In [1]:
## Aca se importan las librerias necesarias, la mas importante para esta prueba es PANDAS
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
%matplotlib inline
In [2]:
##Con este comando leemos los datos de la web
dataframe=pd.read_csv('https://github.com/owid/covid-19-data/blob/master/public/data/owid-covid-data.csv?raw=true', error_bad_lines=False)
In [3]:
# Listados los primeros registros del set de datos
dataframe.tail(5)
Out[3]:
| iso_code | continent | location | date | total_cases | new_cases | new_cases_smoothed | total_deaths | new_deaths | new_deaths_smoothed | ... | gdp_per_capita | extreme_poverty | cardiovasc_death_rate | diabetes_prevalence | female_smokers | male_smokers | handwashing_facilities | hospital_beds_per_thousand | life_expectancy | human_development_index | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78452 | ZWE | Africa | Zimbabwe | 2021-03-26 | 36805.0 | 27.0 | 21.857 | 1518.0 | 0.0 | 1.143 | ... | 1899.775 | 21.4 | 307.846 | 1.82 | 1.6 | 30.7 | 36.791 | 1.7 | 61.49 | 0.571 |
| 78453 | ZWE | Africa | Zimbabwe | 2021-03-27 | 36818.0 | 13.0 | 22.286 | 1519.0 | 1.0 | 1.286 | ... | 1899.775 | 21.4 | 307.846 | 1.82 | 1.6 | 30.7 | 36.791 | 1.7 | 61.49 | 0.571 |
| 78454 | ZWE | Africa | Zimbabwe | 2021-03-28 | 36822.0 | 4.0 | 22.429 | 1520.0 | 1.0 | 1.143 | ... | 1899.775 | 21.4 | 307.846 | 1.82 | 1.6 | 30.7 | 36.791 | 1.7 | 61.49 | 0.571 |
| 78455 | ZWE | Africa | Zimbabwe | 2021-03-29 | 36839.0 | 17.0 | 22.143 | 1520.0 | 0.0 | 0.857 | ... | 1899.775 | 21.4 | 307.846 | 1.82 | 1.6 | 30.7 | 36.791 | 1.7 | 61.49 | 0.571 |
| 78456 | ZWE | Africa | Zimbabwe | 2021-03-30 | 36839.0 | 0.0 | 17.429 | 1520.0 | 0.0 | 0.571 | ... | 1899.775 | 21.4 | 307.846 | 1.82 | 1.6 | 30.7 | 36.791 | 1.7 | 61.49 | 0.571 |
5 rows × 59 columns
In [4]:
## Agregamos nuevas variables al dataset
df=dataframe
dataframe_pais=dataframe[['location','continent','total_vaccinations','people_fully_vaccinated','population']]
dataframe_pais.tail(5)
Out[4]:
| location | continent | total_vaccinations | people_fully_vaccinated | population | |
|---|---|---|---|---|---|
| 78452 | Zimbabwe | Africa | 61093.0 | NaN | 14862927.0 |
| 78453 | Zimbabwe | Africa | 65466.0 | NaN | 14862927.0 |
| 78454 | Zimbabwe | Africa | 68511.0 | NaN | 14862927.0 |
| 78455 | Zimbabwe | Africa | 69751.0 | NaN | 14862927.0 |
| 78456 | Zimbabwe | Africa | NaN | NaN | 14862927.0 |
In [5]:
## aca agrupamos los registros de acuerdo a las variables location y continente, creamos un nuevo dataset y le decimos al nuevo data set (df_pais) las variables adicionales que vamos a utilizar
dataframe_pais=dataframe_pais.groupby(['location',])['continent', 'total_vaccinations','people_fully_vaccinated','population'].last().reset_index()
C:\Users\Tommy\Anaconda3\envs\geo_env\lib\site-packages\ipykernel_launcher.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
In [6]:
## aca listamos ese nuevo dataset pero solo los primeos 5 registros (heading)
dataframe_pais.head()
Out[6]:
| location | continent | total_vaccinations | people_fully_vaccinated | population | |
|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 54000.0 | NaN | 3.892834e+07 |
| 1 | Africa | None | 10305267.0 | 3857478.0 | 1.340598e+09 |
| 2 | Albania | Europe | 64075.0 | 655.0 | 2.877800e+06 |
| 3 | Algeria | Africa | 75000.0 | NaN | 4.385104e+07 |
| 4 | Andorra | Europe | 9288.0 | 1265.0 | 7.726500e+04 |
In [7]:
## ahora ordeno el dataset nuevo por la nueva variable Cobertura en forma descendente y creamos nuevas variables
df1=dataframe[[ 'location', 'date','total_vaccinations',
'people_vaccinated', 'people_fully_vaccinated', 'new_vaccinations',
'population',]]
# df1['new_vaccinations'].fillna(0,inplace=True)
# df1['7-day_average_vaccination']=df1.groupby('location')['new_vaccinations'].transform(lambda x: x.rolling(7).mean())
# df1['Average_daily_doses']=df1['7-day_average_vaccination']/df1['population']
# df1=df1.groupby('location')['Average_daily_doses'].mean().reset_index()
# dataframe_pais['Days_70%_vaccination']=((dataframe_pais['population']*0.7)-(dataframe_pais['total_vaccinations']*0.5))/(df1['Average_daily_doses']*0.5)
dataframe_pais['Cobertura']=(dataframe_pais['total_vaccinations']*0.5)/dataframe_pais['population']*100
dataframe_cobertura=dataframe_pais.sort_values('Cobertura',ascending=False)
In [8]:
## creamos un nuevo dataset "df_cobertura" con las coberturas de los 10 paises con las coberturas de vacunación mas altas
dataframe_cobertura.dropna
dataframe_cobertura_sort=dataframe_cobertura.head(10)
dataframe_cobertura_sort.plot.bar(y='Cobertura',x='location', rot=25)
dataframe_cobertura.columns
Out[8]:
Index(['location', 'continent', 'total_vaccinations',
'people_fully_vaccinated', 'population', 'Cobertura'],
dtype='object')
In [9]:
dataframe_cobertura
Out[9]:
| location | continent | total_vaccinations | people_fully_vaccinated | population | Cobertura | |
|---|---|---|---|---|---|---|
| 75 | Gibraltar | Europe | 59050.0 | 27952.0 | 33691.0 | 87.634680 |
| 96 | Israel | Asia | 10000972.0 | 4764642.0 | 8655541.0 | 57.772079 |
| 172 | Seychelles | Africa | 100552.0 | 36866.0 | 98340.0 | 51.124670 |
| 202 | United Arab Emirates | Asia | 8220783.0 | 2187849.0 | 9890400.0 | 41.559406 |
| 36 | Cayman Islands | North America | 47602.0 | 17958.0 | 65720.0 | 36.215764 |
| ... | ... | ... | ... | ... | ... | ... |
| 206 | Uzbekistan | Asia | NaN | NaN | 33469199.0 | NaN |
| 207 | Vanuatu | Oceania | NaN | NaN | 307150.0 | NaN |
| 208 | Vatican | Europe | NaN | NaN | 809.0 | NaN |
| 212 | Yemen | Asia | NaN | NaN | 29825968.0 | NaN |
| 213 | Zambia | Africa | NaN | NaN | 18383956.0 | NaN |
215 rows × 6 columns
Países mas avanzados en el proceso de vacunación¶
In [10]:
plt.figure(figsize=(12,8))
sns.barplot(x = "location", y = "Cobertura", data = dataframe_cobertura.head(10))
plt.xticks(rotation=70)
plt.show()
In [11]:
dataframe_cobertura=dataframe_pais.sort_values('Cobertura',ascending=True)
dataframe_cobertura.dropna()
dataframe_cobertura.head(10)
Out[11]:
| location | continent | total_vaccinations | people_fully_vaccinated | population | Cobertura | |
|---|---|---|---|---|---|---|
| 56 | Egypt | Africa | 1315.0 | NaN | 102334403.0 | 0.000643 |
| 136 | Namibia | Africa | 152.0 | NaN | 2540916.0 | 0.002991 |
| 14 | Bahamas | North America | 110.0 | NaN | 393248.0 | 0.013986 |
| 210 | Vietnam | Asia | 44000.0 | NaN | 97338583.0 | 0.022602 |
| 190 | Taiwan | Asia | 10891.0 | NaN | 23816775.0 | 0.022864 |
| 209 | Venezuela | South America | 14223.0 | NaN | 28435943.0 | 0.025009 |
| 93 | Iraq | Asia | 26727.0 | NaN | 40222503.0 | 0.033224 |
| 196 | Trinidad and Tobago | North America | 991.0 | NaN | 1399491.0 | 0.035406 |
| 72 | Georgia | Asia | 4924.0 | NaN | 3989175.0 | 0.061717 |
| 173 | Sierra Leone | Africa | 10673.0 | NaN | 7976985.0 | 0.066899 |
Países mas rezagados con procesos de vacunación activos ¶
In [12]:
plt.figure(figsize=(12,8))
sns.barplot(x = "location", y = "Cobertura", data = dataframe_cobertura.head(10))
plt.xticks(rotation=70)
plt.show()
Listando por Continente y Pais¶
Listando los datos de un contiente específico
In [13]:
dataframe_continente=dataframe_pais.groupby('continent')['total_vaccinations','population','Cobertura'].last().reset_index()
dataframe=dataframe.groupby('location')['total_cases','total_deaths','population'].last().reset_index()
dataframe_continente.sort_values('Cobertura',ascending=True)
C:\Users\Tommy\Anaconda3\envs\geo_env\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead. """Entry point for launching an IPython kernel. C:\Users\Tommy\Anaconda3\envs\geo_env\lib\site-packages\ipykernel_launcher.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
Out[13]:
| continent | total_vaccinations | population | Cobertura | |
|---|---|---|---|---|
| 1 | Asia | 44000.0 | 29825968.0 | 0.022602 |
| 5 | South America | 14223.0 | 28435943.0 | 0.025009 |
| 0 | Africa | 69751.0 | 14862927.0 | 0.234648 |
| 4 | Oceania | 41500.0 | 307150.0 | 0.430299 |
| 3 | North America | 147602345.0 | 331002647.0 | 22.296248 |
| 2 | Europe | 34518958.0 | 809.0 | 25.424208 |
Cobertura de vacunación por Continentes ¶
In [14]:
plt.figure(figsize=(12,8))
sns.barplot(x = "continent", y = "Cobertura", data = dataframe_continente.sort_values('Cobertura',ascending=True))
plt.xticks(rotation=70)
plt.show()
In [15]:
dataframe_continente_america=dataframe_pais[dataframe_pais['continent']=='North America']
dataframe_continente_america=dataframe_continente_america.sort_values('Cobertura',ascending=True )
dataframe_continente_america
Out[15]:
| location | continent | total_vaccinations | people_fully_vaccinated | population | Cobertura | |
|---|---|---|---|---|---|---|
| 14 | Bahamas | North America | 110.0 | NaN | 393248.0 | 0.013986 |
| 196 | Trinidad and Tobago | North America | 991.0 | NaN | 1399491.0 | 0.035406 |
| 85 | Honduras | North America | 43073.0 | NaN | 9904608.0 | 0.217439 |
| 79 | Guatemala | North America | 98920.0 | 518.0 | 17915567.0 | 0.276073 |
| 57 | El Salvador | North America | 70000.0 | NaN | 6486201.0 | 0.539607 |
| 98 | Jamaica | North America | 33000.0 | NaN | 2961161.0 | 0.557214 |
| 20 | Belize | North America | 20411.0 | NaN | 397621.0 | 2.566640 |
| 126 | Mexico | North America | 7404912.0 | 850939.0 | 128932753.0 | 2.871618 |
| 44 | Costa Rica | North America | 384355.0 | 160263.0 | 5094114.0 | 3.772540 |
| 54 | Dominican Republic | North America | 910869.0 | 54395.0 | 10847904.0 | 4.198364 |
| 151 | Panama | North America | 364079.0 | 116540.0 | 4314768.0 | 4.218987 |
| 78 | Grenada | North America | 9821.0 | NaN | 112519.0 | 4.364152 |
| 77 | Greenland | North America | 5130.0 | 1203.0 | 56772.0 | 4.518072 |
| 165 | Saint Vincent and the Grenadines | North America | 10519.0 | NaN | 110947.0 | 4.740552 |
| 164 | Saint Lucia | North America | 22011.0 | NaN | 183629.0 | 5.993334 |
| 34 | Canada | North America | 5470884.0 | 679469.0 | 37742157.0 | 7.247710 |
| 163 | Saint Kitts and Nevis | North America | 8573.0 | NaN | 53192.0 | 8.058543 |
| 17 | Barbados | North America | 63689.0 | NaN | 287371.0 | 11.081320 |
| 53 | Dominica | North America | 16058.0 | NaN | 71991.0 | 11.152783 |
| 132 | Montserrat | North America | 1306.0 | 194.0 | 4999.0 | 13.062613 |
| 7 | Antigua and Barbuda | North America | 26424.0 | NaN | 97928.0 | 13.491545 |
| 199 | Turks and Caicos Islands | North America | 12935.0 | NaN | 38718.0 | 16.704117 |
| 6 | Anguilla | North America | 5348.0 | NaN | 15002.0 | 17.824290 |
| 204 | United States | North America | 147602345.0 | 53423486.0 | 331002647.0 | 22.296248 |
| 22 | Bermuda | North America | 37788.0 | 15371.0 | 62273.0 | 30.340597 |
| 36 | Cayman Islands | North America | 47602.0 | 17958.0 | 65720.0 | 36.215764 |
| 47 | Cuba | North America | NaN | NaN | 11326616.0 | NaN |
| 84 | Haiti | North America | NaN | NaN | 11402533.0 | NaN |
| 140 | Nicaragua | North America | NaN | NaN | 6624554.0 | NaN |
Graficando países de Norte y Centro America con respecto a Cobertura¶
In [16]:
## creamos un nuevo dataset para ver los países con las coberturas mas altas en Norte America
dataframe_continente_america[dataframe_continente_america.continent=='North America']
dataframe_continente_america.sort_values('Cobertura',ascending=False,inplace=True)
In [17]:
dataframe_continente_america
Out[17]:
| location | continent | total_vaccinations | people_fully_vaccinated | population | Cobertura | |
|---|---|---|---|---|---|---|
| 36 | Cayman Islands | North America | 47602.0 | 17958.0 | 65720.0 | 36.215764 |
| 22 | Bermuda | North America | 37788.0 | 15371.0 | 62273.0 | 30.340597 |
| 204 | United States | North America | 147602345.0 | 53423486.0 | 331002647.0 | 22.296248 |
| 6 | Anguilla | North America | 5348.0 | NaN | 15002.0 | 17.824290 |
| 199 | Turks and Caicos Islands | North America | 12935.0 | NaN | 38718.0 | 16.704117 |
| 7 | Antigua and Barbuda | North America | 26424.0 | NaN | 97928.0 | 13.491545 |
| 132 | Montserrat | North America | 1306.0 | 194.0 | 4999.0 | 13.062613 |
| 53 | Dominica | North America | 16058.0 | NaN | 71991.0 | 11.152783 |
| 17 | Barbados | North America | 63689.0 | NaN | 287371.0 | 11.081320 |
| 163 | Saint Kitts and Nevis | North America | 8573.0 | NaN | 53192.0 | 8.058543 |
| 34 | Canada | North America | 5470884.0 | 679469.0 | 37742157.0 | 7.247710 |
| 164 | Saint Lucia | North America | 22011.0 | NaN | 183629.0 | 5.993334 |
| 165 | Saint Vincent and the Grenadines | North America | 10519.0 | NaN | 110947.0 | 4.740552 |
| 77 | Greenland | North America | 5130.0 | 1203.0 | 56772.0 | 4.518072 |
| 78 | Grenada | North America | 9821.0 | NaN | 112519.0 | 4.364152 |
| 151 | Panama | North America | 364079.0 | 116540.0 | 4314768.0 | 4.218987 |
| 54 | Dominican Republic | North America | 910869.0 | 54395.0 | 10847904.0 | 4.198364 |
| 44 | Costa Rica | North America | 384355.0 | 160263.0 | 5094114.0 | 3.772540 |
| 126 | Mexico | North America | 7404912.0 | 850939.0 | 128932753.0 | 2.871618 |
| 20 | Belize | North America | 20411.0 | NaN | 397621.0 | 2.566640 |
| 98 | Jamaica | North America | 33000.0 | NaN | 2961161.0 | 0.557214 |
| 57 | El Salvador | North America | 70000.0 | NaN | 6486201.0 | 0.539607 |
| 79 | Guatemala | North America | 98920.0 | 518.0 | 17915567.0 | 0.276073 |
| 85 | Honduras | North America | 43073.0 | NaN | 9904608.0 | 0.217439 |
| 196 | Trinidad and Tobago | North America | 991.0 | NaN | 1399491.0 | 0.035406 |
| 14 | Bahamas | North America | 110.0 | NaN | 393248.0 | 0.013986 |
| 47 | Cuba | North America | NaN | NaN | 11326616.0 | NaN |
| 84 | Haiti | North America | NaN | NaN | 11402533.0 | NaN |
| 140 | Nicaragua | North America | NaN | NaN | 6624554.0 | NaN |
In [18]:
# Construyendo el grafico
##Quitamos los valores Nan
plt.figure(figsize=(12,8))
# dataframe_continente_america.dropna(inplace=True)
sns.barplot(x = "location", y = "Cobertura", data = dataframe_continente_america)
plt.xticks(rotation=60)
plt.show()
In [20]:
# dataframe_cobertura[dataframe_cobertura['location']=='Costa Rica']
# dataframe_cobertura.to_excel("vacunas_final.xlsx")
Aplicando el algoritmo Kmeans (K medios) de clusterización¶
In [21]:
# Aplicando algoritmo Kmeans a nuestro dataset
from sklearn.cluster import KMeans
dataframe_clusters = dataframe_pais
dataframe_clusters = df.reset_index()
In [22]:
inertias = []
K = range(1,10)
dataframe_clusters.fillna(0,inplace=True)
for k in K:
#Crear y ajustar el modelo
kmeanModel = KMeans(n_clusters=k).fit(dataframe_clusters.drop(['continent','location','iso_code','date','tests_units'],axis=1))
inertias.append(kmeanModel.inertia_)
plt.plot(K, inertias, 'bx-')
plt.xlabel('VALORES DE K')
plt.ylabel('Inertia')
plt.title('EL METODO DEL CODO USANDO INERTIA' )
plt.show()
In [23]:
## El numero de clusters o grupos sera de 4
kmeans = KMeans(n_clusters = 4, init = 'k-means++', random_state = 42)
y_kmeans = kmeans.fit_predict(dataframe_clusters.drop(['continent','location','iso_code','date','tests_units'],axis=1))
y_kmeans1=y_kmeans+1
cluster = pd.DataFrame(y_kmeans1)
today_sub=dataframe_clusters.drop(['continent','location','iso_code','date','tests_units'],axis=1)
# aca añadimos la variable cluster a nuestro nuevo dataset
today_sub['cluster'] = cluster
# Promedio de los valores del cluster
kmeans_mean_cluster = pd.DataFrame(round(today_sub.groupby('cluster').mean(),1))
## Listando los valores promedios de las variables utilizadas en cada cluster
kmeans_mean_cluster
Out[23]:
| index | total_cases | new_cases | new_cases_smoothed | total_deaths | new_deaths | new_deaths_smoothed | total_cases_per_million | new_cases_per_million | new_cases_smoothed_per_million | ... | gdp_per_capita | extreme_poverty | cardiovasc_death_rate | diabetes_prevalence | female_smokers | male_smokers | handwashing_facilities | hospital_beds_per_thousand | life_expectancy | human_development_index | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cluster | |||||||||||||||||||||
| 1 | 39686.3 | 341277.7 | 2606.6 | 2574.8 | 9286.9 | 60.7 | 60.0 | 8448.4 | 69.2 | 68.1 | ... | 17757.7 | 8.3 | 239.2 | 7.3 | 7.8 | 23.3 | 23.5 | 2.6 | 70.6 | 0.7 |
| 2 | 3876.5 | 9718148.1 | 63364.1 | 62273.1 | 167582.2 | 983.0 | 974.9 | 2094.5 | 13.7 | 13.4 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3 | 77130.5 | 39710042.8 | 293523.3 | 289811.8 | 999458.7 | 6459.4 | 6393.5 | 5094.4 | 37.7 | 37.2 | ... | 15469.2 | 10.0 | 233.1 | 8.5 | 6.4 | 34.6 | 60.1 | 2.7 | 72.6 | 0.7 |
| 4 | 18179.1 | 4294077.8 | 32872.4 | 32416.9 | 105067.0 | 698.5 | 691.8 | 4768.1 | 38.1 | 37.5 | ... | 5502.5 | 5.5 | 137.2 | 5.1 | 1.0 | 17.5 | 14.9 | 1.2 | 37.0 | 0.4 |
4 rows × 55 columns
In [24]:
## Acá podemos ver el grupo de paises en cada cluster o grupo
dataframe_clusters_2=dataframe_clusters.copy()
dataframe_clusters_2['cluster']= cluster
dataframe_clusters_2
Out[24]:
| index | iso_code | continent | location | date | total_cases | new_cases | new_cases_smoothed | total_deaths | new_deaths | ... | extreme_poverty | cardiovasc_death_rate | diabetes_prevalence | female_smokers | male_smokers | handwashing_facilities | hospital_beds_per_thousand | life_expectancy | human_development_index | cluster | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | AFG | Asia | Afghanistan | 2020-02-24 | 1.0 | 1.0 | 0.000 | 0.0 | 0.0 | ... | 0.0 | 597.029 | 9.59 | 0.0 | 0.0 | 37.746 | 0.5 | 64.83 | 0.511 | 1 |
| 1 | 1 | AFG | Asia | Afghanistan | 2020-02-25 | 1.0 | 0.0 | 0.000 | 0.0 | 0.0 | ... | 0.0 | 597.029 | 9.59 | 0.0 | 0.0 | 37.746 | 0.5 | 64.83 | 0.511 | 1 |
| 2 | 2 | AFG | Asia | Afghanistan | 2020-02-26 | 1.0 | 0.0 | 0.000 | 0.0 | 0.0 | ... | 0.0 | 597.029 | 9.59 | 0.0 | 0.0 | 37.746 | 0.5 | 64.83 | 0.511 | 1 |
| 3 | 3 | AFG | Asia | Afghanistan | 2020-02-27 | 1.0 | 0.0 | 0.000 | 0.0 | 0.0 | ... | 0.0 | 597.029 | 9.59 | 0.0 | 0.0 | 37.746 | 0.5 | 64.83 | 0.511 | 1 |
| 4 | 4 | AFG | Asia | Afghanistan | 2020-02-28 | 1.0 | 0.0 | 0.000 | 0.0 | 0.0 | ... | 0.0 | 597.029 | 9.59 | 0.0 | 0.0 | 37.746 | 0.5 | 64.83 | 0.511 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78452 | 78452 | ZWE | Africa | Zimbabwe | 2021-03-26 | 36805.0 | 27.0 | 21.857 | 1518.0 | 0.0 | ... | 21.4 | 307.846 | 1.82 | 1.6 | 30.7 | 36.791 | 1.7 | 61.49 | 0.571 | 1 |
| 78453 | 78453 | ZWE | Africa | Zimbabwe | 2021-03-27 | 36818.0 | 13.0 | 22.286 | 1519.0 | 1.0 | ... | 21.4 | 307.846 | 1.82 | 1.6 | 30.7 | 36.791 | 1.7 | 61.49 | 0.571 | 1 |
| 78454 | 78454 | ZWE | Africa | Zimbabwe | 2021-03-28 | 36822.0 | 4.0 | 22.429 | 1520.0 | 1.0 | ... | 21.4 | 307.846 | 1.82 | 1.6 | 30.7 | 36.791 | 1.7 | 61.49 | 0.571 | 1 |
| 78455 | 78455 | ZWE | Africa | Zimbabwe | 2021-03-29 | 36839.0 | 17.0 | 22.143 | 1520.0 | 0.0 | ... | 21.4 | 307.846 | 1.82 | 1.6 | 30.7 | 36.791 | 1.7 | 61.49 | 0.571 | 1 |
| 78456 | 78456 | ZWE | Africa | Zimbabwe | 2021-03-30 | 36839.0 | 0.0 | 17.429 | 1520.0 | 0.0 | ... | 21.4 | 307.846 | 1.82 | 1.6 | 30.7 | 36.791 | 1.7 | 61.49 | 0.571 | 1 |
78457 rows × 61 columns
In [25]:
##Para listar los paises dentro de cada cluster separados
dataframe_clusters_2[dataframe_clusters_2['cluster']==2]
Out[25]:
| index | iso_code | continent | location | date | total_cases | new_cases | new_cases_smoothed | total_deaths | new_deaths | ... | extreme_poverty | cardiovasc_death_rate | diabetes_prevalence | female_smokers | male_smokers | handwashing_facilities | hospital_beds_per_thousand | life_expectancy | human_development_index | cluster | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3660 | 3660 | OWID_ASI | 0 | Asia | 2020-01-22 | 556.0 | 0.0 | 0.000 | 17.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2 |
| 3661 | 3661 | OWID_ASI | 0 | Asia | 2020-01-23 | 654.0 | 98.0 | 0.000 | 18.0 | 1.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2 |
| 3662 | 3662 | OWID_ASI | 0 | Asia | 2020-01-24 | 937.0 | 283.0 | 0.000 | 26.0 | 8.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2 |
| 3663 | 3663 | OWID_ASI | 0 | Asia | 2020-01-25 | 1428.0 | 491.0 | 0.000 | 42.0 | 16.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2 |
| 3664 | 3664 | OWID_ASI | 0 | Asia | 2020-01-26 | 2105.0 | 677.0 | 0.000 | 56.0 | 14.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4089 | 4089 | OWID_ASI | 0 | Asia | 2021-03-26 | 27694135.0 | 156385.0 | 136856.286 | 421988.0 | 1111.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2 |
| 4090 | 4090 | OWID_ASI | 0 | Asia | 2021-03-27 | 27846738.0 | 152603.0 | 141559.286 | 423160.0 | 1172.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2 |
| 4091 | 4091 | OWID_ASI | 0 | Asia | 2021-03-28 | 28009737.0 | 162999.0 | 146938.714 | 424218.0 | 1058.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2 |
| 4092 | 4092 | OWID_ASI | 0 | Asia | 2021-03-29 | 28162561.0 | 152824.0 | 151536.857 | 425366.0 | 1148.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2 |
| 4093 | 4093 | OWID_ASI | 0 | Asia | 2021-03-30 | 28323783.0 | 161222.0 | 155385.143 | 426628.0 | 1262.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2 |
434 rows × 61 columns
In [26]:
dataframe_clusters_2[dataframe_clusters_2['cluster']==3]
Out[26]:
| index | iso_code | continent | location | date | total_cases | new_cases | new_cases_smoothed | total_deaths | new_deaths | ... | extreme_poverty | cardiovasc_death_rate | diabetes_prevalence | female_smokers | male_smokers | handwashing_facilities | hospital_beds_per_thousand | life_expectancy | human_development_index | cluster | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 76914 | 76914 | OWID_WRL | 0 | World | 2020-01-22 | 557.0 | 0.0 | 0.000 | 17.0 | 0.0 | ... | 10.0 | 233.07 | 8.51 | 6.434 | 34.635 | 60.13 | 2.705 | 72.58 | 0.737 | 3 |
| 76915 | 76915 | OWID_WRL | 0 | World | 2020-01-23 | 655.0 | 98.0 | 0.000 | 18.0 | 1.0 | ... | 10.0 | 233.07 | 8.51 | 6.434 | 34.635 | 60.13 | 2.705 | 72.58 | 0.737 | 3 |
| 76916 | 76916 | OWID_WRL | 0 | World | 2020-01-24 | 941.0 | 286.0 | 0.000 | 26.0 | 8.0 | ... | 10.0 | 233.07 | 8.51 | 6.434 | 34.635 | 60.13 | 2.705 | 72.58 | 0.737 | 3 |
| 76917 | 76917 | OWID_WRL | 0 | World | 2020-01-25 | 1433.0 | 492.0 | 0.000 | 42.0 | 16.0 | ... | 10.0 | 233.07 | 8.51 | 6.434 | 34.635 | 60.13 | 2.705 | 72.58 | 0.737 | 3 |
| 76918 | 76918 | OWID_WRL | 0 | World | 2020-01-26 | 2118.0 | 685.0 | 0.000 | 56.0 | 14.0 | ... | 10.0 | 233.07 | 8.51 | 6.434 | 34.635 | 60.13 | 2.705 | 72.58 | 0.737 | 3 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 77343 | 77343 | OWID_WRL | 0 | World | 2021-03-26 | 126131572.0 | 641198.0 | 539943.571 | 2767631.0 | 12188.0 | ... | 10.0 | 233.07 | 8.51 | 6.434 | 34.635 | 60.13 | 2.705 | 72.58 | 0.737 | 3 |
| 77344 | 77344 | OWID_WRL | 0 | World | 2021-03-27 | 126715405.0 | 583833.0 | 552113.857 | 2777421.0 | 9790.0 | ... | 10.0 | 233.07 | 8.51 | 6.434 | 34.635 | 60.13 | 2.705 | 72.58 | 0.737 | 3 |
| 77345 | 77345 | OWID_WRL | 0 | World | 2021-03-28 | 127185829.0 | 470424.0 | 558751.000 | 2783885.0 | 6464.0 | ... | 10.0 | 233.07 | 8.51 | 6.434 | 34.635 | 60.13 | 2.705 | 72.58 | 0.737 | 3 |
| 77346 | 77346 | OWID_WRL | 0 | World | 2021-03-29 | 127644757.0 | 458928.0 | 564789.857 | 2791836.0 | 7951.0 | ... | 10.0 | 233.07 | 8.51 | 6.434 | 34.635 | 60.13 | 2.705 | 72.58 | 0.737 | 3 |
| 77347 | 77347 | OWID_WRL | 0 | World | 2021-03-30 | 128212879.0 | 568122.0 | 572509.000 | 2803397.0 | 11561.0 | ... | 10.0 | 233.07 | 8.51 | 6.434 | 34.635 | 60.13 | 2.705 | 72.58 | 0.737 | 3 |
434 rows × 61 columns
In [27]:
dataframe_clusters_2[dataframe_clusters_2['cluster']==4]
Out[27]:
| index | iso_code | continent | location | date | total_cases | new_cases | new_cases_smoothed | total_deaths | new_deaths | ... | extreme_poverty | cardiovasc_death_rate | diabetes_prevalence | female_smokers | male_smokers | handwashing_facilities | hospital_beds_per_thousand | life_expectancy | human_development_index | cluster | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 401 | 401 | OWID_AFR | 0 | Africa | 2020-02-13 | 0.0 | 0.0 | 0.000 | 0.0 | 0.0 | ... | 0.0 | 0.00 | 0.00 | 0.0 | 0.0 | 0.00 | 0.00 | 0.00 | 0.000 | 4 |
| 402 | 402 | OWID_AFR | 0 | Africa | 2020-02-14 | 1.0 | 1.0 | 0.000 | 0.0 | 0.0 | ... | 0.0 | 0.00 | 0.00 | 0.0 | 0.0 | 0.00 | 0.00 | 0.00 | 0.000 | 4 |
| 403 | 403 | OWID_AFR | 0 | Africa | 2020-02-15 | 1.0 | 0.0 | 0.000 | 0.0 | 0.0 | ... | 0.0 | 0.00 | 0.00 | 0.0 | 0.0 | 0.00 | 0.00 | 0.00 | 0.000 | 4 |
| 404 | 404 | OWID_AFR | 0 | Africa | 2020-02-16 | 1.0 | 0.0 | 0.000 | 0.0 | 0.0 | ... | 0.0 | 0.00 | 0.00 | 0.0 | 0.0 | 0.00 | 0.00 | 0.00 | 0.000 | 4 |
| 405 | 405 | OWID_AFR | 0 | Africa | 2020-02-17 | 1.0 | 0.0 | 0.000 | 0.0 | 0.0 | ... | 0.0 | 0.00 | 0.00 | 0.0 | 0.0 | 0.00 | 0.00 | 0.00 | 0.000 | 4 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 32615 | 32615 | IND | Asia | India | 2021-03-26 | 11908910.0 | 62258.0 | 50518.000 | 161240.0 | 291.0 | ... | 21.2 | 282.28 | 10.39 | 1.9 | 20.6 | 59.55 | 0.53 | 69.66 | 0.645 | 4 |
| 32616 | 32616 | IND | Asia | India | 2021-03-27 | 11971624.0 | 62714.0 | 53213.429 | 161552.0 | 312.0 | ... | 21.2 | 282.28 | 10.39 | 1.9 | 20.6 | 59.55 | 0.53 | 69.66 | 0.645 | 4 |
| 32617 | 32617 | IND | Asia | India | 2021-03-28 | 12039644.0 | 68020.0 | 56223.286 | 161843.0 | 291.0 | ... | 21.2 | 282.28 | 10.39 | 1.9 | 20.6 | 59.55 | 0.53 | 69.66 | 0.645 | 4 |
| 32618 | 32618 | IND | Asia | India | 2021-03-29 | 12095855.0 | 56211.0 | 58437.000 | 162114.0 | 271.0 | ... | 21.2 | 282.28 | 10.39 | 1.9 | 20.6 | 59.55 | 0.53 | 69.66 | 0.645 | 4 |
| 32619 | 32619 | IND | Asia | India | 2021-03-30 | 12149335.0 | 53480.0 | 59325.286 | 162468.0 | 354.0 | ... | 21.2 | 282.28 | 10.39 | 1.9 | 20.6 | 59.55 | 0.53 | 69.66 | 0.645 | 4 |
1705 rows × 61 columns
In [28]:
dataframe_clusters_2[dataframe_clusters_2['cluster']==1]
Out[28]:
| index | iso_code | continent | location | date | total_cases | new_cases | new_cases_smoothed | total_deaths | new_deaths | ... | extreme_poverty | cardiovasc_death_rate | diabetes_prevalence | female_smokers | male_smokers | handwashing_facilities | hospital_beds_per_thousand | life_expectancy | human_development_index | cluster | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | AFG | Asia | Afghanistan | 2020-02-24 | 1.0 | 1.0 | 0.000 | 0.0 | 0.0 | ... | 0.0 | 597.029 | 9.59 | 0.0 | 0.0 | 37.746 | 0.5 | 64.83 | 0.511 | 1 |
| 1 | 1 | AFG | Asia | Afghanistan | 2020-02-25 | 1.0 | 0.0 | 0.000 | 0.0 | 0.0 | ... | 0.0 | 597.029 | 9.59 | 0.0 | 0.0 | 37.746 | 0.5 | 64.83 | 0.511 | 1 |
| 2 | 2 | AFG | Asia | Afghanistan | 2020-02-26 | 1.0 | 0.0 | 0.000 | 0.0 | 0.0 | ... | 0.0 | 597.029 | 9.59 | 0.0 | 0.0 | 37.746 | 0.5 | 64.83 | 0.511 | 1 |
| 3 | 3 | AFG | Asia | Afghanistan | 2020-02-27 | 1.0 | 0.0 | 0.000 | 0.0 | 0.0 | ... | 0.0 | 597.029 | 9.59 | 0.0 | 0.0 | 37.746 | 0.5 | 64.83 | 0.511 | 1 |
| 4 | 4 | AFG | Asia | Afghanistan | 2020-02-28 | 1.0 | 0.0 | 0.000 | 0.0 | 0.0 | ... | 0.0 | 597.029 | 9.59 | 0.0 | 0.0 | 37.746 | 0.5 | 64.83 | 0.511 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78452 | 78452 | ZWE | Africa | Zimbabwe | 2021-03-26 | 36805.0 | 27.0 | 21.857 | 1518.0 | 0.0 | ... | 21.4 | 307.846 | 1.82 | 1.6 | 30.7 | 36.791 | 1.7 | 61.49 | 0.571 | 1 |
| 78453 | 78453 | ZWE | Africa | Zimbabwe | 2021-03-27 | 36818.0 | 13.0 | 22.286 | 1519.0 | 1.0 | ... | 21.4 | 307.846 | 1.82 | 1.6 | 30.7 | 36.791 | 1.7 | 61.49 | 0.571 | 1 |
| 78454 | 78454 | ZWE | Africa | Zimbabwe | 2021-03-28 | 36822.0 | 4.0 | 22.429 | 1520.0 | 1.0 | ... | 21.4 | 307.846 | 1.82 | 1.6 | 30.7 | 36.791 | 1.7 | 61.49 | 0.571 | 1 |
| 78455 | 78455 | ZWE | Africa | Zimbabwe | 2021-03-29 | 36839.0 | 17.0 | 22.143 | 1520.0 | 0.0 | ... | 21.4 | 307.846 | 1.82 | 1.6 | 30.7 | 36.791 | 1.7 | 61.49 | 0.571 | 1 |
| 78456 | 78456 | ZWE | Africa | Zimbabwe | 2021-03-30 | 36839.0 | 0.0 | 17.429 | 1520.0 | 0.0 | ... | 21.4 | 307.846 | 1.82 | 1.6 | 30.7 | 36.791 | 1.7 | 61.49 | 0.571 | 1 |
75884 rows × 61 columns
Tiempo esperado para lograr que los países lleguen al 70% de la población vacunada¶
In [29]:
## finding percentage of population populated every day
df1=df[[ 'location', 'date','total_vaccinations',
'people_vaccinated', 'people_fully_vaccinated', 'new_vaccinations',
'population',]]
df1.dropna(subset=['new_vaccinations'],inplace=True)
##taking average rate of daily vaccination for each country and finding the number of days vaccination has been provided
#Average Doses Administered Daily = 7-Day Moving Average of Daily Doses Delivered
# df1['new_vaccinations'].fillna(0,inplace=True)
# df1['Average_daily_doses']=df1.groupby('location')['new_vaccinations'].transform(lambda x: x.rolling(7, center=False).mean()).reset_index(drop=True)
df1.dropna(subset=['new_vaccinations'],inplace=True)
cntry=[]
value=[]
for loc in df1.location.unique():
cntry.append(loc)
value.append(df1[df1['location']==loc]['new_vaccinations'].rolling(7, center=False).mean().mean())
df1=pd.DataFrame({'location':cntry,'Average_daily_doses':value})
df1
# df1['Average_daily_doses']=df1['7-day_average_vaccination']
# df1=df1.groupby('location')['Average_daily_doses'].mean().reset_index()
C:\Users\Tommy\Anaconda3\envs\geo_env\lib\site-packages\ipykernel_launcher.py:5: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy """ C:\Users\Tommy\Anaconda3\envs\geo_env\lib\site-packages\ipykernel_launcher.py:14: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Out[29]:
| location | Average_daily_doses | |
|---|---|---|
| 0 | Africa | 1.597433e+05 |
| 1 | Albania | 9.734857e+02 |
| 2 | Algeria | NaN |
| 3 | Anguilla | NaN |
| 4 | Argentina | 5.506955e+04 |
| ... | ... | ... |
| 107 | United States | 1.775111e+06 |
| 108 | Uruguay | 1.911654e+04 |
| 109 | Vietnam | 2.720429e+03 |
| 110 | World | 5.312850e+06 |
| 111 | Zimbabwe | 1.684192e+03 |
112 rows × 2 columns
In [30]:
df_full=pd.merge(dataframe_cobertura,df1,on='location',how='outer')
df_full['Cobertura']=(df_full['people_fully_vaccinated']/df_full['population'])*100
In [31]:
##Days = [(Population * 0.7) - (Vaccine Doses Delivered * 0.5)] / (Average Daily Vaccine Doses Given * 0.5)
##Percent Vaccinated = (Total Vaccine Doses Administered * 0.5) / Population *100
df_full['Days_70%_vaccination']=(((df_full['population']*0.7)-(df_full['total_vaccinations']*0.5))/(df_full['Average_daily_doses']*0.5))
df_full['Percent Vaccinated']=((df_full['total_vaccinations']*0.5)/df_full['population'])*100
df_full.sort_values('Days_70%_vaccination',ascending=True,inplace=True)
df_full.dropna()
df_full.sort_values('Days_70%_vaccination')
pd.reset_option('display.max_rows')
pd.set_option("display.max_rows",215)
# (df_full)[['location', 'Days_70%_vaccination']]
print(df_full)
location continent total_vaccinations \
163 Gibraltar Europe 59050.0
161 Seychelles Africa 100552.0
162 Israel Asia 10000972.0
160 United Arab Emirates Asia 8220783.0
141 San Marino Europe 11269.0
146 Maldives Asia 235682.0
156 Isle of Man Europe 49787.0
152 United Kingdom Europe 34518958.0
138 Qatar Asia 790676.0
154 Chile South America 10181661.0
149 United States North America 147602345.0
145 Malta Europe 186111.0
132 Faeroe Islands Europe 10770.0
133 Barbados North America 63689.0
143 Serbia Europe 2420995.0
125 Uruguay South America 616374.0
148 Bahrain Asia 755549.0
91 Mongolia Asia 290108.0
131 Morocco Africa 7882300.0
97 Saudi Arabia Asia 4319023.0
87 Dominican Republic North America 910869.0
140 Hungary Europe 2764216.0
73 Belize North America 20411.0
130 Iceland Europe 66156.0
84 China Asia 114690000.0
139 North America None 162671153.0
122 Finland Europe 951354.0
126 Turkey Asia 15301462.0
121 Spain Europe 7736611.0
116 Ireland Europe 802502.0
101 Luxembourg Europe 89684.0
120 Switzerland Europe 1431525.0
127 Lithuania Europe 495383.0
124 Slovakia Europe 958434.0
128 Denmark Europe 1082909.0
77 Hong Kong Asia 490200.0
110 Greece Europe 1668956.0
123 Austria Europe 1569086.0
129 Estonia Europe 255219.0
65 Montenegro Europe 20373.0
118 Norway Europe 892792.0
105 Belgium Europe 1772750.0
119 Europe None 123369807.0
117 Italy Europe 9928663.0
115 European Union None 72218844.0
113 Portugal Europe 1641946.0
112 Germany Europe 13481171.0
106 Romania Europe 2965613.0
107 Czechia Europe 1657364.0
100 Slovenia Europe 295780.0
108 France Europe 10714250.0
80 Macao Asia 47483.0
111 Poland Europe 6081824.0
103 Canada North America 5470884.0
94 Croatia Europe 430396.0
89 Brazil South America 18222559.0
85 Argentina South America 3744566.0
88 Panama North America 364079.0
83 Russia Europe 11183108.0
64 Bangladesh Asia 5319679.0
79 Latvia Europe 133230.0
66 Colombia South America 1965054.0
86 South America None 36143923.0
60 Rwanda Africa 348926.0
78 Bulgaria Europe 458731.0
69 Sri Lanka Asia 903467.0
53 Cambodia Asia 296149.0
58 Bolivia South America 275810.0
81 World None 577923364.0
62 Lebanon Asia 207647.0
76 Mexico North America 7404912.0
71 India Asia 63054353.0
54 Malaysia Asia 621417.0
67 Indonesia Asia 10766490.0
42 Moldova Europe 33885.0
47 Ecuador South America 235000.0
59 Peru South America 805224.0
74 Asia None 244892160.0
57 Australia Oceania 597523.0
52 South Korea Asia 860387.0
38 Malawi Africa 123097.0
56 Albania Europe 64075.0
50 Oceania None 639023.0
61 Oman Asia 139897.0
49 Senegal Africa 247750.0
29 Tunisia Africa 53089.0
40 Japan Asia 949731.0
34 Guatemala North America 98920.0
36 Paraguay South America 41213.0
27 South Africa Africa 251707.0
41 Africa None 10305267.0
33 Ukraine Europe 231566.0
31 Zimbabwe Africa 69751.0
16 Uganda Africa 69701.0
4 Taiwan Asia 10891.0
12 Cote d'Ivoire Africa 37367.0
3 Vietnam Asia 44000.0
0 Egypt Africa 1315.0
1 Namibia Africa 152.0
2 Bahamas North America 110.0
5 Venezuela South America 14223.0
6 Iraq Asia 26727.0
7 Trinidad and Tobago North America 991.0
8 Georgia Asia 4924.0
9 Sierra Leone Africa 10673.0
10 Nigeria Africa 277458.0
11 Afghanistan Asia 54000.0
13 Thailand Asia 102050.0
14 Iran Asia 124193.0
15 Mozambique Africa 46439.0
17 Pakistan Asia 350000.0
18 Kenya Africa 90340.0
19 Algeria Africa 75000.0
20 Gambia Africa 5345.0
21 North Macedonia Europe 5300.0
22 Guinea Africa 34178.0
23 Angola Africa 87022.0
24 Mauritius Africa 3843.0
25 Belarus Europe 30000.0
26 Cape Verde Africa 2184.0
28 Honduras North America 43073.0
30 Philippines Asia 508332.0
32 Togo Africa 42092.0
35 Laos Asia 40732.0
37 Kazakhstan Asia 109995.0
39 Myanmar Asia 380000.0
43 New Zealand Oceania 41500.0
44 Equatorial Guinea Africa 12155.0
45 El Salvador North America 70000.0
46 Jamaica North America 33000.0
48 Ghana Africa 420000.0
51 Palestine Asia 81942.0
55 Guyana South America 15524.0
63 Jordan Asia 320140.0
68 Suriname South America 24528.0
70 Sao Tome and Principe Africa 9724.0
72 Azerbaijan Asia 510420.0
75 Nepal Asia 1600000.0
82 Costa Rica North America 384355.0
90 Grenada North America 9821.0
92 Greenland North America 5130.0
93 Saint Vincent and the Grenadines North America 10519.0
95 Saint Lucia North America 22011.0
96 Andorra Europe 9288.0
98 Netherlands Europe 2378552.0
99 Kuwait Asia 604861.0
102 Liechtenstein Europe 5471.0
104 Cyprus Europe 129438.0
109 Sweden Europe 1588671.0
114 Saint Kitts and Nevis North America 8573.0
134 Dominica North America 16058.0
135 Singapore Asia 1318912.0
136 Montserrat North America 1306.0
137 Antigua and Barbuda North America 26424.0
142 Turks and Caicos Islands North America 12935.0
144 Anguilla North America 5348.0
147 Bhutan Asia 340917.0
150 Monaco Europe 18081.0
151 Guernsey Europe 33400.0
153 Saint Helena Africa 3107.0
155 Jersey Europe 55114.0
157 Bermuda North America 37788.0
158 Falkland Islands South America 2187.0
159 Cayman Islands North America 47602.0
164 Armenia Asia NaN
165 Benin Africa NaN
166 Bosnia and Herzegovina Europe NaN
167 Botswana Africa NaN
168 Brunei Asia NaN
169 Burkina Faso Africa NaN
170 Burundi Africa NaN
171 Cameroon Africa NaN
172 Central African Republic Africa NaN
173 Chad Africa NaN
174 Comoros Africa NaN
175 Congo Africa NaN
176 Cuba North America NaN
177 Democratic Republic of Congo Africa NaN
178 Djibouti Africa NaN
179 Eritrea Africa NaN
180 Eswatini Africa NaN
181 Ethiopia Africa NaN
182 Fiji Oceania NaN
183 Gabon Africa NaN
184 Guinea-Bissau Africa NaN
185 Haiti North America NaN
186 International None NaN
187 Kosovo Europe NaN
188 Kyrgyzstan Asia NaN
189 Lesotho Africa NaN
190 Liberia Africa NaN
191 Libya Africa NaN
192 Madagascar Africa NaN
193 Mali Africa NaN
194 Marshall Islands Oceania NaN
195 Mauritania Africa NaN
196 Micronesia (country) Oceania NaN
197 Nicaragua North America NaN
198 Niger Africa NaN
199 Northern Cyprus Asia 97969.0
200 Papua New Guinea Oceania NaN
201 Samoa Oceania NaN
202 Solomon Islands Oceania NaN
203 Somalia Africa NaN
204 South Sudan Africa NaN
205 Sudan Africa NaN
206 Syria Asia NaN
207 Tajikistan Asia NaN
208 Tanzania Africa NaN
209 Timor Asia NaN
210 Uzbekistan Asia NaN
211 Vanuatu Oceania NaN
212 Vatican Europe NaN
213 Yemen Asia NaN
214 Zambia Africa NaN
people_fully_vaccinated population Cobertura Average_daily_doses \
163 27952.0 3.369100e+04 82.965777 7.466162e+02
161 36866.0 9.834000e+04 37.488306 2.191209e+03
162 4764642.0 8.655541e+06 55.047304 1.034725e+05
160 2187849.0 9.890400e+06 22.120935 8.932830e+04
141 2623.0 3.393800e+04 7.728800 4.550000e+02
146 NaN 5.405420e+05 NaN 4.492826e+03
156 15307.0 8.503200e+04 18.001458 5.888132e+02
152 3838010.0 6.788600e+07 5.653610 4.100137e+05
138 NaN 2.881060e+06 NaN 2.135076e+04
154 3513361.0 1.911621e+07 18.378963 1.073435e+05
149 53423486.0 3.310026e+08 16.139897 1.775111e+06
145 52340.0 4.415390e+05 11.853993 2.394941e+03
132 4129.0 4.886500e+04 8.449811 2.874286e+02
133 NaN 2.873710e+05 NaN 1.617069e+03
143 1006012.0 6.804596e+06 14.784302 3.287406e+04
125 31380.0 3.473727e+06 0.903353 1.911654e+04
148 253008.0 1.701583e+06 14.868978 7.012545e+03
91 NaN 3.278292e+06 NaN 1.605196e+04
131 3566498.0 3.691056e+07 9.662542 1.584368e+05
97 NaN 3.481387e+07 NaN 1.282917e+05
87 54395.0 1.084790e+07 0.501433 4.040200e+04
140 753187.0 9.660350e+06 7.796684 3.005439e+04
73 NaN 3.976210e+05 NaN 1.406952e+03
130 20734.0 3.412500e+05 6.075897 1.045746e+03
84 NaN 1.439324e+09 NaN 4.453286e+06
139 55320336.0 5.920722e+08 9.343512 1.532268e+06
122 88715.0 5.540718e+06 1.601146 1.379434e+04
126 6687094.0 8.433907e+07 7.928821 1.998711e+05
121 2644076.0 4.675478e+07 5.655199 1.083073e+05
116 224861.0 4.937796e+06 4.553874 1.123361e+04
101 21071.0 6.259760e+05 3.366103 1.438083e+03
120 533515.0 8.654618e+06 6.164512 1.939306e+04
127 158925.0 2.722291e+06 5.837914 5.880429e+03
124 259983.0 5.459643e+06 4.761905 1.135297e+04
128 373611.0 5.792203e+06 6.450240 1.169661e+04
77 34300.0 7.496988e+06 0.457517 1.606978e+04
110 588298.0 1.042306e+07 5.644199 2.044337e+04
123 417321.0 9.006400e+06 4.633605 1.717243e+04
129 60861.0 1.326539e+06 4.587954 2.444810e+03
65 2467.0 6.280620e+05 0.392796 1.305449e+03
118 271536.0 5.421242e+06 5.008742 1.001831e+04
105 512447.0 1.158962e+07 4.421605 2.069843e+04
119 31587445.0 7.486801e+08 4.219085 1.314883e+06
117 3112079.0 6.046183e+07 5.147180 1.060699e+05
115 21528920.0 4.449191e+08 4.838840 7.789274e+05
113 472270.0 1.019671e+07 4.631593 1.766508e+04
112 4059469.0 8.378394e+07 4.845163 1.439461e+05
106 1003466.0 1.923768e+07 5.216148 3.247152e+04
107 486702.0 1.070898e+07 4.544802 1.787792e+04
100 104563.0 2.078932e+06 5.029650 3.462595e+03
108 2709292.0 6.814769e+07 3.975618 1.109920e+05
80 NaN 6.493420e+05 NaN 1.127269e+03
111 2019040.0 3.784660e+07 5.334798 5.598163e+04
103 679469.0 3.774216e+07 1.800292 4.940793e+04
94 81748.0 4.105268e+06 1.991295 5.502429e+03
89 4110050.0 2.125594e+08 1.933601 2.709063e+05
85 670936.0 4.519578e+07 1.484510 5.506955e+04
88 116540.0 4.314768e+06 2.700956 5.190560e+03
83 4296849.0 1.459345e+08 2.944369 1.714090e+05
64 NaN 1.646894e+08 NaN 1.899116e+05
79 22950.0 1.886202e+06 1.216731 1.772335e+03
66 228486.0 5.088288e+07 0.449043 4.651790e+04
86 8972366.0 4.307598e+08 2.082916 3.785986e+05
60 NaN 1.295221e+07 NaN 1.069487e+04
78 93081.0 6.948445e+06 1.339595 5.470022e+03
69 NaN 2.141325e+07 NaN 1.585648e+04
53 NaN 1.671897e+07 NaN 1.249961e+04
58 95326.0 1.167303e+07 0.816635 8.461190e+03
81 126779833.0 7.794799e+09 1.626467 5.312850e+06
62 79734.0 6.825442e+06 1.168188 4.738849e+03
76 850939.0 1.289328e+08 0.659987 8.284271e+04
71 9065318.0 1.380004e+09 0.656905 8.768036e+05
54 NaN 3.236600e+07 NaN 2.084963e+04
67 3330639.0 2.735236e+08 1.217679 1.694161e+05
42 NaN 4.033963e+06 NaN 2.332857e+03
47 60358.0 1.764306e+07 0.342106 9.902571e+03
59 262469.0 3.297185e+07 0.796040 1.741662e+04
74 27086291.0 4.639847e+09 0.583775 2.343224e+06
57 NaN 2.549988e+07 NaN 1.305439e+04
52 8185.0 5.126918e+07 0.015965 2.622990e+04
38 NaN 1.912996e+07 NaN 8.783000e+03
56 655.0 2.877800e+06 0.022760 9.734857e+02
50 NaN 4.267781e+07 NaN 1.417513e+04
61 19019.0 5.106622e+06 0.372438 1.625818e+03
49 NaN 1.674393e+07 NaN 5.317989e+03
29 NaN 1.181862e+07 NaN 3.483429e+03
40 96785.0 1.264765e+08 0.076524 3.682799e+04
34 518.0 1.791557e+07 0.002891 4.401464e+03
36 NaN 7.132530e+06 NaN 1.475762e+03
27 251707.0 5.930869e+07 0.424402 7.092566e+03
41 3857478.0 1.340598e+09 0.287743 1.597433e+05
33 2.0 4.373376e+07 0.000005 5.213496e+03
31 NaN 1.486293e+07 NaN 1.684192e+03
16 NaN 4.574100e+07 NaN 4.229729e+03
4 NaN 2.381678e+07 NaN 1.337500e+03
12 NaN 2.637828e+07 NaN 1.458557e+03
3 NaN 9.733858e+07 NaN 2.720429e+03
0 NaN 1.023344e+08 NaN NaN
1 NaN 2.540916e+06 NaN NaN
2 NaN 3.932480e+05 NaN NaN
5 NaN 2.843594e+07 NaN NaN
6 NaN 4.022250e+07 NaN NaN
7 NaN 1.399491e+06 NaN NaN
8 NaN 3.989175e+06 NaN NaN
9 NaN 7.976985e+06 NaN NaN
10 NaN 2.061396e+08 NaN NaN
11 NaN 3.892834e+07 NaN NaN
13 5862.0 6.979998e+07 0.008398 NaN
14 NaN 8.399295e+07 NaN NaN
15 NaN 3.125544e+07 NaN NaN
17 NaN 2.208923e+08 NaN NaN
18 NaN 5.377130e+07 NaN NaN
19 NaN 4.385104e+07 NaN NaN
20 NaN 2.416664e+06 NaN NaN
21 NaN 2.083380e+06 NaN NaN
22 NaN 1.313279e+07 NaN NaN
23 NaN 3.286627e+07 NaN NaN
24 NaN 1.271767e+06 NaN NaN
25 10000.0 9.449321e+06 0.105828 NaN
26 NaN 5.559880e+05 NaN NaN
28 NaN 9.904608e+06 NaN NaN
30 NaN 1.095811e+08 NaN NaN
32 NaN 8.278737e+06 NaN NaN
35 NaN 7.275556e+06 NaN NaN
37 19247.0 1.877671e+07 0.102505 NaN
39 NaN 5.440979e+07 NaN NaN
43 NaN 4.822233e+06 NaN NaN
44 2407.0 1.402985e+06 0.171563 NaN
45 NaN 6.486201e+06 NaN NaN
46 NaN 2.961161e+06 NaN NaN
48 NaN 3.107294e+07 NaN NaN
51 7978.0 5.101416e+06 0.156388 NaN
55 NaN 7.865590e+05 NaN NaN
63 68943.0 1.020314e+07 0.675704 NaN
68 NaN 5.866340e+05 NaN NaN
70 NaN 2.191610e+05 NaN NaN
72 NaN 1.013918e+07 NaN NaN
75 NaN 2.913681e+07 NaN NaN
82 160263.0 5.094114e+06 3.146043 NaN
90 NaN 1.125190e+05 NaN NaN
92 1203.0 5.677200e+04 2.119002 NaN
93 NaN 1.109470e+05 NaN NaN
95 NaN 1.836290e+05 NaN NaN
96 1265.0 7.726500e+04 1.637223 NaN
98 690062.0 1.713487e+07 4.027237 NaN
99 38000.0 4.270563e+06 0.889812 NaN
102 NaN 3.813700e+04 NaN NaN
104 35963.0 8.758990e+05 4.105839 NaN
109 482539.0 1.009927e+07 4.777959 NaN
114 NaN 5.319200e+04 NaN NaN
134 NaN 7.199100e+04 NaN NaN
135 375605.0 5.850343e+06 6.420222 NaN
136 194.0 4.999000e+03 3.880776 NaN
137 NaN 9.792800e+04 NaN NaN
142 NaN 3.871800e+04 NaN NaN
144 NaN 1.500200e+04 NaN NaN
147 NaN 7.716120e+05 NaN NaN
150 8331.0 3.924400e+04 21.228723 NaN
151 7654.0 6.705200e+04 11.415021 NaN
153 NaN 6.071000e+03 NaN NaN
155 11484.0 1.010730e+05 11.362085 NaN
157 15371.0 6.227300e+04 24.683250 NaN
158 NaN 3.483000e+03 NaN NaN
159 17958.0 6.572000e+04 27.325015 NaN
164 NaN 2.963234e+06 NaN NaN
165 NaN 1.212320e+07 NaN NaN
166 NaN 3.280815e+06 NaN NaN
167 NaN 2.351625e+06 NaN NaN
168 NaN 4.374830e+05 NaN NaN
169 NaN 2.090328e+07 NaN NaN
170 NaN 1.189078e+07 NaN NaN
171 NaN 2.654586e+07 NaN NaN
172 NaN 4.829764e+06 NaN NaN
173 NaN 1.642586e+07 NaN NaN
174 NaN 8.695950e+05 NaN NaN
175 NaN 5.518092e+06 NaN NaN
176 NaN 1.132662e+07 NaN NaN
177 NaN 8.956140e+07 NaN NaN
178 NaN 9.880020e+05 NaN NaN
179 NaN 3.546427e+06 NaN NaN
180 NaN 1.160164e+06 NaN NaN
181 NaN 1.149636e+08 NaN NaN
182 NaN 8.964440e+05 NaN NaN
183 NaN 2.225728e+06 NaN NaN
184 NaN 1.967998e+06 NaN NaN
185 NaN 1.140253e+07 NaN NaN
186 NaN NaN NaN NaN
187 NaN 1.932774e+06 NaN NaN
188 NaN 6.524191e+06 NaN NaN
189 NaN 2.142252e+06 NaN NaN
190 NaN 5.057677e+06 NaN NaN
191 NaN 6.871287e+06 NaN NaN
192 NaN 2.769102e+07 NaN NaN
193 NaN 2.025083e+07 NaN NaN
194 NaN 5.919400e+04 NaN NaN
195 NaN 4.649660e+06 NaN NaN
196 NaN 1.150210e+05 NaN NaN
197 NaN 6.624554e+06 NaN NaN
198 NaN 2.420664e+07 NaN NaN
199 44083.0 NaN NaN NaN
200 NaN 8.947027e+06 NaN NaN
201 NaN 1.984100e+05 NaN NaN
202 NaN 6.868780e+05 NaN NaN
203 NaN 1.589322e+07 NaN NaN
204 NaN 1.119373e+07 NaN NaN
205 NaN 4.384927e+07 NaN NaN
206 NaN 1.750066e+07 NaN NaN
207 NaN 9.537642e+06 NaN NaN
208 NaN 5.973421e+07 NaN NaN
209 NaN 1.318442e+06 NaN NaN
210 NaN 3.346920e+07 NaN NaN
211 NaN 3.071500e+05 NaN NaN
212 NaN 8.090000e+02 NaN NaN
213 NaN 2.982597e+07 NaN NaN
214 NaN 1.838396e+07 NaN NaN
Days_70%_vaccination Percent Vaccinated
163 -15.915272 87.634680
161 16.942247 51.124670
162 20.457469 57.772079
160 62.978661 41.559406
141 79.657582 16.602334
146 115.979749 21.800526
156 117.622705 29.275449
152 147.608358 25.424208
138 151.882542 13.721963
154 154.466991 26.630963
149 177.905103 22.296248
145 180.398457 21.075262
132 200.540258 11.020158
133 209.409972 11.081320
143 216.141196 17.789410
125 222.155430 8.871941
148 231.965310 22.201356
91 267.849008 4.424682
131 276.403519 10.677568
97 346.245204 6.203021
87 353.353710 4.198364
140 358.026740 14.307018
73 381.148934 2.566640
130 393.588871 9.693187
84 426.732845 3.984163
139 434.799873 13.737442
122 493.365541 8.585115
126 514.197481 9.071396
121 532.928853 8.273604
116 543.940195 8.126115
101 547.035336 7.163533
120 550.967137 8.270296
127 563.874616 9.098642
124 588.838759 8.777442
128 600.702034 9.347989
77 622.633481 3.269313
110 632.152316 8.006078
123 642.883800 8.710950
129 655.239240 9.619732
65 657.945131 1.621894
118 668.470668 8.234202
105 698.251498 7.648010
119 703.319169 8.239154
117 704.421459 8.210687
115 706.956577 8.115953
113 715.164795 8.051354
112 721.216794 8.045199
106 738.097319 7.707823
107 745.903881 7.738196
100 755.134536 7.113749
108 763.050378 7.861052
80 764.321226 3.656240
111 837.835915 8.034834
103 958.715330 7.247710
94 966.296814 5.241996
89 1031.207465 4.286463
85 1080.987971 4.142606
88 1093.638509 4.218987
83 1126.692080 3.831552
64 1186.054412 1.615064
79 1414.773486 3.531700
66 1489.125366 1.930958
86 1497.416602 4.195369
60 1662.869302 1.346975
78 1694.525461 3.300962
69 1833.640539 2.109598
53 1848.890185 0.885668
58 1898.838071 1.181399
81 1945.244932 3.707109
62 1972.624870 1.521125
76 2089.513346 2.871618
71 2131.551329 2.284571
54 2143.490339 0.959984
67 2196.759888 1.968110
42 2406.346748 0.419996
47 2470.599094 0.665984
59 2604.142993 1.221078
74 2667.646934 2.639011
57 2688.926876 1.171619
52 2703.649708 0.839088
38 3035.277240 0.321739
56 4072.833265 1.113264
50 4169.973777 0.748660
61 4311.290081 1.369761
49 4361.376804 0.739820
29 4734.696252 0.224599
40 4782.159111 0.375458
34 5676.036923 0.276073
36 6738.437256 0.288909
27 11671.440177 0.212201
41 11684.571279 0.384353
33 11699.577205 0.264745
31 12313.527997 0.234648
16 15123.357899 0.076191
4 24921.565607 0.022864
12 25293.639115 0.070829
3 50076.674547 0.022602
0 NaN 0.000643
1 NaN 0.002991
2 NaN 0.013986
5 NaN 0.025009
6 NaN 0.033224
7 NaN 0.035406
8 NaN 0.061717
9 NaN 0.066899
10 NaN 0.067299
11 NaN 0.069358
13 NaN 0.073102
14 NaN 0.073931
15 NaN 0.074289
17 NaN 0.079224
18 NaN 0.084004
19 NaN 0.085517
20 NaN 0.110586
21 NaN 0.127197
22 NaN 0.130125
23 NaN 0.132388
24 NaN 0.151089
25 NaN 0.158742
26 NaN 0.196407
28 NaN 0.217439
30 NaN 0.231943
32 NaN 0.254218
35 NaN 0.279924
37 NaN 0.292903
39 NaN 0.349202
43 NaN 0.430299
44 NaN 0.433184
45 NaN 0.539607
46 NaN 0.557214
48 NaN 0.675829
51 NaN 0.803130
55 NaN 0.986830
63 NaN 1.568831
68 NaN 2.090571
70 NaN 2.218460
72 NaN 2.517069
75 NaN 2.745668
82 NaN 3.772540
90 NaN 4.364152
92 NaN 4.518072
93 NaN 4.740552
95 NaN 5.993334
96 NaN 6.010483
98 NaN 6.940676
99 NaN 7.081748
102 NaN 7.172824
104 NaN 7.388866
109 NaN 7.865276
114 NaN 8.058543
134 NaN 11.152783
135 NaN 11.272091
136 NaN 13.062613
137 NaN 13.491545
142 NaN 16.704117
144 NaN 17.824290
147 NaN 22.091219
150 NaN 23.036643
151 NaN 24.906043
153 NaN 25.588865
155 NaN 27.264452
157 NaN 30.340597
158 NaN 31.395349
159 NaN 36.215764
164 NaN NaN
165 NaN NaN
166 NaN NaN
167 NaN NaN
168 NaN NaN
169 NaN NaN
170 NaN NaN
171 NaN NaN
172 NaN NaN
173 NaN NaN
174 NaN NaN
175 NaN NaN
176 NaN NaN
177 NaN NaN
178 NaN NaN
179 NaN NaN
180 NaN NaN
181 NaN NaN
182 NaN NaN
183 NaN NaN
184 NaN NaN
185 NaN NaN
186 NaN NaN
187 NaN NaN
188 NaN NaN
189 NaN NaN
190 NaN NaN
191 NaN NaN
192 NaN NaN
193 NaN NaN
194 NaN NaN
195 NaN NaN
196 NaN NaN
197 NaN NaN
198 NaN NaN
199 NaN NaN
200 NaN NaN
201 NaN NaN
202 NaN NaN
203 NaN NaN
204 NaN NaN
205 NaN NaN
206 NaN NaN
207 NaN NaN
208 NaN NaN
209 NaN NaN
210 NaN NaN
211 NaN NaN
212 NaN NaN
213 NaN NaN
214 NaN NaN