Argentina and Santa Fe COVID API

Based on handed scraped data on goverment reports:

Santa Fe reports are cumulative. National reports shows new daily cases.

We decided to work with cumulatives time series. There is a smart design decision behind that: if we have cumulative confirmed cases, we don't have to read all the entries, only with the frequentcy we are interested in (imagine weekly analysis).

'Sospechosos' could decrease because some cases can move to 'Confirmados' or 'Descartados'.

Non-Python users

For non python users csv's are generated periodically to be parsed and used (see ./csv/) folder. All with cumulative time series.

For Santa Fe:

csv/SantaFe_AllData.csv

For Argentina:

csv/Argentina_Provinces.csv

Check last update time on csv/last_update.txt

Argentina API

Python API for working with Argentina COVID data reported.

DataTypes exported:

COVIDStats namedtuple
ArgentinaAPI class

API methods:

api.get_stats(date) API public properties:
api.df_provinces : pandas.DataFrame
api.provinces : List[str]

Important data types.

from argentina_api import *
print('COVIDStats namedtuple:', COVIDStats._fields)

COVIDStats namedtuple: ('date', 'place_name', 'confirmados', 'muertos', 'recuperados', 'activos')

Create api instance passing the working directory

When load the data, the API tells if there are no entries in 'Info' sheet for certain city.

api = ArgentinaAPI('./')

Downloading Argentinian provinces table from google drive (https://docs.google.com/spreadsheets/d/e/2PACX-1vTfinng5SDBH9RSJMHJk28dUlW3VVSuvqaBSGzU-fYRTVLCzOkw1MnY17L2tWsSOppHB96fr21Ykbyv/pub#)

`get_stats : Date -> [ COVIDStats ]` of all provinces

Date must be expressed in DD/MM format.

api.get_stats('26/03')[:3]

[COVIDStats(date='26/03', place_name='BUENOS AIRES', confirmados=158, muertos=4, recuperados=15, activos=139),
 COVIDStats(date='26/03', place_name='CABA', confirmados=197, muertos=4, recuperados=53, activos=140),
 COVIDStats(date='26/03', place_name='CATAMARCA', confirmados=0, muertos=0, recuperados=0, activos=0)]

Exported DataFrames

Also exports a DataFrame df_provinces.

With the content of Google Drive data by province (see link above). Provinces names are normalized using normalize_str function.

api.df_provinces.head(3)

.dataframe tbody tr th {
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		03/03	04/03	05/03	06/03	07/03	08/03	09/03	10/03	11/03	12/03	...	02/04	03/04	04/04	05/04	06/04	07/04	08/04	09/04	10/04	11/04
TYPE	PROVINCIA
ACTIVOS	BUENOS AIRES	0	0	1	2	2	3	3	4	5	9	...	289	308	333	365	375	405	421	442	460	493
	CABA	1	1	1	5	5	7	8	9	10	13	...	283	311	345	376	389	411	427	445	455	498
	CATAMARCA	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

3 rows × 40 columns

Exported provinces names

Also exports a List[str] provinces with all the provinces names:

api.provinces

['BUENOS AIRES',
 'CABA',
 'CATAMARCA',
 'CHACO',
 'CHUBUT',
 'CORDOBA',
 'CORRIENTES',
 'ENTRE RIOS',
 'FORMOSA',
 'JUJUY',
 'LA PAMPA',
 'LA RIOJA',
 'MENDOZA',
 'MISIONES',
 'NEUQUEN',
 'RIO NEGRO',
 'SALTA',
 'SAN JUAN',
 'SAN LUIS',
 'SANTA CRUZ',
 'SANTA FE',
 'SANTIAGO DEL ESTERO',
 'TIERRA DEL FUEGO',
 'TUCUMAN']

Example of use

provinces = api.df_provinces.loc['CONFIRMADOS']['26/03']
provinces = provinces[provinces>0]
provinces.plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x7fce3ff5e280>

Santa Fe API

Python API for working with Santa Fe (Argentina) COVID data reported.

DataTypes exported:

CityInfo, COVIDStats namedtuples
SantaFeAPI class

API methods:

api.get_stats(date)
api.get_cities_stats(date)
api.get_departments_stats(date) API public properties:
api.df : pandas.DataFrame
api.all_names : List[str]
api.cities : List[str]
api.departments : List[str]

Exported functions:

is_city(str)
is_deparment(str)
normalize_str(str)

Important data types.

from santa_fe_api import *
print('COVIDStats namedtuple:', COVIDStats._fields)

COVIDStats namedtuple: ('date', 'place_name', 'confirmados', 'descartados', 'sospechosos')

Create api instance passing the working directory

When load the data the API tells if there are no entries in 'Info' sheet for certain city.

api = SantaFeAPI('./')

Download from google drive...

`get_stats : Date -> [ COVIDStats ]` of all places

Date must be expressed in DD/M/YYYY format.

api.get_stats('26/3/2020')[:3]

[COVIDStats(date='26/3/2020', place_name='#D_IRIONDO', confirmados=0.0, descartados=1.0, sospechosos=0.0),
 COVIDStats(date='26/3/2020', place_name='ARMSTRONG', confirmados=0.0, descartados=1.0, sospechosos=0.0),
 COVIDStats(date='26/3/2020', place_name='RAFAELA', confirmados=5.0, descartados=1.0, sospechosos=9.0)]

`get_stats : Date -> [ COVIDStats ]` of only cities

api.get_cities_stats('26/3/2020')[:3]

[COVIDStats(date='26/3/2020', place_name='ARMSTRONG', confirmados=0.0, descartados=1.0, sospechosos=0.0),
 COVIDStats(date='26/3/2020', place_name='RAFAELA', confirmados=5.0, descartados=1.0, sospechosos=9.0),
 COVIDStats(date='26/3/2020', place_name='SAN GENARO', confirmados=0.0, descartados=0.0, sospechosos=1.0)]

`get_stats : Date -> [ COVIDStats ]` of only departments

api.get_departments_stats('26/3/2020')[:10]

[COVIDStats(date='26/3/2020', place_name='#D_IRIONDO', confirmados=0.0, descartados=1.0, sospechosos=0.0),
 COVIDStats(date='26/3/2020', place_name='#D_GENERAL LOPEZ', confirmados=1.0, descartados=5.0, sospechosos=4.0),
 COVIDStats(date='26/3/2020', place_name='#D_SAN CRISTOBAL', confirmados=0.0, descartados=1.0, sospechosos=1.0),
 COVIDStats(date='26/3/2020', place_name='#D_GARAY', confirmados=2.0, descartados=2.0, sospechosos=5.0),
 COVIDStats(date='26/3/2020', place_name='#D_GENERAL OBLIGADO', confirmados=0.0, descartados=2.0, sospechosos=5.0),
 COVIDStats(date='26/3/2020', place_name='#D_SAN JUSTO', confirmados=0.0, descartados=0.0, sospechosos=2.0),
 COVIDStats(date='26/3/2020', place_name='#D_ROSARIO', confirmados=15.0, descartados=88.0, sospechosos=22.0),
 COVIDStats(date='26/3/2020', place_name='#D_CASTELLANOS', confirmados=5.0, descartados=4.0, sospechosos=11.0),
 COVIDStats(date='26/3/2020', place_name='#D_CASEROS', confirmados=2.0, descartados=0.0, sospechosos=1.0),
 COVIDStats(date='26/3/2020', place_name='#D_CONSTITUCION', confirmados=1.0, descartados=3.0, sospechosos=1.0)]

Exported DataFrames

Also exports a pandas.DataFrame df.

With the content of Google Drive 'AllData' with ['TYPE','DEPARTMENT','PLACE'] index.

Values are cumulative. City names are normalized using normalize_str function.

api.df.head(3)

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}

</style>

			14/3/2020	15/3/2020	16/3/2020	17/3/2020	18/3/2020	19/3/2020	20/3/2020	21/3/2020	22/3/2020	23/3/2020	...	31/3/2020	1/4/2020	2/4/2020	3/4/2020	4/4/2020	5/4/2020	6/4/2020	7/4/2020	8/4/2020	9/4/2020
TYPE	DEPARTMENT	PLACE
CONFIRMADOS	##TOTAL	##TOTAL	1.0	1.0	1.0	1.0	1.0	2.0	2.0	4.0	4.0	15.0	...	133.0	144.0	152.0	160.0	165.0	176.0	184.0	187.0	189.0	195.0
	#D_9 DE JULIO	#D_9 DE JULIO	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	#D_9 DE JULIO	TOSTADO	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

3 rows × 27 columns

Exported Dict[CityName, DepartmentName]

to_department(str) property stores CityName to DepartmentName assignations. all_names property stores Set[CityName or DepartmentName]. cities property stores Set[CityName]. departments property stores Set[DepartmentName].

print('Some cities: {}'.format(list(api.cities)[:3]))
print('Some departments: {}'.format(list(api.departments)[:3]))
print('Cities with the respective departments: {}'.format([(c,api.to_department[c]) for c in list(api.cities)[:2]]))

Some cities: ['ARMSTRONG', 'RAFAELA', 'SAN GENARO']
Some departments: ['#D_IRIONDO', '#D_CONSTITUCION', '#D_GENERAL OBLIGADO']
Cities with the respective departments: [('ARMSTRONG', '#D_BELGRANO'), ('RAFAELA', '#D_CASTELLANOS')]

Example of use

Uses is_city(str) is_deparment(str) method to check if a place name is city or department.

ciudades = api.df.loc['CONFIRMADOS'][ api.df.loc['CONFIRMADOS'].index.map(lambda x : is_city(x[1]))  ]['26/3/2020']
ciudades = ciudades[ciudades>0].reset_index(['DEPARTMENT'],drop=True) 
ciudades.plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x7fce3d839dc0>

Authors

Mariano Crosetti | Computer Sciences student @ Facebook | LinkedIn | Twitter | Github
Juan Hernandez Ruiz | Civil Engineer Student @ Facebook | LinkedIn | Twitter

Name		Name	Last commit message	Last commit date
Latest commit History 262 Commits
.github/workflows		.github/workflows
README_files		README_files
barrios_generator		barrios_generator
csvs		csvs
migration1		migration1
msaludopendata		msaludopendata
retrasos		retrasos
tools		tools
.gitignore		.gitignore
README.md		README.md
argentina_api.py		argentina_api.py
argentina_province_parse.py		argentina_province_parse.py
common.py		common.py
csv_generator.py		csv_generator.py
process_departamentos.py		process_departamentos.py
requirements.txt		requirements.txt
santa_fe_api.py		santa_fe_api.py
santa_fe_datosabiertos_joiner.py		santa_fe_datosabiertos_joiner.py

mariano22/argcovidapi

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Argentina and Santa Fe COVID API

Non-Python users

Argentina API

Important data types.

Create api instance passing the working directory

get_stats : Date -> [ COVIDStats ] of all provinces

Exported DataFrames

Exported provinces names

Example of use

Santa Fe API

Important data types.

Create api instance passing the working directory

get_stats : Date -> [ COVIDStats ] of all places

get_stats : Date -> [ COVIDStats ] of only cities

get_stats : Date -> [ COVIDStats ] of only departments

Exported DataFrames

Exported Dict[CityName, DepartmentName]

Example of use

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`get_stats : Date -> [ COVIDStats ]` of all provinces

`get_stats : Date -> [ COVIDStats ]` of all places

`get_stats : Date -> [ COVIDStats ]` of only cities

`get_stats : Date -> [ COVIDStats ]` of only departments