{
"cells": [
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import altair as alt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Look at the metadata"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"metadata_df = pd.read_json('../../data/covidtracking/states-metadata.json')"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
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" <th></th>\n",
" <th>state</th>\n",
" <th>covid19SiteOld</th>\n",
" <th>covid19Site</th>\n",
" <th>covid19SiteSecondary</th>\n",
" <th>twitter</th>\n",
" <th>pui</th>\n",
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" <th>name</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AK</td>\n",
" <td>http://dhss.alaska.gov/dph/Epi/id/Pages/COVID-...</td>\n",
" <td>http://dhss.alaska.gov/dph/Epi/id/Pages/COVID-...</td>\n",
" <td>http://dhss.alaska.gov/dph/Epi/id/Pages/COVID-...</td>\n",
" <td>@Alaska_DHSS</td>\n",
" <td>All data</td>\n",
" <td>False</td>\n",
" <td>Unclear if their reported number means \"person...</td>\n",
" <td>Alaska</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AL</td>\n",
" <td>http://www.alabamapublichealth.gov/infectiousd...</td>\n",
" <td>https://alpublichealth.maps.arcgis.com/apps/op...</td>\n",
" <td>None</td>\n",
" <td>@alpublichealth</td>\n",
" <td>No data</td>\n",
" <td>False</td>\n",
" <td>Last negative count from 3/16. Last update tim...</td>\n",
" <td>Alabama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AR</td>\n",
" <td>https://www.healthy.arkansas.gov/programs-serv...</td>\n",
" <td>https://www.healthy.arkansas.gov/programs-serv...</td>\n",
" <td>None</td>\n",
" <td>@adhpio</td>\n",
" <td>All data</td>\n",
" <td>True</td>\n",
" <td>Pending = \"PUIs\"</td>\n",
" <td>Arkansas</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AS</td>\n",
" <td>http://www.samoagovt.ws/2020/03/ministry-of-he...</td>\n",
" <td>https://www.facebook.com/amsamgov/</td>\n",
" <td>https://www.americansamoa.gov/?fbclid=IwAR2EKT...</td>\n",
" <td>None</td>\n",
" <td>No Data</td>\n",
" <td>False</td>\n",
" <td>American Samoa: No data, no confirmed cases yet.</td>\n",
" <td>American Samoa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AZ</td>\n",
" <td>https://www.azdhs.gov/preparedness/epidemiolog...</td>\n",
" <td>https://www.azdhs.gov/preparedness/epidemiolog...</td>\n",
" <td>None</td>\n",
" <td>@azdhs</td>\n",
" <td>All data</td>\n",
" <td>False</td>\n",
" <td>Negative = “Ruled Out”. Negatives are from pub...</td>\n",
" <td>Arizona</td>\n",
" </tr>\n",
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"</table>\n",
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"text/plain": [
" state covid19SiteOld \\\n",
"0 AK http://dhss.alaska.gov/dph/Epi/id/Pages/COVID-... \n",
"1 AL http://www.alabamapublichealth.gov/infectiousd... \n",
"2 AR https://www.healthy.arkansas.gov/programs-serv... \n",
"3 AS http://www.samoagovt.ws/2020/03/ministry-of-he... \n",
"4 AZ https://www.azdhs.gov/preparedness/epidemiolog... \n",
"\n",
" covid19Site \\\n",
"0 http://dhss.alaska.gov/dph/Epi/id/Pages/COVID-... \n",
"1 https://alpublichealth.maps.arcgis.com/apps/op... \n",
"2 https://www.healthy.arkansas.gov/programs-serv... \n",
"3 https://www.facebook.com/amsamgov/ \n",
"4 https://www.azdhs.gov/preparedness/epidemiolog... \n",
"\n",
" covid19SiteSecondary twitter \\\n",
"0 http://dhss.alaska.gov/dph/Epi/id/Pages/COVID-... @Alaska_DHSS \n",
"1 None @alpublichealth \n",
"2 None @adhpio \n",
"3 https://www.americansamoa.gov/?fbclid=IwAR2EKT... None \n",
"4 None @azdhs \n",
"\n",
" pui pum notes \\\n",
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"1 No data False Last negative count from 3/16. Last update tim... \n",
"2 All data True Pending = \"PUIs\" \n",
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"\n",
" name \n",
"0 Alaska \n",
"1 Alabama \n",
"2 Arkansas \n",
"3 American Samoa \n",
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"execution_count": 60,
"metadata": {},
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],
"source": [
"metadata_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load most recent data and look at it"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"data_df = pd.read_json('../../data/covidtracking/states-daily.json')\n",
"data_df['date'] = pd.to_datetime(data_df['date'], format=\"%Y%m%d\")"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>state</th>\n",
" <th>positive</th>\n",
" <th>negative</th>\n",
" <th>pending</th>\n",
" <th>death</th>\n",
" <th>total</th>\n",
" <th>dateChecked</th>\n",
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" <td>2020-03-20</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>2020-03-20</td>\n",
" <td>AL</td>\n",
" <td>81</td>\n",
" <td>28.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>109</td>\n",
" <td>2020-03-20T20:00:00Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2020-03-20</td>\n",
" <td>AR</td>\n",
" <td>96</td>\n",
" <td>351.0</td>\n",
" <td>203.0</td>\n",
" <td>NaN</td>\n",
" <td>650</td>\n",
" <td>2020-03-20T20:00:00Z</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>2020-03-20</td>\n",
" <td>AS</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>2020-03-20</td>\n",
" <td>AZ</td>\n",
" <td>65</td>\n",
" <td>211.0</td>\n",
" <td>101.0</td>\n",
" <td>0.0</td>\n",
" <td>377</td>\n",
" <td>2020-03-20T20:00:00Z</td>\n",
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],
"text/plain": [
" date state positive negative pending death total \\\n",
"0 2020-03-20 AK 12 686.0 NaN NaN 698 \n",
"1 2020-03-20 AL 81 28.0 NaN 0.0 109 \n",
"2 2020-03-20 AR 96 351.0 203.0 NaN 650 \n",
"3 2020-03-20 AS 0 NaN NaN 0.0 0 \n",
"4 2020-03-20 AZ 65 211.0 101.0 0.0 377 \n",
"\n",
" dateChecked \n",
"0 2020-03-20T20:00:00Z \n",
"1 2020-03-20T20:00:00Z \n",
"2 2020-03-20T20:00:00Z \n",
"3 2020-03-20T20:00:00Z \n",
"4 2020-03-20T20:00:00Z "
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Daily counts and totals"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"# compute daily differences\n",
"tdf = data_df.sort_values(['state', 'date'], ascending=[True, False]).set_index(['state', 'date'])\n",
"diffs_df = tdf[['positive', 'negative', 'death']].groupby(level='state').diff(periods=-1).dropna(how='all')\n",
"tdf_diff=tdf.join(diffs_df, rsuffix='_diff').reset_index()\n",
"\n",
"# \"Normalizing\" the totals\n",
"tdf_diff['total_10'] = tdf_diff['total']/10."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
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\"positive\": 30, \"negative\": 74.0, \"pending\": 20.0, \"death\": 1.0, \"total\": 124, \"dateChecked\": \"2020-03-12T20:00:00Z\", \"positive_diff\": 6.0, \"negative_diff\": 17.0, \"death_diff\": 0.0, \"total_10\": 12.4, \"daily_positive\": 6.0}, {\"state\": \"NJ\", \"date\": \"2020-03-11T00:00:00\", \"positive\": 24, \"negative\": 57.0, \"pending\": 20.0, \"death\": 1.0, \"total\": 100, \"dateChecked\": \"2020-03-11T20:00:00Z\", \"positive_diff\": 9.0, \"negative_diff\": 13.0, \"death_diff\": null, \"total_10\": 10.0, \"daily_positive\": 9.0}, {\"state\": \"NJ\", \"date\": \"2020-03-10T00:00:00\", \"positive\": 15, \"negative\": 44.0, \"pending\": 20.0, \"death\": null, \"total\": 79, \"dateChecked\": \"2020-03-10T20:00:00Z\", \"positive_diff\": 4.0, \"negative_diff\": 9.0, \"death_diff\": null, \"total_10\": 7.9, \"daily_positive\": 4.0}, {\"state\": \"NJ\", \"date\": \"2020-03-09T00:00:00\", \"positive\": 11, \"negative\": 35.0, \"pending\": 14.0, \"death\": null, \"total\": 60, \"dateChecked\": \"2020-03-09T20:00:00Z\", \"positive_diff\": 5.0, \"negative_diff\": 4.0, \"death_diff\": null, \"total_10\": 6.0, \"daily_positive\": 5.0}, {\"state\": \"NJ\", \"date\": \"2020-03-08T00:00:00\", \"positive\": 6, \"negative\": 31.0, \"pending\": 0.0, \"death\": null, \"total\": 37, \"dateChecked\": \"2020-03-08T20:00:00Z\", \"positive_diff\": 2.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 3.7, \"daily_positive\": 2.0}, {\"state\": \"NJ\", \"date\": \"2020-03-07T00:00:00\", \"positive\": 4, \"negative\": null, \"pending\": 1.0, \"death\": null, \"total\": 5, \"dateChecked\": \"2020-03-07T21:00:00Z\", \"positive_diff\": 3.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.5, \"daily_positive\": 3.0}, {\"state\": \"NJ\", \"date\": \"2020-03-06T00:00:00\", \"positive\": 1, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 1, \"dateChecked\": \"2020-03-06T21:00:00Z\", \"positive_diff\": 0.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.1, \"daily_positive\": 0.0}, {\"state\": \"NJ\", \"date\": \"2020-03-05T00:00:00\", \"positive\": 1, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 1, \"dateChecked\": \"2020-03-05T21:00:00Z\", \"positive_diff\": null, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.1, \"daily_positive\": null}], \"data-ddffbf8d2dd76c1c297388ebe03e44ee\": [{\"state\": \"NC\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 137, \"negative\": 3096.0, \"pending\": null, \"death\": 0.0, \"total\": 3233, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"positive_diff\": 40.0, \"negative_diff\": 688.0, \"death_diff\": 0.0, \"total_10\": 323.3, \"daily_positive\": 40.0}, {\"state\": \"NC\", \"date\": \"2020-03-19T00:00:00\", \"positive\": 97, \"negative\": 2408.0, \"pending\": null, \"death\": 0.0, \"total\": 2505, \"dateChecked\": \"2020-03-19T20:00:00Z\", \"positive_diff\": 34.0, \"negative_diff\": 621.0, \"death_diff\": 0.0, \"total_10\": 250.5, \"daily_positive\": 34.0}, {\"state\": \"NC\", \"date\": \"2020-03-18T00:00:00\", \"positive\": 63, \"negative\": 1787.0, \"pending\": null, \"death\": 0.0, \"total\": 1850, \"dateChecked\": \"2020-03-18T20:00:00Z\", \"positive_diff\": 23.0, \"negative_diff\": 1313.0, \"death_diff\": 0.0, \"total_10\": 185.0, \"daily_positive\": 23.0}, {\"state\": \"NC\", \"date\": \"2020-03-17T00:00:00\", \"positive\": 40, \"negative\": 474.0, \"pending\": null, \"death\": 0.0, \"total\": 514, \"dateChecked\": \"2020-03-17T20:00:00Z\", \"positive_diff\": 7.0, \"negative_diff\": 178.0, \"death_diff\": 0.0, \"total_10\": 51.4, \"daily_positive\": 7.0}, {\"state\": \"NC\", \"date\": \"2020-03-16T00:00:00\", \"positive\": 33, \"negative\": 296.0, \"pending\": 151.0, \"death\": 0.0, \"total\": 480, \"dateChecked\": \"2020-03-16T20:00:00Z\", \"positive_diff\": 1.0, \"negative_diff\": 69.0, \"death_diff\": null, \"total_10\": 48.0, \"daily_positive\": 1.0}, {\"state\": \"NC\", \"date\": \"2020-03-15T00:00:00\", \"positive\": 32, \"negative\": 227.0, \"pending\": 151.0, \"death\": null, \"total\": 410, \"dateChecked\": \"2020-03-15T20:00:00Z\", \"positive_diff\": 9.0, \"negative_diff\": 90.0, \"death_diff\": null, \"total_10\": 41.0, \"daily_positive\": 9.0}, {\"state\": \"NC\", \"date\": \"2020-03-14T00:00:00\", \"positive\": 23, \"negative\": 137.0, \"pending\": null, \"death\": null, \"total\": 160, \"dateChecked\": \"2020-03-14T20:00:00Z\", \"positive_diff\": 8.0, \"negative_diff\": 51.0, \"death_diff\": null, \"total_10\": 16.0, \"daily_positive\": 8.0}, {\"state\": \"NC\", \"date\": \"2020-03-13T00:00:00\", \"positive\": 15, \"negative\": 86.0, \"pending\": null, \"death\": null, \"total\": 101, \"dateChecked\": \"2020-03-13T20:00:00Z\", \"positive_diff\": 3.0, \"negative_diff\": 26.0, \"death_diff\": null, \"total_10\": 10.1, \"daily_positive\": 3.0}, {\"state\": \"NC\", \"date\": \"2020-03-12T00:00:00\", \"positive\": 12, \"negative\": 60.0, \"pending\": null, \"death\": null, \"total\": 72, \"dateChecked\": \"2020-03-12T20:00:00Z\", \"positive_diff\": 5.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 7.2, \"daily_positive\": 5.0}, {\"state\": \"NC\", \"date\": \"2020-03-11T00:00:00\", \"positive\": 7, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 7, \"dateChecked\": \"2020-03-11T20:00:00Z\", \"positive_diff\": 0.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.7, \"daily_positive\": 0.0}, {\"state\": \"NC\", \"date\": \"2020-03-10T00:00:00\", \"positive\": 7, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 7, \"dateChecked\": \"2020-03-10T20:00:00Z\", \"positive_diff\": 5.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.7, \"daily_positive\": 5.0}, {\"state\": \"NC\", \"date\": \"2020-03-09T00:00:00\", \"positive\": 2, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 2, \"dateChecked\": \"2020-03-09T20:00:00Z\", \"positive_diff\": 0.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.2, \"daily_positive\": 0.0}, {\"state\": \"NC\", \"date\": \"2020-03-08T00:00:00\", \"positive\": 2, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 2, \"dateChecked\": \"2020-03-08T20:00:00Z\", \"positive_diff\": 0.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.2, \"daily_positive\": 0.0}, {\"state\": \"NC\", \"date\": \"2020-03-07T00:00:00\", \"positive\": 2, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 2, \"dateChecked\": \"2020-03-07T21:00:00Z\", \"positive_diff\": 0.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.2, \"daily_positive\": 0.0}, {\"state\": \"NC\", \"date\": \"2020-03-06T00:00:00\", \"positive\": 2, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 2, \"dateChecked\": \"2020-03-06T21:00:00Z\", \"positive_diff\": 1.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.2, \"daily_positive\": 1.0}, {\"state\": \"NC\", \"date\": \"2020-03-05T00:00:00\", \"positive\": 1, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 1, \"dateChecked\": \"2020-03-05T21:00:00Z\", \"positive_diff\": 0.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.1, \"daily_positive\": 0.0}, {\"state\": \"NC\", \"date\": \"2020-03-04T00:00:00\", \"positive\": 1, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 1, \"dateChecked\": \"2020-03-04T21:00:00Z\", \"positive_diff\": null, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.1, \"daily_positive\": null}], \"data-3d1b17d38cf3d74b0371cfc3b471f3bc\": [{\"state\": \"GA\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 420, \"negative\": 1966.0, \"pending\": null, \"death\": 13.0, \"total\": 2386, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"positive_diff\": 133.0, \"negative_diff\": 422.0, \"death_diff\": 3.0, \"total_10\": 238.6, \"daily_positive\": 133.0}, {\"state\": \"GA\", \"date\": \"2020-03-19T00:00:00\", \"positive\": 287, \"negative\": 1544.0, \"pending\": null, \"death\": 10.0, \"total\": 1831, \"dateChecked\": \"2020-03-19T20:00:00Z\", \"positive_diff\": 90.0, \"negative_diff\": 233.0, \"death_diff\": 9.0, \"total_10\": 183.1, \"daily_positive\": 90.0}, {\"state\": \"GA\", \"date\": \"2020-03-18T00:00:00\", \"positive\": 197, \"negative\": 1311.0, \"pending\": null, \"death\": 1.0, \"total\": 1508, \"dateChecked\": \"2020-03-18T20:00:00Z\", \"positive_diff\": 51.0, \"negative_diff\": null, \"death_diff\": 0.0, \"total_10\": 150.8, \"daily_positive\": 51.0}, {\"state\": \"GA\", \"date\": \"2020-03-17T00:00:00\", \"positive\": 146, \"negative\": null, \"pending\": null, \"death\": 1.0, \"total\": 146, \"dateChecked\": \"2020-03-17T20:00:00Z\", \"positive_diff\": 25.0, \"negative_diff\": null, \"death_diff\": 0.0, \"total_10\": 14.6, \"daily_positive\": 25.0}, {\"state\": \"GA\", \"date\": \"2020-03-16T00:00:00\", \"positive\": 121, \"negative\": null, \"pending\": null, \"death\": 1.0, \"total\": 121, \"dateChecked\": \"2020-03-16T20:00:00Z\", \"positive_diff\": 22.0, \"negative_diff\": null, \"death_diff\": 0.0, \"total_10\": 12.1, \"daily_positive\": 22.0}, {\"state\": \"GA\", \"date\": \"2020-03-15T00:00:00\", \"positive\": 99, \"negative\": null, \"pending\": null, \"death\": 1.0, \"total\": 99, \"dateChecked\": \"2020-03-15T20:00:00Z\", \"positive_diff\": 33.0, \"negative_diff\": null, \"death_diff\": 0.0, \"total_10\": 9.9, \"daily_positive\": 33.0}, {\"state\": \"GA\", \"date\": \"2020-03-14T00:00:00\", \"positive\": 66, \"negative\": null, \"pending\": null, \"death\": 1.0, \"total\": 66, \"dateChecked\": \"2020-03-14T20:00:00Z\", \"positive_diff\": 24.0, \"negative_diff\": null, \"death_diff\": 0.0, \"total_10\": 6.6, \"daily_positive\": 24.0}, {\"state\": \"GA\", \"date\": \"2020-03-13T00:00:00\", \"positive\": 42, \"negative\": null, \"pending\": null, \"death\": 1.0, \"total\": 42, \"dateChecked\": \"2020-03-13T20:00:00Z\", \"positive_diff\": 11.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 4.2, \"daily_positive\": 11.0}, {\"state\": \"GA\", \"date\": \"2020-03-12T00:00:00\", \"positive\": 31, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 31, \"dateChecked\": \"2020-03-12T20:00:00Z\", \"positive_diff\": 9.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 3.1, \"daily_positive\": 9.0}, {\"state\": \"GA\", \"date\": \"2020-03-11T00:00:00\", \"positive\": 22, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 22, \"dateChecked\": \"2020-03-11T20:00:00Z\", \"positive_diff\": 5.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 2.2, \"daily_positive\": 5.0}, {\"state\": \"GA\", \"date\": \"2020-03-10T00:00:00\", \"positive\": 17, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 17, \"dateChecked\": \"2020-03-10T20:00:00Z\", \"positive_diff\": 5.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 1.7, \"daily_positive\": 5.0}, {\"state\": \"GA\", \"date\": \"2020-03-09T00:00:00\", \"positive\": 12, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 12, \"dateChecked\": \"2020-03-09T20:00:00Z\", \"positive_diff\": 5.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 1.2, \"daily_positive\": 5.0}, {\"state\": \"GA\", \"date\": \"2020-03-08T00:00:00\", \"positive\": 7, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 7, \"dateChecked\": \"2020-03-08T20:00:00Z\", \"positive_diff\": 1.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.7, \"daily_positive\": 1.0}, {\"state\": \"GA\", \"date\": \"2020-03-07T00:00:00\", \"positive\": 6, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 6, \"dateChecked\": \"2020-03-07T21:00:00Z\", \"positive_diff\": 4.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.6, \"daily_positive\": 4.0}, {\"state\": \"GA\", \"date\": \"2020-03-06T00:00:00\", \"positive\": 2, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 2, \"dateChecked\": \"2020-03-06T21:00:00Z\", \"positive_diff\": 0.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.2, \"daily_positive\": 0.0}, {\"state\": \"GA\", \"date\": \"2020-03-05T00:00:00\", \"positive\": 2, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 2, \"dateChecked\": \"2020-03-05T21:00:00Z\", \"positive_diff\": 0.0, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.2, \"daily_positive\": 0.0}, {\"state\": \"GA\", \"date\": \"2020-03-04T00:00:00\", \"positive\": 2, \"negative\": null, \"pending\": null, \"death\": null, \"total\": 2, \"dateChecked\": \"2020-03-04T21:00:00Z\", \"positive_diff\": null, \"negative_diff\": null, \"death_diff\": null, \"total_10\": 0.2, \"daily_positive\": null}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# produce the charts for a few states\n",
"\n",
"charts=[]\n",
"for state in ['WA', 'NY', 'MA', 'CA', 'OK','LA','FL','NJ','NC','GA']: \n",
" state_df = tdf_diff[tdf_diff['state'] == state].copy()\n",
"\n",
" state_df.loc[:,'daily_positive'] = state_df['positive'][::-1].diff()\n",
" state_df.loc[:,'total_10'] = state_df['total']/10.\n",
"\n",
" base = alt.Chart(state_df, title=state).encode(alt.X('date', axis=alt.Axis(title='Date'))).properties(width=250, height=150)\n",
" dailies = base.mark_bar(size=10).encode(alt.Y('daily_positive', axis=alt.Axis(title='Daily positive')))\n",
"\n",
" totals = base.mark_line(color='lightgreen').encode(alt.Y('total_10', axis=alt.Axis(title='Tests/10'))) \n",
" positives = totals.mark_line(color='red').encode(alt.Y('positive', axis=alt.Axis(title='# Positive')))\n",
" cumulative = totals + positives\n",
"\n",
" charts.append(alt.layer(dailies, cumulative).resolve_scale(y='independent'))\n",
"\n",
"\n",
"alt.hconcat(*charts)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Counts per 100k"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"pop_df = pd.read_csv('../../data/geodata/us_pop_fung_2019.csv').set_index('ST')"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Most recent test date 2020-03-20 00:00:00\n",
"56 states/territories have data on this date.\n"
]
}
],
"source": [
"most_recent_test_date = data_df['date'].max()\n",
"most_recent_df = data_df[data_df['date'] == most_recent_test_date].set_index('state')\n",
"print(\"Most recent test date\", most_recent_test_date)\n",
"print(len(most_recent_df), \"states/territories have data on this date.\")"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"#create a couple of columns for testing rate analysis\n",
"most_recent_df['total/100k'] = (most_recent_df['total'] / pop_df['Population']) * 100000\n",
"most_recent_df['total/positive'] = (most_recent_df['total'] / most_recent_df['positive'])\n",
"most_recent_df = most_recent_df.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"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>state</th>\n",
" <th>date</th>\n",
" <th>positive</th>\n",
" <th>negative</th>\n",
" <th>pending</th>\n",
" <th>death</th>\n",
" <th>total</th>\n",
" <th>dateChecked</th>\n",
" <th>total/100k</th>\n",
" <th>total/positive</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AK</td>\n",
" <td>2020-03-20</td>\n",
" <td>12</td>\n",
" <td>686.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>698</td>\n",
" <td>2020-03-20T20:00:00Z</td>\n",
" <td>95.414499</td>\n",
" <td>58.166667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AL</td>\n",
" <td>2020-03-20</td>\n",
" <td>81</td>\n",
" <td>28.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>109</td>\n",
" <td>2020-03-20T20:00:00Z</td>\n",
" <td>2.223045</td>\n",
" <td>1.345679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AR</td>\n",
" <td>2020-03-20</td>\n",
" <td>96</td>\n",
" <td>351.0</td>\n",
" <td>203.0</td>\n",
" <td>NaN</td>\n",
" <td>650</td>\n",
" <td>2020-03-20T20:00:00Z</td>\n",
" <td>21.538691</td>\n",
" <td>6.770833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AS</td>\n",
" <td>2020-03-20</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>2020-03-20T20:00:00Z</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AZ</td>\n",
" <td>2020-03-20</td>\n",
" <td>65</td>\n",
" <td>211.0</td>\n",
" <td>101.0</td>\n",
" <td>0.0</td>\n",
" <td>377</td>\n",
" <td>2020-03-20T20:00:00Z</td>\n",
" <td>5.179484</td>\n",
" <td>5.800000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state date positive negative pending death total \\\n",
"0 AK 2020-03-20 12 686.0 NaN NaN 698 \n",
"1 AL 2020-03-20 81 28.0 NaN 0.0 109 \n",
"2 AR 2020-03-20 96 351.0 203.0 NaN 650 \n",
"3 AS 2020-03-20 0 NaN NaN 0.0 0 \n",
"4 AZ 2020-03-20 65 211.0 101.0 0.0 377 \n",
"\n",
" dateChecked total/100k total/positive \n",
"0 2020-03-20T20:00:00Z 95.414499 58.166667 \n",
"1 2020-03-20T20:00:00Z 2.223045 1.345679 \n",
"2 2020-03-20T20:00:00Z 21.538691 6.770833 \n",
"3 2020-03-20T20:00:00Z NaN NaN \n",
"4 2020-03-20T20:00:00Z 5.179484 5.800000 "
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"most_recent_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
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"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chart = alt.Chart(most_recent_df.sort_values('total/100k'), title=\"Tests per 100k\")\n",
"chart.mark_bar().encode(alt.X('state', sort='y'), alt.Y('total/100k'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tests per 100k Population & Total Positives"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"#Load most recent data and city population counts\n",
"data_df = pd.read_json('../../data/covidtracking/states-daily.json')\n",
"data_df['date'] = pd.to_datetime(data_df['date'], format=\"%Y%m%d\")\n",
"pop_df = pd.read_csv('../../data/geodata/us_pop_fung_2019.csv').set_index('ST')"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Most recent test date 2020-03-20 00:00:00\n",
"56 states/territories have data on this date.\n"
]
}
],
"source": [
"most_recent_test_date = data_df['date'].max()\n",
"most_recent_df = data_df[data_df['date'] == most_recent_test_date].set_index('state')\n",
"print(\"Most recent test date\", most_recent_test_date)\n",
"print(len(most_recent_df), \"states/territories have data on this date.\")"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
"most_recent_df['tests/100k'] = round((most_recent_df['total'] / pop_df['Population']) * 100000,2)\n",
"most_recent_df['pos/100k'] = round((most_recent_df['positive'] / pop_df['Population']) * 100000,2)\n",
"most_recent_df['total/positive'] = round((most_recent_df['total'] / most_recent_df['positive']),2)\n",
"most_recent_df = most_recent_df.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
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" <td>NaN</td>\n",
" <td>650</td>\n",
" <td>2020-03-20T20:00:00Z</td>\n",
" <td>21.54</td>\n",
" <td>3.18</td>\n",
" <td>6.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AS</td>\n",
" <td>2020-03-20</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>2020-03-20T20:00:00Z</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AZ</td>\n",
" <td>2020-03-20</td>\n",
" <td>65</td>\n",
" <td>211.0</td>\n",
" <td>101.0</td>\n",
" <td>0.0</td>\n",
" <td>377</td>\n",
" <td>2020-03-20T20:00:00Z</td>\n",
" <td>5.18</td>\n",
" <td>0.89</td>\n",
" <td>5.80</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state date positive negative pending death total \\\n",
"0 AK 2020-03-20 12 686.0 NaN NaN 698 \n",
"1 AL 2020-03-20 81 28.0 NaN 0.0 109 \n",
"2 AR 2020-03-20 96 351.0 203.0 NaN 650 \n",
"3 AS 2020-03-20 0 NaN NaN 0.0 0 \n",
"4 AZ 2020-03-20 65 211.0 101.0 0.0 377 \n",
"\n",
" dateChecked tests/100k pos/100k total/positive \n",
"0 2020-03-20T20:00:00Z 95.41 1.64 58.17 \n",
"1 2020-03-20T20:00:00Z 2.22 1.65 1.35 \n",
"2 2020-03-20T20:00:00Z 21.54 3.18 6.77 \n",
"3 2020-03-20T20:00:00Z NaN NaN NaN \n",
"4 2020-03-20T20:00:00Z 5.18 0.89 5.80 "
]
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null, \"total\": 1420, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 104.43, \"pos/100k\": 3.24, \"total/positive\": 32.27}, {\"state\": \"SD\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 14, \"negative\": 663.0, \"pending\": 270.0, \"death\": 1.0, \"total\": 947, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 107.05, \"pos/100k\": 1.58, \"total/positive\": 67.64}, {\"state\": \"ND\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 20, \"negative\": 800.0, \"pending\": null, \"death\": 0.0, \"total\": 820, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 107.6, \"pos/100k\": 2.62, \"total/positive\": 41.0}, {\"state\": \"VT\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 29, \"negative\": 779.0, \"pending\": null, \"death\": 2.0, \"total\": 808, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 129.49, \"pos/100k\": 4.65, \"total/positive\": 27.86}, {\"state\": \"NY\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 7102, 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"text/plain": [
"alt.LayerChart(...)"
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},
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"source": [
"#resort dataframe in ascending order of testing rates per 100k population\n",
"source_df = most_recent_df.sort_values('tests/100k')\n",
"\n",
"#drop data points without total testing info\n",
"source_df =source_df.dropna(subset=['tests/100k'])\n",
"\n",
"#now create some layers for the chart\n",
"base = alt.Chart(source_df,\n",
" title='COVID-19 in the States : tests and positive cases / 100k Population').encode(\n",
" alt.X('state',sort='x'))\n",
" \n",
"bar = base.mark_bar(color='lightgrey').encode(\n",
" alt.X('state',sort='x'),\n",
" alt.Y('tests/100k',axis=alt.Axis(title='Tests / 100k population',grid=True)),\n",
" tooltip=['state','pos/100k','tests/100k','positive']\n",
")\n",
"\n",
"#positive cases as absolute number\n",
"circle = base.mark_circle(color='blue',size=60).encode(\n",
" alt.X('state',sort='x'),\n",
" alt.Y('positive', axis=alt.Axis(title='Positive COVID-19 Cases')),\n",
" tooltip=['state','positive','tests/100k']\n",
")\n",
"\n",
"#postives per 100,000 pop\n",
"circle2 = base.mark_circle(color='blue',size=60).encode(\n",
" alt.X('state',sort='x'),\n",
" alt.Y('pos/100k', axis=alt.Axis(title='Positive COVID-19 Cases/100k population',tickMinStep=5)),\n",
" tooltip=['state','pos/100k','tests/100k','positive']\n",
")\n",
"\n",
"#assemble the chart with two independent y-axes\n",
"bigChart = alt.layer(bar,circle2).resolve_scale(y='independent').properties(width=800,height=600)\n",
"bigChart"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>state</th>\n",
" <th>date</th>\n",
" <th>positive</th>\n",
" <th>negative</th>\n",
" <th>pending</th>\n",
" <th>death</th>\n",
" <th>total</th>\n",
" <th>dateChecked</th>\n",
" <th>tests/100k</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>DC</td>\n",
" <td>2020-03-19</td>\n",
" <td>39</td>\n",
" <td>153.0</td>\n",
" <td>11.0</td>\n",
" <td>0.0</td>\n",
" <td>203</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>28.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>WI</td>\n",
" <td>2020-03-19</td>\n",
" <td>155</td>\n",
" <td>2192.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2347</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>40.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>CO</td>\n",
" <td>2020-03-19</td>\n",
" <td>216</td>\n",
" <td>2112.0</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>2328</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>40.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>OR</td>\n",
" <td>2020-03-19</td>\n",
" <td>88</td>\n",
" <td>1329.0</td>\n",
" <td>437.0</td>\n",
" <td>3.0</td>\n",
" <td>1854</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>43.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>MA</td>\n",
" <td>2020-03-19</td>\n",
" <td>328</td>\n",
" <td>2804.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3132</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>45.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>WY</td>\n",
" <td>2020-03-19</td>\n",
" <td>18</td>\n",
" <td>271.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>289</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>49.93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>UT</td>\n",
" <td>2020-03-19</td>\n",
" <td>78</td>\n",
" <td>1526.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>1604</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>50.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>MN</td>\n",
" <td>2020-03-19</td>\n",
" <td>89</td>\n",
" <td>2949.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3038</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>53.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AK</td>\n",
" <td>2020-03-19</td>\n",
" <td>6</td>\n",
" <td>400.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>406</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>55.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>NV</td>\n",
" <td>2020-03-19</td>\n",
" <td>95</td>\n",
" <td>1626.0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>1721</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>55.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>NH</td>\n",
" <td>2020-03-19</td>\n",
" <td>39</td>\n",
" <td>621.0</td>\n",
" <td>231.0</td>\n",
" <td>NaN</td>\n",
" <td>891</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>65.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>ND</td>\n",
" <td>2020-03-19</td>\n",
" <td>15</td>\n",
" <td>493.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>508</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>66.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>MT</td>\n",
" <td>2020-03-19</td>\n",
" <td>12</td>\n",
" <td>761.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>773</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>72.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>RI</td>\n",
" <td>2020-03-19</td>\n",
" <td>33</td>\n",
" <td>540.0</td>\n",
" <td>334.0</td>\n",
" <td>NaN</td>\n",
" <td>907</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>85.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>VT</td>\n",
" <td>2020-03-19</td>\n",
" <td>22</td>\n",
" <td>645.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>667</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>106.89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>SD</td>\n",
" <td>2020-03-19</td>\n",
" <td>11</td>\n",
" <td>551.0</td>\n",
" <td>385.0</td>\n",
" <td>1.0</td>\n",
" <td>947</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>107.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>NY</td>\n",
" <td>2020-03-19</td>\n",
" <td>4152</td>\n",
" <td>18132.0</td>\n",
" <td>NaN</td>\n",
" <td>12.0</td>\n",
" <td>22284</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>114.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>NM</td>\n",
" <td>2020-03-19</td>\n",
" <td>35</td>\n",
" <td>2762.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2797</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>133.39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>ME</td>\n",
" <td>2020-03-19</td>\n",
" <td>53</td>\n",
" <td>2004.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2057</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>153.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>WA</td>\n",
" <td>2020-03-19</td>\n",
" <td>1187</td>\n",
" <td>15918.0</td>\n",
" <td>NaN</td>\n",
" <td>66.0</td>\n",
" <td>17105</td>\n",
" <td>2020-03-19T20:00:00Z</td>\n",
" <td>224.63</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state date positive negative pending death total \\\n",
"8 DC 2020-03-19 39 153.0 11.0 0.0 203 \n",
"53 WI 2020-03-19 155 2192.0 NaN NaN 2347 \n",
"6 CO 2020-03-19 216 2112.0 NaN 2.0 2328 \n",
"40 OR 2020-03-19 88 1329.0 437.0 3.0 1854 \n",
"21 MA 2020-03-19 328 2804.0 NaN NaN 3132 \n",
"55 WY 2020-03-19 18 271.0 NaN NaN 289 \n",
"48 UT 2020-03-19 78 1526.0 NaN 0.0 1604 \n",
"25 MN 2020-03-19 89 2949.0 NaN NaN 3038 \n",
"0 AK 2020-03-19 6 400.0 NaN NaN 406 \n",
"36 NV 2020-03-19 95 1626.0 NaN 1.0 1721 \n",
"33 NH 2020-03-19 39 621.0 231.0 NaN 891 \n",
"31 ND 2020-03-19 15 493.0 NaN 0.0 508 \n",
"29 MT 2020-03-19 12 761.0 0.0 NaN 773 \n",
"43 RI 2020-03-19 33 540.0 334.0 NaN 907 \n",
"51 VT 2020-03-19 22 645.0 NaN NaN 667 \n",
"45 SD 2020-03-19 11 551.0 385.0 1.0 947 \n",
"37 NY 2020-03-19 4152 18132.0 NaN 12.0 22284 \n",
"35 NM 2020-03-19 35 2762.0 NaN NaN 2797 \n",
"23 ME 2020-03-19 53 2004.0 NaN NaN 2057 \n",
"52 WA 2020-03-19 1187 15918.0 NaN 66.0 17105 \n",
"\n",
" dateChecked tests/100k \n",
"8 2020-03-19T20:00:00Z 28.76 \n",
"53 2020-03-19T20:00:00Z 40.31 \n",
"6 2020-03-19T20:00:00Z 40.43 \n",
"40 2020-03-19T20:00:00Z 43.96 \n",
"21 2020-03-19T20:00:00Z 45.07 \n",
"55 2020-03-19T20:00:00Z 49.93 \n",
"48 2020-03-19T20:00:00Z 50.03 \n",
"25 2020-03-19T20:00:00Z 53.87 \n",
"0 2020-03-19T20:00:00Z 55.50 \n",
"36 2020-03-19T20:00:00Z 55.87 \n",
"33 2020-03-19T20:00:00Z 65.53 \n",
"31 2020-03-19T20:00:00Z 66.66 \n",
"29 2020-03-19T20:00:00Z 72.33 \n",
"43 2020-03-19T20:00:00Z 85.62 \n",
"51 2020-03-19T20:00:00Z 106.89 \n",
"45 2020-03-19T20:00:00Z 107.05 \n",
"37 2020-03-19T20:00:00Z 114.55 \n",
"35 2020-03-19T20:00:00Z 133.39 \n",
"23 2020-03-19T20:00:00Z 153.03 \n",
"52 2020-03-19T20:00:00Z 224.63 "
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"source_df.tail(20)\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"# Cases per 100,000 population"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"#Load most recent data and city population counts\n",
"data_df = pd.read_json('../../data/covidtracking/states-daily.json')\n",
"data_df['date'] = pd.to_datetime(data_df['date'], format=\"%Y%m%d\")\n",
"pop_df = pd.read_csv('../../data/geodata/us_pop_fung_2019.csv').set_index('ST')\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Most recent test date 2020-03-19 00:00:00\n",
"56 states/territories have data on this date.\n"
]
}
],
"source": [
"most_recent_test_date = data_df['date'].max()\n",
"most_recent_df = data_df[data_df['date'] == most_recent_test_date].set_index('state')\n",
"print(\"Most recent test date\", most_recent_test_date)\n",
"print(len(most_recent_df), \"states/territories have data on this date.\")"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"#add state populations to dataframe\n",
"most_recent_df['population'] = pop_df['Population']\n",
"most_recent_df = most_recent_df.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
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"source": [
"popChart = alt.Chart(most_recent_df, title='Postive Cases vs. Population by US State').mark_circle(size=80).encode(\n",
" alt.X('population',axis=alt.Axis(title='State Population')),\n",
" alt.Y('positive', axis=alt.Axis(title='COVID-19 Positive Individuals')),\n",
" tooltip=['state','population','positive','total']\n",
").properties(width=800, height=600)\n",
"\n",
"popChart = popChart + popChart.transform_regression('population','positive').mark_line()\n",
"\n",
"popChart.interactive()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Cases by State in Order of Number"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
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{\"state\": \"WY\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 19, \"negative\": 331.0, \"pending\": null, \"death\": null, \"total\": 350, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 60.47, \"total/positive\": 18.42}, {\"state\": \"UT\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 112, \"negative\": 2035.0, \"pending\": null, \"death\": 0.0, \"total\": 2147, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 66.97, \"total/positive\": 19.17}, {\"state\": \"NV\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 109, \"negative\": 1992.0, \"pending\": -3.0, \"death\": 1.0, \"total\": 2098, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 68.11, \"total/positive\": 19.25}, {\"state\": \"OK\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 49, \"negative\": 538.0, \"pending\": 374.0, \"death\": 1.0, \"total\": 961, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 24.29, \"total/positive\": 19.61}, {\"state\": \"VA\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 114, \"negative\": 2211.0, \"pending\": null, \"death\": 2.0, \"total\": 2325, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 27.24, \"total/positive\": 20.39}, {\"state\": \"OR\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 114, \"negative\": 2003.0, \"pending\": 433.0, \"death\": 3.0, \"total\": 2550, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 60.46, \"total/positive\": 22.37}, {\"state\": \"NC\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 137, \"negative\": 3096.0, \"pending\": null, \"death\": 0.0, \"total\": 3233, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 30.83, \"total/positive\": 23.6}, {\"state\": \"ID\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 23, \"negative\": 590.0, \"pending\": null, \"death\": null, \"total\": 613, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 34.21, \"total/positive\": 26.65}, {\"state\": \"TX\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 194, \"negative\": 5083.0, \"pending\": null, \"death\": 5.0, \"total\": 5277, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 18.2, \"total/positive\": 27.2}, {\"state\": \"VT\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 29, \"negative\": 779.0, \"pending\": null, \"death\": 2.0, \"total\": 808, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 129.49, \"total/positive\": 27.86}, {\"state\": \"NH\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 44, \"negative\": 745.0, \"pending\": 631.0, \"death\": null, \"total\": 1420, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 104.43, \"total/positive\": 32.27}, {\"state\": \"MN\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 115, \"negative\": 3741.0, \"pending\": null, \"death\": null, \"total\": 3856, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 68.37, \"total/positive\": 33.53}, {\"state\": \"WV\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 7, \"negative\": 219.0, \"pending\": 13.0, \"death\": 0.0, \"total\": 239, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 13.37, \"total/positive\": 34.14}, {\"state\": \"ME\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 57, \"negative\": 2264.0, \"pending\": null, \"death\": null, \"total\": 2321, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 172.67, \"total/positive\": 40.72}, {\"state\": \"ND\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 20, \"negative\": 800.0, \"pending\": null, \"death\": 0.0, \"total\": 820, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 107.6, \"total/positive\": 41.0}, {\"state\": \"AK\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 12, \"negative\": 686.0, \"pending\": null, \"death\": null, \"total\": 698, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 95.41, \"total/positive\": 58.17}, {\"state\": \"MT\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 16, \"negative\": 931.0, \"pending\": null, \"death\": null, \"total\": 947, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 88.61, \"total/positive\": 59.19}, {\"state\": \"SD\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 14, \"negative\": 663.0, \"pending\": 270.0, \"death\": 1.0, \"total\": 947, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 107.05, \"total/positive\": 67.64}, {\"state\": \"NM\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 43, \"negative\": 3771.0, \"pending\": null, \"death\": null, \"total\": 3814, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": 181.89, \"total/positive\": 88.7}, {\"state\": \"AS\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 0, \"negative\": null, \"pending\": null, \"death\": 0.0, \"total\": 0, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": null, \"total/positive\": null}, {\"state\": \"MP\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 0, \"negative\": null, \"pending\": null, \"death\": 0.0, \"total\": 0, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"tests/100k\": null, \"total/positive\": null}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chart = alt.Chart(most_recent_df.sort_values('total/positive'), title=\"Tests per Positve Case\")\n",
"chart.mark_bar().encode(alt.X('state', sort='y'), alt.Y('total/positive')).properties (width=800,height=600)"
]
},
{
"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
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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