freecodecamp-college-algebra/data-graph-explorer
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
640f87cee8
data-graph-explorer/Data Graph Explorer.ipynb

1251 lines
194 KiB
Plaintext
Raw Normal View History

Solution
2023-07-29 05:10:42 +02:00
{
"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": [
"<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>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 src='data:image/png;base64,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
" </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 src='data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA/SklEQVR4nO3dd3hUVf7H8U8CJBAgoQUQCEWwICgICiIugrKgiw121UVsoNjAhg12FXQV0V3UddfesSu7ghUVAbEQ6SDI0kV6FRKkhJA5vz++v8lkIECSyWSSnPfreebRuXNncu7NCfczp90455wTAAAAvBEf6wIAAACgZBEAAQAAPEMABAAA8AwBEAAAwDMEQAAAAM8QAAEAADxDAAQAAPAMARAAAMAzBEAAAADPEAABAAA8QwAEAADwDAEQAADAMwRAAAAAzxAAAQAAPEMABAAA8AwBEAAAwDMEQAAAAM8QAAEAADxDAAQAAPAMARAAAMAzBEAAAADPEAABAAA8QwAEAADwDAEQAADAMwRAAAAAzxAAAQAAPEMABAAA8AwBEAAAwDMEQAAAAM8QAAEAADxDAAQAAPAMARAAAMAzBEAAAADPEAABAAA8QwAEAADwDAEQAADAMwRAAAAAzxAAAQAAPEMABAAA8AwBEAAAwDMEQAAAAM8QAAEAADxDAAQAAPAMARAAAMAzBEAAAADPEAABAAA8QwAEAADwDAEQAADAMwRAAAAAzxAAAQAAPEMABAAA8AwBEAAAwDMEQAAAAM8QAAEAADxDAAQAAPAMARAAAMAzFWNdgLIsEAho/fr1ql69uuLi4mJdHAAAUADOOe3cuVMNGjRQfLyfbWEEwAisX79eaWlpsS4GAAAogjVr1qhRo0axLkZMEAAjUL16dUlWgZKTk2NcGgAAUBCZmZlKS0vLvY77iAAYgWC3b3JyMgEQAIAyxufhW352fAMAAHiMAAgAAOAZAiAAAIBnCIAAAACeIQACAAB4hgAIAADgGQIgAACAZwiAAAAAnvE2AObk5Oi+++5Ts2bNVKVKFTVv3lwPPvignHOxLhoAAEBUeXsnkEcffVTPPvusxowZo1atWmnWrFnq37+/UlJSdMstt8S6eAAAAFHjbQCcNm2aLrzwQvXq1UuS1LRpU73zzjuaMWNGjEsGAAAQXd52AZ9++umaNGmSli5dKkmaP3++vvvuO5177rkxLhkAAEB0edsCOHToUGVmZur4449XhQoVlJOTo5EjR6pfv36HfE9WVpaysrJyn2dmZpZEUQEAAIqVty2A77//vt566y29/fbbmjNnjsaMGaPRo0drzJgxh3zPqFGjlJKSkvtIS0srwRIDAAAUjzjn6bTXtLQ0DR06VIMGDcrd9tBDD+nNN9/U4sWL831Pfi2AaWlpysjIUHJyctTLDAAAIpeZmamUlBSvr9/edgHv3r1b8fHhDaAVKlRQIBA45HsSExOVmJgY7aIBAABElbcB8Pzzz9fIkSPVuHFjtWrVSnPnztXjjz+uAQMGxLpoAAAAUeVtF/DOnTt13333ady4cdq8ebMaNGigvn37avjw4UpISCjQZ9CEDABA2cP12+MAWByoQAAAlD1cvz2eBQwAAOArAiAAAIBnCIAAAACeIQACAAB4hgAIAADgGQIgAACAZwiAAAAAniEAAgAAeIYACAAA4BkCIAAAgGcIgAAAAJ4hAAIAAHiGAAgAAOAZAiAAAIBnCIAAAACeIQACAAB4hgAIAADgGQIgAACAZwiAAAAAniEAAgAAeIYACAAA4BkCIAAAgGcIgAAAAJ4hAAIAAHiGAAgAAOAZAiAAAIBnKsa6AAAQkW3bpNWrpXXrwh8bNkgtW0p33y3VqxfrUkYuK0tKTIx1KQCUEwRAAGXTxo3SrbdK779/6H0mTJCef1667TbpzjulGjVKqnSRc06aM0caN84eixfbMTz0kFSpUqxLB6CMi3POuVgXoqzKzMxUSkqKMjIylJycHOviAH4IBKSXX7aWvR07bFu9elLDhuGP2rWl116TZsywfWrWlIYOlQYPlpKSYlX6w8vJkb77LhT6Vq8+eJ/TTpPeeUdq2rTEiweUF1y/CYARoQIBJWzxYun666VvvrHn7dtLL74onXxy/vs7J40fL917r7RokW1r0EAaPly65hqpYow7Qdats4AafMycKe3cGXo9KUk691ypd28pPl666SYLvTVqSK+8YtsBFBrXbwJgRKhAQAnJypIefVQaOVLat0+qWtW6QgcPLliIy8mR3nxTGjFC+uUX23ak8Fgcdu2S1q8/eHziqlXSrFn22oFq1ZIuuMDC3e9/L1WpEnpt1Srpz3+Wpk+354MGSaNHS5UrR+8YgHKI6zcBMCJUICBKtm+3gBRsGUtPl7ZssdfOPVd69lmpSZPCf25Wlo0JHDHCWtIqVJCGDJHuv794uoUDAStvsAt32bLD71+hgnTiiVKHDqFHy5aHD7XZ2dai+fe/2/O2baV335WOOy7y8gOe4PpNAIwIFQgoRjNnSk89Jf3wg7R06cGv160rPfmkdOmlUlxcZD/rwAkkzZpJzz0n9egRvt+WLVauGTNsVvFRRx081jA5WZo61QLf+PG2X15Vqx78nkaNLLi1a1f04DlhgnTlldLWrdYCOGKEdMcdTBBBdEyeLI0da/W1Th17pKaG/3+tWpH/bZYQrt8EwIhQgYBisHixtWj997/h21u0CG8ZO/nk4u/q/Phj60Zds8aeX365/Zxgy+PPPx/5M+LibKxhUPXqUq9e1oXbvbtNPonWRXH9eumqq6SvvrLnJ51k3dodOkTn55UXW7faZJsDu+bXrbMxmAMHSvfdZ+MufTdzpvSXv4Tq2KFUqGDDM8rIOeP6TQCMCBUIiMDq1db1OmaMdZ3GxUlXXCH17SudeqrN4i0JO3faxf5f/woPckHHH2+BqmlTaznMGxaC3dKpqdKFF1roO/vskl2vzznpjTesK3vbNjuPt9wiPfighdGCysmxFp7166Wbby6fLYmBgPTCC9I990iZmYff9+KLbRZ5aZ0xHm0HfjGrVMm+bNSsafV+61Z7BP+/SpWDW79LMa7fBMCIUIGAItiyRXr4YemZZ6zFQJIuusgmdbRqFbtyzZhhZahQIdTq2L794dcOzMqy4znqKHtfLG3ZYl3Ab7xhz9PSrEv9vPMO3yrjnPTJJ9bKs3ChbRsyRHrsseiXuSQtWiRdd530/ff2/Nhjrb4d2D2/bJkF4Oxs+yLy4Yf2+/WBc9KSJdI//mHhN+8Xs/vvt6ESh7J/f+xn1RcC128CYESoQEAhff65jVsLtpx17SqNGmVr26F4fPmldMMNoe7rBg0sYPfuLZ15ZnjL3tSp0rBhNslGsvGMwZaxd96xGcdlXVaWfeEYNcpCXdWq9nzQoEOH9m+/tfO1bZsF6Y8/ltq0Kdlyl4TNm0NjXIOPX38NvX7hhfalqHXr2JUxSrh+S3IosoyMDCfJZWRkxLooQMn69VfnXn/duT59nDvzTOfefde5nJxD779vn3N33eWctTE417q1c1984VwgUGJF9squXc7dfbdz1auHzrnkXM2azl1xhXNjxjjXs2doe5Uqzg0dar/Xv/zFtiUlOffjj7E+kqIJBJzbvt25L7907rjjQsd53nnO/fJLwT5j+XLnjj/e3le1qnMffXT4n1cWZGc7N2mSc4MHO9esWXjdCD4SEqxupKfHurRRxfXbOVoAI8A3CHhl/Xqb5TpunPT119blk9fJJ1vLSs+e4ZMeVq2ycX0//GDPWbuu5GRlSZMm2e/sww9DLa9BFStat+i994a6OXNypD/8wVoSmze35XhK8y305s+3ZXDWrAkfn7l7d2ifevWkf/9b+tOfCjchZ/t2Gws4aZK97+qrLSblHfu2dau0d6902WXWTdq4cXEfYWT27LHf5bhx1pKZt4VPsmWH8k62OukkKSEhNmUtQVy/6QKOCBUI5Z5zNvvvb3+zWZN5tW4dukPF44+H7mDRpYt1t51+uvTBB3bHjR07pJQUu3tFnz4lfhiQBbtp0ywITJl
" </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
}
Reference in New Issue Copy Permalink