https://github.com/russss/coviddata
Yet another python package for accessing COVID-19 data
https://github.com/russss/coviddata
covid19 data xarray
Last synced: 9 months 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 (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2021-12-14T10:53:42.000Z (about 4 years ago)
- Last Synced: 2025-05-13T03:55:37.492Z (9 months 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:
-
date: 133
-
location: 210
- Coordinates: (2)
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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
array([[ 0., 0., 0., ..., 3778., 4033., 4402.],\n",
" [ nan, nan, nan, ..., 850., 856., 868.],\n",
" [ 0., 0., 0., ..., 5369., 5558., 5723.],\n",
" ...,\n",
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" [ nan, nan, nan, ..., 167., 252., 267.],\n",
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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": [
"\n",
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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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},
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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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- 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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- Data variables: (3)
-
cases
(location, date)
int64
1 1 1 1 ... 1275916 1301095 1322807
array([[ 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 9, 18, 40, 53, 94,\n",
" 207, 275, 387, 538, 721, 1013, 1280,\n",
" 1672, 2142, 3004, 3741, 4842, 6131, 8254,\n",
" 10865, 15063, 20834, 27367, 36334, 47013, 57179,\n",
" 69473, 86789, 105462, 124815, 144297, 165521, 189998,\n",
" 215177, 243235, 275234, 308752, 334718, 363465, 393874,\n",
" 424045, 458260, 492848, 522750, 551906, 576875, 602795,\n",
" 633112, 664129, 696010, 724047, 751646, 776830, 802772,\n",
" 831374, 863123, 897319, 933310, 960343, 981134, 1006137,\n",
" 1033157, 1062503, 1095681, 1125719, 1152006, 1173453, 1195605,\n",
" 1220557, 1248137, 1275916, 1301095, 1322807]])
-
deaths
(location, date)
float64
nan nan nan ... 7.329e+04 7.427e+04
array([[ 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",
" 2.0000e+00, 2.0000e+00, 4.0000e+00, 5.0000e+00, 8.0000e+00,\n",
" 1.1000e+01, 1.4000e+01, 1.6000e+01, 2.0000e+01, 2.6000e+01,\n",
" 2.7000e+01, 3.1000e+01, 3.5000e+01, 3.7000e+01, 4.3000e+01,\n",
" 5.1000e+01, 5.5000e+01, 6.3000e+01, 7.7000e+01, 9.8000e+01,\n",
" 1.2000e+02, 1.4500e+02, 1.8900e+02, 2.5300e+02, 3.0600e+02,\n",
" 4.3700e+02, 5.2100e+02, 7.2500e+02, 9.5300e+02, 1.2310e+03,\n",
" 1.6040e+03, 2.0380e+03, 2.5270e+03, 3.0600e+03, 3.8770e+03,\n",
" 4.8230e+03, 5.9240e+03, 7.1120e+03, 8.4870e+03, 9.7140e+03,\n",
" 1.0937e+04, 1.2841e+04, 1.4737e+04, 1.6659e+04, 1.8751e+04,\n",
" 2.0686e+04, 2.2237e+04, 2.3754e+04, 2.6066e+04, 2.8628e+04,\n",
" 3.0789e+04, 3.2854e+04, 3.4641e+04, 3.6300e+04, 3.7999e+04,\n",
" 4.0554e+04, 4.2595e+04, 4.4409e+04, 4.6251e+04, 4.8069e+04,\n",
" 4.9164e+04, 5.0354e+04, 5.2482e+04, 5.5182e+04, 5.7320e+04,\n",
" 5.9059e+04, 6.0710e+04, 6.1868e+04, 6.2780e+04, 6.5307e+04,\n",
" 6.7256e+04, 7.0002e+04, 7.1762e+04, 7.3291e+04, 7.4270e+04]])
-
tests
(location, date)
int64
1 1 1 1 ... 8408788 8709630 8987524
array([[ 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 9, 41, 121, 282, 527,\n",
" 1597, 2188, 2983, 3753, 4624, 6420, 8832,\n",
" 12580, 17717, 26773, 31273, 38948, 56611, 72185,\n",
" 96858, 124258, 160413, 205167, 249769, 306904, 375517,\n",
" 459004, 559468, 662538, 766904, 855684, 973134, 1084228,\n",
" 1191810, 1309833, 1442065, 1670862, 1790521, 1941251, 2093996,\n",
" 2238429, 2404262, 2562049, 2700617, 2842322, 2975315, 3125858,\n",
" 3262564, 3424690, 3582859, 3725196, 3885586, 4033318, 4181428,\n",
" 4496319, 4688701, 4920802, 5197717, 5404137, 5593495, 5796273,\n",
" 6026170, 6255769, 6552602, 6805241, 7053366, 7285374, 7544328,\n",
" 7786793, 8105513, 8408788, 8709630, 8987524]])
- Attributes: (3)
- date :
- 2020-05-10
- source :
- COVID Tracking Project
- source_url :
- http://covidtracking.com/api/us/daily.csv
"
],
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date
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datetime64[ns]
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" '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",
" '2020-05-11T00:00:00.000000000'], dtype='datetime64[ns]')
-
location
(location)
object
'Afghanistan' ... 'Zimbabwe'
array(['Afghanistan', 'Albania', 'Algeria', ..., 'Yemen', 'Zambia', 'Zimbabwe'],\n",
" dtype=object)
- Data variables: (3)
-
cases
(location, date)
float64
0.0 0.0 0.0 0.0 ... 35.0 36.0 36.0
array([[ 0., 0., 0., ..., 3778., 4033., 4402.],\n",
" [ nan, nan, nan, ..., 850., 856., 868.],\n",
" [ 0., 0., 0., ..., 5369., 5558., 5723.],\n",
" ...,\n",
" [ nan, nan, nan, ..., 34., 34., 51.],\n",
" [ nan, nan, nan, ..., 167., 252., 267.],\n",
" [ nan, nan, nan, ..., 35., 36., 36.]])
-
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.]])
-
tests
(location, date)
float64
nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]])
- Attributes: (3)
- source :
- ['ECDC (Our World in Data)', 'COVID Tracking Project']
- source_url :
- ['https://cowid.netlify.com/data/ecdc/full_data.csv', 'http://covidtracking.com/api/us/daily.csv']
- date :
- [datetime.date(2020, 5, 11), datetime.date(2020, 5, 10)]
"
],
"text/plain": [
"\n",
"Dimensions: (date: 133, location: 210)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2019-12-31 2020-01-01 ... 2020-05-11\n",
" * location (location) object 'Afghanistan' 'Albania' ... 'Zambia' 'Zimbabwe'\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",
" tests (location, date) float64 nan nan nan nan nan ... nan nan nan nan\n",
"Attributes:\n",
" source: ['ECDC (Our World in Data)', 'COVID Tracking Project']\n",
" source_url: ['https://cowid.netlify.com/data/ecdc/full_data.csv', 'http:...\n",
" date: [datetime.date(2020, 5, 11), datetime.date(2020, 5, 10)]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from coviddata import merge\n",
"\n",
"combined_cases = merge(world_cases, us_cases)\n",
"combined_cases"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"(combined_cases.sum(dim='location')\n",
" .to_dataframe()\n",
" .drop(columns=['tests'])\n",
" .plot(logy=True, title=\"Worldwide COVID-19 Cases & Deaths\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## UK NHS Triage Data\n",
"\n",
"The NHS [publishes statistics](https://digital.nhs.uk/data-and-information/publications/statistical/mi-potential-covid-19-symptoms-reported-through-nhs-pathways-and-111-online) on the number of COVID-19 triage decisions made over 999, 111, and 111 Online.\n",
"\n",
"These functions are dependent on screen-scraping the NHS website and may be more unreliable."
]
},
{
"cell_type": "code",
"execution_count": 7,
"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:
-
age_band: 3
-
ccg: 236
-
date: 54
-
sex: 3
-
site_type: 2
- Coordinates: (6)
-
date
(date)
datetime64[ns]
2020-03-18 ... 2020-05-10
array(['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]')
-
age_band
(age_band)
object
'0-18 years' ... '70-120 years'
array(['0-18 years', '19-69 years', '70-120 years'], dtype=object)
-
ccg
(ccg)
object
'E38000001' 'E38000002' ... 'ZC040'
array(['E38000001', 'E38000002', 'E38000004', ..., 'ZC010', 'ZC030', 'ZC040'],\n",
" dtype=object)
-
site_type
(site_type)
int64
111 999
array([111, 999])
-
sex
(sex)
object
'Female' 'Male' 'Unknown'
array(['Female', 'Male', 'Unknown'], dtype=object)
-
ccg_name
(date, age_band, ccg, site_type, sex)
object
'NHS Airedale, Wharfedale and Craven CCG' ... nan
array([[[[['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [['NHS Ashford CCG', 'NHS Ashford CCG', nan],\n",
" [nan, 'NHS Ashford CCG', nan]],\n",
"\n",
" [['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
" [[['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [['NHS Ashford CCG', 'NHS Ashford CCG', nan],\n",
" ['NHS Ashford CCG', 'NHS Ashford CCG', nan]],\n",
"\n",
" [['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
" [[['NHS Airedale, Wharfedale and Craven CCG', nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [['NHS Ashford CCG', 'NHS Ashford CCG', nan],\n",
" [nan, 'NHS Ashford CCG', nan]],\n",
"\n",
" [['NHS Barking and Dagenham CCG', nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]]],\n",
"\n",
"\n",
"\n",
" [[[['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [['NHS Ashford CCG', 'NHS Ashford CCG', nan],\n",
" [nan, 'NHS Ashford CCG', nan]],\n",
"\n",
" [['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
" [[['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [['NHS Ashford CCG', 'NHS Ashford CCG', nan],\n",
" ['NHS Ashford CCG', 'NHS Ashford CCG', nan]],\n",
"\n",
" [['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
" [[['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [['NHS Ashford CCG', 'NHS Ashford CCG', nan],\n",
" ['NHS Ashford CCG', nan, nan]],\n",
"\n",
" [['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]]],\n",
"\n",
"\n",
"\n",
" [[[['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [['NHS Ashford CCG', 'NHS Ashford CCG', nan],\n",
" [nan, 'NHS Ashford CCG', nan]],\n",
"\n",
" [['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
" [[['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [['NHS Ashford CCG', 'NHS Ashford CCG', nan],\n",
" ['NHS Ashford CCG', 'NHS Ashford CCG', nan]],\n",
"\n",
" [['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG', nan],\n",
" ['NHS Barking and Dagenham CCG', nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
" [[['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [['NHS Ashford CCG', 'NHS Ashford CCG', nan],\n",
" ['NHS Ashford CCG', 'NHS Ashford CCG', nan]],\n",
"\n",
" [['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]]],\n",
"\n",
"\n",
"\n",
" ...,\n",
"\n",
"\n",
"\n",
" [[[[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [['NHS Barking and Dagenham CCG', nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
" [[[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
" [[[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, 'NHS Barking and Dagenham CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]]],\n",
"\n",
"\n",
"\n",
" [[[[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
" [[[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
" [[[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, 'NHS Barking and Dagenham CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]]],\n",
"\n",
"\n",
"\n",
" [[[[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG', nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
" [[[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
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" [nan, nan, nan]],\n",
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" [[nan, nan, nan],\n",
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- Data variables: (1)
-
count
(date, age_band, ccg, site_type, sex)
float64
8.0 6.0 nan nan ... nan nan nan nan
array([[[[[ 8., 6., nan],\n",
" [nan, nan, nan]],\n",
"\n",
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"\n",
" ...,\n",
"\n",
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"\n",
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"\n",
" [[ 1., nan, nan],\n",
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"\n",
" [[ 5., 1., nan],\n",
" [nan, nan, nan]],\n",
"\n",
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" [[nan, nan, nan],\n",
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"\n",
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"\n",
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"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
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"\n",
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"\n",
"\n",
"\n",
" ...,\n",
"\n",
"\n",
"\n",
" [[[[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
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" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
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"\n",
"\n",
" [[[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[ 9., 4., nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
" [[[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
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" [nan, nan, nan]],\n",
"\n",
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" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]]],\n",
"\n",
"\n",
"\n",
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"\n",
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"\n",
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"\n",
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" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
" [[[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[ 5., 5., nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
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" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, 2., nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]]],\n",
"\n",
"\n",
"\n",
" [[[[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
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" [nan, nan, nan]],\n",
"\n",
" [[ 1., 3., nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
" [[[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[ 4., 12., nan],\n",
" [nan, nan, nan]],\n",
"\n",
" ...,\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]]],\n",
"\n",
"\n",
" [[[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[ 1., 1., nan],\n",
" [nan, nan, nan]],\n",
"\n",
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"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
" [nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan],\n",
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- Attributes: (3)
- date :
- 2020-05-10
- source :
- NHS England
- source_url :
- https://files.digital.nhs.uk/5F/C91A7A/NHS%20Pathways%20Covid-19%20data%202020-05-10.csv
"
],
"text/plain": [
"\n",
"Dimensions: (age_band: 3, ccg: 236, date: 54, sex: 3, site_type: 2)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2020-03-18 2020-03-19 ... 2020-05-10\n",
" * age_band (age_band) object '0-18 years' '19-69 years' '70-120 years'\n",
" * ccg (ccg) object 'E38000001' 'E38000002' ... 'ZC030' 'ZC040'\n",
" * site_type (site_type) int64 111 999\n",
" * sex (sex) object 'Female' 'Male' 'Unknown'\n",
" ccg_name (date, age_band, ccg, site_type, sex) object 'NHS Airedale, Wharfedale and Craven CCG' ... nan\n",
"Data variables:\n",
" count (date, age_band, ccg, site_type, sex) float64 8.0 6.0 ... nan nan\n",
"Attributes:\n",
" date: 2020-05-10\n",
" source: NHS England\n",
" source_url: https://files.digital.nhs.uk/5F/C91A7A/NHS%20Pathways%20Covi..."
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nhs_pathways = coviddata.uk.triage_nhs_pathways()\n",
"nhs_pathways"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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".xr-icon-database,\n",
".xr-icon-file-text2 {\n",
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xarray.Dataset
- Dimensions:
-
age_band: 3
-
ccg: 209
-
date: 54
-
sex: 2
- Coordinates: (5)
-
date
(date)
datetime64[ns]
2020-03-18 ... 2020-05-10
array(['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]')
-
age_band
(age_band)
object
'0-18 years' ... '70+ years'
array(['0-18 years', '19-69 years', '70+ years'], dtype=object)
-
ccg
(ccg)
object
'E38000001' ... 'E38000248'
array(['E38000001', 'E38000002', 'E38000004', ..., 'E38000246', 'E38000247',\n",
" 'E38000248'], dtype=object)
-
sex
(sex)
object
'Female' 'Male'
array(['Female', 'Male'], dtype=object)
-
ccg_name
(date, age_band, ccg, sex)
object
'NHS Airedale, Wharfedale and Craven CCG' ... 'NHS West Sussex CCG'
array([[[['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG'],\n",
" ['NHS Ashford CCG', 'NHS Ashford CCG'],\n",
" ['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG'],\n",
" ...,\n",
" [nan, nan],\n",
" [nan, nan],\n",
" [nan, nan]],\n",
"\n",
" [['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG'],\n",
" ['NHS Ashford CCG', 'NHS Ashford CCG'],\n",
" ['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG'],\n",
" ...,\n",
" [nan, nan],\n",
" [nan, nan],\n",
" [nan, nan]],\n",
"\n",
" [['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG'],\n",
" ['NHS Ashford CCG', 'NHS Ashford CCG'],\n",
" [nan, 'NHS Barking and Dagenham CCG'],\n",
" ...,\n",
" [nan, nan],\n",
" [nan, nan],\n",
" [nan, nan]]],\n",
"\n",
"\n",
" [[['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG'],\n",
" ['NHS Ashford CCG', 'NHS Ashford CCG'],\n",
" ['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG'],\n",
" ...,\n",
" [nan, nan],\n",
" [nan, nan],\n",
" [nan, nan]],\n",
"\n",
" [['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG'],\n",
" ['NHS Ashford CCG', 'NHS Ashford CCG'],\n",
" ['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG'],\n",
" ...,\n",
" [nan, nan],\n",
" [nan, nan],\n",
" [nan, nan]],\n",
"\n",
" [['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG'],\n",
" ['NHS Ashford CCG', 'NHS Ashford CCG'],\n",
" ['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG'],\n",
" ...,\n",
" [nan, nan],\n",
" [nan, nan],\n",
" [nan, nan]]],\n",
"\n",
"\n",
" [[['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG'],\n",
" ['NHS Ashford CCG', 'NHS Ashford CCG'],\n",
" ['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG'],\n",
" ...,\n",
" [nan, nan],\n",
" [nan, nan],\n",
" [nan, nan]],\n",
"\n",
" [['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG'],\n",
" ['NHS Ashford CCG', 'NHS Ashford CCG'],\n",
" ['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG'],\n",
" ...,\n",
" [nan, nan],\n",
" [nan, nan],\n",
" [nan, nan]],\n",
"\n",
" [['NHS Airedale, Wharfedale and Craven CCG',\n",
" 'NHS Airedale, Wharfedale and Craven CCG'],\n",
" ['NHS Ashford CCG', 'NHS Ashford CCG'],\n",
" ['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG'],\n",
" ...,\n",
" [nan, nan],\n",
" [nan, nan],\n",
" [nan, nan]]],\n",
"\n",
"\n",
" ...,\n",
"\n",
"\n",
" [[[nan, nan],\n",
" [nan, nan],\n",
" [nan, nan],\n",
" ...,\n",
" ['NHS Surrey Heartlands CCG', 'NHS Surrey Heartlands CCG'],\n",
" ['NHS Tees Valley CCG', 'NHS Tees Valley CCG'],\n",
" ['NHS West Sussex CCG', 'NHS West Sussex CCG']],\n",
"\n",
" [[nan, nan],\n",
" [nan, nan],\n",
" ['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG'],\n",
" ...,\n",
" ['NHS Surrey Heartlands CCG', 'NHS Surrey Heartlands CCG'],\n",
" ['NHS Tees Valley CCG', 'NHS Tees Valley CCG'],\n",
" ['NHS West Sussex CCG', 'NHS West Sussex CCG']],\n",
"\n",
" [[nan, nan],\n",
" [nan, nan],\n",
" ['NHS Barking and Dagenham CCG', nan],\n",
" ...,\n",
" ['NHS Surrey Heartlands CCG', 'NHS Surrey Heartlands CCG'],\n",
" ['NHS Tees Valley CCG', nan],\n",
" ['NHS West Sussex CCG', 'NHS West Sussex CCG']]],\n",
"\n",
"\n",
" [[[nan, nan],\n",
" [nan, nan],\n",
" [nan, nan],\n",
" ...,\n",
" ['NHS Surrey Heartlands CCG', 'NHS Surrey Heartlands CCG'],\n",
" ['NHS Tees Valley CCG', 'NHS Tees Valley CCG'],\n",
" ['NHS West Sussex CCG', 'NHS West Sussex CCG']],\n",
"\n",
" [[nan, nan],\n",
" [nan, nan],\n",
" ['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG'],\n",
" ...,\n",
" ['NHS Surrey Heartlands CCG', 'NHS Surrey Heartlands CCG'],\n",
" ['NHS Tees Valley CCG', 'NHS Tees Valley CCG'],\n",
" ['NHS West Sussex CCG', 'NHS West Sussex CCG']],\n",
"\n",
" [[nan, nan],\n",
" [nan, nan],\n",
" [nan, 'NHS Barking and Dagenham CCG'],\n",
" ...,\n",
" ['NHS Surrey Heartlands CCG', 'NHS Surrey Heartlands CCG'],\n",
" ['NHS Tees Valley CCG', 'NHS Tees Valley CCG'],\n",
" ['NHS West Sussex CCG', 'NHS West Sussex CCG']]],\n",
"\n",
"\n",
" [[[nan, nan],\n",
" [nan, nan],\n",
" ['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG'],\n",
" ...,\n",
" ['NHS Surrey Heartlands CCG', 'NHS Surrey Heartlands CCG'],\n",
" ['NHS Tees Valley CCG', 'NHS Tees Valley CCG'],\n",
" ['NHS West Sussex CCG', 'NHS West Sussex CCG']],\n",
"\n",
" [[nan, nan],\n",
" [nan, nan],\n",
" ['NHS Barking and Dagenham CCG',\n",
" 'NHS Barking and Dagenham CCG'],\n",
" ...,\n",
" ['NHS Surrey Heartlands CCG', 'NHS Surrey Heartlands CCG'],\n",
" ['NHS Tees Valley CCG', 'NHS Tees Valley CCG'],\n",
" ['NHS West Sussex CCG', 'NHS West Sussex CCG']],\n",
"\n",
" [[nan, nan],\n",
" [nan, nan],\n",
" [nan, 'NHS Barking and Dagenham CCG'],\n",
" ...,\n",
" ['NHS Surrey Heartlands CCG', 'NHS Surrey Heartlands CCG'],\n",
" [nan, 'NHS Tees Valley CCG'],\n",
" ['NHS West Sussex CCG', 'NHS West Sussex CCG']]]], dtype=object)
- Data variables: (1)
-
count
(date, age_band, ccg, sex)
float64
17.0 16.0 27.0 20.0 ... 2.0 3.0 4.0
array([[[[ 17., 16.],\n",
" [ 27., 20.],\n",
" [ 27., 23.],\n",
" ...,\n",
" [ nan, nan],\n",
" [ nan, nan],\n",
" [ nan, nan]],\n",
"\n",
" [[129., 89.],\n",
" [119., 55.],\n",
" [190., 129.],\n",
" ...,\n",
" [ nan, nan],\n",
" [ nan, nan],\n",
" [ nan, nan]],\n",
"\n",
" [[ 3., 3.],\n",
" [ 2., 2.],\n",
" [ nan, 2.],\n",
" ...,\n",
" [ nan, nan],\n",
" [ nan, nan],\n",
" [ nan, nan]]],\n",
"\n",
"\n",
" [[[ 18., 18.],\n",
" [ 13., 19.],\n",
" [ 22., 24.],\n",
" ...,\n",
" [ nan, nan],\n",
" [ nan, nan],\n",
" [ nan, nan]],\n",
"\n",
" [[174., 89.],\n",
" [153., 103.],\n",
" [229., 168.],\n",
" ...,\n",
" [ nan, nan],\n",
" [ nan, nan],\n",
" [ nan, nan]],\n",
"\n",
" [[ 4., 4.],\n",
" [ 11., 9.],\n",
" [ 4., 2.],\n",
" ...,\n",
" [ nan, nan],\n",
" [ nan, nan],\n",
" [ nan, nan]]],\n",
"\n",
"\n",
" [[[ 23., 19.],\n",
" [ 12., 17.],\n",
" [ 31., 14.],\n",
" ...,\n",
" [ nan, nan],\n",
" [ nan, nan],\n",
" [ nan, nan]],\n",
"\n",
" [[135., 96.],\n",
" [155., 90.],\n",
" [225., 151.],\n",
" ...,\n",
" [ nan, nan],\n",
" [ nan, nan],\n",
" [ nan, nan]],\n",
"\n",
" [[ 4., 3.],\n",
" [ 4., 7.],\n",
" [ 3., 2.],\n",
" ...,\n",
" [ nan, nan],\n",
" [ nan, nan],\n",
" [ nan, nan]]],\n",
"\n",
"\n",
" ...,\n",
"\n",
"\n",
" [[[ nan, nan],\n",
" [ nan, nan],\n",
" [ nan, nan],\n",
" ...,\n",
" [ 6., 8.],\n",
" [ 6., 3.],\n",
" [ 5., 9.]],\n",
"\n",
" [[ nan, nan],\n",
" [ nan, nan],\n",
" [ 9., 12.],\n",
" ...,\n",
" [ 62., 40.],\n",
" [ 62., 24.],\n",
" [ 57., 50.]],\n",
"\n",
" [[ nan, nan],\n",
" [ nan, nan],\n",
" [ 3., nan],\n",
" ...,\n",
" [ 4., 4.],\n",
" [ 2., nan],\n",
" [ 5., 5.]]],\n",
"\n",
"\n",
" [[[ nan, nan],\n",
" [ nan, nan],\n",
" [ nan, nan],\n",
" ...,\n",
" [ 4., 7.],\n",
" [ 3., 10.],\n",
" [ 4., 5.]],\n",
"\n",
" [[ nan, nan],\n",
" [ nan, nan],\n",
" [ 6., 12.],\n",
" ...,\n",
" [ 58., 38.],\n",
" [ 51., 25.],\n",
" [ 50., 34.]],\n",
"\n",
" [[ nan, nan],\n",
" [ nan, nan],\n",
" [ nan, 1.],\n",
" ...,\n",
" [ 4., 2.],\n",
" [ 6., 1.],\n",
" [ 8., 5.]]],\n",
"\n",
"\n",
" [[[ nan, nan],\n",
" [ nan, nan],\n",
" [ 1., 4.],\n",
" ...,\n",
" [ 12., 14.],\n",
" [ 3., 5.],\n",
" [ 8., 5.]],\n",
"\n",
" [[ nan, nan],\n",
" [ nan, nan],\n",
" [ 18., 16.],\n",
" ...,\n",
" [ 76., 38.],\n",
" [ 74., 36.],\n",
" [ 61., 48.]],\n",
"\n",
" [[ nan, nan],\n",
" [ nan, nan],\n",
" [ nan, 2.],\n",
" ...,\n",
" [ 1., 2.],\n",
" [ nan, 2.],\n",
" [ 3., 4.]]]])
- Attributes: (3)
- date :
- 2020-05-10
- source :
- NHS England
- source_url :
- https://files.digital.nhs.uk/DA/564752/111%20Online%20Covid-19%20data_2020-05-10.csv
"
],
"text/plain": [
"\n",
"Dimensions: (age_band: 3, ccg: 209, date: 54, sex: 2)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2020-03-18 2020-03-19 ... 2020-05-10\n",
" * age_band (age_band) object '0-18 years' '19-69 years' '70+ years'\n",
" * ccg (ccg) object 'E38000001' 'E38000002' ... 'E38000247' 'E38000248'\n",
" * sex (sex) object 'Female' 'Male'\n",
" ccg_name (date, age_band, ccg, sex) object 'NHS Airedale, Wharfedale and Craven CCG' ... 'NHS West Sussex CCG'\n",
"Data variables:\n",
" count (date, age_band, ccg, sex) float64 17.0 16.0 27.0 ... 2.0 3.0 4.0\n",
"Attributes:\n",
" date: 2020-05-10\n",
" source: NHS England\n",
" source_url: https://files.digital.nhs.uk/DA/564752/111%20Online%20Covid-..."
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nhs_online = coviddata.uk.triage_nhs_online()\n",
"nhs_online"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"ax = plt.axes()\n",
"(nhs_pathways.sum(['age_band', 'ccg', 'sex', 'site_type'])\n",
" .to_dataframe()\n",
" .plot(ax=ax, label='Pathways', y='count'))\n",
"\n",
"(nhs_online.sum(['age_band', 'ccg', 'sex'])\n",
" .to_dataframe()\n",
" .plot(ax=ax, label='Online', y='count'))\n",
"\n",
"plt.title(\"NHS COVID-19 Triage Rate\")\n",
"plt.ylim(0)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interventions\n",
"\n",
"List of interventions used by the Imperial College London model, in a reasonably simple format:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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"\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",
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xarray.Dataset
- Dimensions:
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location: 83
- Coordinates: (2)
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location
(location)
object
'Austria' ... 'Netherlands'
array(['Austria', 'Austria', 'Austria', 'Austria', 'Austria', 'Belgium',\n",
" 'Belgium', 'Belgium', 'Belgium', 'Belgium', 'Denmark', 'Denmark',\n",
" 'Denmark', 'Denmark', 'Denmark', 'France', 'France', 'France', 'France',\n",
" 'France', 'Germany', 'Germany', 'Germany', 'Germany', 'Germany',\n",
" 'Italy', 'Italy', 'Italy', 'Italy', 'Italy', 'Norway', 'Norway',\n",
" 'Norway', 'Norway', 'Norway', 'Spain', 'Spain', 'Spain', 'Spain',\n",
" 'Spain', 'Sweden', 'Sweden', 'Sweden', 'Sweden', 'Sweden',\n",
" 'Switzerland', 'Switzerland', 'Switzerland', 'Switzerland',\n",
" 'Switzerland', 'United_Kingdom', 'United_Kingdom', 'United_Kingdom',\n",
" 'United_Kingdom', 'United_Kingdom', 'Greece', 'Greece', 'Greece',\n",
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" 'Portugal', 'Portugal', 'Portugal', 'Portugal', 'Portugal', 'Portugal',\n",
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-
date
(location)
datetime64[ns]
2020-03-14 ... 2020-03-12
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- Data variables: (2)
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type
(location)
object
'Schools + Universities' ... 'Advice to work from home'
array(['Schools + Universities', 'Public events', 'Lockdown',\n",
" 'Social distancing encouraged', 'Self-isolating if ill',\n",
" 'Schools + Universities', 'Public events', 'Lockdown',\n",
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" 'Schools + Universities', 'Public events', 'Lockdown',\n",
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" 'Social distancing encouraged', 'Self-isolating if ill',\n",
" 'Closure of cultural institutions', 'Closure of restaurants',\n",
" 'Work from home', 'Stay at home', 'Keep distance from others',\n",
" 'Public gatherings', 'Schools + Universities', 'Public events',\n",
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object
'Nationwide school closures.' ... nan
array(['Nationwide school closures.',\n",
" 'Banning of gatherings of more than 10 people. ',\n",
" 'Banning all access to public spaces and gatherings of more than 5 people. Advice to maintain 1m distance.',\n",
" 'Recommendation to maintain a distance of 1m.',\n",
" 'Implemented at lockdown.', 'Nationwide school closures.',\n",
" 'All recreational activities cancelled regardless of size.',\n",
" 'Citizens are required to stay at home except for work and essential journeys. Going outdoors only with household members or 1 friend.',\n",
" 'Public transport recommended only for essential journeys, work from home encouraged, all public places e.g. restaurants closed.',\n",
" 'Everyone should stay at home if experiencing a cough or fever.',\n",
" 'Secondary schools shut and universities (primary schools also shut on 16th).',\n",
" 'Bans of events >100 people, closes cultural institutions, leisure facilities etc.',\n",
" 'Bans of gatherings of >10 people in public and all public places were shut.',\n",
" 'Limited use of public transport. All cultural institutions shut and recommendation to keep appropriate distance.',\n",
" 'Everyone should stay at home if experiencing a cough or fever.',\n",
" 'Nationwide school closures.', 'Bans of events >100 people.',\n",
" 'Everybody has to stay at home. Need a self-authorisation form to leave home.',\n",
" 'Advice at the time of lockdown.',\n",
" 'Advice at the time of lockdown.', 'Nationwide school closures.',\n",
" 'No gatherings of >1000 people (12th March). Otherwise regional destrictions only until lockdown.',\n",
" 'Gatherings of > 2 people banned, 1.5 m distance',\n",
" 'Avoid social interaction wherever possible recommended by Merkel',\n",
" 'Advice for everyone experiencing symptoms to contact a health care agency to get tested and then self-isolate.',\n",
" 'Nationwide school closures.',\n",
" 'The government bans all public events.',\n",
" 'The government closes all public places. People have to stay at home except for essential travel.',\n",
" 'A distance of more than 1m has to be kept and any other form of alternative aggregation is to be excluded.',\n",
" 'self-isolate if ill and quarantine if tested positive',\n",
" 'Norwegian Directorate of Health closes all educational institutions. Including childcare facilities and all schools',\n",
" 'The Directorate of Health bans all non-necessary social contact.',\n",
" 'Only people living together are allowed outside together. Everyone has to keep a 2m distance.',\n",
" 'The Directorate of Health advises against all travelling and non-necessary social contacts.',\n",
" 'Advice to self-isolate for 7 days if experiencing a cough or fever symptoms.',\n",
" 'Nationwide school closures.',\n",
" 'Banning of all public events by lockdown.', nan,\n",
" 'Advice on social distancing and working remotely from home. ',\n",
" nan,\n",
" 'Colleges and upper secondary schools shut. 18/3/2020 only 48% closed',\n",
" 'Since March 29 the limit is 50 persons.', 'No lockdown occured.',\n",
" 'People even with mild symptoms told to limit social contact, encouragement to work from home.',\n",
" nan, 'No in person teaching until 4th of April.',\n",
" 'The government bans events >100 people.',\n",
" 'Gatherings of more than 5 people are banned.',\n",
" 'Advice on keeping distance. All business where this cannot be realised have been closed in all states (kantons).',\n",
" 'Advice to self-isolate if experiencing a cough or fever symptoms.',\n",
" 'Nationwide school closure. Childminders, nurseries and sixth forms are told to follow the guidance.',\n",
" 'Implemented with lockdown.',\n",
" 'Gatherings of more than 2 people not from the same household banned and police-enforceable.',\n",
" 'Advice to avoid pubs, clubs, theatres and other public institutions.',\n",
" 'Advice to self-isolate for 7 days if experiencing a cough or fever symptoms.',\n",
" 'Nationwide closure of educational institutions',\n",
" 'Ban of gatherings of more than 10 people',\n",
" 'Ban on non-essential transport and movement across the country. Police enforcing restrictions with fines.',\n",
" 'In supermarkets, pharmacies and other essentials keep 2mt distance',\n",
" 'Isolate at home if showing mild symptoms or fever',\n",
" 'closure of Cinemas, theaters, theaters, Libraries, Museums, archaeological and historical sites',\n",
" 'closure of malls, cafes, bars and food outlets',\n",
" 'Govt endorses flexible working hours and teleworking',\n",
" 'Video from Greek government recommending keeping 1m distance (27 secs in) "κρατάμε απόσταση τουλάχιστον 1 μέτρου από άλλους και ιδιαίτερα όσους βήχουν, φτέρνίξονται ή έχουν πυρετό" = "keep a distance of at least 1 meter from others and especially those who cough, sneeze or have a fever"',\n",
" 'Nationwide school closures.',\n",
" 'Gatherings of more than 100 people banned.',\n",
" 'State of emergency declared on the 18th and new measures defined on the 19th, to come into place on 00:00 of 22nd March',\n",
" 'Capacity restrictions of entry to bars and restaurants, encouraged to limit frequency of visits to supermarkets/gyms/public services. Nightclubs closed.',\n",
" 'Mandatory isolation is imposed for people who are sick or being monitored by health authorities.',\n",
" 'The closure of certain types of facilities and establishments (such as, for example, those intended for recreational, cultural, sporting and catering activities, among others)',\n",
" 'The closure of certain types of facilities and establishments (such as, for example, those intended for recreational, cultural, sporting and catering activities, among others)',\n",
" 'É obrigatória a adoção do regime de teletrabalho sempre que as funções em causa o permitam - google translation: It is mandatory to adopt the teleworking regime whenever the functions in question allow',\n",
" "States that everyone who isn't in mandatory confinement or an essential worker should only go out in public for essential reasons e.g. health reasons, acquisition of goods, caring for the vulnerable, exercise.",\n",
" 'Transmission prevention measures in public service establishments: "Maintain distance and space between citizens in all situations" and "Ensure that counter service is provided at the appropriate distance (at least 1 meter, ideally 2)"',\n",
" 'Gatherings of more than 5 people prohibited (except those with family ties).',\n",
" 'exemption for children of essential workers',\n",
" 'Public gatherings >100 people',\n",
" 'Social distancing and self-isolation for people with flue-like symptoms as of Marc 12 for the whole country, previously only for the Noord-Brabant province',\n",
" 'Intelligent lockdown, people are still allowed to meet if keeping their distance',\n",
" 'Social distancing and self-isolation for people with flue-like symptoms as of Marc 12 for the whole country, previously only for the Noord-Brabant province',\n",
" 'museums, concert halls and theatres were closed following the ban of large gatherings',\n",
" 'Schools, restaurants, gyms, sports venues, sex clubs, etc. closed as of 18:00hrs March 15',\n",
" nan], dtype=object)
- Attributes: (3)
- date :
- 2020-05-11 13:47:32.382999
- source :
- Imperial College London
- source_url :
- https://raw.githubusercontent.com/ImperialCollegeLondon/covid19model/master/data/interventions.csv
"
],
"text/plain": [
"\n",
"Dimensions: (location: 83)\n",
"Coordinates:\n",
" * location (location) object 'Austria' 'Austria' ... 'Netherlands'\n",
" date (location) datetime64[ns] 2020-03-14 2020-03-10 ... 2020-03-12\n",
"Data variables:\n",
" type (location) object 'Schools + Universities' ... 'Advice to work from home'\n",
" event (location) object 'Nationwide school closures.' ... nan\n",
"Attributes:\n",
" date: 2020-05-11 13:47:32.382999\n",
" source: Imperial College London\n",
" source_url: https://raw.githubusercontent.com/ImperialCollegeLondon/covi..."
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import coviddata.interventions\n",
"\n",
"coviddata.interventions.imperial_interventions()"
]
}
],
"metadata": {
"celltoolbar": "Tags",
"kernelspec": {
"display_name": "Python 3",
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