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snippsat/gist:06de0d3da08bb46a3eec55915a338030

Created April 27, 2020 13:12
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gistfile1.txt
{
"cells": [
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_html('https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-current-situation/covid-19-current-cases')[0]"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Tile</th>\n",
" <th>Total</th>\n",
" <th>Change in last 24 hours</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Number of confirmed cases in New Zealand</td>\n",
" <td>1122</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Number of probable cases</td>\n",
" <td>347</td>\n",
" <td>-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Number of confirmed and probable cases</td>\n",
" <td>1469</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Number of cases currently in hospital</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Number of recovered cases</td>\n",
" <td>1180</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Number of deaths (as at 1 pm 27 April)</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Tile Total Change in last 24 hours\n",
"0 Number of confirmed cases in New Zealand 1122 1\n",
"1 Number of probable cases 347 -2\n",
"2 Number of confirmed and probable cases 1469 -1\n",
"3 Number of cases currently in hospital 7 0\n",
"4 Number of recovered cases 1180 38\n",
"5 Number of deaths (as at 1 pm 27 April) 19 1"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.rename(columns={'Unnamed: 0': 'Tile'}, inplace=True)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_html('https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-current-situation/covid-19-current-cases')[6]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"df = df.drop(df.index[0])\n",
"df['Date'] = pd.to_datetime(df['Date'], format='%d-%b')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Tests per day</th>\n",
" <th>Total tests (cumulative)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1900-03-09</td>\n",
" <td>12.0</td>\n",
" <td>312</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1900-03-10</td>\n",
" <td>89.0</td>\n",
" <td>401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1900-03-11</td>\n",
" <td>83.0</td>\n",
" <td>484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1900-03-12</td>\n",
" <td>31.0</td>\n",
" <td>515</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1900-03-13</td>\n",
" <td>35.0</td>\n",
" <td>550</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Tests per day Total tests (cumulative)\n",
"1 1900-03-09 12.0 312\n",
"2 1900-03-10 89.0 401\n",
"3 1900-03-11 83.0 484\n",
"4 1900-03-12 31.0 515\n",
"5 1900-03-13 35.0 550"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
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"Date": "1900-03-09T00:00:00",
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"Date": "1900-03-10T00:00:00",
"Tests per day": 89,
"Total tests (cumulative)": 401
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"Date": "1900-03-11T00:00:00",
"Tests per day": 83,
"Total tests (cumulative)": 484
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"Date": "1900-03-12T00:00:00",
"Tests per day": 31,
"Total tests (cumulative)": 515
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"Date": "1900-03-13T00:00:00",
"Tests per day": 35,
"Total tests (cumulative)": 550
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"Date": "1900-03-14T00:00:00",
"Tests per day": 34,
"Total tests (cumulative)": 584
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"Date": "1900-03-15T00:00:00",
"Tests per day": 142,
"Total tests (cumulative)": 726
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"Date": "1900-03-16T00:00:00",
"Tests per day": 325,
"Total tests (cumulative)": 1051
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"Date": "1900-03-17T00:00:00",
"Tests per day": 659,
"Total tests (cumulative)": 1710
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"Date": "1900-03-18T00:00:00",
"Tests per day": 1209,
"Total tests (cumulative)": 2919
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"Date": "1900-03-19T00:00:00",
"Tests per day": 1291,
"Total tests (cumulative)": 4210
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"Date": "1900-03-20T00:00:00",
"Tests per day": 1554,
"Total tests (cumulative)": 5764
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"Date": "1900-03-21T00:00:00",
"Tests per day": 1176,
"Total tests (cumulative)": 6940
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"Date": "1900-03-22T00:00:00",
"Tests per day": 1256,
"Total tests (cumulative)": 8196
},
{
"Date": "1900-03-23T00:00:00",
"Tests per day": 1050,
"Total tests (cumulative)": 9246
},
{
"Date": "1900-03-24T00:00:00",
"Tests per day": 1544,
"Total tests (cumulative)": 10790
},
{
"Date": "1900-03-25T00:00:00",
"Tests per day": 2592,
"Total tests (cumulative)": 13382
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{
"Date": "1900-03-26T00:00:00",
"Tests per day": 2117,
"Total tests (cumulative)": 15499
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"Date": "1900-03-27T00:00:00",
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"Total tests (cumulative)": 17566
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"Date": "1900-03-28T00:00:00",
"Tests per day": 1809,
"Total tests (cumulative)": 19375
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"Date": "1900-03-29T00:00:00",
"Tests per day": 918,
"Total tests (cumulative)": 20293
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{
"Date": "1900-03-30T00:00:00",
"Tests per day": 1391,
"Total tests (cumulative)": 21684
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{
"Date": "1900-03-31T00:00:00",
"Tests per day": 2093,
"Total tests (cumulative)": 23777
},
{
"Date": "1900-04-01T00:00:00",
"Tests per day": 2562,
"Total tests (cumulative)": 26339
},
{
"Date": "1900-04-02T00:00:00",
"Tests per day": 3446,
"Total tests (cumulative)": 29785
},
{
"Date": "1900-04-03T00:00:00",
"Tests per day": 3631,
"Total tests (cumulative)": 33416
},
{
"Date": "1900-04-04T00:00:00",
"Tests per day": 3093,
"Total tests (cumulative)": 36509
},
{
"Date": "1900-04-05T00:00:00",
"Tests per day": 3709,
"Total tests (cumulative)": 40218
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{
"Date": "1900-04-06T00:00:00",
"Tests per day": 2908,
"Total tests (cumulative)": 43126
},
{
"Date": "1900-04-07T00:00:00",
"Tests per day": 4049,
"Total tests (cumulative)": 47175
},
{
"Date": "1900-04-08T00:00:00",
"Tests per day": 3990,
"Total tests (cumulative)": 51165
},
{
"Date": "1900-04-09T00:00:00",
"Tests per day": 4520,
"Total tests (cumulative)": 55685
},
{
"Date": "1900-04-10T00:00:00",
"Tests per day": 3061,
"Total tests (cumulative)": 58746
},
{
"Date": "1900-04-11T00:00:00",
"Tests per day": 2421,
"Total tests (cumulative)": 61167
},
{
"Date": "1900-04-12T00:00:00",
"Tests per day": 1660,
"Total tests (cumulative)": 62827
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"Date": "1900-04-13T00:00:00",
"Tests per day": 1572,
"Total tests (cumulative)": 64399
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"Date": "1900-04-14T00:00:00",
"Tests per day": 2100,
"Total tests (cumulative)": 66499
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"Date": "1900-04-15T00:00:00",
"Tests per day": 3661,
"Total tests (cumulative)": 70160
},
{
"Date": "1900-04-16T00:00:00",
"Tests per day": 4241,
"Total tests (cumulative)": 74401
},
{
"Date": "1900-04-17T00:00:00",
"Tests per day": 4677,
"Total tests (cumulative)": 79078
},
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"Date": "1900-04-18T00:00:00",
"Tests per day": 4146,
"Total tests (cumulative)": 83224
},
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"Date": "1900-04-19T00:00:00",
"Tests per day": 3081,
"Total tests (cumulative)": 86305
},
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"Date": "1900-04-20T00:00:00",
"Tests per day": 3203,
"Total tests (cumulative)": 89508
},
{
"Date": "1900-04-21T00:00:00",
"Tests per day": 5289,
"Total tests (cumulative)": 94797
},
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"Date": "1900-04-22T00:00:00",
"Tests per day": 6480,
"Total tests (cumulative)": 101277
},
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"Date": "1900-04-23T00:00:00",
"Tests per day": 6961,
"Total tests (cumulative)": 108238
},
{
"Date": "1900-04-24T00:00:00",
"Tests per day": 6777,
"Total tests (cumulative)": 115015
},
{
"Date": "1900-04-25T00:00:00",
"Tests per day": 5966,
"Total tests (cumulative)": 120981
},
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"Date": "1900-04-26T00:00:00",
"Tests per day": 2939,
"Total tests (cumulative)": 123920
}
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",
"text/plain": [
"<VegaLite 3 object>\n",
"\n",
"If you see this message, it means the renderer has not been properly enabled\n",
"for the frontend that you are using. For more information, see\n",
"https://altair-viz.github.io/user_guide/troubleshooting.html\n"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import altair as alt\n",
"\n",
"alt.Chart(df).mark_line().encode(\n",
" x='Date',\n",
" y='Tests per day',\n",
" y2='Total tests (cumulative)', \n",
" color='Tests per day'\n",
") "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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