AD4: conexión API del COVID19 y análisis con Pandas

Me conecto a la API https://api.covid19api.com/

Instalo e importo las Pandas

!pip install pandas

Requirement already satisfied: pandas in c:\users\ania alonzo\anaconda3\lib\site-packages (1.4.2)
Requirement already satisfied: python-dateutil>=2.8.1 in c:\users\ania alonzo\anaconda3\lib\site-packages (from pandas) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in c:\users\ania alonzo\anaconda3\lib\site-packages (from pandas) (2021.3)
Requirement already satisfied: numpy>=1.18.5 in c:\users\ania alonzo\anaconda3\lib\site-packages (from pandas) (1.21.5)
Requirement already satisfied: six>=1.5 in c:\users\ania alonzo\anaconda3\lib\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)

pip install numpi

Requirement already satisfied: numpi in c:\users\ania alonzo\anaconda3\lib\site-packages (0.3.0)
Requirement already satisfied: numpy>=1.15.0 in c:\users\ania alonzo\anaconda3\lib\site-packages (from numpi) (1.21.5)
Requirement already satisfied: scipy>=1.1.0 in c:\users\ania alonzo\anaconda3\lib\site-packages (from numpi) (1.7.3)
Note: you may need to restart the kernel to use updated packages.

Coloco URL para traer la lista de países

import pandas as pd

url = 'https://api.covid19api.com/countries'

url

'https://api.covid19api.com/countries'

Añado la variable para pd.read_json

df = pd.read_json(url)

df

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	Country	Slug	ISO2
0	Gibraltar	gibraltar	GI
1	Oman	oman	OM
2	France	france	FR
3	Jersey	jersey	JE
4	Mali	mali	ML
...	...	...	...
243	Puerto Rico	puerto-rico	PR
244	Papua New Guinea	papua-new-guinea	PG
245	Saint Pierre and Miquelon	saint-pierre-and-miquelon	PM
246	Timor-Leste	timor-leste	TL
247	Montenegro	montenegro	ME

248 rows × 3 columns

Extraigo en esta ocasión sólo los datos de España y sucesivamente las de los países que me interesan seleccionar, para este trabajo lo hice con Centroamérica.

df[df['Country'] == 'Spain']

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	Country	Slug	ISO2
141	Spain	spain	ES

url_rt_es = 'https://api.covid19api.com/country/spain/status/confirmed/live'
df_rt_es = pd.read_json(url_rt_es)
df_rt_es

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	Country	CountryCode	Province	City	CityCode	Lat	Lon	Cases	Status	Date
0	Spain	ES				40.46	-3.75	0	confirmed	2020-01-22 00:00:00+00:00
1	Spain	ES				40.46	-3.75	0	confirmed	2020-01-23 00:00:00+00:00
2	Spain	ES				40.46	-3.75	0	confirmed	2020-01-24 00:00:00+00:00
3	Spain	ES				40.46	-3.75	0	confirmed	2020-01-25 00:00:00+00:00
4	Spain	ES				40.46	-3.75	0	confirmed	2020-01-26 00:00:00+00:00
...	...	...	...	...	...	...	...	...	...	...
894	Spain	ES				40.46	-3.75	12818184	confirmed	2022-07-04 00:00:00+00:00
895	Spain	ES				40.46	-3.75	12890002	confirmed	2022-07-05 00:00:00+00:00
896	Spain	ES				40.46	-3.75	12890002	confirmed	2022-07-06 00:00:00+00:00
897	Spain	ES				40.46	-3.75	12890002	confirmed	2022-07-07 00:00:00+00:00
898	Spain	ES				40.46	-3.75	12973615	confirmed	2022-07-08 00:00:00+00:00

899 rows × 10 columns

df_rt_es.head()

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	Country	CountryCode	Lat	Lon	Status	Date
0	Spain	ES	40.46	-3.75	confirmed	2020-01-22 00:00:00+00:00
1	Spain	ES	40.46	-3.75	confirmed	2020-01-23 00:00:00+00:00
2	Spain	ES	40.46	-3.75	confirmed	2020-01-24 00:00:00+00:00
3	Spain	ES	40.46	-3.75	confirmed	2020-01-25 00:00:00+00:00
4	Spain	ES	40.46	-3.75	confirmed	2020-01-26 00:00:00+00:00

df_rt_es.tail()

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	Country	CountryCode	Lat	Lon	Cases	Status	Date
894	Spain	ES	40.46	-3.75	12818184	confirmed	2022-07-04 00:00:00+00:00
895	Spain	ES	40.46	-3.75	12890002	confirmed	2022-07-05 00:00:00+00:00
896	Spain	ES	40.46	-3.75	12890002	confirmed	2022-07-06 00:00:00+00:00
897	Spain	ES	40.46	-3.75	12890002	confirmed	2022-07-07 00:00:00+00:00
898	Spain	ES	40.46	-3.75	12973615	confirmed	2022-07-08 00:00:00+00:00

casos_es = df_rt_es.set_index('Date')['Cases']
casos_es.plot(title='Casos de Covid19 en España 20/1/2020 hasta 29/6/2022')

<AxesSubplot:title={'center':'Casos de Covid19 en España 20/1/2020 hasta 29/6/2022'}, xlabel='Date'>

df[df['Country'] == 'Costa Rica']

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	Country	Slug	ISO2
242	Costa Rica	costa-rica	CR

url_rt_cr = 'https://api.covid19api.com/country/costa-rica/status/confirmed/live'
df_rt_cr = pd.read_json(url_rt_cr)
df_rt_cr

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	Country	CountryCode	Province	City	CityCode	Lat	Lon	Cases	Status	Date
0	Costa Rica	CR				9.75	-83.75	0	confirmed	2020-01-22 00:00:00+00:00
1	Costa Rica	CR				9.75	-83.75	0	confirmed	2020-01-23 00:00:00+00:00
2	Costa Rica	CR				9.75	-83.75	0	confirmed	2020-01-24 00:00:00+00:00
3	Costa Rica	CR				9.75	-83.75	0	confirmed	2020-01-25 00:00:00+00:00
4	Costa Rica	CR				9.75	-83.75	0	confirmed	2020-01-26 00:00:00+00:00
...	...	...	...	...	...	...	...	...	...	...
895	Costa Rica	CR				9.75	-83.75	904934	confirmed	2022-07-05 00:00:00+00:00
896	Costa Rica	CR				9.75	-83.75	904934	confirmed	2022-07-06 00:00:00+00:00
897	Costa Rica	CR				9.75	-83.75	904934	confirmed	2022-07-07 00:00:00+00:00
898	Costa Rica	CR				9.75	-83.75	904934	confirmed	2022-07-08 00:00:00+00:00
899	Costa Rica	CR				9.75	-83.75	904934	confirmed	2022-07-09 00:00:00+00:00

900 rows × 10 columns

df_rt_cr.head()

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	Country	CountryCode	Lat	Lon	Status	Date
0	Costa Rica	CR	9.75	-83.75	confirmed	2020-01-22 00:00:00+00:00
1	Costa Rica	CR	9.75	-83.75	confirmed	2020-01-23 00:00:00+00:00
2	Costa Rica	CR	9.75	-83.75	confirmed	2020-01-24 00:00:00+00:00
3	Costa Rica	CR	9.75	-83.75	confirmed	2020-01-25 00:00:00+00:00
4	Costa Rica	CR	9.75	-83.75	confirmed	2020-01-26 00:00:00+00:00

df_rt_cr.tail()

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	Country	CountryCode	Lat	Lon	Cases	Status	Date
895	Costa Rica	CR	9.75	-83.75	904934	confirmed	2022-07-05 00:00:00+00:00
896	Costa Rica	CR	9.75	-83.75	904934	confirmed	2022-07-06 00:00:00+00:00
897	Costa Rica	CR	9.75	-83.75	904934	confirmed	2022-07-07 00:00:00+00:00
898	Costa Rica	CR	9.75	-83.75	904934	confirmed	2022-07-08 00:00:00+00:00
899	Costa Rica	CR	9.75	-83.75	904934	confirmed	2022-07-09 00:00:00+00:00

casos_cr = df_rt_cr.set_index('Date')['Cases']
casos_cr.plot(title='Casos de Covid19 en Costa Rica 20/1/2020 hasta 29/6/2022')

<AxesSubplot:title={'center':'Casos de Covid19 en Costa Rica 20/1/2020 hasta 29/6/2022'}, xlabel='Date'>

df[df['Country'] == 'Honduras']

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	Country	Slug	ISO2
91	Honduras	honduras	HN

url_rt_hnd = 'https://api.covid19api.com/country/honduras/status/confirmed/live'
df_rt_hnd = pd.read_json(url_rt_hnd)
df_rt_hnd

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	Country	CountryCode	Province	City	CityCode	Lat	Lon	Cases	Status	Date
0	Honduras	HN				15.2	-86.24	0	confirmed	2020-01-22 00:00:00+00:00
1	Honduras	HN				15.2	-86.24	0	confirmed	2020-01-23 00:00:00+00:00
2	Honduras	HN				15.2	-86.24	0	confirmed	2020-01-24 00:00:00+00:00
3	Honduras	HN				15.2	-86.24	0	confirmed	2020-01-25 00:00:00+00:00
4	Honduras	HN				15.2	-86.24	0	confirmed	2020-01-26 00:00:00+00:00
...	...	...	...	...	...	...	...	...	...	...
895	Honduras	HN				15.2	-86.24	427718	confirmed	2022-07-05 00:00:00+00:00
896	Honduras	HN				15.2	-86.24	427718	confirmed	2022-07-06 00:00:00+00:00
897	Honduras	HN				15.2	-86.24	427718	confirmed	2022-07-07 00:00:00+00:00
898	Honduras	HN				15.2	-86.24	429408	confirmed	2022-07-08 00:00:00+00:00
899	Honduras	HN				15.2	-86.24	429408	confirmed	2022-07-09 00:00:00+00:00

900 rows × 10 columns

casos_hn = df_rt_hnd.set_index('Date')['Cases']
casos_hn.plot(title='Casos de Covid19 en Honduras 20/1/2020 hasta 29/6/2022')

<AxesSubplot:title={'center':'Casos de Covid19 en Honduras 20/1/2020 hasta 29/6/2022'}, xlabel='Date'>

df[df['Country'] == 'Guatemala']

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	Country	Slug	ISO2
239	Guatemala	guatemala	GT

url_rt_guat = 'https://api.covid19api.com/country/guatemala/status/confirmed/live'
df_rt_guat = pd.read_json(url_rt_guat)
df_rt_guat

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	Country	CountryCode	Province	City	CityCode	Lat	Lon	Cases	Status	Date
0	Guatemala	GT				15.78	-90.23	0	confirmed	2020-01-22 00:00:00+00:00
1	Guatemala	GT				15.78	-90.23	0	confirmed	2020-01-23 00:00:00+00:00
2	Guatemala	GT				15.78	-90.23	0	confirmed	2020-01-24 00:00:00+00:00
3	Guatemala	GT				15.78	-90.23	0	confirmed	2020-01-25 00:00:00+00:00
4	Guatemala	GT				15.78	-90.23	0	confirmed	2020-01-26 00:00:00+00:00
...	...	...	...	...	...	...	...	...	...	...
895	Guatemala	GT				15.78	-90.23	922340	confirmed	2022-07-05 00:00:00+00:00
896	Guatemala	GT				15.78	-90.23	927473	confirmed	2022-07-06 00:00:00+00:00
897	Guatemala	GT				15.78	-90.23	933259	confirmed	2022-07-07 00:00:00+00:00
898	Guatemala	GT				15.78	-90.23	939300	confirmed	2022-07-08 00:00:00+00:00
899	Guatemala	GT				15.78	-90.23	939300	confirmed	2022-07-09 00:00:00+00:00

900 rows × 10 columns

casos_guat = df_rt_guat.set_index('Date')['Cases']
casos_guat.plot(title='Casos de Covid19 en Guatemala 20/1/2020 hasta 29/6/2022')

<AxesSubplot:title={'center':'Casos de Covid19 en Guatemala 20/1/2020 hasta 29/6/2022'}, xlabel='Date'>

df[df['Country'] == 'El Salvador']

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	Country	Slug	ISO2
139	El Salvador	el-salvador	SV

url_rt_elsalv = 'https://api.covid19api.com/country/el-salvador/status/confirmed/live'
df_rt_elsalv = pd.read_json(url_rt_elsalv)
df_rt_elsalv

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	Country	CountryCode	Province	City	CityCode	Lat	Lon	Cases	Status	Date
0	El Salvador	SV				13.79	-88.9	0	confirmed	2020-01-22 00:00:00+00:00
1	El Salvador	SV				13.79	-88.9	0	confirmed	2020-01-23 00:00:00+00:00
2	El Salvador	SV				13.79	-88.9	0	confirmed	2020-01-24 00:00:00+00:00
3	El Salvador	SV				13.79	-88.9	0	confirmed	2020-01-25 00:00:00+00:00
4	El Salvador	SV				13.79	-88.9	0	confirmed	2020-01-26 00:00:00+00:00
...	...	...	...	...	...	...	...	...	...	...
895	El Salvador	SV				13.79	-88.9	169646	confirmed	2022-07-05 00:00:00+00:00
896	El Salvador	SV				13.79	-88.9	169646	confirmed	2022-07-06 00:00:00+00:00
897	El Salvador	SV				13.79	-88.9	169646	confirmed	2022-07-07 00:00:00+00:00
898	El Salvador	SV				13.79	-88.9	180970	confirmed	2022-07-08 00:00:00+00:00
899	El Salvador	SV				13.79	-88.9	169646	confirmed	2022-07-09 00:00:00+00:00

900 rows × 10 columns

casos_elsalv = df_rt_elsalv.set_index('Date')['Cases']
casos_elsalv.plot(title='Casos de Covid19 en El Salvador 20/1/2020 hasta 29/6/2022')

<AxesSubplot:title={'center':'Casos de Covid19 en El Salvador 20/1/2020 hasta 29/6/2022'}, xlabel='Date'>

df[df['Country'] == 'Nicaragua']

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	Country	Slug	ISO2
36	Nicaragua	nicaragua	NI

url_rt_ni = 'https://api.covid19api.com/country/nicaragua/status/confirmed/live'
df_rt_ni = pd.read_json(url_rt_ni)
df_rt_ni

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	Country	CountryCode	Province	City	CityCode	Lat	Lon	Cases	Status	Date
0	Nicaragua	NI				12.87	-85.21	0	confirmed	2020-01-22 00:00:00+00:00
1	Nicaragua	NI				12.87	-85.21	0	confirmed	2020-01-23 00:00:00+00:00
2	Nicaragua	NI				12.87	-85.21	0	confirmed	2020-01-24 00:00:00+00:00
3	Nicaragua	NI				12.87	-85.21	0	confirmed	2020-01-25 00:00:00+00:00
4	Nicaragua	NI				12.87	-85.21	0	confirmed	2020-01-26 00:00:00+00:00
...	...	...	...	...	...	...	...	...	...	...
895	Nicaragua	NI				12.87	-85.21	14690	confirmed	2022-07-05 00:00:00+00:00
896	Nicaragua	NI				12.87	-85.21	14721	confirmed	2022-07-06 00:00:00+00:00
897	Nicaragua	NI				12.87	-85.21	14721	confirmed	2022-07-07 00:00:00+00:00
898	Nicaragua	NI				12.87	-85.21	14721	confirmed	2022-07-08 00:00:00+00:00
899	Nicaragua	NI				12.87	-85.21	14721	confirmed	2022-07-09 00:00:00+00:00

900 rows × 10 columns

casos_ni = df_rt_ni.set_index('Date')['Cases']
casos_ni.plot(title='Casos de Covid19 en Nicaragua 20/1/2020 hasta 29/6/2022')

<AxesSubplot:title={'center':'Casos de Covid19 en Nicaragua 20/1/2020 hasta 29/6/2022'}, xlabel='Date'>

df[df['Country'] == 'Panama']

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	Country	Slug	ISO2
190	Panama	panama	PA

url_rt_pa = 'https://api.covid19api.com/country/panama/status/confirmed/live'
df_rt_pa = pd.read_json(url_rt_pa)
df_rt_pa

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	Country	CountryCode	Province	City	CityCode	Lat	Lon	Cases	Status	Date
0	Panama	PA				8.54	-80.78	0	confirmed	2020-01-22 00:00:00+00:00
1	Panama	PA				8.54	-80.78	0	confirmed	2020-01-23 00:00:00+00:00
2	Panama	PA				8.54	-80.78	0	confirmed	2020-01-24 00:00:00+00:00
3	Panama	PA				8.54	-80.78	0	confirmed	2020-01-25 00:00:00+00:00
4	Panama	PA				8.54	-80.78	0	confirmed	2020-01-26 00:00:00+00:00
...	...	...	...	...	...	...	...	...	...	...
895	Panama	PA				8.54	-80.78	925254	confirmed	2022-07-05 00:00:00+00:00
896	Panama	PA				8.54	-80.78	925254	confirmed	2022-07-06 00:00:00+00:00
897	Panama	PA				8.54	-80.78	925254	confirmed	2022-07-07 00:00:00+00:00
898	Panama	PA				8.54	-80.78	932710	confirmed	2022-07-08 00:00:00+00:00
899	Panama	PA				8.54	-80.78	925254	confirmed	2022-07-09 00:00:00+00:00

900 rows × 10 columns

casos_pa = df_rt_pa.set_index('Date')['Cases']
casos_pa.plot(title='Casos de Covid19 en Panama 20/1/2020 hasta 29/6/2022')

<AxesSubplot:title={'center':'Casos de Covid19 en Panama 20/1/2020 hasta 29/6/2022'}, xlabel='Date'>

Exporto tabla por cada país centroamericano en CSV para la web

df_ca = pd.concat([casos_cr,casos_hn,casos_guat,casos_elsalv,casos_ni,casos_pa],axis=1)
df_ca

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	Cases	Cases	Cases	Cases	Cases	Cases
Date
2020-01-22 00:00:00+00:00	0	0	0	0	0	0
2020-01-23 00:00:00+00:00	0	0	0	0	0	0
2020-01-24 00:00:00+00:00	0	0	0	0	0	0
2020-01-25 00:00:00+00:00	0	0	0	0	0	0
2020-01-26 00:00:00+00:00	0	0	0	0	0	0
...	...	...	...	...	...	...
2022-07-05 00:00:00+00:00	904934	427718	922340	169646	14690	925254
2022-07-06 00:00:00+00:00	904934	427718	927473	169646	14721	925254
2022-07-07 00:00:00+00:00	904934	427718	933259	169646	14721	925254
2022-07-08 00:00:00+00:00	904934	429408	939300	180970	14721	932710
2022-07-09 00:00:00+00:00	904934	429408	939300	169646	14721	925254

900 rows × 6 columns

df_ca.columns = ['Panama','Costa Rica','Honduras','Nicaragua','El Salvador','Guatemala']
df_ca

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	Panama	Costa Rica	Honduras	Nicaragua	El Salvador	Guatemala
Date
2020-01-22 00:00:00+00:00	0	0	0	0	0	0
2020-01-23 00:00:00+00:00	0	0	0	0	0	0
2020-01-24 00:00:00+00:00	0	0	0	0	0	0
2020-01-25 00:00:00+00:00	0	0	0	0	0	0
2020-01-26 00:00:00+00:00	0	0	0	0	0	0
...	...	...	...	...	...	...
2022-07-05 00:00:00+00:00	904934	427718	922340	169646	14690	925254
2022-07-06 00:00:00+00:00	904934	427718	927473	169646	14721	925254
2022-07-07 00:00:00+00:00	904934	427718	933259	169646	14721	925254
2022-07-08 00:00:00+00:00	904934	429408	939300	180970	14721	932710
2022-07-09 00:00:00+00:00	904934	429408	939300	169646	14721	925254

900 rows × 6 columns

Extraigo la tabla comparativa por países de los que seleccioné, Panamá, Honduras, Costa Rica, Nicaragua, El Salvador,Guatemala

df_ca.plot(title='Comparativa Covid-19 de países Centreoamericanos', logy=True)

<AxesSubplot:title={'center':'Comparativa Covid-19 de países Centreoamericanos'}, xlabel='Date'>

Descripción: hice una comparación de casos de Covid-19 en Centroamérica

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ad4.md

ad4.md

AD4: conexión API del COVID19 y análisis con Pandas

Descripción: hice una comparación de casos de Covid-19 en Centroamérica

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AD4: conexión API del COVID19 y análisis con Pandas

Descripción: hice una comparación de casos de Covid-19 en Centroamérica