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parte-practica.md

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La url utilizada será https://api.covid19api.com/countries

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

Ya estaban instaladas pero así se ve el proceso. Ahora se importarían.

import pandas as pd

Definimos la url

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

Creamos el dataframe.

df = pd.read_json(url)
df
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Country Slug ISO2
0 Falkland Islands (Malvinas) falkland-islands-malvinas FK
1 Panama panama PA
2 Russian Federation russia RU
3 Turkmenistan turkmenistan TM
4 Guinea guinea GN
... ... ... ...
243 Guyana guyana GY
244 Hungary hungary HU
245 Kazakhstan kazakhstan KZ
246 Liberia liberia LR
247 Somalia somalia SO

248 rows × 3 columns

Así se ven solo los primeros:

df.head
<bound method NDFrame.head of                          Country                       Slug ISO2
0    Falkland Islands (Malvinas)  falkland-islands-malvinas   FK
1                         Panama                     panama   PA
2             Russian Federation                     russia   RU
3                   Turkmenistan               turkmenistan   TM
4                         Guinea                     guinea   GN
..                           ...                        ...  ...
243                       Guyana                     guyana   GY
244                      Hungary                    hungary   HU
245                   Kazakhstan                 kazakhstan   KZ
246                      Liberia                    liberia   LR
247                      Somalia                    somalia   SO

[248 rows x 3 columns]>

Así los últimos:

df.tail
<bound method NDFrame.tail of                          Country                       Slug ISO2
0    Falkland Islands (Malvinas)  falkland-islands-malvinas   FK
1                         Panama                     panama   PA
2             Russian Federation                     russia   RU
3                   Turkmenistan               turkmenistan   TM
4                         Guinea                     guinea   GN
..                           ...                        ...  ...
243                       Guyana                     guyana   GY
244                      Hungary                    hungary   HU
245                   Kazakhstan                 kazakhstan   KZ
246                      Liberia                    liberia   LR
247                      Somalia                    somalia   SO

[248 rows x 3 columns]>

Desglosamos las columnas:

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

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

df.set_index('Country')
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Slug ISO2
Country
Falkland Islands (Malvinas) falkland-islands-malvinas FK
Panama panama PA
Russian Federation russia RU
Turkmenistan turkmenistan TM
Guinea guinea GN
... ... ...
Guyana guyana GY
Hungary hungary HU
Kazakhstan kazakhstan KZ
Liberia liberia LR
Somalia somalia SO

248 rows × 2 columns

Vamos a ver los datos de algunos países (España, Francia e Italia) en tiempo real.

url_es = 'https://api.covid19api.com/country/spain/status/confirmed/live'
url_fr = 'https://api.covid19api.com/country/france/status/confirmed/live'
url_it = 'https://api.covid19api.com/country/italy/status/confirmed/live'
df_es = pd.read_json(url_es)
df_fr = pd.read_json(url_fr)
df_it = pd.read_json(url_it)
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
... ... ... ... ... ... ... ... ... ... ...
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 Spain ES 40.46 -3.75 12681820 confirmed 2022-06-27 00:00:00+00:00

888 rows × 10 columns

df_fr
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Country CountryCode Province City CityCode Lat Lon Cases Status Date
0 France FR Martinique 14.64 -61.02 0 confirmed 2020-01-22 00:00:00+00:00
1 France FR New Caledonia -20.90 165.62 0 confirmed 2020-01-22 00:00:00+00:00
2 France FR Reunion -21.12 55.54 0 confirmed 2020-01-22 00:00:00+00:00
3 France FR Wallis and Futuna -14.29 -178.12 0 confirmed 2020-01-22 00:00:00+00:00
4 France FR French Guiana 4.00 -53.00 0 confirmed 2020-01-22 00:00:00+00:00
... ... ... ... ... ... ... ... ... ... ...
10652 France FR Mayotte -12.83 45.17 37877 confirmed 2022-06-27 00:00:00+00:00
10653 France FR Saint Barthelemy 17.90 -62.83 4671 confirmed 2022-06-27 00:00:00+00:00
10654 France FR French Guiana 3.93 -53.13 85596 confirmed 2022-06-27 00:00:00+00:00
10655 France FR Reunion -21.12 55.54 421269 confirmed 2022-06-27 00:00:00+00:00
10656 France FR 46.23 2.21 29823387 confirmed 2022-06-28 00:00:00+00:00

10657 rows × 10 columns

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
... ... ... ... ... ... ... ... ... ... ...
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 Italy IT 41.87 12.57 18259261 confirmed 2022-06-27 00:00:00+00:00

888 rows × 10 columns

Vamos a acotarlo para ver solo los casos por fecha y se entienda mejor la tabla

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
                               ...   
2022-06-23 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
2022-06-27 00:00:00+00:00    12681820
Name: Cases, Length: 888, dtype: int64

En gráfico lo veremos mejor...

df_es.set_index('Date')['Cases'].plot(title='Casos de COVID en España')
<AxesSubplot:title={'center':'Casos de COVID en España'}, xlabel='Date'>

png

Según este gráfico los datos han crecido considerablemente. Podemos interpretar que a pesar de haber más casos gracias a la inmunización los soportamos mejor y no hay tantas muertes.

df_fr.set_index('Date')['Cases'].plot(title='Casos de COVID en Francia')
<AxesSubplot:title={'center':'Casos de COVID en Francia'}, xlabel='Date'>

png

df_it.set_index('Date')['Cases'].plot(title='Casos de COVID en Italia')
<AxesSubplot:title={'center':'Casos de COVID en Italia'}, xlabel='Date'>

png

Vamos a comparar la evolución de nuestros tres países en un solo gráfico.

casos_es = df_es.set_index('Date')['Cases']
casos_fr = df_fr.set_index('Date')['Cases']
casos_it = df_it.set_index('Date')['Cases']

Tras establecer estas nuevas variables, las concatenamos para después hacer el gráfico

pd.concat([casos_es,casos_fr,casos_it],axis=1)
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Cases Cases Cases
Date
2020-01-22 00:00:00+00:00 0.0 0 0.0
2020-01-22 00:00:00+00:00 0.0 0 0.0
2020-01-22 00:00:00+00:00 0.0 0 0.0
2020-01-22 00:00:00+00:00 0.0 0 0.0
2020-01-22 00:00:00+00:00 0.0 0 0.0
... ... ... ...
2022-06-27 00:00:00+00:00 12681820.0 37877 18259261.0
2022-06-27 00:00:00+00:00 12681820.0 4671 18259261.0
2022-06-27 00:00:00+00:00 12681820.0 85596 18259261.0
2022-06-27 00:00:00+00:00 12681820.0 421269 18259261.0
2022-06-28 00:00:00+00:00 NaN 29823387 NaN

10657 rows × 3 columns

Vamos a cambiar los nombres de las columnas a los respectivos países para poder distinguirlos tanto en la tabla como en el gráfico.

vs = pd.concat([casos_es,casos_fr,casos_it],axis=1)
vs.columns = ['España','Francia','Italia']
vs
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España Francia Italia
Date
2020-01-22 00:00:00+00:00 0.0 0 0.0
2020-01-22 00:00:00+00:00 0.0 0 0.0
2020-01-22 00:00:00+00:00 0.0 0 0.0
2020-01-22 00:00:00+00:00 0.0 0 0.0
2020-01-22 00:00:00+00:00 0.0 0 0.0
... ... ... ...
2022-06-27 00:00:00+00:00 12681820.0 37877 18259261.0
2022-06-27 00:00:00+00:00 12681820.0 4671 18259261.0
2022-06-27 00:00:00+00:00 12681820.0 85596 18259261.0
2022-06-27 00:00:00+00:00 12681820.0 421269 18259261.0
2022-06-28 00:00:00+00:00 NaN 29823387 NaN

10657 rows × 3 columns

Y finalmente hacemos plot para visualizarlo gráficamente.

vs.plot(title="Casos de COVID-19: comparativa de España, Francia e Italia")
<AxesSubplot:title={'center':'Casos de COVID-19: comparativa de España, Francia e Italia'}, xlabel='Date'>

png

No funciona muy bien, en las operaciones siguientes probamos con españa e italia, los datos de francia deben ser el problema. El procedimiento seguido para el gráfico de 3 es correcto.

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
... ... ...
2022-06-23 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
2022-06-27 00:00:00+00:00 12681820 18259261

888 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 0
2020-01-22 00:00:00+00:00 0.0 0
2020-01-22 00:00:00+00:00 0.0 0
2020-01-22 00:00:00+00:00 0.0 0
2020-01-22 00:00:00+00:00 0.0 0
... ... ...
2022-06-27 00:00:00+00:00 12681820.0 37877
2022-06-27 00:00:00+00:00 12681820.0 4671
2022-06-27 00:00:00+00:00 12681820.0 85596
2022-06-27 00:00:00+00:00 12681820.0 421269
2022-06-28 00:00:00+00:00 NaN 29823387

10657 rows × 2 columns

vs.columns = ['España','Italia']
vs.plot(title="España vs Italia")
<AxesSubplot:title={'center':'España vs Italia'}, xlabel='Date'>

png