%global _empty_manifest_terminate_build 0
Name: python-covid19dh
Version: 2.3.0
Release: 1
Summary: Unified data hub for a better understanding of COVID-19 https://covid19datahub.io
License: MIT License
URL: https://www.covid19datahub.io
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d8/db/3720cf3390db058dda5e3e9cbb2f42fd9f170191b28d1de91ef433388f85/covid19dh-2.3.0.tar.gz
BuildArch: noarch
Requires: python3-pandas
Requires: python3-requests
%description
# Python Interface to COVID-19 Data Hub
[](https://pypi.org/pypi/covid19dh/) [](https://pypi.org/pypi/covid19dh/) [](https://doi.org/10.21105/joss.02376) [](https://github.com/covid19datahub/Python)
Download COVID-19 data across governmental sources at national, regional, and city level, as described in [Guidotti and Ardia (2020)](https://www.doi.org/10.21105/joss.02376). Includes the time series of vaccines, tests, cases, deaths, recovered, hospitalizations, intensive therapy, and policy measures by [Oxford COVID-19 Government Response Tracker](https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker). Please agree to the [Terms of Use](https://covid19datahub.io/LICENSE.html) and cite the following reference when using it:
**Reference**
Guidotti, E., Ardia, D., (2020).
COVID-19 Data Hub
_Journal of Open Source Software_, **5**(51):2376
[https://doi.org/10.21105/joss.02376](https://doi.org/10.21105/joss.02376)
## Setup and usage
Install from [pip](https://pypi.org/project/covid19dh/) with
```python
pip install covid19dh
```
Importing the main function `covid19()`
```python
from covid19dh import covid19
x, src = covid19()
```
Package is regularly updated. Update with
```bash
pip install --upgrade covid19dh
```
## Return values
The function `covid19()` returns 2 pandas dataframes:
* the data and
* references to the data sources.
## Parametrization
### Country
List of country names (case-insensitive) or ISO codes (alpha-2, alpha-3 or numeric). The list of ISO codes can be found [here](https://github.com/covid19datahub/COVID19/blob/master/inst/extdata/db/ISO.csv).
Fetching data from a particular country:
```python
x, src = covid19("USA") # Unites States
```
Specify multiple countries at the same time:
```python
x, src = covid19(["ESP","PT","andorra",250])
```
If `country` is omitted, the whole dataset is returned:
```python
x, src = covid19()
```
### Raw data
Logical. Skip data cleaning? Default `True`. If `raw=False`, the raw data are cleaned by filling missing dates with `NaN` values. This ensures that all locations share the same grid of dates and no single day is skipped. Then, `NaN` values are replaced with the previous non-`NaN` value or `0`.
```python
x, src = covid19(raw = False)
```
### Date filter
Date can be specified with `datetime.datetime`, `datetime.date` or as a `str` in format `YYYY-mm-dd`.
```python
from datetime import datetime
x, src = covid19("SWE", start = datetime(2020,4,1), end = "2020-05-01")
```
### Level
Integer. Granularity level of the data:
1. Country level
2. State, region or canton level
3. City or municipality level
```python
from datetime import date
x, src = covid19("USA", level = 2, start = date(2020,5,1))
```
### Cache
Logical. Memory caching? Significantly improves performance on successive calls. By default, using the cached data is enabled.
Caching can be disabled (e.g. for long running programs) by:
```python
x, src = covid19("FRA", cache = False)
```
### Vintage
Logical. Retrieve the snapshot of the dataset that was generated at the `end` date instead of using the latest version. Default `False`.
To fetch e.g. US data that were accessible on *22th April 2020* type
```python
x, src = covid19("USA", end = "2020-04-22", vintage = True)
```
The vintage data are collected at the end of the day, but published with approximately 48 hour delay,
once the day is completed in all the timezones.
Hence if `vintage = True`, but `end` is not set, warning is raised and `None` is returned.
```python
x, src = covid19("USA", vintage = True) # too early to get today's vintage
```
```
UserWarning: vintage data not available yet
```
### Data Sources
The data sources are returned as second value.
```python
from covid19dh import covid19
x, src = covid19("USA")
print(src)
```
### Additional information
Find out more at https://covid19datahub.io
## Acknowledgements
Developed and maintained by [Martin Benes](https://pypi.org/user/martinbenes1996/).
## Cite as
*Guidotti, E., Ardia, D., (2020), "COVID-19 Data Hub", Journal of Open Source Software 5(51):2376, doi: 10.21105/joss.02376.*
A BibTeX entry for LaTeX users is
```latex
@Article{,
title = {COVID-19 Data Hub},
year = {2020},
doi = {10.21105/joss.02376},
author = {Emanuele Guidotti and David Ardia},
journal = {Journal of Open Source Software},
volume = {5},
number = {51},
pages = {2376}
}
```
%package -n python3-covid19dh
Summary: Unified data hub for a better understanding of COVID-19 https://covid19datahub.io
Provides: python-covid19dh
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-covid19dh
# Python Interface to COVID-19 Data Hub
[](https://pypi.org/pypi/covid19dh/) [](https://pypi.org/pypi/covid19dh/) [](https://doi.org/10.21105/joss.02376) [](https://github.com/covid19datahub/Python)
Download COVID-19 data across governmental sources at national, regional, and city level, as described in [Guidotti and Ardia (2020)](https://www.doi.org/10.21105/joss.02376). Includes the time series of vaccines, tests, cases, deaths, recovered, hospitalizations, intensive therapy, and policy measures by [Oxford COVID-19 Government Response Tracker](https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker). Please agree to the [Terms of Use](https://covid19datahub.io/LICENSE.html) and cite the following reference when using it:
**Reference**
Guidotti, E., Ardia, D., (2020).
COVID-19 Data Hub
_Journal of Open Source Software_, **5**(51):2376
[https://doi.org/10.21105/joss.02376](https://doi.org/10.21105/joss.02376)
## Setup and usage
Install from [pip](https://pypi.org/project/covid19dh/) with
```python
pip install covid19dh
```
Importing the main function `covid19()`
```python
from covid19dh import covid19
x, src = covid19()
```
Package is regularly updated. Update with
```bash
pip install --upgrade covid19dh
```
## Return values
The function `covid19()` returns 2 pandas dataframes:
* the data and
* references to the data sources.
## Parametrization
### Country
List of country names (case-insensitive) or ISO codes (alpha-2, alpha-3 or numeric). The list of ISO codes can be found [here](https://github.com/covid19datahub/COVID19/blob/master/inst/extdata/db/ISO.csv).
Fetching data from a particular country:
```python
x, src = covid19("USA") # Unites States
```
Specify multiple countries at the same time:
```python
x, src = covid19(["ESP","PT","andorra",250])
```
If `country` is omitted, the whole dataset is returned:
```python
x, src = covid19()
```
### Raw data
Logical. Skip data cleaning? Default `True`. If `raw=False`, the raw data are cleaned by filling missing dates with `NaN` values. This ensures that all locations share the same grid of dates and no single day is skipped. Then, `NaN` values are replaced with the previous non-`NaN` value or `0`.
```python
x, src = covid19(raw = False)
```
### Date filter
Date can be specified with `datetime.datetime`, `datetime.date` or as a `str` in format `YYYY-mm-dd`.
```python
from datetime import datetime
x, src = covid19("SWE", start = datetime(2020,4,1), end = "2020-05-01")
```
### Level
Integer. Granularity level of the data:
1. Country level
2. State, region or canton level
3. City or municipality level
```python
from datetime import date
x, src = covid19("USA", level = 2, start = date(2020,5,1))
```
### Cache
Logical. Memory caching? Significantly improves performance on successive calls. By default, using the cached data is enabled.
Caching can be disabled (e.g. for long running programs) by:
```python
x, src = covid19("FRA", cache = False)
```
### Vintage
Logical. Retrieve the snapshot of the dataset that was generated at the `end` date instead of using the latest version. Default `False`.
To fetch e.g. US data that were accessible on *22th April 2020* type
```python
x, src = covid19("USA", end = "2020-04-22", vintage = True)
```
The vintage data are collected at the end of the day, but published with approximately 48 hour delay,
once the day is completed in all the timezones.
Hence if `vintage = True`, but `end` is not set, warning is raised and `None` is returned.
```python
x, src = covid19("USA", vintage = True) # too early to get today's vintage
```
```
UserWarning: vintage data not available yet
```
### Data Sources
The data sources are returned as second value.
```python
from covid19dh import covid19
x, src = covid19("USA")
print(src)
```
### Additional information
Find out more at https://covid19datahub.io
## Acknowledgements
Developed and maintained by [Martin Benes](https://pypi.org/user/martinbenes1996/).
## Cite as
*Guidotti, E., Ardia, D., (2020), "COVID-19 Data Hub", Journal of Open Source Software 5(51):2376, doi: 10.21105/joss.02376.*
A BibTeX entry for LaTeX users is
```latex
@Article{,
title = {COVID-19 Data Hub},
year = {2020},
doi = {10.21105/joss.02376},
author = {Emanuele Guidotti and David Ardia},
journal = {Journal of Open Source Software},
volume = {5},
number = {51},
pages = {2376}
}
```
%package help
Summary: Development documents and examples for covid19dh
Provides: python3-covid19dh-doc
%description help
# Python Interface to COVID-19 Data Hub
[](https://pypi.org/pypi/covid19dh/) [](https://pypi.org/pypi/covid19dh/) [](https://doi.org/10.21105/joss.02376) [](https://github.com/covid19datahub/Python)
Download COVID-19 data across governmental sources at national, regional, and city level, as described in [Guidotti and Ardia (2020)](https://www.doi.org/10.21105/joss.02376). Includes the time series of vaccines, tests, cases, deaths, recovered, hospitalizations, intensive therapy, and policy measures by [Oxford COVID-19 Government Response Tracker](https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker). Please agree to the [Terms of Use](https://covid19datahub.io/LICENSE.html) and cite the following reference when using it:
**Reference**
Guidotti, E., Ardia, D., (2020).
COVID-19 Data Hub
_Journal of Open Source Software_, **5**(51):2376
[https://doi.org/10.21105/joss.02376](https://doi.org/10.21105/joss.02376)
## Setup and usage
Install from [pip](https://pypi.org/project/covid19dh/) with
```python
pip install covid19dh
```
Importing the main function `covid19()`
```python
from covid19dh import covid19
x, src = covid19()
```
Package is regularly updated. Update with
```bash
pip install --upgrade covid19dh
```
## Return values
The function `covid19()` returns 2 pandas dataframes:
* the data and
* references to the data sources.
## Parametrization
### Country
List of country names (case-insensitive) or ISO codes (alpha-2, alpha-3 or numeric). The list of ISO codes can be found [here](https://github.com/covid19datahub/COVID19/blob/master/inst/extdata/db/ISO.csv).
Fetching data from a particular country:
```python
x, src = covid19("USA") # Unites States
```
Specify multiple countries at the same time:
```python
x, src = covid19(["ESP","PT","andorra",250])
```
If `country` is omitted, the whole dataset is returned:
```python
x, src = covid19()
```
### Raw data
Logical. Skip data cleaning? Default `True`. If `raw=False`, the raw data are cleaned by filling missing dates with `NaN` values. This ensures that all locations share the same grid of dates and no single day is skipped. Then, `NaN` values are replaced with the previous non-`NaN` value or `0`.
```python
x, src = covid19(raw = False)
```
### Date filter
Date can be specified with `datetime.datetime`, `datetime.date` or as a `str` in format `YYYY-mm-dd`.
```python
from datetime import datetime
x, src = covid19("SWE", start = datetime(2020,4,1), end = "2020-05-01")
```
### Level
Integer. Granularity level of the data:
1. Country level
2. State, region or canton level
3. City or municipality level
```python
from datetime import date
x, src = covid19("USA", level = 2, start = date(2020,5,1))
```
### Cache
Logical. Memory caching? Significantly improves performance on successive calls. By default, using the cached data is enabled.
Caching can be disabled (e.g. for long running programs) by:
```python
x, src = covid19("FRA", cache = False)
```
### Vintage
Logical. Retrieve the snapshot of the dataset that was generated at the `end` date instead of using the latest version. Default `False`.
To fetch e.g. US data that were accessible on *22th April 2020* type
```python
x, src = covid19("USA", end = "2020-04-22", vintage = True)
```
The vintage data are collected at the end of the day, but published with approximately 48 hour delay,
once the day is completed in all the timezones.
Hence if `vintage = True`, but `end` is not set, warning is raised and `None` is returned.
```python
x, src = covid19("USA", vintage = True) # too early to get today's vintage
```
```
UserWarning: vintage data not available yet
```
### Data Sources
The data sources are returned as second value.
```python
from covid19dh import covid19
x, src = covid19("USA")
print(src)
```
### Additional information
Find out more at https://covid19datahub.io
## Acknowledgements
Developed and maintained by [Martin Benes](https://pypi.org/user/martinbenes1996/).
## Cite as
*Guidotti, E., Ardia, D., (2020), "COVID-19 Data Hub", Journal of Open Source Software 5(51):2376, doi: 10.21105/joss.02376.*
A BibTeX entry for LaTeX users is
```latex
@Article{,
title = {COVID-19 Data Hub},
year = {2020},
doi = {10.21105/joss.02376},
author = {Emanuele Guidotti and David Ardia},
journal = {Journal of Open Source Software},
volume = {5},
number = {51},
pages = {2376}
}
```
%prep
%autosetup -n covid19dh-2.3.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-covid19dh -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot - 2.3.0-1
- Package Spec generated