Análisis de las vacunas de COVID-19 con Python
El conjunto de datos que usaremos para el análisis de las vacunas de COVID-19 fue tomada de Kaggle. Comenzaremos por importar las librerías de Python necesarias y el conjunto de datos:
in [1]: import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv("vacunacion_paises.csv")
data.head()
out [1]: country iso_code date total_vaccinations people_vaccinated people_fully_vaccinated daily_vaccinations_raw
0 Afghanistan AFG 2021-02-22 0.0 0.0 NaN NaN
1 Afghanistan AFG 2021-02-23 NaN NaN NaN NaN
2 Afghanistan AFG 2021-02-24 NaN NaN NaN NaN
3 Afghanistan AFG 2021-02-25 NaN NaN NaN NaN
4 Afghanistan AFG 2021-02-26 NaN NaN NaN NaN
NOTA: El conjunto de datos tiene más columnas de las que se muestran aquí.
A continuación, exploraremos un poco más el conjunto de datos. La opción max_rows nos permite controlar el número de columnas que pandas imprime. Recibe un entero o None para imprimir todas las filas.
in [2]: pd.set_option("max_rows", None)
Después, contamos el número de entradas que tiene cada país en nuestro conjunto de datos.
in [3]: data.country.value_counts()
out [3]: Scotland 290
England 290
Seychelles 290
Serbia 290
Gibraltar 289
United Kingdom 289
Wales 289
Northern Ireland 289
Name: country, Length: 175, dtype: int64
El Reino Unido está conformado por Inglaterra, Escocia, Gales e Irlanda del Norte. Pero en los datos anteriores, estos países se mencionan por separado con valores muy parecidos. Esto puede ser un error al registrar los datos. Podemos arreglar esto de la siguiente manera:
in [4]: data = data[data.country.apply(lambda x: x not in ["Scotland", "England", "Wales", "Northern Ireland"])]
data.country.value_counts()
out [4]: Serbia 290
Seychelles 290
United Kingdom 289
Gibraltar 289
Singapore 288
Albania 288
Jordan 288
Indonesia 288
Name: country, Length: 171, dtype: int64
Ahora, vamos a revisar las vacunas disponibles en este conjunto de datos:
in [5]: data.vaccines.value_counts()
out [5]: Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech 7102
Oxford/AstraZeneca 4867
Oxford/AstraZeneca, Pfizer/BioNTech 2963
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech 2910
Moderna, Pfizer/BioNTech 2194
Oxford/AstraZeneca, Sinopharm/Beijing 2174
Pfizer/BioNTech 2001
Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V 1876
Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing 1532
Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac, Sputnik V 1102
Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V 1015
Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac 966
CanSino, Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac 823
Oxford/AstraZeneca, Sputnik V 728
Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik V 728
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing 701
Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac, Sputnik V 660
Johnson&Johnson, Moderna, Pfizer/BioNTech 640
Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac, Sputnik V 621
Johnson&Johnson, Oxford/AstraZeneca 582
Sinopharm/Beijing 568
Johnson&Johnson, Oxford/AstraZeneca, Sinopharm/Beijing 562
Pfizer/BioNTech, Sinovac 534
Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac 524
Johnson&Johnson, Pfizer/BioNTech 518
Sinopharm/Beijing, Sputnik V 507
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V 505
Moderna 489
Oxford/AstraZeneca, Pfizer/BioNTech, Sputnik V 486
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac 456
Covaxin, Oxford/AstraZeneca, Sinopharm/Beijing 452
Johnson&Johnson, Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac 443
Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech 433
Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing 417
Oxford/AstraZeneca, Sinovac 343
CanSino, Sinopharm/Beijing, Sinopharm/Wuhan, Sinovac, ZF2001 316
EpiVacCorona, Sputnik V 313
CanSino, Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac, Sputnik V 305
CanSino, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V 302
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sputnik V 296
Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinopharm/Wuhan, Sputnik V 295
Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V 294
Oxford/AstraZeneca, Sinopharm/Beijing, Sputnik V 290
Moderna, Pfizer/BioNTech, Sinovac 288
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac 288
Covaxin, Oxford/AstraZeneca, Sputnik V 285
Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac 284
QazVac, Sinopharm/Beijing, Sputnik V 269
CanSino, Covaxin, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac, Sputnik V 267
Covaxin, Johnson&Johnson, Oxford/AstraZeneca, Sinopharm/Beijing, Sputnik V 267
Pfizer/BioNTech, Sinopharm/Beijing 260
COVIran Barekat, Covaxin, Oxford/AstraZeneca, Sinopharm/Beijing, Soberana02, Sputnik V 260
Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac, Sputnik V 250
Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac 250
Covaxin, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac, Sputnik V 243
Moderna, Oxford/AstraZeneca 243
Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sputnik V 237
Abdala, Sinopharm/Beijing, Sinopharm/Wuhan, Sputnik V 234
Johnson&Johnson, Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik Light, Sputnik V 226
Pfizer/BioNTech, Sputnik V 226
Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac 225
Medigen, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech 220
Moderna, Oxford/AstraZeneca, Sputnik V, ZF2001 210
Sputnik V 210
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik Light, Sputnik V 195
Moderna, Oxford/AstraZeneca, Sinopharm/Beijing, Sputnik V 191
EpiVacCorona, Oxford/AstraZeneca, Sinopharm/Beijing, Sputnik V 183
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac, Sputnik V 167
Covaxin, Oxford/AstraZeneca 163
Abdala, Soberana02 159
Johnson&Johnson, Oxford/AstraZeneca, Sinovac 142
Johnson&Johnson, Moderna 95
Johnson&Johnson 67
Name: vaccines, dtype: int64
Tenemos una gran variedad de vacunas COVID-19 disponibles en este conjunto de datos. Ahora, creamos un DataFrame nuevo donde seleccionamos solo la vacuna y los países para explorar qué vacuna se aplicó en cada país:
in [6]: df = data[['vaccines', 'country']]
df.head()
out [6]: vaccines country
0 Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi... Afghanistan
1 Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi... Afghanistan
2 Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi... Afghanistan
3 Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi... Afghanistan
4 Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi... Afghanistan
A continuación, vamos a ver cuántos países están tomando cada una de las vacunas mencionadas en este conjunto de datos:
in [7]: dict_ = {}
for i in df.vaccines.unique():
dict_[i] = [df["country"][j] for j in df[df["vaccines"]==i].index]
vaccines = {}
for key, value in dict_.items():
vaccines[key] = set(value)
for i, j in vaccines.items():
print(f"{i} -->> {j}\n\n")
out [7]: Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing -->> {'Cameroon', 'Afghanistan'}
Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac, Sputnik V -->> {'Bosnia and Herzegovina', 'Oman', 'Azerbaijan', 'Albania'}
Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik V -->> {'Zimbabwe', 'Algeria', 'Armenia'}
Oxford/AstraZeneca, Pfizer/BioNTech -->> {'Saint Lucia', 'Panama', 'Costa Rica', 'Saint Kitts and Nevis', 'Andorra', 'Bermuda', 'Saudi Arabia', 'Slovenia', 'Cayman Islands', 'Grenada', 'Kosovo'}
Oxford/AstraZeneca -->> {'Papua New Guinea', 'Vanuatu', 'Mali', 'Falkland Islands', 'Anguilla', 'Sao Tome and Principe', 'Madagascar', 'Solomon Islands', 'Kiribati', 'Togo', 'Democratic Republic of Congo', 'Nauru', 'Lesotho', 'Fiji', 'Tonga', 'Liberia', 'Ethiopia', 'Saint Vincent and the Grenadines', 'Pitcairn', 'Nigeria', 'Saint Helena', 'Montserrat', 'Samoa', 'Uganda', 'Tuvalu', 'Niue', 'Angola'}
Oxford/AstraZeneca, Pfizer/BioNTech, Sputnik V -->> {'Nicaragua', 'Antigua and Barbuda'}
CanSino, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V -->> {'Argentina'}
Pfizer/BioNTech -->> {'Kuwait', 'Cook Islands', 'New Caledonia', 'New Zealand', 'Monaco', 'Turks and Caicos Islands', 'Tokelau', 'Aruba', 'Gibraltar'}
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech -->> {'United Kingdom', 'Canada', 'Australia', 'Isle of Man', 'Sint Maarten (Dutch part)', 'Rwanda', 'Guernsey', 'Japan', 'Sweden', 'Jersey', 'Finland'}
Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech -->> {'Germany', 'Croatia', 'Portugal', 'Latvia', 'Ireland', 'Romania', 'South Korea', 'Poland', 'Cyprus', 'Czechia', 'Austria', 'Spain', 'France', 'Iceland', 'Jamaica', 'Estonia', 'Luxembourg', 'Italy', 'Belgium', 'Malta', 'Netherlands', 'Bulgaria', 'Greece', 'Lithuania'}
Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech -->> {'Bahamas', 'Eswatini'}
Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V -->> {'Serbia', 'Jordan', 'Bahrain', 'Iraq', 'Mongolia', 'Montenegro', 'Lebanon'}
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing -->> {'Cape Verde', 'Bangladesh', 'Bhutan'}
Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing -->> {'Maldives', 'Suriname', 'Peru', 'Trinidad and Tobago', 'Barbados', 'Dominica'}
Sinopharm/Beijing, Sputnik V -->> {'Kyrgyzstan', 'Belarus'}
Oxford/AstraZeneca, Sinopharm/Beijing -->> {'Brunei', 'Namibia', 'Niger', 'Mozambique', 'Guinea-Bissau', 'Myanmar', 'Mauritania', 'Senegal', 'Sierra Leone', 'Belize'}
Oxford/AstraZeneca, Sinovac -->> {'Timor', 'Benin'}
Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V -->> {'Bolivia', 'Morocco', 'Moldova', "Cote d'Ivoire"}
Moderna, Pfizer/BioNTech -->> {'Bonaire Sint Eustatius and Saba', 'Qatar', 'Liechtenstein', 'Switzerland', 'Curacao', 'Faeroe Islands', 'Israel', 'Norway'}
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac -->> {'Botswana', 'Ukraine'}
Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac -->> {'Brazil'}
Johnson&Johnson, Oxford/AstraZeneca -->> {'British Virgin Islands', 'Malawi', 'South Sudan'}
Johnson&Johnson, Oxford/AstraZeneca, Sinopharm/Beijing -->> {'Burkina Faso', 'Zambia', 'Gambia'}
Sinopharm/Beijing -->> {'Burundi', 'Equatorial Guinea', 'Gabon', 'Chad'}
Johnson&Johnson, Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac -->> {'Cambodia', 'Somalia'}
Covaxin, Oxford/AstraZeneca -->> {'Central African Republic'}
CanSino, Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac -->> {'Chile', 'Malaysia', 'Ecuador'}
CanSino, Sinopharm/Beijing, Sinopharm/Wuhan, Sinovac, ZF2001 -->> {'China'}
Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac -->> {'Colombia'}
Covaxin, Oxford/AstraZeneca, Sinopharm/Beijing -->> {'Comoros', 'Mauritius'}
Moderna, Oxford/AstraZeneca, Sinopharm/Beijing, Sputnik V -->> {'Congo'}
Abdala, Soberana02 -->> {'Cuba'}
Johnson&Johnson, Moderna, Pfizer/BioNTech -->> {'Denmark', 'United States'}
Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac, Sputnik V -->> {'Egypt', 'Laos', 'Djibouti'}
Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac -->> {'Dominican Republic', 'El Salvador', 'Georgia', 'Thailand'}
Johnson&Johnson, Pfizer/BioNTech -->> {'South Africa', 'French Polynesia'}
Oxford/AstraZeneca, Sputnik V -->> {'Kenya', 'Ghana', 'Guyana'}
Moderna -->> {'Wallis and Futuna', 'Greenland'}
Moderna, Oxford/AstraZeneca -->> {'Guatemala'}
Sputnik V -->> {'Guinea'}
Johnson&Johnson, Moderna -->> {'Haiti'}
Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sputnik V -->> {'Honduras'}
Pfizer/BioNTech, Sinovac -->> {'Hong Kong', 'Turkey'}
Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V -->> {'Hungary'}
Covaxin, Oxford/AstraZeneca, Sputnik V -->> {'India'}
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac -->> {'Indonesia'}
COVIran Barekat, Covaxin, Oxford/AstraZeneca, Sinopharm/Beijing, Soberana02, Sputnik V -->> {'Iran'}
QazVac, Sinopharm/Beijing, Sputnik V -->> {'Kazakhstan'}
Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac, Sputnik V -->> {'Tunisia', 'Philippines', 'Libya'}
Pfizer/BioNTech, Sinopharm/Beijing -->> {'Macao'}
CanSino, Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac, Sputnik V -->> {'Mexico'}
Covaxin, Johnson&Johnson, Oxford/AstraZeneca, Sinopharm/Beijing, Sputnik V -->> {'Nepal'}
Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac, Sputnik V -->> {'North Macedonia'}
Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac -->> {'Uruguay', 'Northern Cyprus'}
CanSino, Covaxin, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac, Sputnik V -->> {'Pakistan'}
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik Light, Sputnik V -->> {'Palestine'}
Covaxin, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac, Sputnik V -->> {'Paraguay'}
EpiVacCorona, Sputnik V -->> {'Russia'}
Pfizer/BioNTech, Sputnik V -->> {'San Marino'}
Oxford/AstraZeneca, Sinopharm/Beijing, Sputnik V -->> {'Seychelles'}
Moderna, Pfizer/BioNTech, Sinovac -->> {'Singapore'}
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sputnik V -->> {'Slovakia'}
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V -->> {'Sri Lanka', 'Vietnam'}
Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac -->> {'Sudan'}
Johnson&Johnson, Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik Light, Sputnik V -->> {'Syria'}
Medigen, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech -->> {'Taiwan'}
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac, Sputnik V -->> {'Tajikistan'}
Johnson&Johnson -->> {'Tanzania'}
EpiVacCorona, Oxford/AstraZeneca, Sinopharm/Beijing, Sputnik V -->> {'Turkmenistan'}
Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinopharm/Wuhan, Sputnik V -->> {'United Arab Emirates'}
Moderna, Oxford/AstraZeneca, Sputnik V, ZF2001 -->> {'Uzbekistan'}
Abdala, Sinopharm/Beijing, Sinopharm/Wuhan, Sputnik V -->> {'Venezuela'}
Johnson&Johnson, Oxford/AstraZeneca, Sinovac -->> {'Yemen'}
Finalmente, visualizamos estos datos para ver qué combinación de vacunas está usando cada país:
in [8]: import plotly.express as px
import plotly.offline as py
mapa_vacunacion = px.choropleth(data, locations = 'iso_code', color = 'vaccines')
mapa_vacunacion.update_layout(height=300, margin={"r":0, "t":0, "l":0, "b":0})
mapa_vacunacion.show()
Así es como podemos analizar el tipo de vacunas que aplicó cada país a sus pobladores en la actualidad. Podemos explorar más a profundidad este conjunto de datos, ya que hay mucho que puede hacer con estos datos.
Puedes descargar el cuaderno del proyecto aquí