https://github.com/russss/coviddata
Yet another python package for accessing COVID-19 data
https://github.com/russss/coviddata
covid19 data xarray
Last synced: about 1 year ago
JSON representation
Yet another python package for accessing COVID-19 data
- Host: GitHub
- URL: https://github.com/russss/coviddata
- Owner: russss
- Created: 2020-04-12T19:52:11.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-12-14T10:53:42.000Z (over 4 years ago)
- Last Synced: 2025-05-13T03:55:37.492Z (about 1 year ago)
- Topics: covid19, data, xarray
- Language: Python
- Size: 530 KB
- Stars: 5
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.ipynb
Awesome Lists containing this project
README
{
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{
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"source": [
"# COVIDdata\n",
"\n",
"Yet another python package for accessing COVID-19 data. Sorry. I have opinions and I don't like all the others.\n",
"\n",
"This package provides methods for fetching various COVID-19 related data sources. Results are provided as [xarray](http://xarray.pydata.org/) datasets, with consistent variable naming and attribution data included.\n",
"\n",
"## Installation\n",
"\n",
"I'm not going to clutter PyPI up with yet another COVID package. Just do `pip install git+https://github.com/russs/coviddata#egg=coviddata`.\n",
"\n",
"\n",
"## Worldwide Data\n",
"\n",
"My preferred wordwide data source is [Our World in Data](https://ourworldindata.org/coronavirus-source-data) which sources their data from the ECDC. The `cases_owid` function downloads this data and returns a Dataset."
]
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xarray.Dataset
- Dimensions:
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date: 133
-
location: 210
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location
(location)
object
'Afghanistan' ... 'Zimbabwe'
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date
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cases
(location, date)
float64
0.0 0.0 0.0 0.0 ... 35.0 36.0 36.0
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-
deaths
(location, date)
float64
0.0 0.0 0.0 0.0 ... 4.0 4.0 4.0 4.0
array([[ 0., 0., 0., ..., 109., 115., 120.],\n",
" [ nan, nan, nan, ..., 31., 31., 31.],\n",
" [ 0., 0., 0., ..., 488., 494., 502.],\n",
" ...,\n",
" [ nan, nan, nan, ..., 7., 7., 8.],\n",
" [ nan, nan, nan, ..., 4., 7., 7.],\n",
" [ nan, nan, nan, ..., 4., 4., 4.]])
- Attributes: (3)
- date :
- 2020-05-11
- source_url :
- https://cowid.netlify.com/data/ecdc/full_data.csv
- source :
- ECDC (Our World in Data)
"
],
"text/plain": [
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"Coordinates:\n",
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" * date (date) datetime64[ns] 2019-12-31 2020-01-01 ... 2020-05-11\n",
"Data variables:\n",
" cases (location, date) float64 0.0 0.0 0.0 0.0 ... 34.0 35.0 36.0 36.0\n",
" deaths (location, date) float64 0.0 0.0 0.0 0.0 0.0 ... 4.0 4.0 4.0 4.0\n",
"Attributes:\n",
" date: 2020-05-11\n",
" source_url: https://cowid.netlify.com/data/ecdc/full_data.csv\n",
" source: ECDC (Our World in Data)"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import coviddata.world\n",
"world_cases = coviddata.world.cases_owid()\n",
"world_cases"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can filter this data by country, and convert it to a [pandas](https://pandas.pydata.org/) dataframe, giving us easy access to pandas' plotting functions."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib\n",
"matplotlib.rcParams['figure.figsize'] = [14, 6]\n",
"\n",
"(world_cases.sel(location=\"United States\")\n",
" .to_dataframe()\n",
" .plot(logy=True, title=\"US COVID-19 Cases & Deaths\"));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Country-specific Data\n",
"\n",
"Some country-specific data sources are more reliable or complete.\n",
"\n",
"### UK Cases Data\n",
"\n",
"UK data can be fetched from [Public Health England](https://www.gov.uk/government/publications/covid-19-track-coronavirus-cases). Note that:\n",
"\n",
"* This data is broken down by constituent UK country whereas global data is usually combined.\n",
"* The cases data is a subset of total UK cases - [more info](https://coronavirus.data.gov.uk/about)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"Show/Hide data repr\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Show/Hide attributes\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
" *\n",
" */\n",
"\n",
":root {\n",
" --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
" --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
" --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
" --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
" --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
" --xr-background-color: var(--jp-layout-color0, white);\n",
" --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
" --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
"}\n",
"\n",
".xr-wrap {\n",
" min-width: 300px;\n",
" max-width: 700px;\n",
"}\n",
"\n",
".xr-header {\n",
" padding-top: 6px;\n",
" padding-bottom: 6px;\n",
" margin-bottom: 4px;\n",
" border-bottom: solid 1px var(--xr-border-color);\n",
"}\n",
"\n",
".xr-header > div,\n",
".xr-header > ul {\n",
" display: inline;\n",
" margin-top: 0;\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-obj-type,\n",
".xr-array-name {\n",
" margin-left: 2px;\n",
" margin-right: 10px;\n",
"}\n",
"\n",
".xr-obj-type {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-section-item input {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label {\n",
" cursor: pointer;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt, dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2 {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"
xarray.Dataset
- Dimensions:
-
date: 92
-
location: 4
- Coordinates: (3)
-
location
(location)
object
'England' ... 'Wales'
array(['England', 'Northern Ireland', 'Scotland', 'Wales'], dtype=object)
-
date
(date)
datetime64[ns]
2020-01-30 ... 2020-05-10
array(['2020-01-30T00:00:00.000000000', '2020-01-31T00:00:00.000000000',\n",
" '2020-02-03T00:00:00.000000000', '2020-02-05T00:00:00.000000000',\n",
" '2020-02-08T00:00:00.000000000', '2020-02-09T00:00:00.000000000',\n",
" '2020-02-11T00:00:00.000000000', '2020-02-12T00:00:00.000000000',\n",
" '2020-02-13T00:00:00.000000000', '2020-02-14T00:00:00.000000000',\n",
" '2020-02-16T00:00:00.000000000', '2020-02-17T00:00:00.000000000',\n",
" '2020-02-19T00:00:00.000000000', '2020-02-21T00:00:00.000000000',\n",
" '2020-02-23T00:00:00.000000000', '2020-02-24T00:00:00.000000000',\n",
" '2020-02-25T00:00:00.000000000', '2020-02-26T00:00:00.000000000',\n",
" '2020-02-27T00:00:00.000000000', '2020-02-28T00:00:00.000000000',\n",
" '2020-02-29T00:00:00.000000000', '2020-03-01T00:00:00.000000000',\n",
" '2020-03-02T00:00:00.000000000', '2020-03-03T00:00:00.000000000',\n",
" '2020-03-04T00:00:00.000000000', '2020-03-05T00:00:00.000000000',\n",
" '2020-03-06T00:00:00.000000000', '2020-03-07T00:00:00.000000000',\n",
" '2020-03-08T00:00:00.000000000', '2020-03-09T00:00:00.000000000',\n",
" '2020-03-10T00:00:00.000000000', '2020-03-11T00:00:00.000000000',\n",
" '2020-03-12T00:00:00.000000000', '2020-03-13T00:00:00.000000000',\n",
" '2020-03-14T00:00:00.000000000', '2020-03-15T00:00:00.000000000',\n",
" '2020-03-16T00:00:00.000000000', '2020-03-17T00:00:00.000000000',\n",
" '2020-03-18T00:00:00.000000000', '2020-03-19T00:00:00.000000000',\n",
" '2020-03-20T00:00:00.000000000', '2020-03-21T00:00:00.000000000',\n",
" '2020-03-22T00:00:00.000000000', '2020-03-23T00:00:00.000000000',\n",
" '2020-03-24T00:00:00.000000000', '2020-03-25T00:00:00.000000000',\n",
" '2020-03-26T00:00:00.000000000', '2020-03-27T00:00:00.000000000',\n",
" '2020-03-28T00:00:00.000000000', '2020-03-29T00:00:00.000000000',\n",
" '2020-03-30T00:00:00.000000000', '2020-03-31T00:00:00.000000000',\n",
" '2020-04-01T00:00:00.000000000', '2020-04-02T00:00:00.000000000',\n",
" '2020-04-03T00:00:00.000000000', '2020-04-04T00:00:00.000000000',\n",
" '2020-04-05T00:00:00.000000000', '2020-04-06T00:00:00.000000000',\n",
" '2020-04-07T00:00:00.000000000', '2020-04-08T00:00:00.000000000',\n",
" '2020-04-09T00:00:00.000000000', '2020-04-10T00:00:00.000000000',\n",
" '2020-04-11T00:00:00.000000000', '2020-04-12T00:00:00.000000000',\n",
" '2020-04-13T00:00:00.000000000', '2020-04-14T00:00:00.000000000',\n",
" '2020-04-15T00:00:00.000000000', '2020-04-16T00:00:00.000000000',\n",
" '2020-04-17T00:00:00.000000000', '2020-04-18T00:00:00.000000000',\n",
" '2020-04-19T00:00:00.000000000', '2020-04-20T00:00:00.000000000',\n",
" '2020-04-21T00:00:00.000000000', '2020-04-22T00:00:00.000000000',\n",
" '2020-04-23T00:00:00.000000000', '2020-04-24T00:00:00.000000000',\n",
" '2020-04-25T00:00:00.000000000', '2020-04-26T00:00:00.000000000',\n",
" '2020-04-27T00:00:00.000000000', '2020-04-28T00:00:00.000000000',\n",
" '2020-04-29T00:00:00.000000000', '2020-04-30T00:00:00.000000000',\n",
" '2020-05-01T00:00:00.000000000', '2020-05-02T00:00:00.000000000',\n",
" '2020-05-03T00:00:00.000000000', '2020-05-04T00:00:00.000000000',\n",
" '2020-05-05T00:00:00.000000000', '2020-05-06T00:00:00.000000000',\n",
" '2020-05-07T00:00:00.000000000', '2020-05-08T00:00:00.000000000',\n",
" '2020-05-09T00:00:00.000000000', '2020-05-10T00:00:00.000000000'],\n",
" dtype='datetime64[ns]')
-
gss_code
(location, date)
object
'E92000001' ... 'W92000004'
array([['E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001',\n",
" 'E92000001', 'E92000001'],\n",
" [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, 'N92000002', 'N92000002', 'N92000002', 'N92000002',\n",
" 'N92000002', 'N92000002', 'N92000002', 'N92000002', 'N92000002',\n",
" 'N92000002', 'N92000002', 'N92000002', 'N92000002', 'N92000002',\n",
" 'N92000002', 'N92000002', 'N92000002', 'N92000002', 'N92000002',\n",
" 'N92000002', 'N92000002', 'N92000002', 'N92000002', 'N92000002',\n",
" 'N92000002', 'N92000002', 'N92000002', 'N92000002', 'N92000002',\n",
" 'N92000002', 'N92000002', 'N92000002', 'N92000002', 'N92000002',\n",
" 'N92000002', 'N92000002', 'N92000002', 'N92000002', 'N92000002',\n",
" 'N92000002', 'N92000002', 'N92000002', 'N92000002', 'N92000002',\n",
" 'N92000002', 'N92000002', 'N92000002', 'N92000002', 'N92000002',\n",
" 'N92000002', 'N92000002', 'N92000002'],\n",
" [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, 'S92000003',\n",
" 'S92000003', 'S92000003', 'S92000003', 'S92000003', 'S92000003',\n",
" 'S92000003', 'S92000003', 'S92000003', 'S92000003', 'S92000003',\n",
" 'S92000003', 'S92000003', 'S92000003', 'S92000003', 'S92000003',\n",
" 'S92000003', 'S92000003', 'S92000003', 'S92000003', 'S92000003',\n",
" 'S92000003', 'S92000003', 'S92000003', 'S92000003', 'S92000003',\n",
" 'S92000003', 'S92000003', 'S92000003', 'S92000003', 'S92000003',\n",
" 'S92000003', 'S92000003', 'S92000003', 'S92000003', 'S92000003',\n",
" 'S92000003', 'S92000003', 'S92000003', 'S92000003', 'S92000003',\n",
" 'S92000003', 'S92000003', 'S92000003', 'S92000003', 'S92000003',\n",
" 'S92000003', 'S92000003', 'S92000003', 'S92000003', 'S92000003',\n",
" 'S92000003', 'S92000003', 'S92000003', 'S92000003', 'S92000003',\n",
" 'S92000003'],\n",
" [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" 'W92000004', 'W92000004', 'W92000004', 'W92000004', 'W92000004',\n",
" 'W92000004', 'W92000004', 'W92000004', 'W92000004', 'W92000004',\n",
" 'W92000004', 'W92000004', 'W92000004', 'W92000004', 'W92000004',\n",
" 'W92000004', 'W92000004', 'W92000004', 'W92000004', 'W92000004',\n",
" 'W92000004', 'W92000004', 'W92000004', 'W92000004', 'W92000004',\n",
" 'W92000004', 'W92000004', 'W92000004', 'W92000004', 'W92000004',\n",
" 'W92000004', 'W92000004', 'W92000004', 'W92000004', 'W92000004',\n",
" 'W92000004', 'W92000004', 'W92000004', 'W92000004', 'W92000004',\n",
" 'W92000004', 'W92000004', 'W92000004', 'W92000004', 'W92000004',\n",
" 'W92000004', 'W92000004', 'W92000004', 'W92000004', 'W92000004',\n",
" 'W92000004', 'W92000004', 'W92000004', 'W92000004', 'W92000004']],\n",
" dtype=object)
- Data variables: (3)
-
cases
(location, date)
float64
1.0 2.0 8.0 9.0 ... nan nan nan nan
array([[1.00000e+00, 2.00000e+00, 8.00000e+00, 9.00000e+00, 1.20000e+01,\n",
" 1.30000e+01, 1.40000e+01, 1.50000e+01, 1.60000e+01, 1.70000e+01,\n",
" 1.80000e+01, 1.90000e+01, 2.00000e+01, 2.10000e+01, 2.50000e+01,\n",
" 2.60000e+01, 3.00000e+01, 3.30000e+01, 3.90000e+01, 5.10000e+01,\n",
" 5.50000e+01, 8.60000e+01, 1.25000e+02, 1.76000e+02, 2.25000e+02,\n",
" 2.72000e+02, 3.45000e+02, 3.92000e+02, 4.42000e+02, 5.67000e+02,\n",
" 7.92000e+02, 1.15700e+03, 1.57900e+03, 1.96900e+03, 2.28400e+03,\n",
" 2.68100e+03, 3.22600e+03, 3.90500e+03, 4.81500e+03, 5.74600e+03,\n",
" 6.82700e+03, 7.86100e+03, 9.06700e+03, 1.10790e+04, 1.30980e+04,\n",
" 1.53600e+04, 1.79700e+04, 2.06230e+04, 2.30000e+04, 2.54360e+04,\n",
" 2.89160e+04, 3.26570e+04, 3.67420e+04, 4.07500e+04, 4.47860e+04,\n",
" 4.81030e+04, 5.11320e+04, 5.54660e+04, 5.99580e+04, 6.42160e+04,\n",
" 6.82450e+04, 7.17740e+04, 7.47760e+04, 7.74650e+04, 8.07990e+04,\n",
" 8.42400e+04, 8.78770e+04, 9.13220e+04, 9.47610e+04, 9.73380e+04,\n",
" 9.94260e+04, 1.02509e+05, 1.05497e+05, 1.08273e+05, 1.11034e+05,\n",
" 1.13815e+05, 1.15634e+05, 1.17032e+05, 1.19256e+05, 1.21554e+05,\n",
" 1.23922e+05, 1.26044e+05, 1.27998e+05, 1.29260e+05, 1.30245e+05,\n",
" 1.31871e+05, 1.33420e+05, 1.34620e+05, 1.35599e+05, 1.35936e+05,\n",
" 1.35982e+05, nan],\n",
" [ nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan,\n",
" nan, nan]])
-
new_cases
(location, date)
float64
0.0 0.0 0.0 0.0 ... nan nan nan nan
array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 2., 0., 2., 0., 0., 1., 0., 2., 0.,\n",
" 0., 1., 7., 0., 5., 2., 8., 0., 9., 1., 6.,\n",
" 0., 0., 0., 0., 6., 5., 4., 4., 0., 0., 1.,\n",
" 13., 4., 4., 15., 11., 12., 4., 0., 5., 12., 4.,\n",
" 4., 9., 13., 9., 12., 18., 0., 0., 0., 13., 63.,\n",
" 452., 274., 46., nan],\n",
" [ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan],\n",
" [ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan],\n",
" [ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
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-
deaths
(location, date)
float64
nan nan nan ... 1.099e+03 1.111e+03
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- Attributes: (3)
- date :
- 2020-05-10
- source :
- Public Health England
- source_url :
- https://c19downloads.azureedge.net/downloads/data/countries_latest.json
"
],
"text/plain": [
"\n",
"Dimensions: (date: 92, location: 4)\n",
"Coordinates:\n",
" * location (location) object 'England' 'Northern Ireland' 'Scotland' 'Wales'\n",
" * date (date) datetime64[ns] 2020-01-30 2020-01-31 ... 2020-05-10\n",
" gss_code (location, date) object 'E92000001' 'E92000001' ... 'W92000004'\n",
"Data variables:\n",
" cases (location, date) float64 1.0 2.0 8.0 9.0 12.0 ... nan nan nan nan\n",
" new_cases (location, date) float64 0.0 0.0 0.0 0.0 0.0 ... nan nan nan nan\n",
" deaths (location, date) float64 nan nan nan ... 1.099e+03 1.111e+03\n",
"Attributes:\n",
" date: 2020-05-10\n",
" source: Public Health England\n",
" source_url: https://c19downloads.azureedge.net/downloads/data/countries_..."
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import coviddata.uk\n",
"uk_cases = coviddata.uk.cases_phe()\n",
"uk_cases"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### US Data\n",
"\n",
"US data from the [Covid Tracking Project](https://covidtracking.com/):"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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