <td>9.338583</td>\n",
       "      <td>8.546648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  CSIRO Adjusted Sea Level  Lower Error Bound  Upper Error Bound  \\\n",
       "0    1880                  0.000000          -0.952756           0.952756   \n",
       "1    1881                  0.220472          -0.732283           1.173228   \n",
       "2    1882                 -0.440945          -1.346457           0.464567   \n",
       "3    1883                 -0.232283          -1.129921           0.665354   \n",
       "4    1884                  0.590551          -0.283465           1.464567   \n",
       "..    ...                       ...                ...                ...   \n",
       "129  2009                  8.586614           8.311024           8.862205   \n",
       "130  2010                  8.901575           8.618110           9.185039   \n",
       "131  2011                  8.964567           8.661417           9.267717   \n",
       "132  2012                  9.326772           8.992126           9.661417   \n",
       "133  2013                  8.980315           8.622047           9.338583   \n",
       "\n",
       "     NOAA Adjusted Sea Level  \n",
       "0                        NaN  \n",
       "1                        NaN  \n",
       "2                        NaN  \n",
       "3                        NaN  \n",
       "4                        NaN  \n",
       "..                       ...  \n",
       "129                 8.046354  \n",
       "130                 8.122973  \n",
       "131                 8.053065  \n",
       "132                 8.457058  \n",
       "133                 8.546648  \n",
       "\n",
       "[134 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for f in uploaded.value:\n",
    "    content = f[\"content\"]\n",
    "    df = pd.read_csv(io.BytesIO(content))\n",
    "    break\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd8b08d5-dff9-48ac-9f9c-fdbc3b099b1b",
   "metadata": {},
   "source": [
    "## Provide CSV file URL as interactive input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "df96980f-c032-43ba-9228-666024333d2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Provide direct url of CSV file:  https://github.com/jasperdebie/VisInfo/raw/master/us-state-capitals.csv\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\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>name</th>\n",
       "      <th>description</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>32.377716</td>\n",
       "      <td>-86.300568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>Juneau</td>\n",
       "      <td>58.301598</td>\n",
       "      <td>-134.420212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>33.448143</td>\n",
       "      <td>-112.096962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Little Rock</td>\n",
       "      <td>34.746613</td>\n",
       "      <td>-92.288986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>38.576668</td>\n",
       "      <td>-121.493629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>Denver</td>\n",
       "      <td>39.739227</td>\n",
       "      <td>-104.984856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>Hartford&lt;br&gt;</td>\n",
       "      <td>41.764046</td>\n",
       "      <td>-72.682198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Dover</td>\n",
       "      <td>39.157307</td>\n",
       "      <td>-75.519722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>Honolulu</td>\n",
       "      <td>21.307442</td>\n",
       "      <td>-157.857376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Tallahassee</td>\n",
       "      <td>30.438118</td>\n",
       "      <td>-84.281296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Atlanta&lt;br&gt;</td>\n",
       "      <td>33.749027</td>\n",
       "      <td>-84.388229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>Boise</td>\n",
       "      <td>43.617775</td>\n",
       "      <td>-116.199722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>Springfield</td>\n",
       "      <td>39.798363</td>\n",
       "      <td>-89.654961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>39.768623</td>\n",
       "      <td>-86.162643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>Des Moines</td>\n",
       "      <td>41.591087</td>\n",
       "      <td>-93.603729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>Topeka</td>\n",
       "      <td>39.048191</td>\n",
       "      <td>-95.677956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Frankfort</td>\n",
       "      <td>38.186722</td>\n",
       "      <td>-84.875374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>Baton Rouge</td>\n",
       "      <td>30.457069</td>\n",
       "      <td>-91.187393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Maine</td>\n",
       "      <td>Augusta</td>\n",
       "      <td>44.307167</td>\n",
       "      <td>-69.781693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>Annapolis</td>\n",
       "      <td>38.978764</td>\n",
       "      <td>-76.490936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>Boston</td>\n",
       "      <td>42.358162</td>\n",
       "      <td>-71.063698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>Lansing</td>\n",
       "      <td>42.733635</td>\n",
       "      <td>-84.555328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>St. Paul</td>\n",
       "      <td>44.955097</td>\n",
       "      <td>-93.102211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>Jackson</td>\n",
       "      <td>32.303848</td>\n",
       "      <td>-90.182106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>Jefferson City</td>\n",
       "      <td>38.579201</td>\n",
       "      <td>-92.172935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Montana</td>\n",
       "      <td>Helena</td>\n",
       "      <td>46.585709</td>\n",
       "      <td>-112.018417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>Lincoln</td>\n",
       "      <td>40.808075</td>\n",
       "      <td>-96.699654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>Carson City</td>\n",
       "      <td>39.163914</td>\n",
       "      <td>-119.766121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>Concord</td>\n",
       "      <td>43.206898</td>\n",
       "      <td>-71.537994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Trenton</td>\n",
       "      <td>40.220596</td>\n",
       "      <td>-74.769913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>35.682240</td>\n",
       "      <td>-105.939728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Raleigh</td>\n",
       "      <td>35.780430</td>\n",
       "      <td>-78.639099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>Bismarck</td>\n",
       "      <td>46.820850</td>\n",
       "      <td>-100.783318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>New York</td>\n",
       "      <td>Albany</td>\n",
       "      <td>42.652843</td>\n",
       "      <td>-73.757874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>39.961346</td>\n",
       "      <td>-82.999069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>35.492207</td>\n",
       "      <td>-97.503342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>Salem</td>\n",
       "      <td>44.938461</td>\n",
       "      <td>-123.030403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Harrisburg</td>\n",
       "      <td>40.264378</td>\n",
       "      <td>-76.883598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>Providence</td>\n",
       "      <td>41.830914</td>\n",
       "      <td>-71.414963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Columbia</td>\n",
       "      <td>34.000343</td>\n",
       "      <td>-81.033211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>Pierre</td>\n",
       "      <td>44.367031</td>\n",
       "      <td>-100.346405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>36.165810</td>\n",
       "      <td>-86.784241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Austin</td>\n",
       "      <td>30.274670</td>\n",
       "      <td>-97.740349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Utah</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>40.777477</td>\n",
       "      <td>-111.888237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>Montpelier</td>\n",
       "      <td>44.262436</td>\n",
       "      <td>-72.580536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>37.538857</td>\n",
       "      <td>-77.433640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Washington</td>\n",
       "      <td>Olympia</td>\n",
       "      <td>47.035805</td>\n",
       "      <td>-122.905014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Charleston</td>\n",
       "      <td>38.336246</td>\n",
       "      <td>-81.612328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Madison</td>\n",
       "      <td>43.074684</td>\n",
       "      <td>-89.384445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Cheyenne</td>\n",
       "      <td>41.140259</td>\n",
       "      <td>-104.820236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name     description   latitude   longitude\n",
       "0          Alabama      Montgomery  32.377716  -86.300568\n",
       "1           Alaska          Juneau  58.301598 -134.420212\n",
       "2          Arizona         Phoenix  33.448143 -112.096962\n",
       "3         Arkansas     Little Rock  34.746613  -92.288986\n",
       "4       California      Sacramento  38.576668 -121.493629\n",
       "5         Colorado          Denver  39.739227 -104.984856\n",
       "6      Connecticut    Hartford<br>  41.764046  -72.682198\n",
       "7         Delaware           Dover  39.157307  -75.519722\n",
       "8           Hawaii        Honolulu  21.307442 -157.857376\n",
       "9          Florida     Tallahassee  30.438118  -84.281296\n",
       "10         Georgia     Atlanta<br>  33.749027  -84.388229\n",
       "11           Idaho           Boise  43.617775 -116.199722\n",
       "12        Illinois     Springfield  39.798363  -89.654961\n",
       "13         Indiana    Indianapolis  39.768623  -86.162643\n",
       "14            Iowa      Des Moines  41.591087  -93.603729\n",
       "15          Kansas          Topeka  39.048191  -95.677956\n",
       "16        Kentucky       Frankfort  38.186722  -84.875374\n",
       "17       Louisiana     Baton Rouge  30.457069  -91.187393\n",
       "18           Maine         Augusta  44.307167  -69.781693\n",
       "19        Maryland       Annapolis  38.978764  -76.490936\n",
       "20   Massachusetts          Boston  42.358162  -71.063698\n",
       "21        Michigan         Lansing  42.733635  -84.555328\n",
       "22       Minnesota        St. Paul  44.955097  -93.102211\n",
       "23     Mississippi         Jackson  32.303848  -90.182106\n",
       "24        Missouri  Jefferson City  38.579201  -92.172935\n",
       "25         Montana          Helena  46.585709 -112.018417\n",
       "26        Nebraska         Lincoln  40.808075  -96.699654\n",
       "27          Nevada     Carson City  39.163914 -119.766121\n",
       "28   New Hampshire         Concord  43.206898  -71.537994\n",
       "29      New Jersey         Trenton  40.220596  -74.769913\n",
       "30      New Mexico        Santa Fe  35.682240 -105.939728\n",
       "31  North Carolina         Raleigh  35.780430  -78.639099\n",
       "32    North Dakota        Bismarck  46.820850 -100.783318\n",
       "33        New York          Albany  42.652843  -73.757874\n",
       "34            Ohio        Columbus  39.961346  -82.999069\n",
       "35        Oklahoma   Oklahoma City  35.492207  -97.503342\n",
       "36          Oregon           Salem  44.938461 -123.030403\n",
       "37    Pennsylvania      Harrisburg  40.264378  -76.883598\n",
       "38    Rhode Island      Providence  41.830914  -71.414963\n",
       "39  South Carolina        Columbia  34.000343  -81.033211\n",
       "40    South Dakota          Pierre  44.367031 -100.346405\n",
       "41       Tennessee       Nashville  36.165810  -86.784241\n",
       "42           Texas          Austin  30.274670  -97.740349\n",
       "43            Utah  Salt Lake City  40.777477 -111.888237\n",
       "44         Vermont      Montpelier  44.262436  -72.580536\n",
       "45        Virginia        Richmond  37.538857  -77.433640\n",
       "46      Washington         Olympia  47.035805 -122.905014\n",
       "47   West Virginia      Charleston  38.336246  -81.612328\n",
       "48       Wisconsin         Madison  43.074684  -89.384445\n",
       "49         Wyoming        Cheyenne  41.140259 -104.820236"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = input(\"Provide direct url of CSV file: \")\n",
    "df = pd.read_csv(url)\n",
    "df\n",
    "# https://github.com/jasperdebie/VisInfo/raw/master/us-state-capitals.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c6bb796a-be2e-4ea8-83ac-8c99097c2f1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "[[-64.843729999672, -64.8306170004155, -64.8166740000574, -64.8003289998509, -64.7850010003439, -64.7780049999629, -64.756923000007, -64.7438040000044, -64.7376500004207, -64.7298299996841, -64.727025000328, -64.7081970000824, -64.7016040001075, -64.6832930000708, -64.6752489999265, -64.6684810000495, -64.6404370000478, -64.6391440000621, -64.643689000191, -64.6377349997255, -64.6638549997298, -64.6613010004438, -64.6642589999609, -64.6782290000832, -64.6944889999008, -64.7079069996541, -64.720173999712, -64.7487359997476, -64.7577690001039, -64.7734199996511, -64.7828909996753, -64.8012889997506, -64.7992609999961, -64.8027730004303, -64.8068909998636, -64.8185169998639, -64.8333530001747, -64.849551999893, -64.8580350004284, -64.8725339995569, -64.8897519996245, -64.8997750000177, -64.9052399998634, -64.9061510002576, -64.921034000424, -64.9337140000354, -64.9420610001283, -64.9464720003629, -64.9609639998188, -64.9804139998553, -64.9978610002517, -65.0198210000917, -65.0315650000928, -65.0485809995957, -65.0501940001468, -65.0577479998463, -65.0677319998815, -65.0809529999913, -65.10083099965, -65.1172670000931, -65.1312199995987, -65.1426220001913, -65.1477229999889, -65.154117999597, -65.1536439999441, -65.1506989999476, -65.1448089999545, -65.137688999725, -65.1399409997855, -65.1351379999137, -65.1303469997376, -65.1174269999267, -65.1130569997, -65.1137599996818, -65.112214999801, -65.1004130000736, -65.091790999621, -65.0805670002019, -65.0606650002537, -65.0415220003638, -65.0172989996944, -65.007769000119, -64.9995900002555, -64.9823889999064, -64.9691820000403, -64.950455000077, -64.9426920001401, -64.9276419995691, -64.907594999856, -64.8950240000245, -64.8910329999146, -64.8739119999314, -64.8649759996594, -64.8599500002049, -64.843729999672], [18.3937130003038, 18.3952020001978, 18.4029059996947, 18.4075949997621, 18.4038019999373, 18.386209000173, 18.3766859999062, 18.3781310002826, 18.3762170003899, 18.3779449996133, 18.3747250001871, 18.373742000052, 18.3706359999168, 18.3725669997141, 18.3674629998913, 18.3655769999245, 18.363997000316, 18.3551899997673, 18.3441069997742, 18.3197589996555, 18.2853760001861, 18.2673589997501, 18.2612709996959, 18.2523160002306, 18.2487579998992, 18.2487249997681, 18.2506900002989, 18.2630590000803, 18.2608550002774, 18.2605059997401, 18.2526930002856, 18.2451059996837, 18.2293219998289, 18.2133720003843, 18.2051629999707, 18.1920609998845, 18.1834559998114, 18.1796829999945, 18.1795870003758, 18.1834839999737, 18.1942359996752, 18.2065249995874, 18.2185139997731, 18.2241929999785, 18.2288429997134, 18.2379699999478, 18.2480099999376, 18.2572230000658, 18.2560599998827, 18.2569120001884, 18.252492999917, 18.2523549996564, 18.2562770001029, 18.2667770001428, 18.2691289998076, 18.2541629999811, 18.2439080002976, 18.2364819999318, 18.2326290001953, 18.23477199986, 18.2417569995878, 18.2507890004071, 18.2590850003178, 18.2774250002881, 18.2931389997995, 18.302844999769, 18.3128859999509, 18.3205580003919, 18.3362909997264, 18.3508549999627, 18.3615369997632, 18.3802330002905, 18.3957579996876, 18.4068750001337, 18.418827, 18.4395440001859, 18.4471019997825, 18.4528070003069, 18.4597469996518, 18.4591020001661, 18.4493229999302, 18.4407029996101, 18.4455259999609, 18.4507079999187, 18.4509469999009, 18.4462940001965, 18.4541570002839, 18.4619490000909, 18.4649839999656, 18.4633640003554, 18.4621600003593, 18.4062549996746, 18.3959270002777, 18.3942250002094, 18.3937130003038]]\n"
     ]
    }
   ],
   "source": [
    "# source: https://public.opendatasoft.com/explore/dataset/us-state-boundaries/table/\n",
    "with open(\"us-state-boundaries.csv\") as f:\n",
    "    sb = [line.split(';')[-2] for line in f.readlines()[1:]] # state boundaries\n",
    "    sb = ['[['+b[b.find(\"[[[\")+3: b.find(\"]]]\")+1].strip('[').strip(']')+']]' for b in sb]\n",
    "\n",
    "sb2 = []\n",
    "for i in range(len(sb)):\n",
    "    try:\n",
    "        sb2.append(json.loads(sb[i]))\n",
    "    except BaseException as e:\n",
    "        print(i)\n",
    "\n",
    "sb3 = []\n",
    "for b in sb2:\n",
    "    x = []\n",
    "    y = []\n",
    "    for xy in b:\n",
    "        x.append(xy[0])\n",
    "        y.append(xy[1])\n",
    "    sb3.append([x, y])\n",
    "\n",
    "print(sb3[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "422402ed-4ca9-44b1-9d2d-81949961af55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "913a98642708421fb05ec33e19acd1ef",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axis = plt.subplots()\n",
    "x_col = \"longitude\"\n",
    "y_col = \"latitude\"\n",
    "xlo = df[x_col].min() - 5\n",
    "xhi = df[x_col].max() + 5\n",
    "ylo = df[y_col].min() - 5\n",
    "yhi = df[y_col].max() + 5\n",
    "\n",
    "x = df[x_col].to_numpy()\n",
    "y = df[y_col].to_numpy()\n",
    "\n",
    "plt.axis([xlo, xhi, ylo, yhi])\n",
    "plt.plot(x, y, \"ro\")\n",
    "for b in sb3:\n",
    "    plt.plot(b[0], b[1], \"b\")\n",
    "plt.title(\"USA states and their capital location\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47995e7b-d2dc-4c9d-bf70-ee6c791c6f94",
   "metadata": {},
   "source": [
    "## Edit CSV file URL variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "d5418a6b-4b81-4523-90f1-1ab328f6adfa",
   "metadata": {},
   "outputs": [
    {
     "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>Macquarie Island MAX</th>\n",
       "      <th>Macquarie Island MIN</th>\n",
       "      <th>Mawson MAX</th>\n",
       "      <th>Mawson MIN</th>\n",
       "      <th>Casey MAX</th>\n",
       "      <th>Casey MIN</th>\n",
       "      <th>Davis MAX</th>\n",
       "      <th>Davis MIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>1950</td>\n",
       "      <td>6.050</td>\n",
       "      <td>2.625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>1951</td>\n",
       "      <td>6.100</td>\n",
       "      <td>2.660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>1952</td>\n",
       "      <td>6.140</td>\n",
       "      <td>2.660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2011</td>\n",
       "      <td>6.740</td>\n",
       "      <td>3.200</td>\n",
       "      <td>-8.04</td>\n",
       "      <td>-14.28</td>\n",
       "      <td>-5.86</td>\n",
       "      <td>-12.50</td>\n",
       "      <td>-7.00</td>\n",
       "      <td>-12.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2012</td>\n",
       "      <td>6.840</td>\n",
       "      <td>3.260</td>\n",
       "      <td>-8.24</td>\n",
       "      <td>-14.40</td>\n",
       "      <td>-6.28</td>\n",
       "      <td>-12.96</td>\n",
       "      <td>-7.10</td>\n",
       "      <td>-13.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2013</td>\n",
       "      <td>6.825</td>\n",
       "      <td>3.225</td>\n",
       "      <td>-8.10</td>\n",
       "      <td>-14.30</td>\n",
       "      <td>-6.30</td>\n",
       "      <td>-12.90</td>\n",
       "      <td>-7.05</td>\n",
       "      <td>-13.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Macquarie Island MAX  Macquarie Island MIN  Mawson MAX  Mawson MIN  \\\n",
       "0   1948                   NaN                   NaN         NaN         NaN   \n",
       "1   1949                   NaN                   NaN         NaN         NaN   \n",
       "2   1950                 6.050                 2.625         NaN         NaN   \n",
       "3   1951                 6.100                 2.660         NaN         NaN   \n",
       "4   1952                 6.140                 2.660         NaN         NaN   \n",
       "..   ...                   ...                   ...         ...         ...   \n",
       "63  2011                 6.740                 3.200       -8.04      -14.28   \n",
       "64  2012                 6.840                 3.260       -8.24      -14.40   \n",
       "65  2013                 6.825                 3.225       -8.10      -14.30   \n",
       "66  2014                   NaN                   NaN         NaN         NaN   \n",
       "67  2015                   NaN                   NaN         NaN         NaN   \n",
       "\n",
       "    Casey MAX  Casey MIN  Davis MAX  Davis MIN  \n",
       "0         NaN        NaN        NaN        NaN  \n",
       "1         NaN        NaN        NaN        NaN  \n",
       "2         NaN        NaN        NaN        NaN  \n",
       "3         NaN        NaN        NaN        NaN  \n",
       "4         NaN        NaN        NaN        NaN  \n",
       "..        ...        ...        ...        ...  \n",
       "63      -5.86     -12.50      -7.00     -12.98  \n",
       "64      -6.28     -12.96      -7.10     -13.08  \n",
       "65      -6.30     -12.90      -7.05     -13.05  \n",
       "66        NaN        NaN        NaN        NaN  \n",
       "67        NaN        NaN        NaN        NaN  \n",
       "\n",
       "[68 rows x 9 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://data.gov.au/data/dataset/7169894e-b518-4933-a900-f79ebc4ec6a3/resource/5d9edabe-b6af-4975-9340-88f55b872a00/download/soe2016antarctic5-year-smoothed-annual-max-min-temperature-by-stationaad.csv\"\n",
    "df = pd.read_csv(url)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "436124b0-56ab-4af1-b3e4-abfd142ae4d1",
   "metadata": {},
   "outputs": [
    {
     "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>Macquarie Island MAX</th>\n",
       "      <th>Macquarie Island MIN</th>\n",
       "      <th>Mawson MAX</th>\n",
       "      <th>Mawson MIN</th>\n",
       "      <th>Casey MAX</th>\n",
       "      <th>Casey MIN</th>\n",
       "      <th>Davis MAX</th>\n",
       "      <th>Davis MIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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": [
       "   Year  Macquarie Island MAX  Macquarie Island MIN  Mawson MAX  Mawson MIN  \\\n",
       "0  1948                   NaN                   NaN         NaN         NaN   \n",
       "1  1949                   NaN                   NaN         NaN         NaN   \n",
       "\n",
       "   Casey MAX  Casey MIN  Davis MAX  Davis MIN  \n",
       "0        NaN        NaN        NaN        NaN  \n",
       "1        NaN        NaN        NaN        NaN  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "b54f2a53-31c7-4f70-8d8a-7a14a665f933",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Year' 'Macquarie Island MAX' 'Macquarie Island MIN' 'Mawson MAX'\n",
      " 'Mawson MIN' 'Casey MAX' 'Casey MIN' 'Davis MAX' 'Davis MIN']\n"
     ]
    }
   ],
   "source": [
    "col_names = df.columns.values\n",
    "print(col_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "e1e204bc-dce9-4917-aaba-663b6e12a65b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Year\n",
      "1 Macquarie Island MAX\n",
      "2 Macquarie Island MIN\n",
      "3 Mawson MAX\n",
      "4 Mawson MIN\n",
      "5 Casey MAX\n",
      "6 Casey MIN\n",
      "7 Davis MAX\n",
      "8 Davis MIN\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Input index number of column to be used for x-axis:  0\n",
      "Input index number of column to be used for y-axis:  2\n",
      "Input index number of column 2 (optional) to be used for y-axis:  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x-axis: Year, y-axis: Macquarie Island MIN, y-axis (second): 1\n"
     ]
    }
   ],
   "source": [
    "[print(i, col_names[i]) for i in range(len(col_names))]\n",
    "x_col = int(input(\"Input index number of column to be used for x-axis: \"))\n",
    "y_col = int(input(\"Input index number of column to be used for y-axis: \"))\n",
    "y_col2 = input(\"Input index number of column 2 (optional) to be used for y-axis: \")\n",
    "y_col2 = int(y_col2) if y_col2 else None\n",
    "print(f\"x-axis: {col_names[x_col]}, y-axis: {col_names[y_col]}, y-axis (second): {y_col2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "4a8fdb26-5b04-49ab-8f37-311ec1e0dccd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25808103e4f24542a5bf087d3335347e",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axis = plt.subplots()\n",
    "xlo = df[col_names[x_col]].min() - 5\n",
    "xhi = df[col_names[x_col]].max() + 5\n",
    "ylo = df[col_names[y_col]].min() - 5\n",
    "yhi = df[col_names[y_col]].max() + 5\n",
    "if y_col2:\n",
    "    ylo = min(ylo, df[col_names[y_col2]].min() - 5)\n",
    "    yhi = max(yhi, df[col_names[y_col]].max() + 5)\n",
    "\n",
    "x = df[col_names[x_col]].to_numpy()\n",
    "y = df[col_names[y_col]].to_numpy()\n",
    "if y_col2:\n",
    "    y2 = df[col_names[y_col2]].to_numpy()\n",
    "\n",
    "plt.axis([xlo, xhi, ylo, yhi])\n",
    "# plt.plot(x, y, \"bo\")\n",
    "plt.plot(x, y, \"b\")\n",
    "if y_col2:\n",
    "    # plt.plot(x, y2, \"ro\")\n",
    "    plt.plot(x, y2, \"r\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8176fbac-b605-41a7-9319-f762d95f2364",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}