en esta practica vamos a utilizar "Pandas", la librería de Phyton, para trabjar sobre los datos del Covid y analizarlos.
La URL: https://api.covid19api.com/countries
Para la instalación de las librerías usaremos la función !pip. La excavación se debe a que estamos ejecutando bash.
!pip install pandasRequirement already satisfied: pandas in c:\users\gabri\anaconda3\lib\site-packages (1.4.4)
Requirement already satisfied: pytz>=2020.1 in c:\users\gabri\anaconda3\lib\site-packages (from pandas) (2022.1)
Requirement already satisfied: python-dateutil>=2.8.1 in c:\users\gabri\anaconda3\lib\site-packages (from pandas) (2.8.2)
Requirement already satisfied: numpy>=1.18.5 in c:\users\gabri\anaconda3\lib\site-packages (from pandas) (1.21.5)
Requirement already satisfied: six>=1.5 in c:\users\gabri\anaconda3\lib\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)
Para Importar utilizaremos pd de pandas
import pandas as pdlas variables se asignan con el simbolo = y escribimos los links en comillas al ser una cadena de caracteres.
miurl = "https://api.covid19api.com/countries"Para comprobar si esta bien hecho solo se tiene que escribir miurl y ver que el resultado sea igual al link.
miurl'https://api.covid19api.com/countries'
Al poner miurl dentro del parentecis de la funcion type podemos ver que es un cadena de caracteres.
type(miurl)str
La abreviatura de dataframe es df. Con función read_json() que lee el formato json. dentro del parentecer ponemos lo que queremos leer. Ejemplo, un url.
df = pd.read_json(url)Para visualizar los datos llamamos el objeto y panda identifica una de las entradas del dataframe.
para visualizar los datos llamos al objeto. Observamos una tabla de Panda que identifica las entradas del dataframe.
df
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| Country | Slug | ISO2 | |
|---|---|---|---|
| 0 | Angola | angola | AO |
| 1 | Georgia | georgia | GE |
| 2 | Ireland | ireland | IE |
| 3 | Slovenia | slovenia | SI |
| 4 | French Guiana | french-guiana | GF |
| ... | ... | ... | ... |
| 243 | Sri Lanka | sri-lanka | LK |
| 244 | Canada | canada | CA |
| 245 | Kuwait | kuwait | KW |
| 246 | Libya | libya | LY |
| 247 | Seychelles | seychelles | SC |
248 rows × 3 columns
Para ver las primeras entradas de la tabla utilizaremos la siguiente función: 6 para ver las seis primeras.
df.head(6)
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| Country | Slug | ISO2 | |
|---|---|---|---|
| 0 | Fiji | fiji | FJ |
| 1 | Hong Kong, SAR China | hong-kong-sar-china | HK |
| 2 | Palestinian Territory | palestine | PS |
| 3 | Sierra Leone | sierra-leone | SL |
| 4 | Turkey | turkey | TR |
| 5 | Uzbekistan | uzbekistan | UZ |
Con df.tail() Vemos las ultimas
df.tail(6)
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| Country | Slug | ISO2 | |
|---|---|---|---|
| 242 | Republic of Kosovo | kosovo | XK |
| 243 | Zambia | zambia | ZM |
| 244 | Argentina | argentina | AR |
| 245 | Burundi | burundi | BI |
| 246 | Monaco | monaco | MC |
| 247 | Seychelles | seychelles | SC |
Para ver las informaciones sobre las variables que contiene el df usamos la siguiente función:
df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 248 entries, 0 to 247
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Country 248 non-null object
1 Slug 248 non-null object
2 ISO2 248 non-null object
dtypes: object(3)
memory usage: 5.9+ KB
Para visualizar una sola variable
df['Country']0 Fiji
1 Hong Kong, SAR China
2 Palestinian Territory
3 Sierra Leone
4 Turkey
...
243 Zambia
244 Argentina
245 Burundi
246 Monaco
247 Seychelles
Name: Country, Length: 248, dtype: object
Para ver un valor concreto de una de las varibles:
df['Country'][66]'British Indian Ocean Territory'
df['ISO2'].head()0 FJ
1 HK
2 PS
3 SL
4 TR
Name: ISO2, dtype: object
La URL que utilizamos ahora es la siguiente: https://api.covid19api.com/country/colombia/status/confirmed/live
Guardamos los datos, pero ahora añadiendo co (abreviatura de colombia) para identificar y solo trabajar con este país df_co.
url_co = 'https://api.covid19api.com/country/colombia/status/confirmed/live'
df_co = pd.read_json(url_co)
df_co
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| Country | CountryCode | Province | City | CityCode | Lat | Lon | Cases | Status | Date | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-22 00:00:00+00:00 | |||
| 1 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-23 00:00:00+00:00 | |||
| 2 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-24 00:00:00+00:00 | |||
| 3 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-25 00:00:00+00:00 | |||
| 4 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-26 00:00:00+00:00 | |||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1037 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-24 00:00:00+00:00 | |||
| 1038 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-25 00:00:00+00:00 | |||
| 1039 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-26 00:00:00+00:00 | |||
| 1040 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-27 00:00:00+00:00 | |||
| 1041 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-28 00:00:00+00:00 |
1042 rows × 10 columns
Por columnas
df_co.columnsIndex(['Country', 'CountryCode', 'Province', 'City', 'CityCode', 'Lat', 'Lon',
'Cases', 'Status', 'Date'],
dtype='object')
Cabecera
df_co.head(10)
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| Country | CountryCode | Province | City | CityCode | Lat | Lon | Cases | Status | Date | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-22 00:00:00+00:00 | |||
| 1 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-23 00:00:00+00:00 | |||
| 2 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-24 00:00:00+00:00 | |||
| 3 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-25 00:00:00+00:00 | |||
| 4 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-26 00:00:00+00:00 | |||
| 5 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-27 00:00:00+00:00 | |||
| 6 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-28 00:00:00+00:00 | |||
| 7 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-29 00:00:00+00:00 | |||
| 8 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-30 00:00:00+00:00 | |||
| 9 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-31 00:00:00+00:00 |
df_co.tail(10)
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| Country | CountryCode | Province | City | CityCode | Lat | Lon | Cases | Status | Date | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1032 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-19 00:00:00+00:00 | |||
| 1033 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-20 00:00:00+00:00 | |||
| 1034 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-21 00:00:00+00:00 | |||
| 1035 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-22 00:00:00+00:00 | |||
| 1036 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-23 00:00:00+00:00 | |||
| 1037 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-24 00:00:00+00:00 | |||
| 1038 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-25 00:00:00+00:00 | |||
| 1039 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-26 00:00:00+00:00 | |||
| 1040 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-27 00:00:00+00:00 | |||
| 1041 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-28 00:00:00+00:00 |
df_co.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1042 entries, 0 to 1041
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Country 1042 non-null object
1 CountryCode 1042 non-null object
2 Province 1042 non-null object
3 City 1042 non-null object
4 CityCode 1042 non-null object
5 Lat 1042 non-null float64
6 Lon 1042 non-null float64
7 Cases 1042 non-null int64
8 Status 1042 non-null object
9 Date 1042 non-null datetime64[ns, UTC]
dtypes: datetime64[ns, UTC](1), float64(2), int64(1), object(6)
memory usage: 81.5+ KB
Para Obtener una descripción estadística de las variables del df (numero total, media, moda, desviación, mínimo, máximo y cuartiles):
df_co.describe()
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| Lat | Lon | Cases | |
|---|---|---|---|
| count | 1.042000e+03 | 1.042000e+03 | 1.042000e+03 |
| mean | 4.570000e+00 | -7.430000e+01 | 3.430246e+06 |
| std | 2.043791e-14 | 1.464421e-12 | 2.436522e+06 |
| min | 4.570000e+00 | -7.430000e+01 | 0.000000e+00 |
| 25% | 4.570000e+00 | -7.430000e+01 | 8.882092e+05 |
| 50% | 4.570000e+00 | -7.430000e+01 | 4.109543e+06 |
| 75% | 4.570000e+00 | -7.430000e+01 | 6.076698e+06 |
| max | 4.570000e+00 | -7.430000e+01 | 6.312657e+06 |
Para la elaboración del mismo en el eje X (fechas) y Y (Casos).
Vamos a establcer la fecha como índice.
df_co.set_index('Date')
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| Country | CountryCode | Province | City | CityCode | Lat | Lon | Cases | Status | |
|---|---|---|---|---|---|---|---|---|---|
| Date | |||||||||
| 2020-01-22 00:00:00+00:00 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | |||
| 2020-01-23 00:00:00+00:00 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | |||
| 2020-01-24 00:00:00+00:00 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | |||
| 2020-01-25 00:00:00+00:00 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | |||
| 2020-01-26 00:00:00+00:00 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | |||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2022-11-24 00:00:00+00:00 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | |||
| 2022-11-25 00:00:00+00:00 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | |||
| 2022-11-26 00:00:00+00:00 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | |||
| 2022-11-27 00:00:00+00:00 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | |||
| 2022-11-28 00:00:00+00:00 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed |
1042 rows × 9 columns
df_co
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| Country | CountryCode | Province | City | CityCode | Lat | Lon | Cases | Status | Date | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-22 00:00:00+00:00 | |||
| 1 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-23 00:00:00+00:00 | |||
| 2 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-24 00:00:00+00:00 | |||
| 3 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-25 00:00:00+00:00 | |||
| 4 | Colombia | CO | 4.57 | -74.3 | 0 | confirmed | 2020-01-26 00:00:00+00:00 | |||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1037 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-24 00:00:00+00:00 | |||
| 1038 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-25 00:00:00+00:00 | |||
| 1039 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-26 00:00:00+00:00 | |||
| 1040 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-27 00:00:00+00:00 | |||
| 1041 | Colombia | CO | 4.57 | -74.3 | 6312657 | confirmed | 2022-11-28 00:00:00+00:00 |
1042 rows × 10 columns
Como la tabla debe reflejar tambien los casos, creamos una vista para ver datos por fechas.
df_co.set_index('Date')['Cases']Date
2020-01-22 00:00:00+00:00 0
2020-01-23 00:00:00+00:00 0
2020-01-24 00:00:00+00:00 0
2020-01-25 00:00:00+00:00 0
2020-01-26 00:00:00+00:00 0
...
2022-11-24 00:00:00+00:00 6312657
2022-11-25 00:00:00+00:00 6312657
2022-11-26 00:00:00+00:00 6312657
2022-11-27 00:00:00+00:00 6312657
2022-11-28 00:00:00+00:00 6312657
Name: Cases, Length: 1042, dtype: int64
Sobre esta vista creamos el gráfico con la funcion plot.
df_co.set_index('Date')['Cases'].plot()Matplotlib is building the font cache; this may take a moment.
<AxesSubplot:xlabel='Date'>
Para nombrar el gráfico usamos el atributo title:
df_co.set_index('Date')['Cases'].plot(title= "Casos de Covid19 en Colombia")<AxesSubplot:title={'center':'Casos de Covid19 en Colombia'}, xlabel='Date'>
Repetimos con los datos de España, Republica Dominicana y Ecuador
La URL que utilizamos ahora es la siguiente: https://api.covid19api.com/country/spain/status/confirmed/live
Guardamos los datos, pero ahora añadiendo es (abreviatura de España) para identificar y solo trabajar con este país df_es.
url_es = 'https://api.covid19api.com/country/spain/status/confirmed/live'
df_es = pd.read_json(url_es)
df_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 | |||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1037 | Spain | ES | 40.46 | -3.75 | 13573721 | confirmed | 2022-11-24 00:00:00+00:00 | |||
| 1038 | Spain | ES | 40.46 | -3.75 | 13595504 | confirmed | 2022-11-25 00:00:00+00:00 | |||
| 1039 | Spain | ES | 40.46 | -3.75 | 13595504 | confirmed | 2022-11-26 00:00:00+00:00 | |||
| 1040 | Spain | ES | 40.46 | -3.75 | 13595504 | confirmed | 2022-11-27 00:00:00+00:00 | |||
| 1041 | Spain | ES | 40.46 | -3.75 | 13595504 | confirmed | 2022-11-28 00:00:00+00:00 |
1042 rows × 10 columns
df_es.set_index('Date')['Cases'].plot(title= "Casos de Covid19 en España")<AxesSubplot:title={'center':'Casos de Covid19 en España'}, xlabel='Date'>
La URL que utilizamos ahora es la siguiente: https://api.covid19api.com/country/Dominican%20Republic/status/confirmed/live
Guardamos los datos, pero ahora añadiendo do (abreviatura de Dominican Republic) para identificar y solo trabajar con este país df_do.
url_do = 'https://api.covid19api.com/country/Dominican%20Republic/status/confirmed/live'
df_do = pd.read_json(url_do)
df_do
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| Country | CountryCode | Province | City | CityCode | Lat | Lon | Cases | Status | Date | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Dominican Republic | DO | 18.74 | -70.16 | 0 | confirmed | 2020-01-22 00:00:00+00:00 | |||
| 1 | Dominican Republic | DO | 18.74 | -70.16 | 0 | confirmed | 2020-01-23 00:00:00+00:00 | |||
| 2 | Dominican Republic | DO | 18.74 | -70.16 | 0 | confirmed | 2020-01-24 00:00:00+00:00 | |||
| 3 | Dominican Republic | DO | 18.74 | -70.16 | 0 | confirmed | 2020-01-25 00:00:00+00:00 | |||
| 4 | Dominican Republic | DO | 18.74 | -70.16 | 0 | confirmed | 2020-01-26 00:00:00+00:00 | |||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1037 | Dominican Republic | DO | 18.74 | -70.16 | 648456 | confirmed | 2022-11-24 00:00:00+00:00 | |||
| 1038 | Dominican Republic | DO | 18.74 | -70.16 | 649150 | confirmed | 2022-11-25 00:00:00+00:00 | |||
| 1039 | Dominican Republic | DO | 18.74 | -70.16 | 649150 | confirmed | 2022-11-26 00:00:00+00:00 | |||
| 1040 | Dominican Republic | DO | 18.74 | -70.16 | 649834 | confirmed | 2022-11-27 00:00:00+00:00 | |||
| 1041 | Dominican Republic | DO | 18.74 | -70.16 | 649834 | confirmed | 2022-11-28 00:00:00+00:00 |
1042 rows × 10 columns
df_do.set_index('Date')['Cases'].plot(title= "Casos de Covid19 en República Dominicana")<AxesSubplot:title={'center':'Casos de Covid19 en República Dominicana'}, xlabel='Date'>
La URL que utilizamos ahora es la siguiente: https://api.covid19api.com/country/ecuador/status/confirmed/live
Guardamos los datos, pero ahora añadiendo ec (abreviatura de Ecuador) para identificar y solo trabajar con este país df_ec.
url_ec = 'https://api.covid19api.com/country/ecuador/status/confirmed/live'
df_ec = pd.read_json(url_ec)
df_ec
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| Country | CountryCode | Province | City | CityCode | Lat | Lon | Cases | Status | Date | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ecuador | EC | -1.83 | -78.18 | 0 | confirmed | 2020-01-22 00:00:00+00:00 | |||
| 1 | Ecuador | EC | -1.83 | -78.18 | 0 | confirmed | 2020-01-23 00:00:00+00:00 | |||
| 2 | Ecuador | EC | -1.83 | -78.18 | 0 | confirmed | 2020-01-24 00:00:00+00:00 | |||
| 3 | Ecuador | EC | -1.83 | -78.18 | 0 | confirmed | 2020-01-25 00:00:00+00:00 | |||
| 4 | Ecuador | EC | -1.83 | -78.18 | 0 | confirmed | 2020-01-26 00:00:00+00:00 | |||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1037 | Ecuador | EC | -1.83 | -78.18 | 1009958 | confirmed | 2022-11-24 00:00:00+00:00 | |||
| 1038 | Ecuador | EC | -1.83 | -78.18 | 1009958 | confirmed | 2022-11-25 00:00:00+00:00 | |||
| 1039 | Ecuador | EC | -1.83 | -78.18 | 1009958 | confirmed | 2022-11-26 00:00:00+00:00 | |||
| 1040 | Ecuador | EC | -1.83 | -78.18 | 1009958 | confirmed | 2022-11-27 00:00:00+00:00 | |||
| 1041 | Ecuador | EC | -1.83 | -78.18 | 1011132 | confirmed | 2022-11-28 00:00:00+00:00 |
1042 rows × 10 columns
df_ec.set_index('Date')['Cases'].plot(title= "Casos de Covid19 en Ecuador")<AxesSubplot:title={'center':'Casos de Covid19 en Ecuador'}, xlabel='Date'>
