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Christine Choirat
covid-19-public-data
Commits
c42bb0d6
Commit
c42bb0d6
authored
5 years ago
by
Chandrasekhar Ramakrishnan
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feat: example for working with covidtracking data
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notebooks/examples/covidtracking-example.ipynb
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c42bb0d6
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"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": 2,
"metadata": {},
"outputs": [],
"source": [
"metadata_df = pd.read_json('../../data/covidtracking/states-metadata.json')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>dataSite</th>\n",
" <th>covid19Site</th>\n",
" <th>twitter</th>\n",
" <th>pui</th>\n",
" <th>pum</th>\n",
" <th>notes</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <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>@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>http://www.alabamapublichealth.gov/infectiousd...</td>\n",
" <td>@alpublichealth</td>\n",
" <td>No data</td>\n",
" <td>False</td>\n",
" <td>Last negative count from 3/16.</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>@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>AZ</td>\n",
" <td>https://www.azdhs.gov/preparedness/epidemiolog...</td>\n",
" <td>https://www.azdhs.gov/preparedness/epidemiolog...</td>\n",
" <td>@azdhs</td>\n",
" <td>All data</td>\n",
" <td>False</td>\n",
" <td>Negative = “Ruled Out”. Our total is slightly ...</td>\n",
" <td>Arizona</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>CA</td>\n",
" <td>https://www.cdph.ca.gov/Programs/CID/DCDC/Page...</td>\n",
" <td>https://www.cdph.ca.gov/Programs/CID/DCDC/Page...</td>\n",
" <td>@CAPublicHealth</td>\n",
" <td>Only positives</td>\n",
" <td>False</td>\n",
" <td>Only positives reported regularly. Add deaths ...</td>\n",
" <td>California</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state dataSite \\\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 AZ https://www.azdhs.gov/preparedness/epidemiolog... \n",
"4 CA https://www.cdph.ca.gov/Programs/CID/DCDC/Page... \n",
"\n",
" covid19Site twitter \\\n",
"0 http://dhss.alaska.gov/dph/Epi/id/Pages/COVID-... @Alaska_DHSS \n",
"1 http://www.alabamapublichealth.gov/infectiousd... @alpublichealth \n",
"2 https://www.healthy.arkansas.gov/programs-serv... @adhpio \n",
"3 https://www.azdhs.gov/preparedness/epidemiolog... @azdhs \n",
"4 https://www.cdph.ca.gov/Programs/CID/DCDC/Page... @CAPublicHealth \n",
"\n",
" pui pum notes \\\n",
"0 All data False Unclear if their reported number means \"person... \n",
"1 No data False Last negative count from 3/16. \n",
"2 All data True Pending = \"PUIs\" \n",
"3 All data False Negative = “Ruled Out”. Our total is slightly ... \n",
"4 Only positives False Only positives reported regularly. Add deaths ... \n",
"\n",
" name \n",
"0 Alaska \n",
"1 Alabama \n",
"2 Arkansas \n",
"3 Arizona \n",
"4 California "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metadata_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Look at the data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" 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>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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2020-03-18</td>\n",
" <td>AK</td>\n",
" <td>6.0</td>\n",
" <td>406.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>412.0</td>\n",
" <td>2020-03-18T20:00:00Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2020-03-18</td>\n",
" <td>AL</td>\n",
" <td>46.0</td>\n",
" <td>28.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>74.0</td>\n",
" <td>2020-03-18T20:00:00Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2020-03-18</td>\n",
" <td>AR</td>\n",
" <td>33.0</td>\n",
" <td>236.0</td>\n",
" <td>50.0</td>\n",
" <td>NaN</td>\n",
" <td>319.0</td>\n",
" <td>2020-03-18T20:00:00Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2020-03-18</td>\n",
" <td>AZ</td>\n",
" <td>28.0</td>\n",
" <td>148.0</td>\n",
" <td>102.0</td>\n",
" <td>0.0</td>\n",
" <td>278.0</td>\n",
" <td>2020-03-18T20:00:00Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2020-03-18</td>\n",
" <td>CA</td>\n",
" <td>611.0</td>\n",
" <td>7981.0</td>\n",
" <td>NaN</td>\n",
" <td>13.0</td>\n",
" <td>8592.0</td>\n",
" <td>2020-03-18T20:00:00Z</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date state positive negative pending death total \\\n",
"0 2020-03-18 AK 6.0 406.0 NaN NaN 412.0 \n",
"1 2020-03-18 AL 46.0 28.0 NaN 0.0 74.0 \n",
"2 2020-03-18 AR 33.0 236.0 50.0 NaN 319.0 \n",
"3 2020-03-18 AZ 28.0 148.0 102.0 0.0 278.0 \n",
"4 2020-03-18 CA 611.0 7981.0 NaN 13.0 8592.0 \n",
"\n",
" dateChecked \n",
"0 2020-03-18T20:00:00Z \n",
"1 2020-03-18T20:00:00Z \n",
"2 2020-03-18T20:00:00Z \n",
"3 2020-03-18T20:00:00Z \n",
"4 2020-03-18T20:00:00Z "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How many data points per state?"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AZ</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>6</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>CA</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>FL</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>14</td>\n",
" <td>8</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>GA</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>IL</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>12</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>MA</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>NC</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>NH</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>NY</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>13</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>OR</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>SC</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>TX</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>WA</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>13</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>WI</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>CO</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>DC</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>MD</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>MI</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>NE</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>NJ</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>9</td>\n",
" <td>12</td>\n",
" <td>8</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>NV</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>OH</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>TN</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>VA</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AK</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AR</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>DE</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>IA</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>IN</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>KS</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>KY</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>MN</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>NM</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>PA</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>VT</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>WV</td>\n",
" <td>13</td>\n",
" <td>12</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AL</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>CT</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>HI</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>ID</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>LA</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>ME</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>MO</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>MS</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>MT</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>ND</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>12</td>\n",
" <td>8</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>OK</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>RI</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>SD</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>UT</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>WY</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AS</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>GU</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>MP</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>PR</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>VI</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state date positive negative pending death total dateChecked\n",
"4 AZ 15 15 15 15 6 15 15\n",
"5 CA 15 15 15 0 7 15 15\n",
"10 FL 15 15 15 14 8 15 15\n",
"11 GA 15 15 1 0 6 15 15\n",
"16 IL 15 15 15 12 2 15 15\n",
"21 MA 15 15 6 0 0 14 15\n",
"30 NC 15 15 7 2 3 15 15\n",
"33 NH 15 15 15 15 0 15 15\n",
"37 NY 15 15 13 4 4 15 15\n",
"40 OR 15 15 15 15 1 15 15\n",
"44 SC 15 15 15 3 3 15 15\n",
"47 TX 15 15 2 0 2 15 15\n",
"52 WA 15 15 13 2 8 15 15\n",
"53 WI 15 15 15 5 0 15 15\n",
"6 CO 14 14 14 4 5 14 14\n",
"8 DC 14 14 14 12 0 14 14\n",
"22 MD 14 14 14 2 0 14 14\n",
"24 MI 14 14 14 12 0 14 14\n",
"32 NE 14 14 14 11 0 14 14\n",
"34 NJ 14 14 9 12 8 14 14\n",
"36 NV 14 14 14 1 3 14 14\n",
"38 OH 14 14 14 12 0 14 14\n",
"46 TN 14 14 8 0 0 14 14\n",
"49 VA 14 14 14 5 4 13 14\n",
"0 AK 13 13 13 7 0 13 13\n",
"2 AR 13 13 13 13 0 13 13\n",
"9 DE 13 13 13 11 0 13 13\n",
"14 IA 13 13 13 8 0 13 13\n",
"17 IN 13 13 9 0 5 13 13\n",
"18 KS 13 13 13 5 5 13 13\n",
"19 KY 13 13 13 1 3 13 13\n",
"25 MN 13 13 13 0 0 13 13\n",
"35 NM 13 13 13 4 0 13 13\n",
"41 PA 13 13 8 5 0 12 13\n",
"51 VT 13 13 13 0 0 13 13\n",
"54 WV 13 12 13 13 4 13 13\n",
"1 AL 12 11 9 4 4 12 12\n",
"7 CT 12 11 11 1 0 12 12\n",
"13 HI 12 12 1 1 0 12 12\n",
"15 ID 12 12 12 0 0 12 12\n",
"20 LA 12 12 11 0 4 12 12\n",
"23 ME 12 12 12 6 0 12 12\n",
"26 MO 12 12 7 0 5 12 12\n",
"28 MS 12 12 8 0 0 12 12\n",
"29 MT 12 12 12 5 0 12 12\n",
"31 ND 12 11 12 8 6 12 12\n",
"39 OK 12 12 11 11 0 12 12\n",
"43 RI 12 12 11 11 0 12 12\n",
"45 SD 12 12 12 12 1 12 12\n",
"48 UT 12 12 7 0 4 12 12\n",
"55 WY 12 12 3 1 0 12 12\n",
"3 AS 3 3 0 0 1 3 3\n",
"12 GU 3 3 3 0 0 3 3\n",
"27 MP 3 3 0 0 1 3 3\n",
"42 PR 3 3 3 2 0 3 3\n",
"50 VI 3 3 0 0 1 3 3"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary_df = data_df.groupby('state').count()\n",
"summary_df.reset_index().sort_values(['date', 'state'], ascending=[False, True])"
]
}
],
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
%% Cell type:code id: tags:
```
python
import
pandas
as
pd
import
altair
as
alt
```
%% Cell type:markdown id: tags:
# Look at the metadata
%% Cell type:code id: tags:
```
python
metadata_df
=
pd
.
read_json
(
'
../../data/covidtracking/states-metadata.json
'
)
```
%% Cell type:code id: tags:
```
python
metadata_df
.
head
()
```
%% Output
state dataSite \
0 AK http://dhss.alaska.gov/dph/Epi/id/Pages/COVID-...
1 AL http://www.alabamapublichealth.gov/infectiousd...
2 AR https://www.healthy.arkansas.gov/programs-serv...
3 AZ https://www.azdhs.gov/preparedness/epidemiolog...
4 CA https://www.cdph.ca.gov/Programs/CID/DCDC/Page...
covid19Site twitter \
0 http://dhss.alaska.gov/dph/Epi/id/Pages/COVID-... @Alaska_DHSS
1 http://www.alabamapublichealth.gov/infectiousd... @alpublichealth
2 https://www.healthy.arkansas.gov/programs-serv... @adhpio
3 https://www.azdhs.gov/preparedness/epidemiolog... @azdhs
4 https://www.cdph.ca.gov/Programs/CID/DCDC/Page... @CAPublicHealth
pui pum notes \
0 All data False Unclear if their reported number means "person...
1 No data False Last negative count from 3/16.
2 All data True Pending = "PUIs"
3 All data False Negative = “Ruled Out”. Our total is slightly ...
4 Only positives False Only positives reported regularly. Add deaths ...
name
0 Alaska
1 Alabama
2 Arkansas
3 Arizona
4 California
%% Cell type:markdown id: tags:
# Look at the data
%% Cell type:code id: tags:
```
python
data_df
=
pd
.
read_json
(
'
../../data/covidtracking/states-daily.json
'
)
data_df
[
'
date
'
]
=
pd
.
to_datetime
(
data_df
[
'
date
'
],
format
=
"
%Y%m%d
"
)
```
%% Cell type:code id: tags:
```
python
data_df
.
head
()
```
%% Output
date state positive negative pending death total \
0 2020-03-18 AK 6.0 406.0 NaN NaN 412.0
1 2020-03-18 AL 46.0 28.0 NaN 0.0 74.0
2 2020-03-18 AR 33.0 236.0 50.0 NaN 319.0
3 2020-03-18 AZ 28.0 148.0 102.0 0.0 278.0
4 2020-03-18 CA 611.0 7981.0 NaN 13.0 8592.0
dateChecked
0 2020-03-18T20:00:00Z
1 2020-03-18T20:00:00Z
2 2020-03-18T20:00:00Z
3 2020-03-18T20:00:00Z
4 2020-03-18T20:00:00Z
%% Cell type:markdown id: tags:
## How many data points per state?
%% Cell type:code id: tags:
```
python
summary_df
=
data_df
.
groupby
(
'
state
'
).
count
()
summary_df
.
reset_index
().
sort_values
([
'
date
'
,
'
state
'
],
ascending
=
[
False
,
True
])
```
%% Output
state date positive negative pending death total dateChecked
4 AZ 15 15 15 15 6 15 15
5 CA 15 15 15 0 7 15 15
10 FL 15 15 15 14 8 15 15
11 GA 15 15 1 0 6 15 15
16 IL 15 15 15 12 2 15 15
21 MA 15 15 6 0 0 14 15
30 NC 15 15 7 2 3 15 15
33 NH 15 15 15 15 0 15 15
37 NY 15 15 13 4 4 15 15
40 OR 15 15 15 15 1 15 15
44 SC 15 15 15 3 3 15 15
47 TX 15 15 2 0 2 15 15
52 WA 15 15 13 2 8 15 15
53 WI 15 15 15 5 0 15 15
6 CO 14 14 14 4 5 14 14
8 DC 14 14 14 12 0 14 14
22 MD 14 14 14 2 0 14 14
24 MI 14 14 14 12 0 14 14
32 NE 14 14 14 11 0 14 14
34 NJ 14 14 9 12 8 14 14
36 NV 14 14 14 1 3 14 14
38 OH 14 14 14 12 0 14 14
46 TN 14 14 8 0 0 14 14
49 VA 14 14 14 5 4 13 14
0 AK 13 13 13 7 0 13 13
2 AR 13 13 13 13 0 13 13
9 DE 13 13 13 11 0 13 13
14 IA 13 13 13 8 0 13 13
17 IN 13 13 9 0 5 13 13
18 KS 13 13 13 5 5 13 13
19 KY 13 13 13 1 3 13 13
25 MN 13 13 13 0 0 13 13
35 NM 13 13 13 4 0 13 13
41 PA 13 13 8 5 0 12 13
51 VT 13 13 13 0 0 13 13
54 WV 13 12 13 13 4 13 13
1 AL 12 11 9 4 4 12 12
7 CT 12 11 11 1 0 12 12
13 HI 12 12 1 1 0 12 12
15 ID 12 12 12 0 0 12 12
20 LA 12 12 11 0 4 12 12
23 ME 12 12 12 6 0 12 12
26 MO 12 12 7 0 5 12 12
28 MS 12 12 8 0 0 12 12
29 MT 12 12 12 5 0 12 12
31 ND 12 11 12 8 6 12 12
39 OK 12 12 11 11 0 12 12
43 RI 12 12 11 11 0 12 12
45 SD 12 12 12 12 1 12 12
48 UT 12 12 7 0 4 12 12
55 WY 12 12 3 1 0 12 12
3 AS 3 3 0 0 1 3 3
12 GU 3 3 3 0 0 3 3
27 MP 3 3 0 0 1 3 3
42 PR 3 3 3 2 0 3 3
50 VI 3 3 0 0 1 3 3
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