Me conecto a la API https://covid19api.com/
!pip install pandas
Requirement already satisfied: pandas in c:\users\hp\anaconda3\lib\site-packages (1.4.2)
Requirement already satisfied: numpy>=1.18.5 in c:\users\hp\anaconda3\lib\site-packages (from pandas) (1.21.5)
Requirement already satisfied: pytz>=2020.1 in c:\users\hp\anaconda3\lib\site-packages (from pandas) (2021.3)
Requirement already satisfied: python-dateutil>=2.8.1 in c:\users\hp\anaconda3\lib\site-packages (from pandas) (2.8.2)
Requirement already satisfied: six>=1.5 in c:\users\hp\anaconda3\lib\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)
import pandas as pd
Se tiene que instalar e importar panda
url = 'https://api.covid19api.com/countries'
Se coloca la url para llamar a la lista de países
url
'https://api.covid19api.com/countries'
df = pd.read_json(url)
Añado una variable para la función read_json
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
El código permite extraer la lista parte de las filas
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
Aplicando la url y la funciòn read_json llamo a una lista de casos de España
df_rt_es.head()
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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 |
df_rt_es.tail()
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Country | CountryCode | Province | City | CityCode | 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 |
df.describe()
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Country | Slug | ISO2 | |
---|---|---|---|
count | 248 | 248 | 248 |
unique | 248 | 248 | 248 |
top | Gibraltar | gibraltar | GI |
freq | 1 | 1 | 1 |
plot_rt_es = df_rt_es.set_index('Date')['Cases'].plot(title="Casos de Covid-19 en España desde 20/01/2020 hasta 29/06/2022")
df[df['Country'] == 'Pan']
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Country | Slug | ISO2 |
---|
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 Covid-19 en Panama desde 20/01/2020 hasta 29/06/2022")
<AxesSubplot:title={'center':'Casos de Covid-19 en Panama desde 20/01/2020 hasta 29/06/2022'}, xlabel='Date'>
Repito el mismo proceso que he hecho con Panamá, con Costa Rica, Honduras, Nicaragua, Guatemala y El Salvador
df[df['Country'] == 'cr']
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Country | Slug | ISO2 |
---|
url_casos_cr = 'https://api.covid19api.com/country/cr/status/confirmed/live'
df_rt_cr = pd.read_json(url_casos_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
casos_cr = df_rt_cr.set_index('Date')['Cases']
casos_cr.plot(title="Casos de Covid-19 en Costa Rica desde 20/01/2020 hasta 29/06/2022")
<AxesSubplot:title={'center':'Casos de Covid-19 en Costa Rica desde 20/01/2020 hasta 29/06/2022'}, xlabel='Date'>
url_casos_hnd = 'https://api.covid19api.com/country/hnd/status/confirmed/live'
df_rt_hnd = pd.read_json(url_casos_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_hnd = df_rt_hnd.set_index('Date')['Cases']
casos_hnd.plot(title="Casos de Covid-19 en Honduras desde 20/01/2020 hasta 29/06/2022")
<AxesSubplot:title={'center':'Casos de Covid-19 en Honduras desde 20/01/2020 hasta 29/06/2022'}, xlabel='Date'>
url_casos_guat = 'https://api.covid19api.com/country/guatemala/status/confirmed/live'
df_rt_guat = pd.read_json(url_casos_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 Covid-19 en Guatemala desde 20/01/2020 hasta 29/06/2022")
<AxesSubplot:title={'center':'Casos de Covid-19 en Guatemala desde 20/01/2020 hasta 29/06/2022'}, xlabel='Date'>
url_casos_elsalv = 'https://api.covid19api.com/country/el-salvador/status/confirmed/live'
df_rt_elsalv = pd.read_json(url_casos_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 Covid-19 en el-salavdor desde 20/01/2020 hasta 29/06/2022")
<AxesSubplot:title={'center':'Casos de Covid-19 en el-salavdor desde 20/01/2020 hasta 29/06/2022'}, xlabel='Date'>
url_casos_ni = 'https://api.Covid19api.com/country/nicaragua/status/confirmed/live'
df_rt_ni = pd.read_json(url_casos_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 Covid-19 en Nicaragua desde 20/01/2020 hasta 29/06/2022")
<AxesSubplot:title={'center':'Casos de Covid-19 en Nicaragua desde 20/01/2020 hasta 29/06/2022'}, xlabel='Date'>
df_ca = pd.concat([casos_pa,casos_cr,casos_hnd,casos_elsalv,casos_guat,casos_ni],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 | 925254 | 904934 | 904934 | 169646 | 922340 | 922340 |
2022-07-06 00:00:00+00:00 | 925254 | 904934 | 904934 | 169646 | 927473 | 927473 |
2022-07-07 00:00:00+00:00 | 925254 | 904934 | 904934 | 169646 | 933259 | 933259 |
2022-07-08 00:00:00+00:00 | 932710 | 904934 | 904934 | 180970 | 939300 | 939300 |
2022-07-09 00:00:00+00:00 | 925254 | 904934 | 904934 | 169646 | 939300 | 939300 |
900 rows × 6 columns
df_ca.columns = ['Panamá','Costa Rica','Honduras','Guatemala','El Salvador','Nicaragua']
df_ca
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Panamá | Costa Rica | Honduras | Guatemala | El Salvador | Nicaragua | |
---|---|---|---|---|---|---|
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 | 925254 | 904934 | 904934 | 169646 | 922340 | 922340 |
2022-07-06 00:00:00+00:00 | 925254 | 904934 | 904934 | 169646 | 927473 | 927473 |
2022-07-07 00:00:00+00:00 | 925254 | 904934 | 904934 | 169646 | 933259 | 933259 |
2022-07-08 00:00:00+00:00 | 932710 | 904934 | 904934 | 180970 | 939300 | 939300 |
2022-07-09 00:00:00+00:00 | 925254 | 904934 | 904934 | 169646 | 939300 | 939300 |
900 rows × 6 columns
df_ca.plot(title="Comparativa Covid-19 de países Centroamericanos", logy=True)
<AxesSubplot:title={'center':'Comparativa Covid19 de países Centroamericanos'}, xlabel='Date'>
- Para lograrlo, lo primero que hice fue extraer los casos por paìs y posteriormente realicé la comparación de todas las regiones.