En esta actividad dirigida 4 nos conectaremos con el API del COVID19 y analizaremos con Panda
Nos conectaremos al API: https://api.covid19api.com/
Lo siguiente sería instalar la librería de Panda
A continuación los códigos para la instalación e importación de la librería Pandas
pip install pandas
Defaulting to user installation because normal site-packages is not writeableNote: you may need to restart the kernel to use updated packages.
Requirement already satisfied: pandas in d:\anaconda\lib\site-packages (1.4.2)
Requirement already satisfied: python-dateutil>=2.8.1 in d:\anaconda\lib\site-packages (from pandas) (2.8.2)
Requirement already satisfied: numpy>=1.18.5 in d:\anaconda\lib\site-packages (from pandas) (1.21.5)
Requirement already satisfied: pytz>=2020.1 in d:\anaconda\lib\site-packages (from pandas) (2021.3)
Requirement already satisfied: six>=1.5 in d:\anaconda\lib\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)
import pandas as pd
Creamos una variable llamada url
y la definimos como la dirección del API que tiene la información del COVID19 en los distintos países.
url = 'https://api.covid19api.com/countries'
Comprobamos la variable invocando url
url
'https://api.covid19api.com/countries'
Como el API tiene la información en un lista de datos JSON, creamos un Data Frame df
(estructuras de lista de datos de Python) compuesto por la función de Pandas que permite leer el formato JSON.
df = pd.read_json(url)
Comprobamos el df
invocándolo y nos aparece el JSON en formato de tabla.
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
Para obtener los datos específicamente de España, utilizamos el siguiente código:
df[df['Country'] == 'Spain']
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Country | Slug | ISO2 | |
---|---|---|---|
141 | Spain | spain | ES |
A continuación creamos una nueva variable llamada url_rt_es
y la definimos con la sección del API que tiene los datos en tiempo real de los casos en España.
También hacemos un data frame que ayudado por la función de Pandas, lea el JSON de estos datos específicos y terminamos invocándo este mismo data frame para obtener la tabla.
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
Para obtener la cabecera de esta tabla utilizamos el siguiente código:
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 |
Para obtener la parte final de esta tabla utilizamos el siguiente código:
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 |
A continuación definimos los datos que queremos representar en la visualización gráfica. En esta ocasión escogimos la fecha y la cantidad de casos. Además, le pedimos que plotée, le añada el título y sumamos el parametro kind "area" para determinar este tipo de gráfica.
casos_es = df_rt_es.set_index('Date')['Cases']
casos_es.plot(title="Casos de Covid-19 en España desde 20/01/2020 hasta 9/07/2022", kind = "area")
<AxesSubplot:title={'center':'Casos de Covid-19 en España desde 20/01/2020 hasta 9/07/2022'}, xlabel='Date'>
Seguiremos el mismo procedimiento que hemos hecho con España para obtener y visualizar los datos de COVID19 en Panamá
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 | 932710 | confirmed | 2022-07-10 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 Panamá desde 20/01/2020 hasta 9/07/2022", kind = "area")
<AxesSubplot:title={'center':'Casos de Covid-19 en Panamá desde 20/01/2020 hasta 9/07/2022'}, xlabel='Date'>
Después de obtener los datos de ambos países, ahora contrastaremos el comportamiento del virus en España y Panamá a través de una gráfica comparativa.
Para lograr esto primero tendremos que concatenar los resultados basándonos en el eje de las filas (axis=1) creando la variable pa_vs_es
pa_vs_es = pd.concat([casos_es,casos_pa],axis=1)
pa_vs_es
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Cases | Cases | |
---|---|---|
Date | ||
2020-01-22 00:00:00+00:00 | 0.0 | 0 |
2020-01-23 00:00:00+00:00 | 0.0 | 0 |
2020-01-24 00:00:00+00:00 | 0.0 | 0 |
2020-01-25 00:00:00+00:00 | 0.0 | 0 |
2020-01-26 00:00:00+00:00 | 0.0 | 0 |
... | ... | ... |
2022-07-05 00:00:00+00:00 | 12890002.0 | 925254 |
2022-07-06 00:00:00+00:00 | 12890002.0 | 925254 |
2022-07-07 00:00:00+00:00 | 12890002.0 | 925254 |
2022-07-08 00:00:00+00:00 | 12973615.0 | 932710 |
2022-07-10 00:00:00+00:00 | NaN | 932710 |
900 rows × 2 columns
Luego tendremos que titular las columnas con el nombre del país al que pertenecen los datos (porque solo aparece la variable cases en ambas columnas.
Para eso creamos un nuevo objeto llamado pa_vs_es.columns
pa_vs_es.columns = ['España', 'Panamá']
pa_vs_es
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España | Panamá | |
---|---|---|
Date | ||
2020-01-22 00:00:00+00:00 | 0.0 | 0 |
2020-01-23 00:00:00+00:00 | 0.0 | 0 |
2020-01-24 00:00:00+00:00 | 0.0 | 0 |
2020-01-25 00:00:00+00:00 | 0.0 | 0 |
2020-01-26 00:00:00+00:00 | 0.0 | 0 |
... | ... | ... |
2022-07-05 00:00:00+00:00 | 12890002.0 | 925254 |
2022-07-06 00:00:00+00:00 | 12890002.0 | 925254 |
2022-07-07 00:00:00+00:00 | 12890002.0 | 925254 |
2022-07-08 00:00:00+00:00 | 12973615.0 | 932710 |
2022-07-10 00:00:00+00:00 | NaN | 932710 |
900 rows × 2 columns
Ahora plotearemos la variable pa_vs_es
colocándole un título acorde.
pa_vs_es.plot(title="Comparativa Covid19 España-Panamá")
<AxesSubplot:title={'center':'Comparativa Covid19 España-Panamá'}, xlabel='Date'>
A continuación contrastaremos los datos de Panamá con el resto de países centroamericanos (Costa Rica, Honduras, El Salvador, Guatemala y Nicaragua).
Para conseguir esto, aplicaremos el procedimiento antes descrito para cada país.
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 | 2022-07-10 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 9/07/2022")
<AxesSubplot:title={'center':'Casos de Covid-19 en Costa Rica desde 20/01/2020 hasta 9/07/2022'}, xlabel='Date'>
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 | 2022-07-10 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 9/07/2022")
<AxesSubplot:title={'center':'Casos de Covid-19 en Honduras desde 20/01/2020 hasta 9/07/2022'}, 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 | 180970 | confirmed | 2022-07-10 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 desde 20/01/2020 hasta 9/07/2022")
<AxesSubplot:title={'center':'Casos de Covid19 en El Salvador desde 20/01/2020 hasta 9/07/2022'}, 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 | 944993 | confirmed | 2022-07-10 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 desde 20/01/2020 hasta 9/07/2022")
<AxesSubplot:title={'center':'Casos de Covid19 en Guatemala desde 20/01/2020 hasta 9/07/2022'}, 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 | 2022-07-10 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 Nicaraguas desde 20/01/2020 hasta 9/07/2022")
<AxesSubplot:title={'center':'Casos de Covid19 en Nicaraguas desde 20/01/2020 hasta 9/07/2022'}, xlabel='Date'>
Aquí aplicaremos una concatenación de los datos de todos los países antes presentados. Crearemos una variable llamada df_centroamerica
para agrupar todos los datos de los países centroamericanos.
df_centroamerica = pd.concat([casos_pa,casos_cr,casos_hnd,casos_elsalv,casos_guat,casos_ni],axis=1)
df_centroamerica
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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 | 427718 | 169646 | 922340 | 14690 |
2022-07-06 00:00:00+00:00 | 925254 | 904934 | 427718 | 169646 | 927473 | 14721 |
2022-07-07 00:00:00+00:00 | 925254 | 904934 | 427718 | 169646 | 933259 | 14721 |
2022-07-08 00:00:00+00:00 | 932710 | 904934 | 429408 | 180970 | 939300 | 14721 |
2022-07-10 00:00:00+00:00 | 932710 | 904934 | 429408 | 180970 | 944993 | 14721 |
900 rows × 6 columns
Colocamos el nombre del país a cada columna.
df_centroamerica.columns = ['Panamá','Costa Rica','Honduras','El Salvador','Guatemala','Nicaragua']
df_centroamerica
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Panamá | Costa Rica | Honduras | El Salvador | Guatemala | 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 | 427718 | 169646 | 922340 | 14690 |
2022-07-06 00:00:00+00:00 | 925254 | 904934 | 427718 | 169646 | 927473 | 14721 |
2022-07-07 00:00:00+00:00 | 925254 | 904934 | 427718 | 169646 | 933259 | 14721 |
2022-07-08 00:00:00+00:00 | 932710 | 904934 | 429408 | 180970 | 939300 | 14721 |
2022-07-10 00:00:00+00:00 | 932710 | 904934 | 429408 | 180970 | 944993 | 14721 |
900 rows × 6 columns
Finalmente podremos plotear una gráfica comparativa con los datos de todos los países centroamericanos.
df_centroamerica.plot(title="Comparativa de los casos de Covid19 en países Centroamericanos desde 20/01/2020 hasta 9/07/2022")
<AxesSubplot:title={'center':'Comparativa de los casos de Covid19 en países Centroamericanos desde 20/01/2020 hasta 9/07/2022'}, xlabel='Date'>