imagina-examen

Hola

hola hola hola

La url que usaremos es: https://api.covid19api.com/countries

Primero vamos a importar las librerías.

!pip install pandas

Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.3.1)
Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.21.1)
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.1)
Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2020.4)
Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7.3->pandas) (1.14.0)

Perdón, lo de antes era instalar las librerías. Ahora sí, vamos a importarlas.

import pd as pandas

---------------------------------------------------------------------------

ModuleNotFoundError                       Traceback (most recent call last)

<ipython-input-4-d001e6b1a3c9> in <module>
----> 1 import pd as pandas


ModuleNotFoundError: No module named 'pd'

import pandas as pd

Ahora definimos la url que vamos a usar como variable.

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

Comprobamos que se ha fijado...

url

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

Bien. Ahora ya podemos crear el dataframe.

En este caso, a diferencia del de las farolas, se trata de un archivo json.

df = pd.read_json(url)

df

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	Country	Slug	ISO2
0	Malaysia	malaysia	MY
1	Maldives	maldives	MV
2	Mozambique	mozambique	MZ
3	Turks and Caicos Islands	turks-and-caicos-islands	TC
4	Viet Nam	vietnam	VN
...	...	...	...
243	Oman	oman	OM
244	Zimbabwe	zimbabwe	ZW
245	Gibraltar	gibraltar	GI
246	Germany	germany	DE
247	Marshall Islands	marshall-islands	MH

248 rows × 3 columns

Juguemos un poco con la tabla acotándola. ¿Qué columnas tiene?

df.columns

Index(['Country', 'Slug', 'ISO2'], dtype='object')

df.head()

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	Country	Slug	ISO2
0	Malaysia	malaysia	MY
1	Maldives	maldives	MV
2	Mozambique	mozambique	MZ
3	Turks and Caicos Islands	turks-and-caicos-islands	TC
4	Viet Nam	vietnam	VN

df.tail()

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	Country	Slug	ISO2
243	Oman	oman	OM
244	Zimbabwe	zimbabwe	ZW
245	Gibraltar	gibraltar	GI
246	Germany	germany	DE
247	Marshall Islands	marshall-islands	MH

df.info

<bound method DataFrame.info of                       Country                      Slug ISO2
0                    Malaysia                  malaysia   MY
1                    Maldives                  maldives   MV
2                  Mozambique                mozambique   MZ
3    Turks and Caicos Islands  turks-and-caicos-islands   TC
4                    Viet Nam                   vietnam   VN
..                        ...                       ...  ...
243                      Oman                      oman   OM
244                  Zimbabwe                  zimbabwe   ZW
245                 Gibraltar                 gibraltar   GI
246                   Germany                   germany   DE
247          Marshall Islands          marshall-islands   MH

[248 rows x 3 columns]>

Vamos a centrarnos en uno de los países, españa.

url_es = 'https://api.covid19api.com/country/spain/status/confirmed/live'

url_es

'https://api.covid19api.com/country/spain/status/confirmed/live'

df = pd.read_json(url_es)

df = pd.read_json(url)

df

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	Country	Slug	ISO2
0	Malaysia	malaysia	MY
1	Maldives	maldives	MV
2	Mozambique	mozambique	MZ
3	Turks and Caicos Islands	turks-and-caicos-islands	TC
4	Viet Nam	vietnam	VN
...	...	...	...
243	Oman	oman	OM
244	Zimbabwe	zimbabwe	ZW
245	Gibraltar	gibraltar	GI
246	Germany	germany	DE
247	Marshall Islands	marshall-islands	MH

248 rows × 3 columns

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
...	...	...	...	...	...	...	...	...	...	...
882	Spain	ES				40.46	-3.75	12613634	confirmed	2022-06-22 00:00:00+00:00
883	Spain	ES				40.46	-3.75	12613634	confirmed	2022-06-23 00:00:00+00:00
884	Spain	ES				40.46	-3.75	12681820	confirmed	2022-06-24 00:00:00+00:00
885	Spain	ES				40.46	-3.75	12681820	confirmed	2022-06-25 00:00:00+00:00
886	Spain	ES				40.46	-3.75	12681820	confirmed	2022-06-26 00:00:00+00:00

887 rows × 10 columns

url_it = 'https://api.covid19api.com/country/italy/status/confirmed/live'

url_it

'https://api.covid19api.com/country/italy/status/confirmed/live'

df_it = pd.read_json(url_it)

df_it

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	Country	CountryCode	Province	City	CityCode	Lat	Lon	Cases	Status	Date
0	Italy	IT				41.87	12.57	0	confirmed	2020-01-22 00:00:00+00:00
1	Italy	IT				41.87	12.57	0	confirmed	2020-01-23 00:00:00+00:00
2	Italy	IT				41.87	12.57	0	confirmed	2020-01-24 00:00:00+00:00
3	Italy	IT				41.87	12.57	0	confirmed	2020-01-25 00:00:00+00:00
4	Italy	IT				41.87	12.57	0	confirmed	2020-01-26 00:00:00+00:00
...	...	...	...	...	...	...	...	...	...	...
882	Italy	IT				41.87	12.57	18014202	confirmed	2022-06-22 00:00:00+00:00
883	Italy	IT				41.87	12.57	18071634	confirmed	2022-06-23 00:00:00+00:00
884	Italy	IT				41.87	12.57	18128044	confirmed	2022-06-24 00:00:00+00:00
885	Italy	IT				41.87	12.57	18184917	confirmed	2022-06-25 00:00:00+00:00
886	Italy	IT				41.87	12.57	18234242	confirmed	2022-06-26 00:00:00+00:00

887 rows × 10 columns

url_gr = 'https://api.covid19api.com/country/greece/status/confirmed/live'

url_gr

'https://api.covid19api.com/country/greece/status/confirmed/live'

df_gr = pd.read_json(url_gr)

df_gr

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	Country	CountryCode	Province	City	CityCode	Lat	Lon	Cases	Status	Date
0	Greece	GR				39.07	21.82	0	confirmed	2020-01-22 00:00:00+00:00
1	Greece	GR				39.07	21.82	0	confirmed	2020-01-23 00:00:00+00:00
2	Greece	GR				39.07	21.82	0	confirmed	2020-01-24 00:00:00+00:00
3	Greece	GR				39.07	21.82	0	confirmed	2020-01-25 00:00:00+00:00
4	Greece	GR				39.07	21.82	0	confirmed	2020-01-26 00:00:00+00:00
...	...	...	...	...	...	...	...	...	...	...
883	Greece	GR				39.07	21.82	3583526	confirmed	2022-06-23 00:00:00+00:00
884	Greece	GR				39.07	21.82	3595498	confirmed	2022-06-24 00:00:00+00:00
885	Greece	GR				39.07	21.82	3607580	confirmed	2022-06-25 00:00:00+00:00
886	Greece	GR				39.07	21.82	3616874	confirmed	2022-06-26 00:00:00+00:00
887	Greece	GR				39.07	21.82	3616874	confirmed	2022-06-27 00:00:00+00:00

888 rows × 10 columns

df_es.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	Spain	ES				40.46	-3.75	0	confirmed
2020-01-23 00:00:00+00:00	Spain	ES				40.46	-3.75	0	confirmed
2020-01-24 00:00:00+00:00	Spain	ES				40.46	-3.75	0	confirmed
2020-01-25 00:00:00+00:00	Spain	ES				40.46	-3.75	0	confirmed
2020-01-26 00:00:00+00:00	Spain	ES				40.46	-3.75	0	confirmed
...	...	...	...	...	...	...	...	...	...
2022-06-22 00:00:00+00:00	Spain	ES				40.46	-3.75	12613634	confirmed
2022-06-23 00:00:00+00:00	Spain	ES				40.46	-3.75	12613634	confirmed
2022-06-24 00:00:00+00:00	Spain	ES				40.46	-3.75	12681820	confirmed
2022-06-25 00:00:00+00:00	Spain	ES				40.46	-3.75	12681820	confirmed
2022-06-26 00:00:00+00:00	Spain	ES				40.46	-3.75	12681820	confirmed

887 rows × 9 columns

df_es.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
                               ...   
2022-06-22 00:00:00+00:00    12613634
2022-06-23 00:00:00+00:00    12613634
2022-06-24 00:00:00+00:00    12681820
2022-06-25 00:00:00+00:00    12681820
2022-06-26 00:00:00+00:00    12681820
Name: Cases, Length: 887, dtype: int64

df_it.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
                               ...   
2022-06-22 00:00:00+00:00    18014202
2022-06-23 00:00:00+00:00    18071634
2022-06-24 00:00:00+00:00    18128044
2022-06-25 00:00:00+00:00    18184917
2022-06-26 00:00:00+00:00    18234242
Name: Cases, Length: 887, dtype: int64

df_gr.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
                              ...   
2022-06-23 00:00:00+00:00    3583526
2022-06-24 00:00:00+00:00    3595498
2022-06-25 00:00:00+00:00    3607580
2022-06-26 00:00:00+00:00    3616874
2022-06-27 00:00:00+00:00    3616874
Name: Cases, Length: 888, dtype: int64

df_es.set_index('Date')['Cases'].plot(title='Casos de COVID-19 en España')

<AxesSubplot:title={'center':'Casos de COVID-19 en España'}, xlabel='Date'>

df_es.set_index('Cases')['Date'].plot(title='prueba de al revés')

<AxesSubplot:title={'center':'prueba de al revés'}, xlabel='Cases'>

<AxesSubplot:title={'center':'Casos de COVID-19 en Italia'}, xlabel='Date'>

df_gr.set_index('Date')['Cases'].plot(title='Casos de COVID-19 en Grecia')

<AxesSubplot:title={'center':'Casos de COVID-19 en Grecia'}, xlabel='Date'>

casos_es = df_es.set_index('Cases')['Date']

casos_it = df_it.set_index('Cases')['Date']

casos_gr = df_gr.set_index('Cases')['Date']

casos_es = df_es.set_index('Date')['Cases']

casos_es

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
                               ...   
2022-06-22 00:00:00+00:00    12613634
2022-06-23 00:00:00+00:00    12613634
2022-06-24 00:00:00+00:00    12681820
2022-06-25 00:00:00+00:00    12681820
2022-06-26 00:00:00+00:00    12681820
Name: Cases, Length: 887, dtype: int64

casos_it = df_it.set_index('Date')['Cases']

casos_it

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
                               ...   
2022-06-22 00:00:00+00:00    18014202
2022-06-23 00:00:00+00:00    18071634
2022-06-24 00:00:00+00:00    18128044
2022-06-25 00:00:00+00:00    18184917
2022-06-26 00:00:00+00:00    18234242
Name: Cases, Length: 887, dtype: int64

casos_gr = df_gr.set_index('Date')['Cases']

casos_gr

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
                              ...   
2022-06-23 00:00:00+00:00    3583526
2022-06-24 00:00:00+00:00    3595498
2022-06-25 00:00:00+00:00    3607580
2022-06-26 00:00:00+00:00    3616874
2022-06-27 00:00:00+00:00    3616874
Name: Cases, Length: 888, dtype: int64

vs = pd.concat([casos_es,casos_it];axis=1)

  File "<ipython-input-56-8995b982f51b>", line 1
    vs = pd.concat([casos_es,casos_it];axis=1)
                                      ^
SyntaxError: invalid syntax

pd.concat([casos_es,casos_it],axis=1)

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	Cases	Cases
Date
2020-01-22 00:00:00+00:00	0	0
2020-01-23 00:00:00+00:00	0	0
2020-01-24 00:00:00+00:00	0	0
2020-01-25 00:00:00+00:00	0	0
2020-01-26 00:00:00+00:00	0	0
...	...	...
2022-06-22 00:00:00+00:00	12613634	18014202
2022-06-23 00:00:00+00:00	12613634	18071634
2022-06-24 00:00:00+00:00	12681820	18128044
2022-06-25 00:00:00+00:00	12681820	18184917
2022-06-26 00:00:00+00:00	12681820	18234242

887 rows × 2 columns

vs = pd.concat([casos_es,casos_it],axis=1)

vs

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	Cases	Cases
Date
2020-01-22 00:00:00+00:00	0	0
2020-01-23 00:00:00+00:00	0	0
2020-01-24 00:00:00+00:00	0	0
2020-01-25 00:00:00+00:00	0	0
2020-01-26 00:00:00+00:00	0	0
...	...	...
2022-06-22 00:00:00+00:00	12613634	18014202
2022-06-23 00:00:00+00:00	12613634	18071634
2022-06-24 00:00:00+00:00	12681820	18128044
2022-06-25 00:00:00+00:00	12681820	18184917
2022-06-26 00:00:00+00:00	12681820	18234242

887 rows × 2 columns

pd.concat([casos_es,casos_it,casos_gr],axis=1)

.dataframe tbody tr th {
    vertical-align: top;
}

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    text-align: right;
}

</style>

	Cases	Cases	Cases
Date
2020-01-22 00:00:00+00:00	0.0	0.0	0
2020-01-23 00:00:00+00:00	0.0	0.0	0
2020-01-24 00:00:00+00:00	0.0	0.0	0
2020-01-25 00:00:00+00:00	0.0	0.0	0
2020-01-26 00:00:00+00:00	0.0	0.0	0
...	...	...	...
2022-06-23 00:00:00+00:00	12613634.0	18071634.0	3583526
2022-06-24 00:00:00+00:00	12681820.0	18128044.0	3595498
2022-06-25 00:00:00+00:00	12681820.0	18184917.0	3607580
2022-06-26 00:00:00+00:00	12681820.0	18234242.0	3616874
2022-06-27 00:00:00+00:00	NaN	NaN	3616874

888 rows × 3 columns

vs = pd.concat([casos_es,casos_it,casos_gr],axis=1)

vs

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	Cases	Cases	Cases
Date
2020-01-22 00:00:00+00:00	0.0	0.0	0
2020-01-23 00:00:00+00:00	0.0	0.0	0
2020-01-24 00:00:00+00:00	0.0	0.0	0
2020-01-25 00:00:00+00:00	0.0	0.0	0
2020-01-26 00:00:00+00:00	0.0	0.0	0
...	...	...	...
2022-06-23 00:00:00+00:00	12613634.0	18071634.0	3583526
2022-06-24 00:00:00+00:00	12681820.0	18128044.0	3595498
2022-06-25 00:00:00+00:00	12681820.0	18184917.0	3607580
2022-06-26 00:00:00+00:00	12681820.0	18234242.0	3616874
2022-06-27 00:00:00+00:00	NaN	NaN	3616874

888 rows × 3 columns

vs.columns = ['España','Italia','Grecia']

vs.columns

Index(['España', 'Italia', 'Grecia'], dtype='object')

vs.plot(title='Casos de COVID-19 en España, Italia y Grecia')

<AxesSubplot:title={'center':'Casos de COVID-19 en España, Italia y Grecia'}, xlabel='Date'>

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