
# Install
! pip install plotly

# Import Libraries 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.formula.api as sm
import plotly.express as px
import plotly.graph_objects as go
import warnings
import statsmodels.formula.api as smf

# From Libraries
from sklearn import linear_model
from scipy import stats
from patsy.builtins import Q
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import LabelEncoder

Collecting plotly
  Downloading plotly-5.4.0-py2.py3-none-any.whl (25.3 MB)
     |████████████████████████████████| 25.3 MB 17.1 MB/s eta 0:00:01   |███████████▍                    | 9.0 MB 5.4 MB/s eta 0:00:04
Requirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from plotly) (1.15.0)
Collecting tenacity>=6.2.0
  Downloading tenacity-8.0.1-py3-none-any.whl (24 kB)
Installing collected packages: tenacity, plotly
Successfully installed plotly-5.4.0 tenacity-8.0.1


# Read the csv file on Covid 19 Data in the United States
covid_df = pd.read_csv("Fat_Supply_Quantity_Data.csv")

# Order all the Countries in the 'Country' column by alphabetical order
covid_df = covid_df.sort_values('Country', ascending = True)

# Print all the columns of the dataset
pd.set_option('display.max_columns', None)

# Print all the rows of the dataset
pd.set_option('display.max_rows', None)

# Print out dataset
covid_df


# Read the csv file on Covid 19 Data in the United States
covid_food_df = pd.read_csv("Food_Supply_Quantity_kg_Data.csv")

# Order all the Countries in the 'Country' column by alphabetical order
covid_food_df = covid_food_df.sort_values('Country', ascending = True)

# Print all the columns of the dataset
pd.set_option('display.max_columns', None)

# Print all the rows of the dataset
pd.set_option('display.max_rows', None)

# Print out dataset
covid_food_df


# Reset all rows of dataframe so all the rows are not displayed in the table below
pd.reset_option('all')

# Ignore all warnings that occur after output
warnings.filterwarnings('ignore')

# Drop all necessary columns except the ones that have a direct relation to COVID-19 tests, deaths, and various age groups
covid_obesity_df = covid_df.drop(['Alcoholic Beverages', 'Animal Products', 'Animal fats', 'Aquatic Products, Other', 
                                  'Cereals - Excluding Beer', 'Eggs', 'Fish, Seafood', 'Fruits - Excluding Wine', 'Meat', 
                                  'Miscellaneous', 'Milk - Excluding Butter', 'Offals', 'Oilcrops', 'Pulses', 'Spices', 
                                  'Starchy Roots', 'Stimulants', 'Sugar Crops', 'Sugar & Sweeteners', 'Treenuts', 
                                  'Vegetal Products', 'Vegetable Oils', 'Vegetables', 'Undernourished', 'Population', 
                                  'Unit (all except Population)'], axis = 1)

# Drop any rows of data that contain missing values or 'NaN' values
covid_obesity_df = covid_obesity_df.dropna(how = 'any', thresh = None, subset = ['Obesity', 'Deaths'], inplace = False)

# Print new table 
covid_obesity_df

: boolean
    use_inf_as_null had been deprecated and will be removed in a future
    version. Use `use_inf_as_na` instead.

/opt/conda/lib/python3.8/site-packages/pandas/_config/config.py:622: FutureWarning: 
: boolean
    use_inf_as_null had been deprecated and will be removed in a future
    version. Use `use_inf_as_na` instead.

  warnings.warn(d.msg, FutureWarning)


plt.figure(figsize = (12, 8))

x = np.array(covid_obesity_df['Obesity'])

y = np.array(covid_obesity_df['Deaths'])

plt.plot(x, y, 'o', color = 'red')

# Linear Regression using Polyfit
m, b = np.polyfit(x, y, 1)

plt.plot(x, (m * x) + b, color = 'black')

plt.grid()

plt.title("Obesity vs. COVID-19 Deaths", fontsize = 20)

plt.ylabel("Death Rates", fontsize = 18)

plt.xlabel("Obesity Rates", fontsize = 18)

Text(0.5, 0, 'Obesity Rates')


# Print Regression Results for Obesity
regression_results = sm.ols(formula =  'Deaths ~ Obesity', data = covid_obesity_df).fit()

# Create first parameter for linear regression model 
first_param = [[x] for x in covid_obesity_df['Obesity']]

# Create second parameter for linear regression model
second_param = [[y] for y in covid_obesity_df['Deaths']]

# Create a variable to call the linearRegression import above
lin_regression = linear_model.LinearRegression()

# Create variable to store linear regression model data
lin_reg_model = lin_regression.fit(first_param, second_param) 

# Create coefficient for the above variable
format_one = lin_reg_model.coef_[0]

#Obtain y-intercept of above variable 
format_two = lin_reg_model.intercept_

# Print the data for the fitted model 
print (regression_results.summary())

# Print new line for spacing purposes
print ()

# prints the output in slope intercept form 
print ("Linear Regression Model (Slope Intercept Form): Y = {0}x + {1}".format(format_one, format_two))

# Print new line for spacing purposes
print ()

# Find the p-value 
slope, intercept, r_value, p_value, stderr = stats.linregress(covid_obesity_df['Obesity'], covid_obesity_df['Deaths'])

# Print the p-value 
print ("The p-Value is: ", p_value, ", which is = 0.00000000006530504174828157")

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Deaths   R-squared:                       0.233
Model:                            OLS   Adj. R-squared:                  0.229
Method:                 Least Squares   F-statistic:                     49.01
Date:                Mon, 20 Dec 2021   Prob (F-statistic):           6.53e-11
Time:                        16:39:06   Log-Likelihood:                 283.24
No. Observations:                 163   AIC:                            -562.5
Df Residuals:                     161   BIC:                            -556.3
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
Intercept     -0.0072      0.007     -0.958      0.340      -0.022       0.008
Obesity        0.0025      0.000      7.001      0.000       0.002       0.003
==============================================================================
Omnibus:                       16.002   Durbin-Watson:                   2.047
Prob(Omnibus):                  0.000   Jarque-Bera (JB):               17.638
Skew:                           0.776   Prob(JB):                     0.000148
Kurtosis:                       3.436   Cond. No.                         46.7
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Linear Regression Model (Slope Intercept Form): Y = [0.00250087]x + [-0.00715848]

The p-Value is:  6.530504174828157e-11 , which is = 0.00000000006530504174828157


# Drop all necessary columns except the ones that have a direct relation to COVID-19 tests, deaths, and undernourishment
covid_undernourished_df = covid_df.drop(['Alcoholic Beverages', 'Animal Products', 'Animal fats', 'Aquatic Products, Other', 
                                  'Cereals - Excluding Beer', 'Eggs', 'Fish, Seafood', 'Fruits - Excluding Wine', 'Meat', 
                                  'Miscellaneous', 'Milk - Excluding Butter', 'Offals', 'Oilcrops', 'Pulses', 'Spices', 
                                  'Starchy Roots', 'Stimulants', 'Sugar Crops', 'Sugar & Sweeteners', 'Treenuts', 
                                  'Vegetal Products', 'Vegetable Oils', 'Vegetables', 'Obesity', 'Population', 
                                  'Unit (all except Population)'], axis = 1)

# Drop any rows of data that contain missing values or 'NaN' values
covid_undernourished_df = covid_undernourished_df.dropna(how = 'any', thresh = None, subset = ['Undernourished', 'Deaths'], 
                                                         inplace = False)

# Delete all occurences of '<' so data will only be represented with whole numbers (not str characters)
covid_undernourished_df['Undernourished'] = covid_undernourished_df['Undernourished'].str.replace('<', '')

# Convert all data in the 'Undernourished' into float representation
covid_undernourished_df['Undernourished'] = pd.to_numeric(covid_undernourished_df['Undernourished'], downcast = 'float')

# Print new table 
covid_undernourished_df


plt.figure(figsize = (12, 8))

x = np.array(covid_undernourished_df['Undernourished'])

y = np.array(covid_undernourished_df['Deaths'])

plt.plot(x, y, 'o', color = 'red')

m, b = np.polyfit(x, y, 1)

plt.plot(x, (m * x) + b, color = 'black')

plt.grid()

plt.title("Undernourishment vs. COVID-19 Deaths", fontsize = 20)

plt.ylabel("Death Rates", fontsize = 18)

plt.xlabel("Undernourised Rates", fontsize = 18)

Text(0.5, 0, 'Undernourised Rates')


# Print Regression Results for Undernourished
regression_results = sm.ols(formula =  'Deaths ~ Undernourished', data = covid_undernourished_df).fit()

# Create first parameter for linear regression model 
first_param = [[x] for x in covid_undernourished_df['Undernourished']]

# Create second parameter for linear regression model
second_param = [[y] for y in covid_undernourished_df['Deaths']]

# Create a variable to call the linearRegression import above
lin_regression = linear_model.LinearRegression()

# Create variable to store linear regression model data
lin_reg_model = lin_regression.fit(first_param, second_param) 

# Create coefficient for the above variable
format_one = lin_reg_model.coef_[0]

#Obtain y-intercept of above variable 
format_two = lin_reg_model.intercept_

# Print the data for the fitted model 
print (regression_results.summary())

# Print new line for spacing purposes
print ()

# prints the output in slope intercept form 
print ("Linear Regression Model (Slopt Intercept Form): Y = {0}x + {1}".format(format_one, format_two))

# Print new line for spacing purposes
print ()

# Find the p-value 
slope, intercept, r_value, p_value, stderr = stats.linregress(covid_undernourished_df['Undernourished'], 
                                                              covid_undernourished_df['Deaths'])

# Print the p-value 
print ("The p-Value is: ", p_value, ", which is = 0.00000002243538027485908")

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Deaths   R-squared:                       0.183
Model:                            OLS   Adj. R-squared:                  0.178
Method:                 Least Squares   F-statistic:                     34.77
Date:                Mon, 20 Dec 2021   Prob (F-statistic):           2.24e-08
Time:                        16:39:06   Log-Likelihood:                 266.67
No. Observations:                 157   AIC:                            -529.3
Df Residuals:                     155   BIC:                            -523.2
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
Intercept          0.0601      0.005     12.223      0.000       0.050       0.070
Undernourished    -0.0018      0.000     -5.897      0.000      -0.002      -0.001
==============================================================================
Omnibus:                       16.312   Durbin-Watson:                   2.068
Prob(Omnibus):                  0.000   Jarque-Bera (JB):               19.242
Skew:                           0.852   Prob(JB):                     6.63e-05
Kurtosis:                       2.805   Cond. No.                         22.4
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Linear Regression Model (Slopt Intercept Form): Y = [-0.00179543]x + [0.06012591]

The p-Value is:  2.243538027485908e-08 , which is = 0.00000002243538027485908


# Drop all necessary columns except the ones that have a direct relation to COVID-19 tests, deaths, and vegetables
covid_vegetables_df = covid_df.drop(['Alcoholic Beverages', 'Animal fats', 'Animal Products', 'Aquatic Products, Other', 
                                  'Cereals - Excluding Beer', 'Eggs', 'Fish, Seafood', 'Fruits - Excluding Wine', 'Meat', 
                                  'Miscellaneous', 'Milk - Excluding Butter', 'Offals', 'Oilcrops', 'Pulses', 'Spices', 
                                  'Starchy Roots', 'Stimulants', 'Sugar Crops', 'Sugar & Sweeteners', 'Treenuts', 
                                  'Vegetal Products', 'Vegetable Oils', 'Obesity', 'Undernourished', 
                                  'Population', 
                                  'Unit (all except Population)'], axis = 1)

# Drop any rows of data that contain missing values or 'NaN' values
covid_vegetables_df = covid_vegetables_df.dropna(how = 'any', thresh = None, 
                                                           subset = ['Vegetables', 'Deaths'], inplace = False)

# Print new table 
covid_vegetables_df


plt.figure(figsize = (12, 8))

x = np.array(covid_vegetables_df['Vegetables'])

y = np.array(covid_vegetables_df['Deaths'])

plt.plot(x, y, 'o', color = 'red')

m, b = np.polyfit(x, y, 1)

plt.plot(x, (m * x) + b, color = 'black')

plt.grid()

plt.title("Vegetable Consumption vs. COVID-19 Deaths", fontsize = 20)

plt.ylabel("Death Rates", fontsize = 18)

plt.xlabel("Vegetables Consumption Rates", fontsize = 18)

Text(0.5, 0, 'Vegetables Consumption Rates')


# Print Regression Results for Vegetable Consumption
regression_results = sm.ols(formula =  'Deaths ~ Vegetables', data = covid_vegetables_df).fit()

# Create first parameter for linear regression model 
first_param = [[x] for x in covid_vegetables_df['Vegetables']]

# Create second parameter for linear regression model
second_param = [[y] for y in covid_vegetables_df['Deaths']]

# Create a variable to call the linearRegression import above
lin_regression = linear_model.LinearRegression()

# Create variable to store linear regression model data
lin_reg_model = lin_regression.fit(first_param, second_param) 

# Create coefficient for the above variable
format_one = lin_reg_model.coef_[0]

#Obtain y-intercept of above variable 
format_two = lin_reg_model.intercept_

# Print the data for the fitted model 
print (regression_results.summary())

# Print new line for spacing purposes
print ()

# prints the output in slope intercept form 
print ("Linear Regression Model (Slopt Intercept Form): Y = {0}x + {1}".format(format_one, format_two))

# Print new line for spacing purposes
print ()

# Find the p-value 
slope, intercept, r_value, p_value, stderr = stats.linregress(covid_vegetables_df['Vegetables'], 
                                                              covid_vegetables_df['Deaths'])

# Print the p-value 
print ("The P-Value is: ", p_value)

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Deaths   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                 -0.006
Method:                 Least Squares   F-statistic:                  0.004717
Date:                Mon, 20 Dec 2021   Prob (F-statistic):              0.945
Time:                        16:39:07   Log-Likelihood:                 263.36
No. Observations:                 164   AIC:                            -522.7
Df Residuals:                     162   BIC:                            -516.5
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
Intercept      0.0398      0.007      5.804      0.000       0.026       0.053
Vegetables    -0.0013      0.019     -0.069      0.945      -0.038       0.035
==============================================================================
Omnibus:                       27.504   Durbin-Watson:                   2.041
Prob(Omnibus):                  0.000   Jarque-Bera (JB):               37.012
Skew:                           1.162   Prob(JB):                     9.18e-09
Kurtosis:                       3.114   Cond. No.                         5.33
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Linear Regression Model (Slopt Intercept Form): Y = [-0.00127219]x + [0.03976037]

The P-Value is:  0.9453309860444901


# Drop all necessary columns except the ones that have a direct relation to COVID-19 tests, deaths, and treenuts
covid_treenuts_df = covid_df.drop(['Alcoholic Beverages', 'Animal fats', 'Animal Products', 'Aquatic Products, Other', 
                                  'Cereals - Excluding Beer', 'Eggs', 'Fish, Seafood', 'Fruits - Excluding Wine', 'Meat', 
                                  'Miscellaneous', 'Milk - Excluding Butter', 'Offals', 'Oilcrops', 'Pulses', 'Spices', 
                                  'Starchy Roots', 'Stimulants', 'Sugar Crops', 'Sugar & Sweeteners', 
                                  'Vegetal Products', 'Vegetable Oils', 'Vegetables', 'Obesity', 'Undernourished', 
                                  'Population', 
                                  'Unit (all except Population)'], axis = 1)

# Drop any rows of data that contain missing values or 'NaN' values
covid_treenuts_df = covid_treenuts_df.dropna(how = 'any', thresh = None, 
                                                           subset = ['Treenuts', 'Deaths'], inplace = False)

# Print new table 
covid_treenuts_df


plt.figure(figsize = (12, 8))

x = np.array(covid_treenuts_df['Treenuts'])

y = np.array(covid_treenuts_df['Deaths'])

plt.plot(x, y, 'o', color = 'red')

m, b = np.polyfit(x, y, 1)

plt.plot(x, (m * x) + b, color = 'black')

plt.grid()

plt.title("Treenut Consumption vs. COVID-19 Deaths", fontsize = 20)

plt.ylabel("Death Rates", fontsize = 18)

plt.xlabel("Treenut Consumption Rates", fontsize = 18)

Text(0.5, 0, 'Treenut Consumption Rates')


# Print Regression Results for Treenut Consumption
regression_results = sm.ols(formula =  'Deaths ~ Treenuts', data = covid_treenuts_df).fit()

# Create first parameter for linear regression model 
first_param = [[x] for x in covid_treenuts_df['Treenuts']]

# Create second parameter for linear regression model
second_param = [[y] for y in covid_treenuts_df['Deaths']]

# Create a variable to call the linearRegression import above
lin_regression = linear_model.LinearRegression()

# Create variable to store linear regression model data
lin_reg_model = lin_regression.fit(first_param, second_param) 

# Create coefficient for the above variable
format_one = lin_reg_model.coef_[0]

#Obtain y-intercept of above variable 
format_two = lin_reg_model.intercept_

# Print the data for the fitted model 
print (regression_results.summary())

# Print new line for spacing purposes
print ()

# prints the output in slope intercept form 
print ("Linear Regression Model (Slopt Intercept Form): Y = {0}x + {1}".format(format_one, format_two))

# Print new line for spacing purposes
print ()

# Find the p-value 
slope, intercept, r_value, p_value, stderr = stats.linregress(covid_treenuts_df['Treenuts'], 
                                                              covid_treenuts_df['Deaths'])

# Print the p-value 
print ("The P-Value is: ", p_value)

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Deaths   R-squared:                       0.024
Model:                            OLS   Adj. R-squared:                  0.018
Method:                 Least Squares   F-statistic:                     3.970
Date:                Mon, 20 Dec 2021   Prob (F-statistic):             0.0480
Time:                        16:39:07   Log-Likelihood:                 265.34
No. Observations:                 164   AIC:                            -526.7
Df Residuals:                     162   BIC:                            -520.5
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
Intercept      0.0329      0.005      6.585      0.000       0.023       0.043
Treenuts       0.0091      0.005      1.993      0.048    8.18e-05       0.018
==============================================================================
Omnibus:                       27.561   Durbin-Watson:                   2.041
Prob(Omnibus):                  0.000   Jarque-Bera (JB):               37.011
Skew:                           1.161   Prob(JB):                     9.19e-09
Kurtosis:                       3.143   Cond. No.                         2.20
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Linear Regression Model (Slopt Intercept Form): Y = [0.00913388]x + [0.03285637]

The P-Value is:  0.04798943887205049


# Drop all necessary columns except the ones that have a direct relation to COVID-19 tests, deaths, and fruits
covid_fruits_df = covid_df.drop(['Alcoholic Beverages', 'Animal fats', 'Animal Products', 'Aquatic Products, Other', 
                                 'Cereals - Excluding Beer', 'Eggs', 'Fish, Seafood', 'Meat', 'Miscellaneous', 
                                 'Milk - Excluding Butter', 'Offals', 'Oilcrops', 'Pulses', 'Spices', 'Starchy Roots', 
                                 'Stimulants', 'Sugar Crops', 'Sugar & Sweeteners', 'Treenuts', 'Vegetal Products', 
                                 'Vegetable Oils', 'Vegetables', 'Obesity', 'Undernourished', 'Population', 
                                 'Unit (all except Population)'], axis = 1)

# Drop any rows of data that contain missing values or 'NaN' values
covid_fruits_df = covid_fruits_df.dropna(how = 'any', thresh = None, 
                                         subset = ['Fruits - Excluding Wine', 'Deaths'], inplace = False)

# Print new table 
covid_fruits_df


plt.figure(figsize = (12, 8))

x = np.array(covid_fruits_df['Fruits - Excluding Wine'])

y = np.array(covid_fruits_df['Deaths'])

plt.plot(x, y, 'o', color = 'red')

m, b = np.polyfit(x, y, 1)

plt.plot(x, (m * x) + b, color = 'black')

plt.grid()

plt.title("Fruits-Excluding Wine Consumption vs. COVID-19 Deaths", fontsize = 20)

plt.ylabel("Death Rates", fontsize = 18)

plt.xlabel("Fruit Consumption Rates", fontsize = 18)

Text(0.5, 0, 'Fruit Consumption Rates')


# Recreating 'Fruit - Excluding Wine' column to encompass OLS Regression Results
covid_fruits_df['temp_fruits_excluding_wine'] = covid_fruits_df['Fruits - Excluding Wine']

# Print Regression Results for Fruit Consumption
regression_results = sm.ols(formula =  'Deaths ~ temp_fruits_excluding_wine', data = covid_fruits_df).fit()

# Create first parameter for linear regression model 
first_param = [[x] for x in covid_fruits_df['Fruits - Excluding Wine']]

# Create second parameter for linear regression model
second_param = [[y] for y in covid_fruits_df['Deaths']]

# Create a variable to call the linearRegression import above
lin_regression = linear_model.LinearRegression()

# Create variable to store linear regression model data
lin_reg_model = lin_regression.fit(first_param, second_param) 

# Create coefficient for the above variable
format_one = lin_reg_model.coef_[0]

#Obtain y-intercept of above variable 
format_two = lin_reg_model.intercept_

# Print the data for the fitted model 
print (regression_results.summary())

# Print new line for spacing purposes
print ()

# prints the output in slope intercept form 
print ("Linear Regression Model (Slopt Intercept Form): Y = {0}x + {1}".format(format_one, format_two))

# Print new line for spacing purposes
print ()

# Find the p-value 
slope, intercept, r_value, p_value, stderr = stats.linregress(covid_fruits_df['Fruits - Excluding Wine'], 
                                                              covid_fruits_df['Deaths'])

# Print the p-value 
print ("The P-Value is: ", p_value)

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Deaths   R-squared:                       0.010
Model:                            OLS   Adj. R-squared:                  0.004
Method:                 Least Squares   F-statistic:                     1.667
Date:                Mon, 20 Dec 2021   Prob (F-statistic):              0.198
Time:                        16:39:07   Log-Likelihood:                 264.19
No. Observations:                 164   AIC:                            -524.4
Df Residuals:                     162   BIC:                            -518.2
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================================
                                 coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------------------
Intercept                      0.0425      0.005      9.407      0.000       0.034       0.051
temp_fruits_excluding_wine    -0.0058      0.004     -1.291      0.198      -0.015       0.003
==============================================================================
Omnibus:                       26.668   Durbin-Watson:                   2.057
Prob(Omnibus):                  0.000   Jarque-Bera (JB):               35.553
Skew:                           1.140   Prob(JB):                     1.90e-08
Kurtosis:                       3.090   Cond. No.                         1.84
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Linear Regression Model (Slopt Intercept Form): Y = [-0.00577016]x + [0.04254187]

The P-Value is:  0.19848679481002798


countries_list = covid_df['Country'].unique()
country_arr = []

for c in countries_list:
    country = covid_df[covid_df.Country == c].Deaths
    # Extracted value from the filtered dataframe for the deaths percentage of Coronavirus for each country
    val = country.values[0]
    country_arr.append(val)
    
# Make a dataframe with these two columns to plot on Violin plot
covid_data = {'Country': countries_list, 'Death_Rate': country_arr}
cut_df = pd.DataFrame(covid_data)

#ax = sns.violinplot(x= cut_df['Death_Rate'])
fig = px.violin(cut_df, y='Death_Rate', box = True, points = 'all')
fig.show()


# This has the codes for the countries so that we can make a map which plots the different traits from our main dataset
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')

country_codes = []

# This loop will allow us to now get the correct countries for the map
for i in df['COUNTRY']:
    # This will figure out if the country exists in our dataset (Some of the countries in the above dataset aren't in our data)
    if i == "Bahamas, The":
            country_codes.append("NaN")
    if i == "Gambia, The":
            country_codes.append("NaN")
    if i == "Czech Republic":
            country_codes.append("NaN")
    if i == "Congo, Democratic Republic of the" or i == "Congo, Republic of the":
            country_codes.append("NaN")
            # We don't want anymore NaN's since there's technically only one Congo in our actual dataset      
            if i == "Congo, Democratic Republic of the":
                country_codes.pop()  
    elif cut_df['Country'].str.contains(i).any():
        # This will get the proper code for the country
        c = df.loc[df.COUNTRY == i, 'CODE']
        #Extracts the code
        country_codes.append(c.values[0])
        # This will make changes to the list in the current iteration so we can have the proper countries with their codes
        if i == "Estonia":
            country_codes.append("NaN")
        if i == "Kyrgyzstan":
            country_codes.append("LAO")
        if i == "Nigeria":
            country_codes.append("NaN")
        if i == "Macedonia":
            country_codes.pop()
            country_codes.append("MDG")
        
cut_df['Country_Code'] = country_codes

# Need to specifically change some country codes
cut_df.loc[cut_df.Country == 'Bahamas', 'Country_Code'] = 'BHM'
cut_df.loc[cut_df.Country == 'Congo', 'Country_Code'] = 'COD'
cut_df.loc[cut_df.Country == 'Czechia', 'Country_Code'] = 'CZE'
cut_df.loc[cut_df.Country == 'Eswatini', 'Country_Code'] = 'NaN'
cut_df.loc[cut_df.Country == 'Gambia', 'Country_Code'] = 'GMB'
cut_df.loc[cut_df.Country == 'Guinea-Bissau', 'Country_Code'] = 'GNB'
cut_df.loc[cut_df.Country == 'Guinea', 'Country_Code'] = 'GIN'
cut_df.loc[cut_df.Country == 'Myanmar', 'Country_Code'] = 'NaN'
cut_df.loc[cut_df.Country == 'Nigeria', 'Country_Code'] = 'NGA'
cut_df.loc[cut_df.Country == 'Niger', 'Country_Code'] = 'NER'
cut_df.loc[cut_df.Country == 'North Macedonia', 'Country_Code'] = 'NaN'
cut_df.loc[cut_df.Country == 'Republic of Moldova', 'Country_Code'] = 'MDA'
cut_df.loc[cut_df.Country == 'Malawi', 'Country_Code'] = 'MWI'
cut_df.loc[cut_df.Country == 'Malaysia', 'Country_Code'] = 'MYS'
cut_df.loc[cut_df.Country == 'Maldives', 'Country_Code'] = 'MDV'
cut_df.loc[cut_df.Country == 'Mali', 'Country_Code'] = 'MLI'
cut_df.loc[cut_df.Country == 'Malta', 'Country_Code'] = 'MLT'
cut_df.loc[cut_df.Country == 'Mauritania', 'Country_Code'] = 'MRT'
cut_df.loc[cut_df.Country == 'Mauritius', 'Country_Code'] = 'MUS'
cut_df.loc[cut_df.Country == 'Mexico', 'Country_Code'] = 'MEX'
cut_df.loc[cut_df.Country == 'Mongolia', 'Country_Code'] = 'MNG'
cut_df.loc[cut_df.Country == 'Montenegro', 'Country_Code'] = 'MNE'
cut_df.loc[cut_df.Country == 'Morocco', 'Country_Code'] = 'MAR'
cut_df.loc[cut_df.Country == 'Mozambique', 'Country_Code'] = 'MOZ'
cut_df.loc[cut_df.Country == 'Namibia', 'Country_Code'] = 'NAM'
cut_df.loc[cut_df.Country == 'Nepal', 'Country_Code'] = 'NPL'
cut_df.loc[cut_df.Country == 'Netherlands', 'Country_Code'] = 'NLD'
cut_df.loc[cut_df.Country == 'New Caledonia', 'Country_Code'] = 'NCL'
cut_df.loc[cut_df.Country == 'New Zealand', 'Country_Code'] = 'NZL'
cut_df.loc[cut_df.Country == 'Nicaragua', 'Country_Code'] = 'NIC'
cut_df.loc[cut_df.Country == 'Norway', 'Country_Code'] = 'NOR'
cut_df.loc[cut_df.Country == 'Oman', 'Country_Code'] = 'OMN'
cut_df.loc[cut_df.Country == 'Pakistan', 'Country_Code'] = 'PAK'
cut_df.loc[cut_df.Country == 'Panama', 'Country_Code'] = 'PAN'
cut_df.loc[cut_df.Country == 'Paraguay', 'Country_Code'] = 'PRY'
cut_df.loc[cut_df.Country == 'Peru', 'Country_Code'] = 'PER'
cut_df.loc[cut_df.Country == 'Philippines', 'Country_Code'] = 'PHL'
cut_df.loc[cut_df.Country == 'Poland', 'Country_Code'] = 'POL'
cut_df.loc[cut_df.Country == 'Portugal', 'Country_Code'] = 'PRT'
cut_df.loc[cut_df.Country == 'Thailand', 'Country_Code'] = 'THA'
cut_df.loc[cut_df.Country == 'Timor-Leste', 'Country_Code'] = 'TLS'
cut_df.loc[cut_df.Country == 'Togo', 'Country_Code'] = 'TGO'
cut_df.loc[cut_df.Country == 'Trinidad and Tobago', 'Country_Code'] = 'TTO'
cut_df.loc[cut_df.Country == 'Tunisia', 'Country_Code'] = 'TUN'
cut_df.loc[cut_df.Country == 'Turkey', 'Country_Code'] = 'TUR'
cut_df.loc[cut_df.Country == 'Turkmenistan', 'Country_Code'] = 'TKM'
cut_df.loc[cut_df.Country == 'Uganda', 'Country_Code'] = 'UGA'
cut_df.loc[cut_df.Country == 'Ukraine', 'Country_Code'] = 'UKR'
cut_df.loc[cut_df.Country == 'United Arab Emirates', 'Country_Code'] = 'ARE'
cut_df.loc[cut_df.Country == 'United Kingdom', 'Country_Code'] = 'GBR'
cut_df.loc[cut_df.Country == 'United Republic of Tanzania', 'Country_Code'] = 'TZA'

# Need loop to put codes in Dataframe
fig = go.Figure(data=go.Choropleth(
    locations = cut_df['Country_Code'],
    z = cut_df['Death_Rate'],
    text = cut_df['Country'],
    colorscale = 'Reds',
    autocolorscale=False,
    reversescale=False,
    marker_line_color='darkgray',
    marker_line_width=0.5,
    colorbar_tickprefix = '',
    colorbar_title = 'Death Rate<br>Percentage %',
))

fig.update_layout(
    title_text='Covid-19 Death Rates per Country',
    autosize = False,
    width = 800,
    height = 800,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))
    
fig.show()


# Creating a dataframe to include the regions of Countries
regions_covid_df = covid_df
food_regions_df = covid_food_df
regions_list = []

Asia = ['Bangladesh', 'Cambodia', 'China', 'India', 'Indonesia', 'Japan', 'Kazakhstan', 'Korea, North',
       'Korea, South', 'Kyrgyzstan', "Lao People's Democratic Republic", 'Malaysia', 'Maldives', 'Mongolia', 'Myanmar', 'Nepal',
       'Philippines', 'Sri Lanka', "Taiwan*", 'Tajikistan', 'Thailand', "Turkmenistan", 'Uzbekistan', 'Vietnam']
Middle_East_North_Africa_Greater_Arabia= ['Afghanistan', 'Algeria', 'Azerbaijan', 'Egypt', 'Iran (Islamic Republic of)', 'Iraq', 'Israel', 'Jordan',
              'Kuwait', 'Lebanon', 'Morocco', 'Oman', 'Pakistan', 'Saudi Arabia', 'Tunisia', 'Turkey', 'United Arab Emirates', 'Yemen']
Europe = ['Albania', 'Armenia', 'Austria', 'Belarus', 'Belgium', 'Bosnia and Herzegovina', 'Bulgaria', 'Croatia', 'Cyprus', 
         'Czechia', 'Denmark', 'Estonia', 'Finland', 'France', 'Georgia', 'Germany', 'Greece', 'Hungary', 'Iceland', 
         'Ireland', 'Italy', "Latvia", 'Lithuania', 'Luxembourg', 'North Macedonia', 'Malta', 'Republic of Moldova', 'Montenegro', 'Netherlands',
         'Norway', 'Poland', 'Portugal', 'Romania', 'Russian Federation', 'Serbia', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'Switzerland',
         'Ukraine', 'United Kingdom'] 
North_America = ['Canada', 'Mexico', "United States of America"]
Central_America_and_Caribbean = ['Antigua and Barbuda', 'Bahamas', 'Barbados', 'Belize', 'Costa Rica', 'Cuba', 'Dominica', 'Dominican Republic', 'El Salvador', 'Grenada',
                'Guatemala', 'Haiti', 'Honduras', 'Jamaica', 'Nicaragua', 'Panama', 'Saint Kitts and Nevis', 'Saint Lucia',
                'Saint Vincent and the Grenadines', 'Trinidad and Tobago']
South_America = ['Argentina', 'Bolivia', 'Brazil', 'Chile', 'Colombia', 'Ecuador','Guyana', 'Paraguay', 'Peru', 'Suriname', 'Uruguay', 'Venezuela (Bolivarian Republic of)']
Sub_Saharan_Africa = ['Angola', 'Benin', 'Botswana', 'Burkina Faso', 'Cabo Verde', 'Cameroon', 'Central African Republic', 'Chad',
                     'Congo', "Cote d'Ivoire", 'Djibouti', 'Eswatini', 'Ethiopia', 'Gabon', 'Gambia', 'Ghana', 'Guinea', 'Guinea-Bissau', 'Kenya', 'Lesotho', 'Liberia',
                     'Madagascar', 'Malawi', 'Mali', 'Mauritania', 'Mauritius', 'Mozambique', 'Namibia', 'Niger', 'Nigeria',  'Rwanda',
                     'Sao Tome and Principe', 'Senegal', 'Sierra Leone', 'South Africa', 'Sudan',  'United Republic of Tanzania',
                     'Togo', 'Uganda', 'Zambia', 'Zimbabwe']
Australia_and_Oceania = ['Australia',  'Fiji', 'French Polynesia', 'Kiribati', 'New Caledonia', 'New Zealand', 'Samoa', 'Solomon Islands', 'Timor-Leste', 'Vanuatu' ]

# Figuring out which countries are in which regions and then appending those to a list that will be combined into the dataframe
for i in regions_covid_df['Country']:
    if i in Asia:
        regions_list.append('Asia')
    if i in Middle_East_North_Africa_Greater_Arabia:
        regions_list.append('Middle East, North Africa, Greater Arabia')
    if i in Europe:
        regions_list.append('Europe')
    if i in North_America:
        regions_list.append('North America')
    if i in Central_America_and_Caribbean:
        regions_list.append('Central America, Caribbean')
    if i in South_America:
        regions_list.append('South America')
    if i in Sub_Saharan_Africa:
        regions_list.append('Sub-Saharan Africa')
        
    if i in Australia_and_Oceania:
        regions_list.append('Australia, Oceania')
    
# Fat Supply Quantity Dataset      
regions_covid_df['Region'] = regions_list
# Food Quantity Dataset
food_regions_df['Region'] = regions_list

# Placed the Region column farther up front in the dataframe, next to the Country
regions_covid_df = regions_covid_df[['Country', 'Region', 'Alcoholic Beverages', 'Animal fats', 'Animal Products', 'Aquatic Products, Other', 
                                  'Cereals - Excluding Beer', 'Eggs', 'Fish, Seafood', 'Fruits - Excluding Wine', 'Meat', 
                                  'Miscellaneous', 'Milk - Excluding Butter', 'Vegetables', 'Offals', 'Oilcrops', 'Pulses', 'Spices', 
                                  'Starchy Roots', 'Stimulants', 'Sugar Crops', 'Sugar & Sweeteners', 'Treenuts', 
                                  'Vegetal Products', 'Vegetable Oils', 'Fruits - Excluding Wine', 'Deaths', 'Confirmed', 'Obesity', 'Undernourished', 
                                  'Population', 'Recovered', 'Active',
                                  'Unit (all except Population)']]

regions_covid_df.head()


food_regions_df = food_regions_df[['Country', 'Region', 'Alcoholic Beverages', 'Animal fats', 'Animal Products', 'Aquatic Products, Other', 
                                  'Cereals - Excluding Beer', 'Eggs', 'Fish, Seafood', 'Fruits - Excluding Wine', 'Meat', 
                                  'Miscellaneous', 'Milk - Excluding Butter', 'Vegetables', 'Offals', 'Oilcrops', 'Pulses', 'Spices', 
                                  'Starchy Roots', 'Stimulants', 'Sugar Crops', 'Sugar & Sweeteners', 'Treenuts', 
                                  'Vegetal Products', 'Vegetable Oils', 'Fruits - Excluding Wine', 'Deaths', 'Confirmed', 'Obesity', 'Undernourished', 
                                  'Population', 'Recovered', 'Active',
                                  'Unit (all except Population)']]

food_regions_df.head()


regions = regions_covid_df['Region'].unique()



region_data = regions_covid_df[['Country', 'Region', 'Deaths', 'Obesity', 'Meat', 'Pulses', 'Sugar & Sweeteners', 'Vegetables', 'Fruits - Excluding Wine', 'Undernourished', 'Population',
                               'Confirmed', 'Animal Products', 'Treenuts']]
food_regions_data =  food_regions_df[['Country', 'Region', 'Deaths', 'Obesity', 'Meat', 'Pulses', 'Sugar & Sweeteners', 'Vegetables', 'Fruits - Excluding Wine', 'Undernourished', 'Population',
                               'Confirmed', 'Animal Products', 'Treenuts']]
fig = px.violin(region_data, x = 'Region', y='Deaths', points = 'all', color = 'Region', box = True)
fig.update_layout(
    title_text='Covid Death Percentage For Each Region of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))
fig.show()


fig = px.violin(region_data, x = 'Region', y='Obesity', points = 'all', color = 'Region', box = True)
fig.update_layout(
    title_text='Obesity Percentage compared by each region of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))
fig.show()


model = smf.ols(formula='Deaths ~ Obesity + C(Region) + Deaths:Obesity', data=region_data).fit()
summary = model.summary()
print(summary)

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Deaths   R-squared:                       0.974
Model:                            OLS   Adj. R-squared:                  0.973
Method:                 Least Squares   F-statistic:                     639.2
Date:                Mon, 20 Dec 2021   Prob (F-statistic):          1.43e-116
Time:                        16:39:09   Log-Likelihood:                 559.32
No. Observations:                 163   AIC:                            -1099.
Df Residuals:                     153   BIC:                            -1068.
Df Model:                           9                                         
Covariance Type:            nonrobust                                         
==========================================================================================================================
                                                             coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------------------------
Intercept                                                  0.0085      0.002      4.041      0.000       0.004       0.013
C(Region)[T.Australia, Oceania]                            0.0073      0.004      1.756      0.081      -0.001       0.016
C(Region)[T.Central America, Caribbean]                    0.0078      0.003      2.514      0.013       0.002       0.014
C(Region)[T.Europe]                                        0.0110      0.003      3.424      0.001       0.005       0.017
C(Region)[T.Middle East, North Africa, Greater Arabia]     0.0050      0.003      1.515      0.132      -0.002       0.012
C(Region)[T.North America]                                -0.0173      0.006     -2.813      0.006      -0.029      -0.005
C(Region)[T.South America]                                 0.0143      0.004      4.054      0.000       0.007       0.021
C(Region)[T.Sub-Saharan Africa]                           -0.0003      0.002     -0.120      0.905      -0.005       0.004
Obesity                                                   -0.0006      0.000     -4.878      0.000      -0.001      -0.000
Deaths:Obesity                                             0.0386      0.001     46.833      0.000       0.037       0.040
==============================================================================
Omnibus:                       17.285   Durbin-Watson:                   1.824
Prob(Omnibus):                  0.000   Jarque-Bera (JB):               56.823
Skew:                           0.224   Prob(JB):                     4.58e-13
Kurtosis:                       5.858   Cond. No.                         260.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.


fig = px.violin(region_data, x = 'Region', y='Meat', points = 'all', color = 'Region', box = True)
fig.update_layout(
    title_text='Meat Fat Percentage compared by each region of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))

fig.show()


fig = px.violin(food_regions_data, x = 'Region', y='Meat', points = 'all', color = 'Region', box = True)
fig.update_layout(
    title_text='Meat Consumption Percentage compared by each region of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))

fig.show()


fig = px.violin(region_data, x = 'Region', y='Vegetables', points = 'all', color = 'Region', box = True)
fig.update_layout(
    title_text='Vegetable Fat Percentage compared by each region of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))

fig.show()


fig = px.violin(food_regions_data, x = 'Region', y='Vegetables', points = 'all', color = 'Region', box = True)
fig.update_layout(
    title_text='Vegetable Consumption Percentage compared by each region of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))

fig.show()


fig = px.violin(region_data, x = 'Region', y='Animal Products', points = 'all', color = 'Region', box = True)
fig.update_layout(
    title_text='Animal Products Fat Percentage compared by each region of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))

fig.show()


fig = px.violin(food_regions_data, x = 'Region', y='Animal Products', points = 'all', color = 'Region', box = True)
fig.update_layout(
    title_text='Animal Product Consumption Percentage compared by each region of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))
fig.show()


Asia = ['Afghanistan', 'Bangladesh', 'Cambodia', 'China', 'India', 'Indonesia', 'Japan', 'Kazakhstan', 'Korea, North',
       'Korea, South', 'Kyrgyzstan', "Lao People's Democratic Republic", 'Malaysia', 'Maldives', 'Mongolia', 'Myanmar', 'Nepal',
       'Philippines', 'Sri Lanka', "Taiwan*", 'Tajikistan', 'Thailand', "Turkmenistan", 'Uzbekistan', 'Vietnam',
        'Iran (Islamic Republic of)', 'Iraq', 'Israel', 'Jordan','Kuwait','Lebanon', 'Oman', 'Pakistan', 'Saudi Arabia', 'Yemen',
        'United Arab Emirates']

Europe = ['Albania', 'Armenia', 'Austria', 'Belarus', 'Belgium', 'Bosnia and Herzegovina', 'Bulgaria', 'Croatia', 'Cyprus', 
         'Czechia', 'Denmark', 'Estonia', 'Finland', 'France', 'Georgia', 'Germany', 'Greece', 'Hungary', 'Iceland', 
         'Ireland', 'Italy', "Latvia", 'Lithuania', 'Luxembourg', 'North Macedonia', 'Malta', 'Republic of Moldova', 'Montenegro', 'Netherlands',
         'Norway', 'Poland', 'Portugal', 'Romania', 'Russian Federation', 'Serbia', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'Switzerland',
         'Ukraine', 'United Kingdom', 'Turkey','Azerbaijan'] 

Americas = ['Canada', 'Mexico', "United States of America", 'Argentina', 'Bolivia', 'Brazil', 'Chile', 'Colombia', 'Ecuador','Guyana', 'Paraguay', 'Peru',
            'Suriname', 'Uruguay', 'Venezuela (Bolivarian Republic of)','Antigua and Barbuda', 'Bahamas', 'Barbados', 'Belize', 'Costa Rica', 'Cuba', 'Dominica', 'Dominican Republic', 'El Salvador', 'Grenada',
                'Guatemala', 'Haiti', 'Honduras', 'Jamaica', 'Nicaragua', 'Panama', 'Saint Kitts and Nevis', 'Saint Lucia',
                'Saint Vincent and the Grenadines', 'Trinidad and Tobago']

Africa = ['Algeria','Angola', 'Benin', 'Botswana', 'Burkina Faso', 'Cabo Verde', 'Cameroon', 'Central African Republic', 'Chad',
                     'Congo', "Cote d'Ivoire", 'Djibouti', 'Egypt','Eswatini', 'Ethiopia', 'Gabon', 'Gambia', 'Ghana', 'Guinea', 'Guinea-Bissau', 'Kenya', 'Lesotho', 'Liberia',
                     'Madagascar', 'Malawi', 'Mali', 'Mauritania', 'Mauritius', 'Mozambique', 'Namibia', 'Niger', 'Nigeria',  'Rwanda',
                     'Sao Tome and Principe', 'Senegal', 'Sierra Leone', 'South Africa', 'Sudan',  'United Republic of Tanzania',
                     'Togo', 'Uganda', 'Zambia', 'Zimbabwe','Morocco', 'Tunisia']

Oceania = ['Australia',  'Fiji', 'French Polynesia', 'Kiribati', 'New Caledonia', 'New Zealand', 'Samoa', 'Solomon Islands', 'Timor-Leste', 'Vanuatu' ]

continent_list = []

for i in regions_covid_df['Country']:
    if i in Asia:
        continent_list.append('Asia')
    if i in Africa:
        continent_list.append('Africa')
    if i in Europe:
        continent_list.append('Europe')
    if i in Americas:
        continent_list.append('Americas')     
    if i in Australia_and_Oceania:
        continent_list.append('Oceania')
# Fat Percentage Dataset
regions_covid_df['Continent'] = continent_list
# Food Supply Dataset
food_regions_df['Continent'] = continent_list

continent_data = regions_covid_df[['Country', 'Continent', 'Region', 'Deaths', 'Obesity', 'Meat', 'Pulses', 'Sugar & Sweeteners', 'Vegetables', 'Fruits - Excluding Wine', 'Undernourished', 'Population',
                               'Confirmed', 'Animal Products']]
continent_data.head()


food_continent_data = food_regions_df[['Country', 'Continent', 'Region', 'Deaths', 'Obesity', 'Meat', 'Pulses', 'Sugar & Sweeteners', 'Vegetables', 'Fruits - Excluding Wine', 'Undernourished', 'Population',
                               'Confirmed', 'Animal Products', 'Treenuts']]
food_continent_data.head()


fig = px.violin(continent_data, x = 'Continent', y='Deaths', points = 'all', color = 'Continent', box = True)
fig.update_layout(
    title_text='Covid Death Percentage compared by each Continent of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))

fig.show()


fig = px.violin(continent_data, x = 'Continent', y='Obesity', points = 'all', color = 'Continent', box = True)
fig.update_layout(
    title_text='Obesity Percentage compared by each Continent of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))
fig.show()


model = smf.ols(formula='Deaths ~ Obesity + C(Continent) + Deaths:Obesity', data=continent_data).fit()
summary = model.summary()

print(summary)

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Deaths   R-squared:                       0.967
Model:                            OLS   Adj. R-squared:                  0.966
Method:                 Least Squares   F-statistic:                     769.7
Date:                Mon, 20 Dec 2021   Prob (F-statistic):          3.82e-113
Time:                        16:39:10   Log-Likelihood:                 540.40
No. Observations:                 163   AIC:                            -1067.
Df Residuals:                     156   BIC:                            -1045.
Df Model:                           6                                         
Covariance Type:            nonrobust                                         
============================================================================================
                               coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------
Intercept                    0.0083      0.002      4.785      0.000       0.005       0.012
C(Continent)[T.Americas]     0.0074      0.002      3.023      0.003       0.003       0.012
C(Continent)[T.Asia]         0.0010      0.002      0.476      0.635      -0.003       0.005
C(Continent)[T.Europe]       0.0113      0.003      4.261      0.000       0.006       0.017
C(Continent)[T.Oceania]      0.0057      0.004      1.418      0.158      -0.002       0.014
Obesity                     -0.0005      0.000     -4.988      0.000      -0.001      -0.000
Deaths:Obesity               0.0377      0.001     46.380      0.000       0.036       0.039
==============================================================================
Omnibus:                       43.657   Durbin-Watson:                   1.756
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              492.449
Skew:                          -0.496   Prob(JB):                    1.16e-107
Kurtosis:                      11.457   Cond. No.                         134.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.


fig = px.violin(continent_data, x = 'Continent', y='Meat', points = 'all', color = 'Continent', box = True)
fig.update_layout(
    title_text='Meat Fat Percentage compared by each Continent of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))

fig.show()


fig = px.violin(food_continent_data, x = 'Continent', y='Meat', points = 'all', color = 'Continent', box = True)
fig.update_layout(
    title_text='Meat Consumption Percentage compared by each Continent of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))
fig.show()


fig = px.violin(continent_data, x = 'Continent', y='Vegetables', points = 'all', color = 'Continent', box = True)
fig.update_layout(
    title_text='Vegetables Fat Consumption compared by each Continent of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))

fig.show()


fig = px.violin(food_continent_data, x = 'Continent', y='Vegetables', points = 'all', color = 'Continent', box = True)
fig.update_layout(
    title_text='Vegetables Consumption compared by each Continent of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))

fig.show()


fig = px.violin(continent_data, x = 'Continent', y='Animal Products', points = 'all', color = 'Continent', box = True)
fig.update_layout(
    title_text='Animal Products Fat Consumption compared by each Continent of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))

fig.show()


fig = px.violin(food_continent_data, x = 'Continent', y='Animal Products', points = 'all', color = 'Continent', box = True)
fig.update_layout(
    title_text='Animal Products Consumption compared by each Continent of the World',
    autosize = False,
    width = 1500,
    height = 900,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ))

fig.show()


model = smf.ols(formula='Deaths ~ Obesity + Meat + C(Region) + Obesity:Meat', data=food_regions_df).fit()
summary = model.summary()

print(summary)

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Deaths   R-squared:                       0.603
Model:                            OLS   Adj. R-squared:                  0.577
Method:                 Least Squares   F-statistic:                     23.11
Date:                Mon, 20 Dec 2021   Prob (F-statistic):           6.67e-26
Time:                        16:39:10   Log-Likelihood:                 336.92
No. Observations:                 163   AIC:                            -651.8
Df Residuals:                     152   BIC:                            -617.8
Df Model:                          10                                         
Covariance Type:            nonrobust                                         
==========================================================================================================================
                                                             coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------------------------
Intercept                                                 -0.0005      0.013     -0.037      0.971      -0.026       0.025
C(Region)[T.Australia, Oceania]                           -0.0175      0.016     -1.074      0.285      -0.050       0.015
C(Region)[T.Central America, Caribbean]                    0.0079      0.012      0.647      0.519      -0.016       0.032
C(Region)[T.Europe]                                        0.0770      0.012      6.636      0.000       0.054       0.100
C(Region)[T.Middle East, North Africa, Greater Arabia]     0.0124      0.014      0.914      0.362      -0.014       0.039
C(Region)[T.North America]                                 0.0853      0.023      3.765      0.000       0.041       0.130
C(Region)[T.South America]                                 0.0541      0.013      4.011      0.000       0.027       0.081
C(Region)[T.Sub-Saharan Africa]                            0.0019      0.009      0.218      0.828      -0.016       0.019
Obesity                                                    0.0005      0.001      0.645      0.520      -0.001       0.002
Meat                                                       0.0001      0.004      0.038      0.969      -0.007       0.008
Obesity:Meat                                            3.285e-05      0.000      0.186      0.853      -0.000       0.000
==============================================================================
Omnibus:                        9.016   Durbin-Watson:                   2.289
Prob(Omnibus):                  0.011   Jarque-Bera (JB):               17.259
Skew:                           0.148   Prob(JB):                     0.000179
Kurtosis:                       4.566   Cond. No.                     1.11e+03
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.11e+03. This might indicate that there are
strong multicollinearity or other numerical problems.


model = smf.ols(formula='Deaths ~ Obesity + Meat + C(Continent) + Obesity:Meat', data=food_continent_data).fit()
summary = model.summary()

print(summary)

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Deaths   R-squared:                       0.520
Model:                            OLS   Adj. R-squared:                  0.498
Method:                 Least Squares   F-statistic:                     23.95
Date:                Mon, 20 Dec 2021   Prob (F-statistic):           7.07e-22
Time:                        16:39:10   Log-Likelihood:                 321.34
No. Observations:                 163   AIC:                            -626.7
Df Residuals:                     155   BIC:                            -601.9
Df Model:                           7                                         
Covariance Type:            nonrobust                                         
============================================================================================
                               coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------
Intercept                    0.0040      0.011      0.354      0.724      -0.018       0.026
C(Continent)[T.Americas]     0.0228      0.010      2.263      0.025       0.003       0.043
C(Continent)[T.Asia]         0.0009      0.008      0.114      0.910      -0.015       0.017
C(Continent)[T.Europe]       0.0669      0.009      7.061      0.000       0.048       0.086
C(Continent)[T.Oceania]     -0.0276      0.016     -1.770      0.079      -0.058       0.003
Obesity                      0.0005      0.001      0.712      0.478      -0.001       0.002
Meat                        -0.0024      0.004     -0.595      0.553      -0.010       0.006
Obesity:Meat                 0.0002      0.000      0.900      0.370      -0.000       0.001
==============================================================================
Omnibus:                        6.123   Durbin-Watson:                   2.152
Prob(Omnibus):                  0.047   Jarque-Bera (JB):                5.927
Skew:                           0.368   Prob(JB):                       0.0516
Kurtosis:                       3.576   Cond. No.                         612.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.


food_regions_df.columns.values[5] = "AnimalProducts"
model = smf.ols(formula='Deaths ~ Obesity + C(Region) + AnimalProducts + Obesity:Vegetables', data=food_regions_df).fit()
summary = model.summary()
print(summary)

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Deaths   R-squared:                       0.603
Model:                            OLS   Adj. R-squared:                  0.577
Method:                 Least Squares   F-statistic:                     23.06
Date:                Mon, 20 Dec 2021   Prob (F-statistic):           7.30e-26
Time:                        16:39:11   Log-Likelihood:                 336.82
No. Observations:                 163   AIC:                            -651.6
Df Residuals:                     152   BIC:                            -617.6
Df Model:                          10                                         
Covariance Type:            nonrobust                                         
==========================================================================================================================
                                                             coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------------------------
Intercept                                                 -0.0009      0.008     -0.102      0.919      -0.018       0.016
C(Region)[T.Australia, Oceania]                           -0.0172      0.017     -1.010      0.314      -0.051       0.016
C(Region)[T.Central America, Caribbean]                    0.0083      0.013      0.654      0.514      -0.017       0.033
C(Region)[T.Europe]                                        0.0764      0.012      6.583      0.000       0.053       0.099
C(Region)[T.Middle East, North Africa, Greater Arabia]     0.0106      0.013      0.801      0.425      -0.016       0.037
C(Region)[T.North America]                                 0.0859      0.023      3.755      0.000       0.041       0.131
C(Region)[T.South America]                                 0.0543      0.014      3.911      0.000       0.027       0.082
C(Region)[T.Sub-Saharan Africa]                            0.0017      0.009      0.184      0.855      -0.016       0.020
Obesity                                                    0.0007      0.001      1.230      0.221      -0.000       0.002
AnimalProducts                                         -1.688e-05      0.019     -0.001      0.999      -0.038       0.038
Obesity:Vegetables                                      3.026e-06   4.18e-05      0.072      0.942   -7.95e-05    8.56e-05
==============================================================================
Omnibus:                        8.939   Durbin-Watson:                   2.278
Prob(Omnibus):                  0.011   Jarque-Bera (JB):               17.046
Skew:                           0.146   Prob(JB):                     0.000199
Kurtosis:                       4.557   Cond. No.                     1.92e+03
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.92e+03. This might indicate that there are
strong multicollinearity or other numerical problems.


food_continent_data.columns.values[15] = "AnimalProducts"
model = smf.ols(formula='Deaths ~ Obesity + C(Continent) + AnimalProducts + Obesity:Vegetables', data=food_continent_data).fit()
summary = model.summary()
print(summary)

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Deaths   R-squared:                       0.525
Model:                            OLS   Adj. R-squared:                  0.503
Method:                 Least Squares   F-statistic:                     24.46
Date:                Mon, 20 Dec 2021   Prob (F-statistic):           3.10e-22
Time:                        16:39:11   Log-Likelihood:                 322.23
No. Observations:                 163   AIC:                            -628.5
Df Residuals:                     155   BIC:                            -603.7
Df Model:                           7                                         
Covariance Type:            nonrobust                                         
============================================================================================
                               coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------
Intercept                   -0.0058      0.007     -0.855      0.394      -0.019       0.008
C(Continent)[T.Americas]     0.0229      0.010      2.357      0.020       0.004       0.042
C(Continent)[T.Asia]        -0.0003      0.008     -0.036      0.971      -0.017       0.016
C(Continent)[T.Europe]       0.0649      0.009      7.106      0.000       0.047       0.083
C(Continent)[T.Oceania]     -0.0276      0.016     -1.731      0.085      -0.059       0.004
Obesity                      0.0013      0.001      2.434      0.016       0.000       0.002
AnimalProducts               0.0309      0.020      1.549      0.123      -0.009       0.070
Obesity:Vegetables       -3.418e-05   4.35e-05     -0.786      0.433      -0.000    5.17e-05
==============================================================================
Omnibus:                        5.804   Durbin-Watson:                   2.216
Prob(Omnibus):                  0.055   Jarque-Bera (JB):                5.684
Skew:                           0.341   Prob(JB):                       0.0583
Kurtosis:                       3.610   Cond. No.                     1.16e+03
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.16e+03. This might indicate that there are
strong multicollinearity or other numerical problems.


model = smf.ols(formula='Deaths ~ Obesity + Vegetables + C(Region) + Obesity:Vegetables', data=food_regions_df).fit()
summary = model.summary()

print(summary)

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Deaths   R-squared:                       0.603
Model:                            OLS   Adj. R-squared:                  0.577
Method:                 Least Squares   F-statistic:                     23.06
Date:                Mon, 20 Dec 2021   Prob (F-statistic):           7.30e-26
Time:                        16:39:11   Log-Likelihood:                 336.82
No. Observations:                 163   AIC:                            -651.6
Df Residuals:                     152   BIC:                            -617.6
Df Model:                          10                                         
Covariance Type:            nonrobust                                         
==========================================================================================================================
                                                             coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------------------------
Intercept                                                 -0.0010      0.015     -0.066      0.948      -0.031       0.029
C(Region)[T.Australia, Oceania]                           -0.0171      0.017     -0.996      0.321      -0.051       0.017
C(Region)[T.Central America, Caribbean]                    0.0083      0.013      0.638      0.525      -0.017       0.034
C(Region)[T.Europe]                                        0.0764      0.012      6.361      0.000       0.053       0.100
C(Region)[T.Middle East, North Africa, Greater Arabia]     0.0107      0.014      0.750      0.454      -0.017       0.039
C(Region)[T.North America]                                 0.0859      0.023      3.720      0.000       0.040       0.132
C(Region)[T.South America]                                 0.0543      0.014      3.837      0.000       0.026       0.082
C(Region)[T.Sub-Saharan Africa]                            0.0017      0.010      0.170      0.865      -0.018       0.022
Obesity                                                    0.0007      0.001      1.000      0.319      -0.001       0.002
Vegetables                                              1.923e-05      0.002      0.011      0.991      -0.004       0.004
Obesity:Vegetables                                      2.191e-06    8.8e-05      0.025      0.980      -0.000       0.000
==============================================================================
Omnibus:                        8.937   Durbin-Watson:                   2.278
Prob(Omnibus):                  0.011   Jarque-Bera (JB):               17.041
Skew:                           0.146   Prob(JB):                     0.000199
Kurtosis:                       4.557   Cond. No.                     2.02e+03
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 2.02e+03. This might indicate that there are
strong multicollinearity or other numerical problems.


model = smf.ols(formula='Deaths ~ Obesity + C(Continent) + Vegetables + Obesity:Vegetables', data=food_continent_data).fit()
summary = model.summary()

print(summary)

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Deaths   R-squared:                       0.518
Model:                            OLS   Adj. R-squared:                  0.496
Method:                 Least Squares   F-statistic:                     23.76
Date:                Mon, 20 Dec 2021   Prob (F-statistic):           9.77e-22
Time:                        16:39:11   Log-Likelihood:                 320.99
No. Observations:                 163   AIC:                            -626.0
Df Residuals:                     155   BIC:                            -601.2
Df Model:                           7                                         
Covariance Type:            nonrobust                                         
============================================================================================
                               coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------
Intercept                   -0.0049      0.011     -0.435      0.664      -0.027       0.017
C(Continent)[T.Americas]     0.0216      0.010      2.131      0.035       0.002       0.042
C(Continent)[T.Asia]         0.0013      0.009      0.140      0.889      -0.016       0.019
C(Continent)[T.Europe]       0.0654      0.010      6.852      0.000       0.047       0.084
C(Continent)[T.Oceania]     -0.0284      0.016     -1.734      0.085      -0.061       0.004
Obesity                      0.0014      0.001      1.849      0.066   -9.35e-05       0.003
Vegetables                   0.0002      0.002      0.121      0.904      -0.003       0.004
Obesity:Vegetables       -3.103e-05   8.86e-05     -0.350      0.727      -0.000       0.000
==============================================================================
Omnibus:                        5.994   Durbin-Watson:                   2.190
Prob(Omnibus):                  0.050   Jarque-Bera (JB):                5.778
Skew:                           0.363   Prob(JB):                       0.0556
Kurtosis:                       3.568   Cond. No.                     1.09e+03
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.09e+03. This might indicate that there are
strong multicollinearity or other numerical problems.

	Country	Alcoholic Beverages	Animal Products	Animal fats	Aquatic Products, Other	Cereals - Excluding Beer	Eggs	Fish, Seafood	Fruits - Excluding Wine	Meat	Miscellaneous	Milk - Excluding Butter	Offals	Oilcrops	Pulses	Spices	Starchy Roots	Stimulants	Sugar Crops	Sugar & Sweeteners	Treenuts	Vegetal Products	Vegetable Oils	Vegetables	Obesity	Undernourished	Confirmed	Deaths	Recovered	Active	Population	Unit (all except Population)
0	Afghanistan	0.0000	21.6397	6.2224	0.0000	8.0353	0.6859	0.0327	0.4246	6.1244	0.0163	8.2803	0.3103	1.0452	0.1960	0.2776	0.0490	0.0980	0.0000	0.0000	0.7513	28.3684	17.0831	0.3593	4.5	29.8	0.142134	0.006186	0.123374	0.012574	3.892800e+07	%
1	Albania	0.0000	32.0002	3.4172	0.0000	2.6734	1.6448	0.1445	0.6418	8.7428	0.0170	17.7576	0.2933	3.1622	0.1148	0.0000	0.0510	0.5270	0.0000	0.0000	0.9181	17.9998	9.2443	0.6503	22.3	6.2	2.967301	0.050951	1.792636	1.123714	2.838000e+06	%
2	Algeria	0.0000	14.4175	0.8972	0.0000	4.2035	1.2171	0.2008	0.5772	3.8961	0.0439	8.0934	0.1067	1.1983	0.2698	0.1568	0.1129	0.2886	0.0000	0.0000	0.8595	35.5857	27.3606	0.5145	26.6	3.9	0.244897	0.006558	0.167572	0.070767	4.435700e+07	%
3	Angola	0.0000	15.3041	1.3130	0.0000	6.5545	0.1539	1.4155	0.3488	11.0268	0.0308	1.2309	0.1539	3.9902	0.3282	0.0103	0.7078	0.1128	0.0000	0.0000	0.0308	34.7010	22.4638	0.1231	6.8	25	0.061687	0.001461	0.056808	0.003419	3.252200e+07	%
4	Antigua and Barbuda	0.0000	27.7033	4.6686	0.0000	3.2153	0.3872	1.5263	1.2177	14.3202	0.0898	6.6607	0.1347	1.3579	0.0673	0.3591	0.0449	1.0549	0.0000	0.0000	0.2020	22.2995	14.4436	0.2469	19.1	NaN	0.293878	0.007143	0.190816	0.095918	9.800000e+04	%
5	Argentina	0.0000	30.3572	3.3076	0.0000	1.3316	1.5706	0.1664	0.2091	19.2693	0.0000	5.8512	0.1878	0.0640	0.0213	0.0213	0.1110	0.2475	0.0000	0.0000	0.1366	19.6449	17.3147	0.1878	28.5	4.6	4.356147	0.108227	3.905192	0.342729	4.537700e+07	%
6	Armenia	0.0000	29.6642	6.2619	0.0000	2.5068	1.6196	0.2218	0.5468	10.8165	0.0361	10.4709	0.2734	0.6602	0.0774	0.0103	0.0567	1.8002	0.0000	0.0000	0.9542	20.3384	12.8127	0.8717	20.9	4.3	5.681225	0.105345	5.398410	0.177470	2.956000e+06	%
7	Australia	0.0000	24.1099	4.6030	0.0000	0.9908	0.7017	0.4515	0.4028	11.6002	0.0520	6.5196	0.2339	1.2929	0.0260	0.1007	0.0422	0.7926	0.0000	0.0000	1.6145	25.8901	20.3612	0.2144	30.4	<2.5	0.112025	0.003530	0.101289	0.007207	2.575400e+07	%
8	Austria	0.0000	27.8268	12.8517	0.0000	1.2297	1.2147	0.4259	0.2249	8.1099	0.0000	5.1497	0.0750	1.1367	0.0120	0.1020	0.0450	0.4439	0.0000	0.0000	0.8398	22.1762	17.9323	0.2039	21.9	<2.5	4.739982	0.089679	4.496870	0.153433	8.914000e+06	%
9	Azerbaijan	0.0000	32.1198	7.7987	0.0000	5.4481	2.0197	0.2122	0.5940	11.9993	0.0170	9.9202	0.1612	0.1867	0.0255	0.0170	0.1697	1.3663	0.0000	0.0000	2.2573	17.8802	7.1538	0.6534	19.9	<2.5	2.285536	0.031223	2.225574	0.028740	1.010800e+07	%
10	Bahamas	0.0000	30.2259	4.5600	0.0000	3.6327	1.2829	1.4991	0.8995	17.4941	0.3556	5.1318	0.2580	2.6077	0.0209	0.2719	0.0349	0.9343	0.0000	0.0000	0.4532	19.7741	10.1659	0.3974	32.1	NaN	2.100763	0.044784	1.735115	0.320865	3.930000e+05	%
11	Bangladesh	0.0000	9.8365	1.2369	0.0000	6.9946	1.2664	2.1646	0.4270	1.8407	0.0147	3.2249	0.1178	0.6332	0.5448	1.0602	0.2209	0.0295	0.0147	0.0000	0.2651	40.1561	29.6422	0.3092	3.4	14.7	0.316691	0.004823	0.284344	0.027524	1.698090e+08	%
12	Barbados	0.0000	25.8451	3.9733	0.0000	2.1187	1.2130	1.8060	0.4636	13.1382	0.3235	5.6176	0.0970	3.2616	0.2318	0.3181	0.1078	0.6038	0.0000	0.0000	0.7817	24.1576	15.6882	0.2534	24.8	3.9	0.583972	0.006272	0.470035	0.107666	2.870000e+05	%
13	Belarus	0.0000	23.8181	7.8662	0.0000	4.8075	1.5772	0.5151	0.2316	11.5636	0.0519	2.0644	0.2316	0.4392	0.0000	0.0160	0.1957	0.8385	0.0000	0.0000	1.0462	26.1859	18.2119	0.3394	26.6	<2.5	2.740896	0.018912	2.612704	0.109280	9.375000e+06	%
14	Belgium	0.0000	26.7378	12.6234	0.0000	1.3531	1.1703	0.6188	0.2373	5.6817	0.0000	6.5057	0.1379	0.5226	0.0353	0.0994	0.0770	0.0834	0.0000	0.0000	1.2056	23.2622	19.3504	0.2982	24.5	<2.5	6.286322	0.185428	0.000000	6.100894	1.151500e+07	%
15	Belize	0.0000	23.7877	9.8102	0.0000	3.0510	0.6759	0.4155	0.3039	10.3435	0.1984	2.5053	0.0310	1.4511	0.3783	0.0496	0.0310	0.8062	0.0000	0.0000	0.3845	26.2123	19.3538	0.2108	22.4	7.5	2.872792	0.073031	2.739618	0.060143	4.190000e+05	%
16	Benin	0.0000	6.1742	0.4816	0.0000	4.6952	0.2064	0.8599	0.1978	3.2849	0.0258	1.2469	0.0946	7.3007	0.6535	2.5970	1.0835	0.1118	0.0000	0.0000	1.9176	43.8301	24.8775	0.3612	8.2	10.1	0.034344	0.000450	0.029183	0.004710	1.220900e+07	%
17	Bolivia	0.0000	30.4177	4.1685	0.0000	2.7296	1.5174	0.1831	0.5407	21.1476	0.0436	3.2703	0.1395	1.3604	0.2093	0.1483	0.4796	0.7849	0.0000	0.0000	0.7413	19.5779	12.3485	0.1919	18.7	17.1	1.952446	0.092435	1.438614	0.421396	1.163300e+07	%
18	Bosnia and Herzegovina	0.0000	24.6478	2.4453	0.0000	3.6588	0.8354	0.3049	0.5976	9.6957	0.0915	11.2629	0.1037	0.9208	0.2012	2.2196	0.1342	2.4819	0.0000	0.0000	0.9574	25.3552	13.2996	0.7866	19.4	<2.5	3.762085	0.145535	2.960317	0.656233	3.281000e+06	%
19	Botswana	0.0000	16.9634	4.1310	0.0000	7.1830	0.2697	0.3622	0.1696	3.4759	0.1927	8.5241	0.2081	1.1715	0.1156	0.5472	0.1927	0.7399	0.0000	0.0000	0.1541	33.0328	22.3815	0.1850	16.1	26.4	1.014372	0.007035	0.844799	0.162538	2.317000e+06	%
20	Brazil	0.0000	24.8515	4.3627	0.0000	1.1278	0.7706	0.1886	0.2528	14.2920	0.0000	5.1654	0.0763	1.7097	0.2528	0.0080	0.1204	0.1164	0.0361	0.0120	0.1766	25.1445	21.2153	0.1204	22.3	<2.5	4.460165	0.108603	3.979469	0.372094	2.118120e+08	%
21	Bulgaria	0.0000	27.1456	8.0274	0.0000	1.8485	1.2201	0.3404	0.2723	9.3523	0.0262	7.9698	0.2356	1.6547	0.0471	0.6179	0.0367	0.1885	0.0000	0.0000	0.3404	22.8518	17.5787	0.2461	27.4	3.6	3.227256	0.134416	2.788956	0.303883	6.927000e+06	%
22	Burkina Faso	0.0000	7.2367	0.5981	0.0000	10.5524	0.4061	0.3692	0.0443	3.5741	0.0148	2.1636	0.1255	11.9406	0.5391	0.0960	0.0369	0.0222	0.0000	0.0000	0.2658	42.7706	19.1774	0.0665	4.5	20	0.053710	0.000641	0.048031	0.005038	2.090300e+07	%
23	Cabo Verde	0.0000	21.9578	4.2923	0.0000	5.1318	0.8468	0.5256	0.1898	9.9204	0.1679	6.2851	0.0803	1.4819	0.4526	0.9709	0.0949	0.4161	0.0000	0.0000	0.1752	28.0385	18.6656	0.3066	10.6	12.6	2.586331	0.024281	2.461151	0.100899	5.560000e+05	%
24	Cambodia	0.0000	18.8510	0.8839	0.0000	9.4179	0.5639	4.8461	0.2438	11.7799	0.1372	0.6248	0.1372	8.2902	0.2743	0.5029	0.3657	0.0610	0.0762	0.0000	0.3048	31.1490	11.2313	0.2591	3.5	16.4	0.003059	0.000000	0.002930	0.000129	1.549700e+07	%
25	Cameroon	0.0000	6.3264	0.7838	0.0000	6.9796	0.0746	0.9051	0.7931	3.3685	0.0280	1.1197	0.0840	9.7509	0.8491	0.7465	0.5319	0.1213	0.0000	0.0000	0.1026	43.6689	23.2155	0.5505	9.5	9.9	0.118031	0.001782	0.110914	0.005335	2.659800e+07	%
26	Canada	0.0000	23.1663	9.7895	0.0000	1.5440	1.2437	0.4244	0.4244	7.8211	0.0620	3.8812	0.0065	2.3600	0.1991	0.0979	0.0588	0.1795	0.0000	0.0000	1.2110	26.8353	20.4733	0.2220	31.3	<2.5	2.109961	0.054203	1.909848	NaN	3.819000e+07	%
27	Central African Republic	0.0000	18.5453	2.8767	0.0000	2.7645	0.1122	0.5509	0.4998	12.9144	0.0102	1.7342	0.3468	7.0183	0.4794	0.0102	1.0609	0.0408	0.0000	0.0000	0.0000	31.4598	19.4838	0.0918	6.3	59.6	0.103292	0.001304	0.101139	0.000849	4.830000e+06	%
28	Chad	0.0000	16.8564	3.1261	0.0000	7.9860	0.0701	0.6480	0.0701	7.8546	0.0088	4.4308	0.7268	16.7863	0.5342	0.0000	0.1926	0.0263	0.0000	0.0000	0.0000	33.1436	7.5131	0.0263	4.8	37.5	0.020578	0.000741	0.016881	0.002957	1.687700e+07	%
29	Chile	0.0000	32.5206	5.2608	0.0000	3.1847	1.4085	0.5204	0.4186	20.2172	0.0339	5.0798	0.0396	1.1596	0.2093	0.0283	0.1810	0.4356	0.0000	0.0000	0.7241	17.4737	10.8440	0.2602	28.8	2.7	3.842229	0.097047	3.626194	NaN	1.947000e+07	%
30	China	0.0000	31.0465	3.4468	0.0097	2.5989	2.3066	0.9817	0.2344	21.6062	0.1517	2.2156	0.4784	1.8351	0.0234	0.0676	0.0565	0.9362	0.0000	0.0179	1.7979	18.9535	10.6590	0.5763	6.6	8.5	0.007156	0.000344	0.006685	0.000126	1.402385e+09	%
31	Colombia	0.0000	21.4008	1.7819	0.0000	1.7347	1.7819	0.3717	1.2273	11.3996	0.0177	5.9535	0.1180	0.9854	0.1652	0.1239	0.2832	0.2655	0.0000	0.0000	0.5251	28.5992	23.0824	0.1829	22.1	4.8	4.350795	0.112639	4.090144	0.148012	4.944400e+07	%
32	Congo	0.0000	12.9993	0.3390	0.0000	2.7016	0.1377	1.8540	0.8370	9.6514	0.0530	0.6780	0.3390	5.0429	0.1483	0.0106	1.0806	0.5191	0.0000	0.0000	0.0212	36.9954	26.0091	0.5827	8.4	40.3	0.571675	0.014516	0.379721	0.177437	5.518000e+06	%
33	Costa Rica	0.0000	20.5571	6.1000	0.0000	1.6733	1.1492	0.9286	0.5700	5.6449	0.0138	6.6700	0.0552	1.0389	0.2436	0.0414	0.0506	0.4045	0.0000	0.0138	0.3540	29.4475	24.8736	0.1701	25.7	4.8	3.843436	0.052279	3.055782	0.735375	5.111000e+06	%
34	Cote d'Ivoire	0.0000	5.3253	0.2726	0.0000	4.9709	0.4271	1.2268	0.5271	2.5809	0.0091	0.5634	0.2545	4.8891	0.1000	0.6089	1.2359	1.9357	0.0000	0.0000	2.3355	44.6747	27.7354	0.3272	9.0	19	0.112959	0.000619	0.106105	0.006235	2.617500e+07	%
35	Croatia	0.0000	29.3768	7.4457	0.0000	2.9565	0.9476	0.5633	0.2577	11.4896	0.0000	8.7951	0.1310	1.8123	0.0175	0.0655	0.0393	2.6726	0.0000	0.0000	0.6987	20.6254	11.7123	0.3930	27.1	<2.5	5.848905	0.127960	5.648706	0.072239	4.020000e+06	%
36	Cuba	0.0000	25.9025	3.2213	0.0000	4.4779	1.4788	0.2638	0.6179	16.0233	0.0139	4.7695	0.1458	2.6937	0.6248	0.0417	0.3749	0.1458	0.0000	0.0000	0.0069	24.0975	14.5446	0.5554	26.7	<2.5	0.283760	0.002065	0.230822	0.050873	1.128100e+07	%
37	Cyprus	0.0000	20.9578	1.8698	0.0000	3.4616	1.0044	0.7309	0.3632	10.5506	0.1614	6.6945	0.1031	2.1164	0.0628	0.0538	0.0404	1.9236	0.0000	0.0000	0.8609	29.0467	19.7381	0.2601	22.6	5.6	2.621707	0.017481	0.170423	2.433803	1.207000e+06	%
38	Czechia	0.0000	25.1685	9.7011	0.0000	1.1998	0.8222	0.3000	0.1870	8.6212	0.0212	5.6463	0.0776	0.4941	0.0353	0.0635	0.0565	0.9563	0.0000	0.0000	0.4164	24.8333	21.2267	0.1729	28.5	<2.5	9.612841	0.159845	8.555328	0.897667	1.071600e+07	%
39	Denmark	0.0000	35.4131	14.2498	0.0000	1.4741	1.7484	1.1313	0.5257	9.2408	0.0000	8.8142	0.2285	0.7161	0.0152	0.1752	0.0533	1.6874	0.0000	0.0000	1.1846	14.5850	8.4752	0.2819	21.3	<2.5	3.463218	0.038017	3.307360	0.117842	5.829000e+06	%
40	Djibouti	0.0000	11.1065	1.3966	0.0000	4.3977	0.2494	0.2910	0.1247	5.1210	0.1413	3.8573	0.1912	0.4323	0.5570	0.3159	0.0333	0.5653	0.0000	0.0000	0.4157	38.8977	31.4490	0.4572	12.2	18.9	0.601316	0.006377	0.592308	0.002632	9.880000e+05	%
41	Dominica	0.0000	26.9250	3.8063	0.0000	1.4913	0.3619	1.6536	1.1856	13.7339	0.1123	7.2320	0.1373	4.8172	0.1373	0.4680	0.4867	1.7846	0.0000	0.0000	0.0312	23.0812	12.3237	0.2309	28.2	6.2	0.168056	0.000000	0.152778	0.015278	7.200000e+04	%
42	Dominican Republic	0.0000	15.2779	2.2261	0.0000	1.4528	1.1248	0.2484	3.6133	7.3718	0.0422	4.1194	0.1875	2.9103	0.1921	0.0609	0.0797	0.2906	0.0000	0.0000	0.0703	34.7221	25.8881	0.1218	26.9	9.5	2.115695	0.026676	1.577705	0.511314	1.050000e+07	%
43	Ecuador	0.0000	20.9283	4.7198	0.0000	3.6672	1.0246	0.2519	0.4367	8.7453	0.0280	6.0803	0.1008	0.8566	0.0616	0.0392	0.0840	0.1848	0.0000	0.0000	0.0784	29.0745	23.5541	0.0840	19.3	7.9	1.468306	0.085683	1.198184	0.184438	1.751100e+07	%
44	Egypt	0.0000	15.9979	3.4231	0.0000	13.4988	0.8448	1.3200	0.7216	6.4678	0.0088	3.6343	0.2992	4.4615	0.1584	0.2376	0.1936	0.0792	0.0704	0.0000	0.2992	34.0109	13.6132	0.6600	31.1	4.5	0.167759	0.009527	0.131002	0.027229	1.008030e+08	%
45	El Salvador	0.0000	23.3032	6.3901	0.0000	8.1958	1.6034	0.4514	1.4321	6.6547	0.1012	8.1491	0.0545	5.0669	0.5837	0.0311	0.1401	0.3269	0.0000	0.0000	0.1167	26.6968	10.2506	0.4514	22.7	9	0.867721	0.025814	0.778136	0.063771	6.481000e+06	%
46	Estonia	0.0000	33.2190	6.5339	0.0000	2.8982	1.5830	0.2818	0.3805	10.4279	0.0188	14.2750	0.1174	0.3664	0.0658	0.1926	0.0892	2.7855	0.0000	0.0000	1.2495	16.7833	8.4457	0.2865	23.8	2.9	3.569647	0.034260	2.828550	0.706837	1.331000e+06	%
47	Eswatini	0.0000	20.8055	3.1415	0.0000	12.0023	0.4718	0.3682	0.4718	11.1162	0.1151	5.4315	0.2762	3.4062	0.2071	0.8170	0.3222	0.0575	0.0000	0.0000	0.6559	29.1945	10.9436	0.1956	13.5	20.6	1.465670	0.054710	1.038859	0.372101	1.104000e+06	%
48	Ethiopia	0.0000	11.1691	1.4358	0.0000	11.3929	0.2238	0.0559	0.4102	4.1768	0.0000	5.1091	0.1678	2.6851	2.5545	2.6851	0.8018	0.0186	0.0000	0.0000	0.7459	38.8216	17.4156	0.1305	3.6	20.6	0.123093	0.001867	0.109237	0.011989	1.149160e+08	%
49	Fiji	0.0000	20.0472	7.6883	0.0000	1.5114	0.5773	1.4694	0.0840	8.4230	0.0262	1.7948	0.0945	8.9635	0.2991	0.1312	0.3569	0.4408	0.0000	0.0000	0.1469	29.9554	17.8168	0.1732	30.0	3.7	0.006250	0.000223	0.005915	0.000112	8.960000e+05	%
50	Finland	0.0000	36.7250	6.3787	0.0000	1.7872	0.8524	0.9384	0.2901	16.6649	0.1146	11.8155	0.0788	0.6017	0.0179	0.0430	0.0537	0.7844	0.0000	0.0000	0.5157	13.2732	8.8607	0.2041	24.9	<2.5	0.854802	0.012443	0.560680	0.281678	5.529000e+06	%
51	France	0.0000	30.2594	9.1996	0.0000	1.4334	0.9894	0.8752	0.3869	11.0864	0.0159	7.9533	0.1554	0.6850	0.0254	0.0285	0.0412	0.6279	0.0000	0.0000	0.8530	19.7406	15.4658	0.1776	23.2	<2.5	5.199055	0.121558	0.367877	4.709620	6.494000e+07	%
52	French Polynesia	0.0000	25.1596	4.6648	0.0000	3.7271	0.8220	1.5842	0.1995	14.0782	0.2913	3.8827	0.1277	4.7566	0.0399	0.0200	0.0758	1.2690	0.0000	0.0000	0.3192	24.8404	14.0024	0.1397	NaN	4.2	NaN	NaN	NaN	NaN	2.800000e+05	%
53	Gabon	0.0000	19.2656	0.7379	0.0000	4.1074	0.1956	2.0359	0.6401	13.4068	0.1422	2.2937	0.5957	8.9438	0.0089	0.0000	0.5779	0.7112	0.0000	0.0000	0.0178	30.7344	15.3805	0.2045	13.4	10.5	0.513537	0.003182	0.483953	0.026401	2.231000e+06	%
54	Gambia	0.0000	6.0418	0.2548	0.0000	4.9416	0.2113	1.3488	0.0373	1.3488	0.0622	2.8468	0.0311	12.4751	0.0311	0.0000	0.0124	0.0995	0.0000	0.0000	0.2362	43.9582	25.9883	0.0746	8.7	10.2	0.175300	0.005544	0.160778	0.008978	2.417000e+06	%
55	Georgia	0.0000	25.2749	7.2612	0.0000	4.5695	1.1781	0.4284	0.2071	7.2326	0.1071	9.0818	0.1000	0.6854	0.0000	0.0714	0.0928	2.7774	0.0000	0.0000	1.1566	24.7180	14.8579	0.1999	23.3	7.9	7.042261	0.087995	6.805249	0.149017	3.715000e+06	%
56	Germany	0.0000	28.1419	11.0084	0.0000	1.1778	1.0462	0.7019	0.2632	8.9127	0.0000	6.4289	0.0472	0.8909	0.0101	0.0844	0.0607	0.6614	0.0000	0.0000	1.6739	21.8581	16.8163	0.2160	25.7	<2.5	2.743101	0.073891	2.451366	0.217844	8.330000e+07	%
57	Ghana	0.0000	7.6875	0.8060	0.0000	5.6789	0.3472	2.7774	1.2151	3.1618	0.0496	0.4216	0.1736	10.9113	0.3968	1.2771	2.0087	0.0000	0.0000	0.0000	0.0868	42.3063	20.4464	0.2480	9.7	5.5	0.225424	0.001445	0.204364	0.019615	3.107300e+07	%
58	Greece	0.0000	17.9063	1.6989	0.0000	1.3280	0.7749	0.3245	0.2484	6.6267	0.0298	8.3355	0.1424	1.8413	0.0795	0.0497	0.0431	0.7749	0.0000	0.0000	1.8810	32.0970	25.5332	0.2848	27.4	<2.5	1.525355	0.055617	0.876299	0.593439	1.070000e+07	%
59	Grenada	0.0000	23.0842	2.4889	0.0000	1.4883	1.4440	1.2856	2.8436	12.9576	0.1267	4.8195	0.0823	3.3186	0.1900	0.8803	0.0697	0.8740	0.0000	0.0000	0.1393	26.9158	16.8841	0.1077	20.2	NaN	0.130973	0.000885	0.129204	0.000885	1.130000e+05	%
60	Guatemala	0.0000	15.4397	1.7622	0.0000	9.3956	2.6186	0.1482	1.2516	7.0735	0.0247	3.7385	0.0988	3.7879	0.5764	0.1235	0.0247	0.3788	0.0000	0.0000	0.9305	34.5603	17.8113	0.2553	18.8	15.2	0.901899	0.032403	0.824294	0.045201	1.806600e+07	%
61	Guinea	0.0000	6.0747	0.7557	0.0000	4.7086	0.3125	0.5014	0.3415	2.8993	0.0363	1.4896	0.1163	6.6633	0.1744	0.0000	0.3343	0.2616	0.0000	0.0000	0.0073	43.9253	30.6133	0.7848	6.6	16.5	0.116760	0.000669	0.114013	0.002078	1.256000e+07	%
62	Guinea-Bissau	0.0000	10.8323	0.6274	0.0000	2.8828	0.1509	0.0715	0.2938	8.0527	0.0556	1.8027	0.1271	1.7154	0.0794	0.0000	0.1985	0.2224	0.0000	0.0000	3.3116	39.1677	30.3288	0.0794	8.2	28	0.143851	0.002387	0.125947	0.015516	1.927000e+06	%
63	Guyana	0.0000	22.7418	1.0020	0.0000	2.9072	0.3340	1.1690	1.5030	12.5550	0.0911	7.6287	0.0531	9.3518	0.3871	0.8653	0.1290	0.9261	0.0000	0.0000	0.8805	27.2582	9.0633	1.1538	19.2	8.1	1.008767	0.022872	0.884625	0.101271	7.870000e+05	%
64	Haiti	0.0000	12.1462	1.7481	0.0000	5.7517	0.1353	0.3722	1.6804	7.8944	0.0564	1.8608	0.1353	3.3157	0.8458	0.0000	0.5526	0.3609	0.0000	0.0226	0.0000	37.8482	25.1494	0.1241	20.5	49.3	0.103113	0.002149	0.080295	0.020670	1.140300e+07	%
65	Honduras	0.0000	16.7113	2.6611	0.0000	8.3159	0.7448	0.1302	0.3760	7.8603	0.0651	5.2281	0.0868	0.5857	0.4194	0.0289	0.0217	0.3833	0.0000	0.0000	0.1012	33.2924	22.8722	0.1157	19.4	12.9	1.560270	0.037612	0.626369	0.896290	9.864000e+06	%
66	Hungary	0.0000	28.9972	12.3082	0.0000	1.2735	1.2018	0.1954	0.1270	10.0576	0.0000	5.2308	0.0033	0.3583	0.0391	0.1107	0.0391	0.7393	0.0000	0.0000	0.0456	21.0012	18.0699	0.2019	28.6	<2.5	3.840739	0.133367	2.845797	0.861575	9.767000e+06	%
67	Iceland	0.0000	34.4402	7.1086	0.0000	4.3005	0.8991	1.6872	0.4975	13.6029	0.1199	11.0046	0.1349	0.8931	0.0120	0.1079	0.0300	1.5254	0.0000	0.0000	0.5005	15.5628	7.4053	0.1678	23.1	<2.5	1.636141	0.007880	1.619293	0.008967	3.680000e+05	%
68	India	0.0000	16.0538	7.4859	0.0000	5.1196	0.7653	0.3783	0.4046	0.9061	0.0000	6.4919	0.0264	3.7386	1.1084	0.8005	0.0792	0.0176	0.0000	0.0000	0.5014	33.9462	21.7804	0.3958	3.8	14.5	0.773256	0.011070	0.751561	0.010625	1.400100e+09	%
69	Indonesia	0.0000	9.1722	0.7049	0.0000	6.6871	1.4549	2.5122	0.7139	3.6237	0.0000	0.7049	0.1627	7.3649	0.0452	0.3886	0.3072	0.4970	0.0000	0.0633	0.3976	40.8368	24.0918	0.2711	6.9	8.3	0.422100	0.011553	0.345620	0.064927	2.717390e+08	%
70	Iran (Islamic Republic of)	0.0000	16.9273	6.0372	0.0000	5.5867	1.3259	0.6114	0.6243	6.1724	0.0000	2.6389	0.1416	0.4055	0.2961	0.2124	0.1738	0.0451	0.0000	0.0000	4.9044	33.0759	20.2935	0.5278	25.5	4.9	1.734248	0.069414	1.482322	0.182512	8.415000e+07	%
71	Iraq	0.0000	11.1327	3.8118	0.0000	6.2761	1.9023	0.1441	0.2306	3.5164	0.0432	1.7366	0.0288	0.9584	0.1369	0.0576	0.0288	0.7278	0.0000	0.0000	0.6341	38.8673	29.6152	0.1513	27.4	29	1.579398	0.033004	1.508287	0.038107	3.972500e+07	%
72	Ireland	0.0000	27.1689	8.6472	0.0000	2.7091	0.9231	0.9372	0.2829	10.0477	0.0283	6.5464	0.0707	0.8630	0.0389	0.0707	0.0531	1.5137	0.0000	0.0000	0.8417	22.8329	16.1839	0.2405	26.9	<2.5	4.048531	0.073436	0.467000	3.508095	5.003000e+06	%
73	Israel	0.0000	15.8138	1.2809	0.0000	1.6083	0.8700	0.5746	1.6051	8.1541	0.0257	4.8443	0.0867	3.0787	0.1605	0.0610	0.0353	0.8315	0.0000	0.0000	1.3194	34.1862	25.1782	0.2857	26.7	<2.5	7.439052	0.055024	6.609635	0.774392	9.216000e+06	%
74	Italy	0.0000	21.8727	5.3931	0.0000	1.3598	1.0529	0.7327	0.2541	8.7134	0.0198	5.9146	0.0627	0.4753	0.0858	0.0099	0.0297	0.1056	0.0000	0.0000	1.4324	28.1306	24.1270	0.2277	22.9	<2.5	4.353685	0.150927	3.494529	0.708229	6.029600e+07	%
75	Jamaica	0.0000	23.0867	6.1721	0.0064	2.2375	0.3242	1.2204	0.4322	9.5156	0.1081	5.7081	0.1462	3.4389	0.0890	1.4302	0.2161	0.3496	0.0000	0.0000	0.1398	26.9133	18.1732	0.2924	24.4	8	0.599537	0.012709	0.435208	0.151620	2.809000e+06	%
76	Japan	0.0000	19.4516	1.9098	0.0057	1.8642	3.1241	3.2666	0.1881	7.8331	0.0285	3.2096	0.1026	2.9816	0.0342	0.1425	0.0513	0.8266	0.0057	0.0000	0.6499	30.5456	23.4308	0.3478	4.4	<2.5	0.320821	0.005059	0.284770	0.030991	1.259670e+08	%
77	Jordan	0.0000	12.2622	1.8696	0.0000	3.7391	0.5224	0.3189	0.1980	5.8122	0.0385	3.6512	0.0880	2.5074	0.2584	0.1155	0.0550	0.5994	0.0000	0.0000	0.9018	37.7378	29.0553	0.2694	33.4	12.2	3.126276	0.040912	2.991760	0.093604	1.067900e+07	%
78	Kazakhstan	0.0000	23.4158	3.0343	0.0000	1.7405	0.8975	0.1321	0.1787	9.6740	0.0389	9.5031	0.1748	0.6488	0.0078	0.0272	0.1088	0.9946	0.0000	0.0000	0.7654	26.5822	21.7258	0.3497	21.3	<2.5	1.299402	0.016651	1.137764	0.144987	1.873200e+07	%
79	Kenya	0.0000	17.7332	1.8742	0.0000	9.9701	0.3438	0.2773	1.0757	6.5654	0.0000	8.4951	0.1885	1.6746	0.8429	0.0555	0.2551	0.0665	0.0000	0.0000	0.5989	32.2724	17.5003	0.2107	6.0	29.4	0.189975	0.003318	0.157491	0.029166	5.352800e+07	%
80	Kiribati	0.0000	10.5699	0.7467	0.0000	1.9560	0.2650	3.0833	0.3517	6.1955	0.0434	0.2553	0.0193	28.5639	0.0000	0.0145	0.2409	0.0193	0.0000	0.0000	0.8094	39.4325	7.2939	0.1397	45.6	2.7	NaN	NaN	NaN	NaN	1.250000e+05	%
81	Korea, North	0.0000	11.4709	0.0262	0.0000	7.2234	1.6911	0.5375	0.6424	8.7179	0.0131	0.4195	0.0918	0.9832	0.6555	0.0000	0.3146	0.0393	0.0000	0.0000	0.0262	38.5160	27.5957	1.0357	7.1	47.8	NaN	NaN	NaN	NaN	2.577900e+07	%
82	Korea, South	0.0976	20.0483	4.2523	0.0511	1.8403	1.3524	1.9147	0.1766	11.1860	0.0000	1.1386	0.1534	1.8914	0.0186	0.0372	0.0325	0.2370	0.0000	0.0000	1.2036	29.9563	23.7197	0.6924	4.9	<2.5	0.156227	0.002841	0.136855	0.016531	5.178100e+07	%
83	Kuwait	0.0000	19.8923	2.1588	0.0000	3.0847	2.3242	0.3732	0.3496	9.5706	0.1086	5.2766	0.1890	1.3416	0.1984	0.5385	0.0472	1.5353	0.0000	0.0000	1.1810	30.1101	21.2150	0.5055	37.0	2.8	3.624728	0.020550	3.434087	0.170092	4.691000e+06	%
84	Kyrgyzstan	0.0000	29.1225	3.4472	0.0000	4.0964	0.8655	0.0895	0.2388	10.1776	0.0448	14.2068	0.3358	0.0895	0.1940	0.0970	0.1865	2.1564	0.0000	0.0000	1.5893	20.8849	11.4610	0.7163	15.4	7.1	1.288929	0.021580	1.236609	0.030740	6.594000e+06	%
85	Lao People's Democratic Republic	0.0000	21.6306	3.9594	0.0000	9.0989	0.4974	1.6871	0.8777	15.0283	0.0878	0.2243	0.2243	5.6076	0.1365	1.0435	0.3121	0.0780	0.1853	0.0000	0.0683	28.3792	9.7620	1.1118	4.5	16.5	0.000621	0.000000	0.000566	0.000055	7.242000e+06	%
86	Latvia	0.0000	30.4390	10.4430	0.0000	1.5314	1.3430	0.8819	0.2526	9.7976	0.0361	7.8613	0.1122	0.6334	0.0000	0.0321	0.1323	2.0044	0.0000	0.0000	0.8819	19.5630	13.7943	0.2606	25.7	<2.5	3.749737	0.069769	3.122976	0.556993	1.902000e+06	%
87	Lebanon	0.0000	12.4024	2.1637	0.0000	2.2530	0.3290	0.3681	0.5800	5.1361	0.0390	4.3553	0.0502	2.8385	0.2175	0.0836	0.1171	0.7250	0.0000	0.0000	2.9110	37.5976	27.4593	0.3736	31.3	11	4.656938	0.052190	2.947062	1.657685	6.825000e+06	%
88	Lesotho	0.0000	12.9600	0.5290	0.0000	18.3763	0.2415	0.2530	0.1495	9.1881	0.0460	2.4149	0.3450	0.7820	0.4025	0.3910	0.1840	0.1495	0.0000	0.0000	0.0345	37.0285	16.3523	0.1725	13.5	13.1	0.437908	0.008543	0.126331	0.303035	2.142000e+06	%
89	Liberia	0.0000	7.4228	0.4154	0.0000	3.3592	0.2528	0.3702	0.1445	6.0051	0.0361	0.2438	0.1355	1.5803	0.0361	0.0000	0.3070	0.1174	0.0000	0.0000	0.3883	42.5772	36.4186	0.1896	8.6	37.2	0.038671	0.001661	0.034994	0.002017	5.058000e+06	%
90	Lithuania	0.0000	32.3048	9.3880	0.0000	1.7819	1.9226	1.2380	0.3283	14.2134	0.0094	5.4302	0.1125	0.8441	0.2345	0.1970	0.0610	0.3423	0.0000	0.0000	0.9238	17.6975	12.7128	0.2579	28.4	<2.5	6.667072	0.104760	4.939621	1.622691	2.794000e+06	%
91	Luxembourg	0.0000	32.4517	6.4786	0.0000	1.8437	1.6645	0.6987	0.3256	16.3118	0.0658	7.2286	0.0658	0.6402	0.0146	0.1646	0.0329	3.3838	0.0000	0.0000	0.1280	17.5519	10.6855	0.2634	24.2	<2.5	8.150633	0.093354	7.661551	0.395728	6.320000e+05	%
92	Madagascar	0.0000	19.0932	1.7454	0.0000	10.0681	0.3406	0.7450	1.2984	11.5581	0.0213	4.4274	0.2980	1.5113	0.3193	0.2554	0.9153	0.1916	0.0213	0.0000	0.2129	30.9068	15.8791	0.1916	4.5	44.4	0.068849	0.001015	0.065779	0.002055	2.769100e+07	%
93	Malawi	0.0000	18.8711	2.6263	0.0000	13.0891	0.2753	0.6672	1.3767	14.6458	0.0106	0.4024	0.2542	5.6232	0.7519	0.0529	1.3555	0.0318	0.0000	0.0000	0.2012	31.1236	8.4189	0.2224	4.7	17.5	0.140486	0.004375	0.059320	0.076790	1.913000e+07	%
94	Malaysia	0.0000	17.1723	1.4813	0.0000	2.2933	2.7596	1.8599	0.1756	9.8041	0.0713	1.2289	0.0329	1.7502	0.0658	0.6584	0.0329	0.0000	0.0000	0.0000	0.3731	32.8304	27.1740	0.2359	15.3	2.5	0.728230	0.002614	0.570361	0.155255	3.278100e+07	%
95	Maldives	0.0000	29.7398	4.8409	0.0000	4.5092	3.2756	8.4068	0.4665	5.8360	0.4561	7.3909	0.0000	1.3683	0.0518	1.1713	0.0622	1.7207	0.0000	0.0000	4.9756	20.2550	4.9549	0.5183	7.9	10.3	3.078743	0.009982	2.690573	0.378189	5.410000e+05	%
96	Mali	0.0000	17.4631	1.2195	0.0000	12.0873	0.1327	0.4065	0.2987	5.6745	0.0249	9.7146	0.3152	3.3765	0.5475	0.1244	0.1327	0.0332	0.0000	0.0000	1.3357	32.5369	14.1945	0.3816	7.1	6.3	0.040294	0.001669	0.029895	0.008730	2.025100e+07	%
97	Malta	0.0000	29.3856	6.6557	0.0000	4.8786	1.3878	1.3074	0.2666	10.6541	0.0169	9.3298	0.0508	1.4978	0.0804	0.0804	0.0296	2.6783	0.0000	0.0000	1.1720	20.6144	9.3975	0.5162	31.0	<2.5	3.558621	0.053448	3.045211	0.459962	5.220000e+05	%
98	Mauritania	0.0000	17.4021	1.7485	0.0000	4.3092	0.4337	0.5369	0.0895	5.7479	0.0688	8.6460	0.2822	0.5025	0.3373	0.0000	0.0207	0.1652	0.0000	0.0000	0.0000	32.6014	26.9567	0.1514	11.3	10.4	0.361118	0.009140	0.343763	0.008215	4.650000e+06	%
99	Mauritius	0.0000	12.8934	1.4559	0.0000	3.2471	0.7355	1.0457	0.1501	6.7594	0.1001	2.8418	0.0550	1.5660	0.2502	0.3452	0.0250	0.9856	0.0000	0.0000	0.2702	37.1041	29.9945	0.1751	11.5	6.5	0.046166	0.000791	0.042688	0.002688	1.265000e+06	%
100	Mexico	0.0000	23.7741	3.2525	0.0000	6.4604	2.2807	0.4661	0.5305	13.2133	0.0050	4.3433	0.2182	1.1850	0.2380	0.2529	0.0248	0.2033	0.0000	0.0000	0.7437	26.2234	16.3419	0.2429	28.4	3.6	1.507199	0.129731	1.159805	0.217663	1.277920e+08	%
101	Mongolia	0.0000	36.9018	5.5153	0.0000	2.7445	0.5930	0.0315	0.0420	21.0223	0.0525	9.0628	0.6717	0.1259	0.0105	0.0052	0.0420	1.6373	0.0000	0.0000	0.0787	13.0982	8.2231	0.1417	19.6	13.4	0.060190	0.000060	0.043112	0.017019	3.361000e+06	%
102	Montenegro	0.0000	32.7855	3.8887	0.0000	3.7312	1.1566	0.3473	0.3688	14.7778	0.1003	12.5363	0.0752	0.5837	0.0788	0.0430	0.0251	2.5531	0.0000	0.0000	0.8415	17.2163	8.6332	0.2578	24.9	<2.5	10.408199	0.134405	9.039871	1.233923	6.220000e+05	%
103	Morocco	0.0000	15.4227	3.5807	0.0000	7.1539	1.1517	1.2329	0.7014	6.9989	0.0074	2.2296	0.2289	0.7309	0.1477	0.3987	0.0960	0.1993	0.0000	0.0000	1.9048	34.5736	22.9457	0.2953	25.6	3.4	1.321111	0.023312	1.262575	0.035225	3.595200e+07	%
104	Mozambique	0.0000	11.5910	1.1197	0.0000	8.8101	0.5537	0.8490	0.1846	6.8783	0.0369	2.0795	0.1107	1.1936	0.7014	0.0246	0.8490	0.0984	0.0000	0.0000	0.8244	38.4152	25.5199	0.1600	6.0	27.9	0.141539	0.001447	0.085959	0.054133	3.116600e+07	%
105	Myanmar	0.0000	26.0470	3.0066	0.0000	3.8192	0.9251	2.5441	0.2750	18.1335	0.0375	1.2814	0.1563	5.1194	0.6938	0.5313	0.0625	0.0250	0.0000	0.0813	0.2688	23.9530	12.7453	0.2938	5.7	10.6	NaN	NaN	NaN	NaN	5.470400e+07	%
106	Namibia	0.0000	16.7373	3.3171	0.0000	6.8020	0.2718	0.6075	0.2798	7.7772	0.0959	4.5480	0.2078	0.8792	0.4316	0.4236	0.5915	0.5995	0.0000	0.0000	0.1199	33.2667	22.9078	0.1359	15.0	27.3	1.371271	0.014601	1.312830	0.043841	2.541000e+06	%
107	Nepal	0.0000	12.1853	3.3076	0.0000	7.1664	0.4361	0.1563	0.5513	2.7069	0.0082	5.4139	0.1563	0.4525	0.5513	1.1437	0.1975	0.2468	0.1892	0.0000	0.5019	37.8147	26.2136	0.6006	3.8	8.7	0.906141	0.006784	0.892826	0.006531	2.999600e+07	%
108	Netherlands	0.0000	30.1494	6.6484	0.0000	1.2702	1.4787	0.7451	0.3475	10.8567	0.0000	10.3934	0.0270	0.3591	0.0309	0.1235	0.0849	0.0541	0.0000	0.0000	1.4980	19.8525	15.8449	0.2355	23.1	<2.5	5.815292	0.082830	0.076063	5.656398	1.746700e+07	%
109	New Caledonia	0.0000	22.2697	3.6599	0.0000	5.0979	1.0439	0.8385	0.2222	11.6086	0.2851	5.0476	0.0755	3.3036	0.0293	0.0629	0.0335	1.3961	0.0000	0.0000	0.2557	27.7282	16.8532	0.1887	NaN	7.1	NaN	NaN	NaN	NaN	2.950000e+05	%
110	New Zealand	0.0000	34.1264	13.9753	0.0000	1.3271	1.2111	0.6485	0.3049	13.9409	0.0000	4.2948	0.0601	2.0228	0.0773	0.0859	0.0687	0.0945	0.0000	0.0000	1.3357	15.8736	10.2689	0.2835	32.0	<2.5	0.046521	0.000501	0.044696	0.001323	4.987000e+06	%
111	Nicaragua	0.0000	17.9935	2.3309	0.0000	9.2369	1.0080	0.4174	0.1339	7.6069	0.1102	6.5123	0.1102	1.9214	0.4646	0.0079	0.1024	0.4252	0.0000	0.0000	0.0315	32.0104	19.3952	0.1811	21.8	17	0.095497	0.002577	0.064054	0.028866	6.596000e+06	%
112	Niger	0.0000	10.9769	2.6573	0.0000	12.4360	0.0580	0.1353	0.2416	3.6332	0.0097	4.3193	0.1836	13.4216	1.4398	0.3479	0.0483	0.0290	0.0000	0.0000	0.0000	39.0279	10.5904	0.4445	4.7	16.5	0.019009	0.000682	0.016330	0.001997	2.418900e+07	%
113	Nigeria	0.0000	5.0182	0.8078	0.0000	7.1082	0.5755	0.5957	0.3433	2.4838	0.0101	0.4645	0.0909	4.3922	0.4241	0.4746	1.2621	0.0404	0.0000	0.0000	0.2322	44.9818	30.1494	0.5452	7.8	13.4	0.067547	0.000799	0.054602	0.012146	2.061400e+08	%
114	North Macedonia	0.0000	19.6420	7.1263	0.0000	1.9013	0.7305	0.2371	0.4112	6.5409	0.0435	4.8476	0.1500	2.0174	0.1355	0.4886	0.0774	1.7078	0.0000	0.0000	1.1708	30.3628	21.6642	0.7450	23.9	3.2	4.548029	0.140192	4.025721	0.382115	2.080000e+06	%
115	Norway	0.0000	27.2211	6.4445	0.0000	1.9601	1.0645	1.5545	0.4055	10.6992	0.3244	7.4043	0.0541	1.7032	0.0912	0.0473	0.0473	1.6796	0.0000	0.0000	0.8888	22.7806	15.4642	0.1656	25.0	<2.5	1.200167	0.010804	0.334101	0.855263	5.387000e+06	%
116	Oman	0.0000	23.6146	2.3712	0.0000	3.7744	1.4422	0.9290	0.7926	9.0171	0.0325	9.6407	0.2144	1.1174	0.1689	0.7276	0.0390	1.0979	0.0000	0.0000	0.6107	26.3821	17.4495	0.5782	22.9	6.8	2.865288	0.032506	2.700318	0.132463	4.713000e+06	%
117	Pakistan	0.0000	24.8245	8.9701	0.0000	4.2124	0.6203	0.0818	0.1227	3.4967	0.0068	11.5125	0.1431	1.0906	0.2726	0.2249	0.0341	0.0204	0.0613	0.0000	0.1909	25.1789	18.8603	0.0750	7.8	20.3	0.250961	0.005416	0.230941	0.014604	2.209400e+08	%
118	Panama	0.0000	25.9557	9.1864	0.0000	3.8355	0.8991	0.8614	0.5722	9.1046	0.1320	5.7281	0.1823	1.4273	0.1509	0.0377	0.0566	1.0375	0.0000	0.0000	0.4339	24.0317	16.2412	0.1258	22.5	10	7.622321	0.127364	6.773850	0.721107	4.283000e+06	%
119	Paraguay	0.0000	19.8084	3.8811	0.0000	4.9807	1.5622	0.1742	0.3974	10.2335	0.0272	3.7069	0.2558	2.0576	0.2123	0.0218	0.5552	0.5334	0.0000	0.0000	0.0163	30.1889	21.2672	0.1198	19.0	10.7	1.896664	0.038691	1.562591	0.295382	7.255000e+06	%
120	Peru	0.0000	18.0402	1.1055	0.0000	5.5879	2.1206	1.9497	1.6382	6.8241	0.0302	5.6080	0.4322	2.5628	0.6030	0.0704	0.6834	0.4523	0.0000	0.0000	0.2312	31.9598	19.6482	0.4523	19.1	9.7	3.573742	0.127751	3.310514	0.135477	3.282400e+07	%
121	Philippines	0.0000	29.7607	7.3714	0.0000	3.4748	1.0544	1.6045	0.7243	18.8870	0.0275	0.5684	0.2751	2.2371	0.0550	0.0642	0.2200	0.3576	0.0000	0.0183	0.7701	20.2439	11.9281	0.3576	6.0	13.3	0.488699	0.010139	0.445757	0.032803	1.095810e+08	%
122	Poland	0.0000	32.7460	12.3719	0.0000	1.6183	0.7141	0.4851	0.2057	13.3072	0.0000	5.8600	0.0078	0.4967	0.0310	0.0427	0.1087	0.0737	0.0000	0.0000	0.4385	17.2540	13.9747	0.2639	25.6	<2.5	4.028175	0.101632	3.392335	0.534208	3.836800e+07	%
123	Portugal	0.0000	26.4556	9.5983	0.0000	2.1808	0.7993	1.0267	0.2825	10.3459	0.0138	4.5442	0.1378	0.4203	0.0689	0.0413	0.0586	1.3540	0.0000	0.0000	0.7648	23.5444	18.0803	0.2825	23.2	<2.5	7.429605	0.136070	5.846836	1.446699	1.025500e+07	%
124	Republic of Moldova	0.0000	26.5244	6.8551	0.0000	2.5570	1.5517	0.6921	0.2987	9.7764	0.0656	7.5326	0.1238	0.2841	0.0291	0.0146	0.0874	0.8305	0.0000	0.0000	0.1093	23.4720	18.8388	0.3570	20.1	NaN	4.646195	0.099859	4.333692	0.212645	3.535000e+06	%
125	Romania	0.0000	27.4359	6.8987	0.0000	2.5519	1.5914	0.1994	0.2930	7.9894	0.0366	10.6349	0.1221	0.5495	0.0407	0.4965	0.1099	0.8506	0.0000	0.0000	0.6797	22.5600	16.5120	0.4477	24.5	<2.5	3.863328	0.097755	3.583358	0.182215	1.924100e+07	%
126	Russian Federation	0.0000	27.9360	7.2000	0.0000	1.6511	1.9603	0.9415	0.2138	10.8433	0.0091	6.8453	0.1455	0.7823	0.0546	0.0273	0.1410	0.4184	0.0000	0.0000	0.1274	22.0640	18.3753	0.2638	25.7	<2.5	2.663104	0.051120	2.316142	0.295842	1.467330e+08	%
127	Rwanda	0.0000	11.0727	1.0364	0.0000	4.8545	0.1636	0.6364	9.6727	5.2182	0.0182	3.7818	0.2364	3.6545	2.6909	0.0545	2.1636	0.0545	0.0000	0.0000	0.0182	38.9091	15.5091	0.2545	4.8	36.8	0.126135	0.001675	0.093013	0.031447	1.295200e+07	%
128	Saint Kitts and Nevis	0.0000	24.3013	4.7370	0.0000	2.0602	0.5600	0.7296	0.3596	14.3547	0.1439	3.7916	0.1233	2.9028	0.1541	0.2055	0.0462	0.1798	0.0000	0.0000	0.5189	25.7039	19.0043	0.1233	23.1	NaN	0.074074	0.000000	0.066667	0.007407	5.400000e+04	%
129	Saint Lucia	0.0000	33.3454	3.8130	0.0000	1.6712	0.5249	1.5867	0.3741	22.8778	0.1629	4.3137	0.2353	2.4012	0.1810	0.4163	0.0965	0.9894	0.0000	0.0000	0.5551	16.6516	9.6531	0.1508	19.8	NaN	0.996154	0.009890	0.480769	0.505495	1.820000e+05	%
130	Saint Vincent and the Grenadines	0.0000	24.4653	1.0850	0.0000	4.3274	0.7589	0.7150	0.7526	17.5290	0.2258	4.2521	0.1254	0.6773	0.2759	1.2919	0.1756	1.7184	0.0000	0.0000	0.4516	25.5315	15.3904	0.2509	23.8	5.7	1.082883	0.002703	0.361261	0.718919	1.110000e+05	%
131	Samoa	0.0000	21.6806	4.7780	0.0036	1.5771	0.2227	1.8968	0.6287	13.2418	0.0683	1.4693	0.0683	16.8666	0.0000	0.1150	0.3341	0.2910	0.0000	0.0000	0.0611	28.3194	8.3345	0.0431	45.5	2.7	0.001000	0.000000	0.001000	0.000000	2.000000e+05	%
132	Sao Tome and Principe	0.0000	7.3284	0.6241	0.0000	2.9864	0.1074	1.4361	1.2013	4.1809	0.0537	0.9731	0.0134	20.7704	0.1342	0.0336	0.1544	0.0738	0.0000	0.0000	0.0268	42.6616	17.1666	0.0738	10.6	7	0.643333	0.008571	0.504762	0.130000	2.100000e+05	%
133	Saudi Arabia	0.0000	15.3954	2.6475	0.0000	5.2126	0.9019	0.4122	0.4219	6.9243	0.0873	4.4125	0.0970	0.6207	0.1794	0.4800	0.0291	1.2510	0.0000	0.0000	0.7128	34.6070	25.4182	0.1891	35.0	7.1	1.055795	0.018256	1.030898	0.006641	3.504100e+07	%
134	Senegal	0.0000	6.1040	0.7876	0.0000	5.7374	0.2580	0.7129	0.0815	3.2319	0.0068	0.9845	0.1222	3.9041	0.1222	0.1697	0.0543	0.2037	0.0000	0.0000	0.3870	43.9028	33.0391	0.1901	7.4	11.3	0.170145	0.004031	0.139107	0.027007	1.674400e+07	%
135	Serbia	0.0000	33.0354	4.0698	0.0000	2.1213	1.5195	0.3396	0.5780	18.1385	0.0060	8.7594	0.2145	1.1262	0.0775	0.0834	0.0596	1.5016	0.0000	0.0000	0.9355	16.9646	10.1061	0.3635	23.5	5.7	5.840908	0.059106	0.000000	5.781803	6.957000e+06	%
136	Sierra Leone	0.0000	5.9931	0.1678	0.0000	3.2063	0.2983	1.2117	0.2610	2.0412	0.0373	2.1996	0.0746	4.8560	0.4660	0.2889	0.4474	0.0746	0.0000	0.0000	0.0559	44.0022	34.0479	0.2703	7.5	25.6	0.047286	0.000990	0.030738	0.015557	7.977000e+06	%
137	Slovakia	0.0000	25.4930	14.9373	0.0000	2.0003	1.1183	0.2331	0.1386	5.8496	0.0095	3.2697	0.0851	0.7686	0.0221	0.0599	0.0410	0.7655	0.0000	0.0000	0.4253	24.5070	20.1474	0.1292	22.4	3.4	4.792640	0.094013	4.361424	0.337202	5.462000e+06	%
138	Slovenia	0.0000	26.6163	10.0008	0.0000	3.5771	1.1287	0.3693	0.4606	8.5899	0.0622	6.4155	0.1120	0.8714	0.0415	0.1784	0.0539	2.4110	0.0000	0.0000	1.5852	23.3878	13.8684	0.2697	22.5	<2.5	8.235901	0.171755	7.312934	0.751213	2.103000e+06	%
139	Solomon Islands	0.0000	13.6649	3.4726	0.0000	3.0802	0.2943	2.5505	0.5690	6.5431	0.0981	0.7553	0.0490	16.5686	0.5199	0.1962	2.1778	0.9810	0.0000	0.0000	0.3139	36.3351	11.7520	0.0785	20.5	8.9	0.002378	0.000000	0.001399	0.000979	7.150000e+05	%
140	South Africa	0.0000	19.7743	0.8542	0.0000	6.4308	1.0982	0.2379	0.0854	14.1489	0.0000	3.1056	0.3295	1.1836	0.0427	0.1037	0.0549	0.2807	0.0000	0.0000	0.0915	30.2257	21.7877	0.1647	27.0	6.2	2.471490	0.077447	2.248100	0.145943	5.962800e+07	%
141	Spain	0.0000	19.5253	2.4641	0.0000	1.0780	1.2521	1.0814	0.3750	9.2772	0.0000	5.3601	0.0937	1.0513	0.0971	0.0134	0.0469	0.4955	0.0000	0.0000	1.3626	30.4731	25.6922	0.2611	27.1	<2.5	6.176110	0.128867	0.315684	5.731559	4.763500e+07	%
142	Sri Lanka	0.0000	10.1348	0.3195	0.0000	2.7159	1.1682	2.2766	0.5791	2.0270	0.0100	4.2336	0.0999	27.1892	0.3794	1.4279	0.0899	0.1198	0.0000	0.0100	0.4194	39.8602	6.7000	0.2396	5.4	9	0.312604	0.001600	0.285335	0.025669	2.193700e+07	%
143	Sudan	0.0000	20.1511	1.7092	0.0000	5.6998	0.2347	0.0587	0.3961	7.0789	0.0073	10.8055	0.2641	3.7265	0.4108	0.1174	0.0587	0.0587	0.0000	0.0000	0.0000	29.8489	19.0214	0.3521	7.4	20.1	0.067582	0.004176	0.049702	0.013704	4.384900e+07	%
144	Suriname	0.0000	14.8751	0.7849	0.0000	3.1593	0.9601	0.7720	0.2335	10.6390	0.1557	1.6867	0.0324	3.1139	0.0649	0.0584	0.1103	0.8823	0.0000	0.0000	0.1557	35.1281	26.9089	0.2789	26.5	8.5	1.429752	0.026446	1.318017	0.085289	6.050000e+05	%
145	Sweden	0.0000	28.4111	9.7148	0.0000	1.9481	1.3741	0.8778	0.3556	8.8481	0.0481	7.5926	0.0074	0.9519	0.0296	0.1111	0.0481	0.2333	0.0000	0.0000	0.9704	21.5852	16.6630	0.2296	22.1	<2.5	5.666975	0.116749	0.000000	5.550226	1.037700e+07	%
146	Switzerland	0.0000	28.1054	6.2293	0.0000	1.1241	0.9300	0.4048	0.3379	12.0906	0.0000	8.3804	0.0703	0.5286	0.0268	0.0836	0.0368	0.1740	0.0000	0.0000	1.9772	21.8962	17.3898	0.2141	21.2	<2.5	6.160215	0.111258	3.678480	2.370477	8.634000e+06	%
147	Taiwan*	0.0000	19.3217	2.6952	0.0000	1.1629	1.4818	0.4667	0.2956	13.2817	0.0194	1.3223	0.0700	2.8197	0.0506	0.1050	0.0194	0.4823	0.0000	0.0000	0.6145	30.6783	24.8016	0.3111	NaN	3.5	0.003914	0.000038	0.003571	0.000305	2.361000e+07	%
148	Tajikistan	0.0000	12.6827	0.0348	0.0000	10.2035	0.5741	0.0174	0.3566	7.1590	0.0348	4.6799	0.2175	1.2700	0.1131	0.0174	0.0783	1.1308	0.0000	0.0000	0.4175	37.3173	22.7557	0.9395	12.6	NaN	0.141139	0.000955	0.140185	0.000000	9.429000e+06	%
149	Thailand	0.0000	19.5718	1.6660	0.0000	2.5380	2.8961	1.7984	0.5060	11.6621	0.0078	1.4636	0.0934	4.6166	0.1012	1.7594	0.0701	0.1246	0.0000	0.0000	0.2647	30.4165	20.2336	0.2102	10.8	7.8	0.035126	0.000119	0.025464	0.009544	6.653400e+07	%
150	Timor-Leste	0.0000	17.8988	2.0345	0.0000	10.2168	0.2446	0.7893	1.3007	14.5525	0.1112	0.1779	0.0889	3.5353	0.2779	0.1445	0.1779	0.2001	0.0000	0.0000	0.0111	32.1067	15.9644	0.1668	2.9	24.9	0.006070	0.000000	0.004628	0.001442	1.318000e+06	%
151	Togo	0.0000	5.3063	0.5351	0.0000	8.9093	0.1962	0.5708	0.0624	3.3889	0.0803	0.5440	0.0713	2.9608	0.8918	0.2230	0.6064	0.1694	0.0000	0.0000	0.0000	44.6892	30.6430	0.1516	7.1	16.1	0.065660	0.000954	0.054777	0.009929	8.279000e+06	%
152	Trinidad and Tobago	0.0000	23.3455	5.3472	0.0000	2.5134	0.6185	0.7816	0.3205	11.3860	0.1012	5.1279	0.0843	3.1993	0.3430	0.4386	0.0843	0.4386	0.0000	0.0000	0.5960	26.6573	18.4369	0.1799	19.7	5.5	0.556026	0.009861	0.529949	0.016216	1.369000e+06	%
153	Tunisia	0.0000	12.7886	2.6543	0.0000	3.2543	0.9255	0.5085	0.4068	4.0323	0.0102	4.5764	0.0915	1.4085	0.2034	0.3407	0.0407	0.3000	0.0000	0.0000	2.3289	37.2114	28.1552	0.7627	27.3	4.3	1.817216	0.060205	1.453304	0.303707	1.189600e+07	%
154	Turkey	0.0000	13.6766	2.9392	0.0000	2.0428	0.8250	0.1428	0.3292	3.8475	0.0079	5.8586	0.0674	1.7453	0.3808	0.3729	0.0516	0.0912	0.0000	0.0000	2.8162	36.3175	27.8172	0.6703	32.2	<2.5	3.015138	0.031868	2.881050	0.102220	8.373700e+07	%
155	Turkmenistan	0.0000	32.6886	6.4856	0.0000	2.9938	1.1060	0.1274	0.3069	17.4764	0.0347	7.4411	0.0521	0.3938	0.0405	0.0058	0.0521	1.0829	0.0000	0.0000	0.1969	17.3085	11.7610	0.4459	17.5	5.4	NaN	NaN	NaN	NaN	6.031000e+06	%
156	Uganda	0.0000	12.3139	0.9627	0.0000	3.4479	0.2239	0.8620	0.7164	6.1010	0.0112	4.0188	0.1343	6.5599	1.0411	0.0336	0.5709	0.1008	0.0000	0.0000	0.0000	37.6917	25.0532	0.1567	4.1	41	0.087058	0.000715	0.031370	0.054973	4.574100e+07	%
157	Ukraine	0.0000	26.9100	5.4940	0.0000	3.0837	2.4481	0.5160	0.3084	9.6350	0.0063	8.6910	0.1196	0.1007	0.0629	0.0378	0.2266	0.4972	0.0000	0.0000	0.7867	23.0900	17.4638	0.5223	26.1	3.5	3.075923	0.059206	2.653382	0.363335	4.177800e+07	%
158	United Arab Emirates	0.0000	21.4437	4.0036	0.0000	3.0418	1.1407	0.8108	0.6766	9.4610	0.1006	5.8600	0.1677	3.2040	1.0624	1.1295	0.0168	0.1454	0.0000	0.0000	3.8246	28.5563	14.9240	0.4306	29.9	2.6	3.307445	0.009348	3.079168	0.218930	9.778000e+06	%
159	United Kingdom	0.0000	25.8633	4.2615	0.0000	1.4678	1.0778	0.5176	0.4219	12.5789	0.0780	7.3530	0.0745	1.1664	0.0638	0.1276	0.0851	1.4642	0.0000	0.0000	0.7020	24.1332	18.3507	0.2127	29.5	<2.5	5.868483	0.167220	0.015161	5.686102	6.716000e+07	%
160	United Republic of Tanzania	0.0000	7.5907	1.5442	0.0000	4.2140	0.0744	0.4558	0.2698	3.0140	0.0000	2.5023	0.0000	8.3628	1.0233	0.1302	0.7163	0.0279	0.0000	0.0000	0.3628	42.4093	27.0605	0.2419	7.1	30.7	0.000852	0.000035	0.000306	0.000511	5.973400e+07	%
161	United States of America	0.0000	21.2045	3.3013	0.0000	1.2829	1.2500	0.3887	0.4426	9.4166	0.0000	6.8359	0.0090	1.7464	0.0449	0.0867	0.0478	0.3977	0.0000	0.0000	0.9420	28.7970	23.6117	0.1944	37.3	<2.5	8.159922	0.140103	0.000000	8.019819	3.298780e+08	%
162	Uruguay	0.0000	25.5069	3.4811	0.0000	2.5698	1.2804	0.3281	0.1777	12.2841	0.0456	8.0603	0.0729	1.5127	0.0729	0.0319	0.1002	0.7564	0.0000	0.0000	0.1367	24.4908	18.8819	0.2096	28.9	<2.5	1.269102	0.013622	1.089465	0.166015	3.531000e+06	%
163	Uzbekistan	0.0000	25.9903	2.4884	0.0000	2.7168	1.0639	0.0962	0.5830	10.3624	0.0120	11.8050	0.1743	0.3366	0.0000	0.0180	0.0902	0.1683	0.0000	0.0000	0.7754	24.0067	18.4589	0.8535	15.3	6.3	0.231302	0.001817	0.226751	0.002733	3.417400e+07	%
164	Vanuatu	0.0000	14.9179	1.9531	0.0000	4.1249	0.3509	1.4343	0.3052	9.9334	0.0254	1.1495	0.0966	23.1779	0.0000	0.1068	0.4578	0.2187	0.0000	0.0000	0.0356	35.0847	6.4849	0.1424	23.5	7.2	0.000312	0.000000	0.000312	0.000000	3.210000e+05	%
165	Venezuela (Bolivarian Republic of)	0.0000	16.3261	2.2673	0.0000	2.5449	0.6555	0.5707	0.9640	7.0949	0.0077	5.5217	0.2082	0.1003	0.1157	0.0000	0.1311	0.0848	0.0000	0.0000	0.0154	33.6855	29.5211	0.1851	25.2	21.2	0.452585	0.004287	0.424399	0.023899	2.864500e+07	%
166	Vietnam	0.0000	33.2484	3.8238	0.0000	3.7155	0.7839	1.1217	0.4079	26.4292	0.0064	0.7520	0.3378	4.2891	0.0765	0.6309	0.0701	0.0829	0.0382	0.0000	1.1726	16.7548	5.6211	0.6373	2.1	9.3	0.002063	0.000036	0.001526	0.000501	9.620900e+07	%
167	Yemen	0.0000	12.5401	2.0131	0.0000	11.5271	0.5514	0.3847	0.2564	8.0010	0.0256	1.3463	0.2436	0.7565	0.5001	0.1026	0.0513	0.3718	0.0000	0.0000	0.0769	37.4535	23.6312	0.1667	14.1	38.9	0.007131	0.002062	0.004788	0.000282	2.982600e+07	%
168	Zambia	0.0783	9.6005	1.6113	0.0000	14.3225	0.6266	1.0070	0.1343	4.9010	0.0224	1.2756	0.1790	9.8915	0.0783	0.1567	0.2014	0.0671	0.0000	0.0000	0.0112	40.3939	15.2848	0.1567	6.5	46.7	0.334133	0.004564	0.290524	0.039045	1.838400e+07	%
169	Zimbabwe	0.0000	10.3796	2.9543	0.0000	9.7922	0.3682	0.2455	0.0614	4.5674	0.0175	2.1040	0.1315	1.9462	0.1140	0.0526	0.0877	0.1052	0.0000	0.0000	0.4296	39.6248	26.9396	0.0789	12.3	51.3	0.232033	0.008854	0.190964	0.032214	1.486300e+07	%

	Country	Alcoholic Beverages	Animal fats	Animal Products	Aquatic Products, Other	Cereals - Excluding Beer	Eggs	Fish, Seafood	Fruits - Excluding Wine	Meat	Milk - Excluding Butter	Miscellaneous	Offals	Oilcrops	Pulses	Spices	Starchy Roots	Stimulants	Sugar & Sweeteners	Sugar Crops	Treenuts	Vegetable Oils	Vegetables	Vegetal Products	Obesity	Undernourished	Confirmed	Deaths	Recovered	Active	Population	Unit (all except Population)
0	Afghanistan	0.0014	0.1973	9.4341	0.0000	24.8097	0.2099	0.0350	5.3495	1.2020	7.5828	0.0728	0.2057	0.0700	0.2953	0.0574	0.8802	0.3078	1.3489	0.0000	0.0770	0.5345	6.7642	40.5645	4.5	29.8	0.142134	0.006186	0.123374	0.012574	3.892800e+07	%
1	Albania	1.6719	0.1357	18.7684	0.0000	5.7817	0.5815	0.2126	6.7861	1.8845	15.7213	0.1123	0.2324	0.9377	0.2380	0.0008	1.8096	0.1055	1.5367	0.0000	0.1515	0.3261	11.7753	31.2304	22.3	6.2	2.967301	0.050951	1.792636	1.123714	2.838000e+06	%
2	Algeria	0.2711	0.0282	9.6334	0.0000	13.6816	0.5277	0.2416	6.3801	1.1305	7.6189	0.1671	0.0870	0.3493	0.4783	0.0557	4.1340	0.2216	1.8342	0.0000	0.1152	1.0310	11.6484	40.3651	26.6	3.9	0.244897	0.006558	0.167572	0.070767	4.435700e+07	%
3	Angola	5.8087	0.0560	4.9278	0.0000	9.1085	0.0587	1.7707	6.0005	2.0571	0.8311	0.1165	0.1550	0.4186	0.6507	0.0009	18.1102	0.0508	1.8495	0.0000	0.0061	0.6463	2.3041	45.0722	6.8	25	0.061687	0.001461	0.056808	0.003419	3.252200e+07	%
4	Antigua and Barbuda	3.5764	0.0087	16.6613	0.0000	5.9960	0.2274	4.1489	10.7451	5.6888	6.3663	0.7139	0.2219	0.2172	0.1840	0.1524	1.4522	0.1564	3.8749	0.0000	0.0253	0.8102	5.4495	33.3233	19.1	NaN	0.293878	0.007143	0.190816	0.095918	9.800000e+04	%
5	Argentina	4.2672	0.2234	19.3454	0.0000	8.4102	0.9979	0.4693	6.0435	7.0421	10.2328	0.0000	0.3779	0.0116	0.0528	0.0122	3.0420	0.4378	3.0536	0.0000	0.0200	0.9541	4.3503	30.6559	28.5	4.6	4.356147	0.108227	3.905192	0.342729	4.537700e+07	%
6	Armenia	0.4014	0.1833	13.5640	0.0000	7.2982	0.5783	0.2896	6.0989	2.2675	9.9407	0.2355	0.3040	0.0899	0.1441	0.0055	2.0359	0.1863	2.6579	0.0000	0.1108	0.4705	16.7019	36.4358	20.9	4.3	5.681225	0.105345	5.398410	0.177470	2.956000e+06	%
7	Australia	5.5436	0.3143	21.4175	0.0033	5.4979	0.4428	1.4264	4.1883	6.7049	12.1018	0.5293	0.4240	0.3694	0.0546	0.0458	2.7884	0.2928	2.5364	0.0000	0.3176	1.2798	5.1406	28.5806	30.4	<2.5	0.112025	0.003530	0.101289	0.007207	2.575400e+07	%
8	Austria	7.0215	0.8555	19.5654	0.0011	6.2116	0.7884	0.7562	4.6069	4.6810	12.3776	0.0005	0.1052	0.2683	0.0456	0.0494	3.0548	0.4106	2.6094	0.0000	0.2367	0.8109	5.1098	30.4338	21.9	<2.5	4.739982	0.089679	4.496870	0.153433	8.914000e+06	%
9	Azerbaijan	3.5969	0.2544	11.6416	0.0000	13.0898	0.5593	0.2020	4.7988	2.1513	8.3212	0.0145	0.1534	0.0145	0.0347	0.0044	4.7041	0.0587	1.5523	0.0000	0.1906	0.2235	10.0755	38.3584	19.9	<2.5	2.285536	0.031223	2.225574	0.028740	1.010800e+07	%
10	Bahamas	2.0175	0.0062	11.4718	0.0009	3.7246	0.6250	2.4144	13.2718	5.9269	2.1757	2.9316	0.3218	0.6993	0.0504	0.0884	1.0547	0.1556	6.2151	0.0000	0.0495	0.5110	7.7649	38.5229	32.1	NaN	2.100763	0.044784	1.735115	0.320865	3.930000e+05	%
11	Bangladesh	0.0031	0.0156	5.1926	0.0000	29.8045	0.2957	2.5221	2.8655	0.4191	1.8778	0.0197	0.0633	0.0820	0.7065	0.3195	5.3378	0.0529	0.9285	0.1950	0.0934	0.7615	3.6405	44.8033	3.4	14.7	0.316691	0.004823	0.284344	0.027524	1.698090e+08	%
12	Barbados	3.5650	0.2212	12.4976	0.0000	8.0666	0.7792	3.2750	5.8723	5.8477	2.2041	3.3020	0.1704	0.8734	0.6391	0.1565	4.0812	0.1721	4.3344	0.0000	0.0852	0.8677	5.4725	37.5167	24.8	3.9	0.583972	0.006272	0.470035	0.107666	2.870000e+05	%
13	Belarus	3.7563	0.3353	12.7089	0.0000	6.2814	0.7565	0.8372	3.5665	4.1893	6.2104	0.5035	0.3795	0.0720	0.0010	0.0067	9.1691	0.1142	4.0155	0.0000	0.1399	0.8804	8.7859	37.2904	26.6	<2.5	2.740896	0.018912	2.612704	0.109280	9.375000e+06	%
14	Belgium	5.3730	0.8559	17.7279	0.0010	6.6704	0.6487	1.1325	4.1623	3.2370	11.6344	0.0000	0.2185	0.0965	0.1132	0.0409	4.4157	0.2461	3.6514	0.0000	0.1309	0.5640	6.8161	32.2637	24.5	<2.5	6.286322	0.185428	0.000000	6.100894	1.151500e+07	%
15	Belize	3.3803	0.0832	8.6856	0.0000	11.0700	0.3923	1.0325	12.6094	3.4890	3.6317	1.7653	0.0569	0.3940	1.1259	0.0195	1.0614	0.0849	3.9798	0.0000	0.0340	1.0198	4.7721	41.3122	22.4	7.5	2.872792	0.073031	2.739618	0.060143	4.190000e+05	%
16	Benin	5.9019	0.0132	3.7504	0.0000	9.6230	0.0711	1.2267	2.9523	1.4107	0.9458	0.1450	0.0829	0.6628	0.7367	0.4461	19.7771	0.0042	0.6635	0.0000	0.4028	0.7381	4.1964	46.2493	8.2	10.1	0.034344	0.000450	0.029183	0.004710	1.220900e+07	%
17	Bolivia	2.8956	0.0889	11.7799	0.0000	12.3079	0.6626	0.2307	7.5088	6.4098	4.2873	0.1661	0.1006	0.1374	0.3654	0.0422	7.7315	0.2083	2.5724	0.0000	0.7084	0.3573	3.2224	38.2165	18.7	17.1	1.952446	0.092435	1.438614	0.421396	1.163300e+07	%
18	Bosnia and Herzegovina	4.7876	0.0585	12.4319	0.0000	8.7152	0.2576	0.2465	4.6409	1.8165	9.9443	0.4860	0.1089	0.0963	0.2718	0.3922	4.0495	0.3504	1.6391	0.0000	0.0822	0.4205	11.6394	37.5645	19.4	<2.5	3.762085	0.145535	2.960317	0.656233	3.281000e+06	%
19	Botswana	4.2717	0.3402	14.4486	0.0000	11.4320	0.1402	0.3551	2.8465	2.2840	11.0217	1.6092	0.3056	0.2019	0.2729	0.1626	4.7614	0.1336	4.7857	0.0009	0.0150	1.0065	4.0558	35.5491	16.1	26.4	1.014372	0.007035	0.844799	0.162538	2.317000e+06	%
20	Brazil	4.3629	0.2803	17.3470	0.0000	8.4292	0.5042	0.5899	6.0936	6.4784	9.3416	0.0006	0.1512	0.7093	0.9254	0.0071	3.3959	0.3024	2.7755	1.0111	0.0513	1.2823	3.3070	32.6537	22.3	<2.5	4.460165	0.108603	3.979469	0.372094	2.118120e+08	%
21	Bulgaria	7.0834	0.2488	17.2483	0.0103	8.8750	0.5924	0.4845	3.9812	4.0073	11.5992	0.2254	0.3058	0.3450	0.1512	0.6048	1.8576	0.2818	2.3627	0.0000	0.0797	0.7085	6.2037	32.7435	27.4	3.6	3.227256	0.134416	2.788956	0.303883	6.927000e+06	%
22	Burkina Faso	15.3706	0.0231	4.6611	0.0000	21.2005	0.2406	0.7077	0.6596	1.2029	2.3195	0.1373	0.1664	1.8664	1.4294	0.0321	0.9723	0.0211	1.1287	0.0000	0.0712	0.9503	1.5036	45.3359	4.5	20	0.053710	0.000641	0.048031	0.005038	2.090300e+07	%
23	Cabo Verde	3.9814	0.0363	9.7559	0.0029	14.7961	0.4602	1.0598	4.6402	3.0725	5.0192	1.1190	0.1050	0.5394	1.1390	0.2731	3.8430	0.1671	2.1396	0.0000	0.0258	0.9471	6.6338	40.2437	10.6	12.6	2.586331	0.024281	2.461151	0.100899	5.560000e+05	%
24	Cambodia	4.5783	0.0377	6.0800	0.0000	24.8906	0.1310	4.2369	2.3184	1.2247	0.3285	0.4853	0.1211	0.7900	0.4903	0.0784	3.0519	0.1260	2.9923	0.8367	0.0238	0.2511	3.0072	43.9195	3.5	16.4	0.003059	0.000000	0.002930	0.000129	1.549700e+07	%
25	Cameroon	5.1560	0.0146	2.9330	0.0000	10.3112	0.0256	1.3206	7.2151	0.8760	0.6289	0.0717	0.0673	0.8658	1.4734	0.2830	11.3883	0.0234	0.6479	0.0000	0.1324	0.6398	8.8590	47.0670	9.5	9.9	0.118031	0.001782	0.110914	0.005335	2.659800e+07	%
26	Canada	4.8441	0.8084	16.0365	0.0060	6.4236	0.7668	1.2148	5.0692	4.4707	8.7596	0.6071	0.0097	0.4334	0.6304	0.0574	3.4940	0.4053	5.0237	0.0000	0.1439	1.3441	5.4961	33.9551	31.3	<2.5	2.109961	0.054203	1.909848	NaN	3.819000e+07	%
27	Central African Republic	1.7435	0.1228	6.9032	0.0000	3.8844	0.0510	0.8041	4.5543	3.7346	1.7581	0.0614	0.4338	0.6970	0.8936	0.0031	27.7128	0.2008	0.7022	0.0000	0.0000	0.7240	1.9214	43.0942	6.3	59.6	0.103292	0.001304	0.101139	0.000849	4.830000e+06	%
28	Chad	0.8297	0.2023	12.2804	0.0000	21.6017	0.0495	1.0451	1.0669	3.8543	5.9037	0.0640	1.2256	3.0304	1.4133	0.0000	6.9910	0.0087	1.3449	0.0000	0.0000	0.4556	0.9141	37.7189	4.8	37.5	0.020578	0.000741	0.016881	0.002957	1.687700e+07	%
29	Chile	4.9690	0.1782	15.0394	0.0155	10.8579	0.7757	0.8711	3.9109	6.1492	6.9966	0.3024	0.0525	0.2588	0.2677	0.0133	4.7183	0.1353	3.5582	0.0000	0.1190	0.5435	5.3092	34.9584	28.8	2.7	3.842229	0.097047	3.626194	NaN	1.947000e+07	%
30	China	2.3224	0.0866	13.4240	0.0542	8.9298	1.2059	3.3435	4.2451	6.0732	1.7962	1.3773	0.8643	0.4401	0.0704	0.0364	1.9016	0.1781	3.1065	0.0253	0.2665	0.5790	13.0990	36.5744	6.6	8.5	0.007156	0.000344	0.006685	0.000126	1.402385e+09	%
31	Colombia	3.1032	0.0814	12.8210	0.0000	8.1055	0.8132	0.4552	11.3276	3.5540	7.7443	0.0776	0.1729	0.2276	0.3503	0.0343	5.0309	0.0057	4.7518	0.0280	0.0947	0.9454	3.0981	37.1774	22.1	4.8	4.350795	0.112639	4.090144	0.148012	4.944400e+07	%
32	Congo	4.9612	0.0122	6.1258	0.0000	6.2564	0.0457	2.3945	4.0015	3.2735	0.0963	0.1730	0.3044	0.3632	0.2408	0.0033	23.2457	0.0171	1.3907	0.0000	0.0016	0.6700	2.5520	43.8709	8.4	40.3	0.571675	0.014516	0.379721	0.177437	5.518000e+06	%
33	Costa Rica	1.6982	0.1529	18.6770	0.0007	8.6611	0.7582	1.3317	9.5203	3.8631	12.4535	0.0274	0.1161	0.2914	0.8354	0.0433	1.5676	0.3181	3.9006	0.0000	0.0397	1.4363	2.9837	31.3238	25.7	4.8	3.843436	0.052279	3.055782	0.735375	5.111000e+06	%
34	Cote d'Ivoire	1.4487	0.0132	2.9144	0.0000	11.4585	0.1587	1.3914	4.8741	0.9586	0.1285	0.0163	0.2648	0.6055	0.1727	0.1247	23.3080	0.0759	0.7859	0.0000	0.5443	0.8656	2.8098	47.0806	9.0	19	0.112959	0.000619	0.106105	0.006235	2.617500e+07	%
35	Croatia	4.9846	0.2296	17.3110	0.0005	5.7726	0.3955	0.9196	3.4094	3.6945	11.8824	0.0000	0.1889	0.3494	0.0373	0.0236	1.8078	0.2458	3.0880	0.0000	0.0761	0.3808	12.5188	32.6838	27.1	<2.5	5.848905	0.127960	5.648706	0.072239	4.020000e+06	%
36	Cuba	2.0589	0.1202	10.0096	0.0000	9.9309	0.5263	0.3368	9.0347	3.2837	5.5971	0.0787	0.1456	0.5346	1.1336	0.0089	5.1555	0.0657	3.1617	0.0000	0.0006	0.4535	8.3740	39.9895	26.7	<2.5	0.283760	0.002065	0.230822	0.050873	1.128100e+07	%
37	Cyprus	3.4447	0.0277	16.1736	0.0015	7.0646	0.5746	1.8209	6.1980	5.3167	8.2744	1.4858	0.1584	0.4089	0.2110	0.0307	1.7705	0.2387	4.2194	0.0000	0.1781	1.1937	7.3888	33.8191	22.6	5.6	2.621707	0.017481	0.170423	2.433803	1.207000e+06	%
38	Czechia	9.8498	0.8945	17.8065	0.0006	5.6937	0.4964	0.5355	3.3962	4.7618	10.9830	0.1099	0.1340	0.0949	0.1139	0.0316	3.3991	0.1340	3.8081	0.0000	0.0477	0.9854	4.5289	32.1944	28.5	<2.5	9.612841	0.159845	8.555328	0.897667	1.071600e+07	%
39	Denmark	4.8866	1.2415	23.2141	0.0010	6.7226	0.8206	1.1797	3.1098	3.6804	15.9557	0.0000	0.3347	0.1185	0.0369	0.0774	3.1113	0.4069	2.8728	0.0000	0.1138	0.0915	5.2431	26.7812	21.3	<2.5	3.463218	0.038017	3.307360	0.117842	5.829000e+06	%
40	Djibouti	0.3778	0.0579	5.5215	0.0000	20.2702	0.1275	0.4276	3.2735	1.7428	2.9085	1.1831	0.2572	0.0568	1.5724	0.1506	1.8633	0.1309	4.2005	0.0000	0.0406	1.6153	9.7637	44.4582	12.2	18.9	0.601316	0.006377	0.592308	0.002632	9.880000e+05	%
41	Dominica	0.9410	0.0319	8.9011	0.0000	3.4014	0.1244	1.4188	19.3028	2.7820	4.4217	0.6745	0.1219	1.1058	0.2255	0.1512	6.8477	0.2553	3.1738	0.0000	0.0035	0.3797	4.6371	41.0989	28.2	6.2	0.168056	0.000000	0.152778	0.015278	7.200000e+04	%
42	Dominican Republic	3.2100	0.1122	11.2417	0.0006	6.9232	0.6445	0.6059	18.4431	3.5009	6.1202	0.0906	0.2567	1.1395	0.5881	0.0260	2.0198	0.1420	2.1332	0.0000	0.0108	1.2948	2.7385	38.7577	26.9	9.5	2.115695	0.026676	1.577705	0.511314	1.050000e+07	%
43	Ecuador	3.2929	0.1277	15.3469	0.0328	14.9132	0.6902	0.6893	8.0390	4.0115	9.5745	0.1526	0.2200	0.1765	0.1570	0.0177	2.2701	0.0071	1.7370	0.0000	0.0160	1.3777	2.4981	34.6522	19.3	7.9	1.468306	0.085683	1.198184	0.184438	1.751100e+07	%
44	Egypt	0.0722	0.0896	6.7374	0.0000	17.5311	0.2307	1.5844	6.9387	1.7583	2.7568	0.0187	0.3170	0.6541	0.3063	0.0655	2.6919	0.0863	1.7984	2.0338	0.0268	0.3839	10.6559	43.2622	31.1	4.5	0.167759	0.009527	0.131002	0.027229	1.008030e+08	%
45	El Salvador	2.1651	0.1113	12.8880	0.0000	11.7265	0.7533	0.5730	5.9517	2.6337	8.7381	0.5920	0.0794	1.4273	1.4661	0.0121	2.2462	0.2649	3.5260	0.0000	0.0155	0.4565	7.2703	37.1029	22.7	9	0.867721	0.025814	0.778136	0.063771	6.481000e+06	%
46	Estonia	6.1949	0.4556	22.3154	0.0005	6.3498	0.6145	0.7127	3.5061	3.0810	17.3068	0.1501	0.1457	0.0518	0.1472	0.0576	3.2403	0.2121	2.3886	0.0000	0.0886	0.3472	4.9558	27.6779	23.8	2.9	3.569647	0.034260	2.828550	0.706837	1.331000e+06	%
47	Eswatini	6.2239	0.1024	7.4118	0.0019	14.7814	0.1578	0.3643	8.5537	2.5917	3.9299	0.6695	0.2620	0.3005	0.3578	0.1615	5.3187	0.0648	3.1796	0.0000	0.0892	0.3258	2.5701	42.5816	13.5	20.6	1.465670	0.054710	1.038859	0.372101	1.104000e+06	%
48	Ethiopia	2.4543	0.0276	5.2956	0.0000	22.0779	0.0594	0.0608	1.1774	0.7462	4.2647	0.0180	0.1382	0.2322	3.0195	0.4298	11.2918	0.2957	1.3170	0.0000	0.0802	0.4699	1.8449	44.6989	3.6	20.6	0.123093	0.001867	0.109237	0.011989	1.149160e+08	%
49	Fiji	1.8598	0.2922	7.8511	0.0021	10.1440	0.2682	2.3832	2.1636	3.0004	1.7421	0.2586	0.1642	4.3799	0.7746	0.0500	7.8599	0.1238	9.7259	0.0000	0.0157	0.8683	3.9255	42.1469	30.0	3.7	0.006250	0.000223	0.005915	0.000112	8.960000e+05	%
50	Finland	4.5004	0.5190	26.8865	0.0000	5.1978	0.3979	1.5306	3.1864	3.4915	20.8378	0.7944	0.1097	0.0883	0.0533	0.0168	2.7671	0.5450	1.8388	0.0000	0.0460	0.2468	3.8325	23.1132	24.9	<2.5	0.854802	0.012443	0.560680	0.281678	5.529000e+06	%
51	France	4.1631	0.7907	21.7097	0.0011	6.9951	0.6110	1.8214	4.8780	4.4005	13.7840	0.1918	0.3010	0.1479	0.0991	0.0164	2.6963	0.2925	2.6110	0.0000	0.2093	0.8675	5.1223	28.2903	23.2	<2.5	5.199055	0.121558	0.367877	4.709620	6.494000e+07	%
52	French Polynesia	4.6997	0.0309	14.3585	0.0169	7.6479	0.5386	3.4809	4.3512	7.0798	2.9364	3.2613	0.2748	3.1818	0.1452	0.0111	3.5436	0.1260	2.8715	0.6809	0.0405	0.9675	4.0992	35.6559	NaN	4.2	NaN	NaN	NaN	NaN	2.800000e+05	%
53	Gabon	6.1881	0.0109	8.1825	0.0000	9.1158	0.0701	2.2578	9.0341	4.3200	0.9486	0.4751	0.5758	0.8509	0.0139	0.0015	11.8727	0.0212	1.2508	0.1248	0.0051	0.4824	2.3979	41.8004	13.4	10.5	0.513537	0.003182	0.483953	0.026401	2.231000e+06	%
54	Gambia	2.8419	0.0114	10.1417	0.0000	21.8908	0.2008	3.9197	0.8742	1.1134	4.8167	0.9824	0.0812	3.1637	0.1281	0.0043	0.8614	0.2719	4.1176	0.0000	0.0897	2.2026	2.4575	39.8291	8.7	10.2	0.175300	0.005544	0.160778	0.008978	2.417000e+06	%
55	Georgia	2.5343	0.2356	15.5347	0.0000	15.6253	0.5032	0.6622	3.1735	2.6217	11.3605	0.7882	0.1507	0.0840	0.0025	0.0247	3.7542	0.2397	3.3110	0.0000	0.1631	0.6680	4.0993	34.4636	23.3	7.9	7.042261	0.087995	6.805249	0.149017	3.715000e+06	%
56	Germany	6.7714	0.6715	20.8485	0.0011	6.0363	0.5947	0.6758	3.6131	4.6524	14.1789	0.0000	0.0747	0.1701	0.0419	0.0429	3.5712	0.3493	2.5070	0.0000	0.1903	0.8104	5.0542	29.1443	25.7	<2.5	2.743101	0.073891	2.451366	0.217844	8.330000e+07	%
57	Ghana	0.9444	0.0088	2.8956	0.0000	6.3230	0.0705	1.4992	10.4274	0.8069	0.4178	0.1657	0.0917	0.7957	0.3920	0.2380	24.6325	0.0053	0.8128	0.2815	0.0364	0.3655	1.6849	47.1041	9.7	5.5	0.225424	0.001445	0.204364	0.019615	3.107300e+07	%
58	Greece	2.2879	0.2116	17.6914	0.0005	7.2801	0.4539	1.0260	6.3296	3.7836	12.0221	0.0596	0.1947	0.8112	0.2539	0.0243	3.1307	0.2560	1.7744	0.0000	0.6676	1.5237	7.9134	32.3038	27.4	<2.5	1.525355	0.055617	0.876299	0.593439	1.070000e+07	%
59	Grenada	3.1315	0.1276	12.1319	0.0019	7.6569	0.9226	2.5802	11.0608	5.6546	2.7183	1.2873	0.1266	1.3768	0.6417	0.3171	2.2222	0.1104	5.4480	0.0000	0.0143	0.9959	3.6085	37.8648	20.2	NaN	0.130973	0.000885	0.129204	0.000885	1.130000e+05	%
60	Guatemala	1.7037	0.0957	10.3765	0.0000	15.1579	1.4676	0.2807	6.7124	3.9589	4.4093	0.0348	0.1632	0.5276	1.6504	0.0403	1.3316	0.2872	5.3058	0.0000	0.1980	0.8747	5.7997	39.6240	18.8	15.2	0.901899	0.032403	0.824294	0.045201	1.806600e+07	%
61	Guinea	0.3110	0.0213	3.8776	0.0000	17.2911	0.1648	0.8939	7.9395	1.1561	1.4724	0.2312	0.1692	0.9506	0.3925	0.0000	11.7958	0.1196	1.3368	0.0000	0.0009	1.3271	4.4286	46.1202	6.6	16.5	0.116760	0.000669	0.114013	0.002078	1.256000e+07	%
62	Guinea-Bissau	2.4603	0.0423	4.9295	0.0000	21.6069	0.1038	0.1666	6.3660	2.2155	2.1899	0.5331	0.2114	0.3280	0.2499	0.0013	8.1087	0.0167	0.7893	0.0000	0.4485	1.8016	2.3642	45.0666	8.2	28	0.143851	0.002387	0.125947	0.015516	1.927000e+06	%
63	Guyana	1.8708	0.0206	9.1155	0.0000	9.7847	0.1038	1.7011	5.7954	2.5237	4.7277	0.3516	0.0385	1.8367	0.3907	0.1942	1.8384	0.0742	3.2325	0.0000	0.0435	0.2779	15.2061	40.8722	19.2	8.1	1.008767	0.022872	0.884625	0.101271	7.870000e+05	%
64	Haiti	1.2733	0.0402	3.5986	0.0000	13.4196	0.0525	0.5064	12.0278	1.9687	0.8765	0.3309	0.1532	0.4829	2.0067	0.0022	11.4800	0.3801	2.0961	0.0000	0.0000	0.8742	2.0302	46.3997	20.5	49.3	0.103113	0.002149	0.080295	0.020670	1.140300e+07	%
65	Honduras	2.0662	0.1378	13.6048	0.0000	14.1699	0.4601	0.3339	6.8464	3.5080	9.0154	0.6923	0.1505	0.1749	1.3962	0.0138	0.6796	0.4686	5.9591	0.0000	0.0276	1.2467	2.6599	36.3883	19.4	12.9	1.560270	0.037612	0.626369	0.896290	9.864000e+06	%
66	Hungary	6.7533	1.0595	20.0268	0.0007	7.5908	0.8885	0.4113	3.0852	5.2042	12.4568	0.0039	0.0052	0.0679	0.1906	0.1802	3.0460	0.1031	2.5433	0.0000	0.0183	0.7214	5.6788	29.9644	28.6	<2.5	3.840739	0.133367	2.845797	0.861575	9.767000e+06	%
67	Iceland	4.7545	0.9461	23.9700	0.0025	4.1196	0.5557	4.6117	4.8405	4.6269	13.0028	1.4022	0.2232	0.1759	0.0376	0.0676	1.9909	0.4626	3.9472	0.0000	0.0752	0.4667	3.6874	26.0331	23.1	<2.5	1.636141	0.007880	1.619293	0.008967	3.680000e+05	%
68	India	0.1959	0.0028	11.3357	0.0000	17.3821	0.3061	0.6499	5.4333	0.3560	9.9889	0.0000	0.0311	0.7280	1.5709	0.3268	2.7906	0.0753	1.7471	0.0000	0.1526	0.7487	7.5213	38.6568	3.8	14.5	0.773256	0.011070	0.751561	0.010625	1.400100e+09	%
69	Indonesia	0.0794	0.0309	6.2575	0.0009	24.3918	0.4911	3.9386	5.5134	1.0316	0.5943	0.0026	0.1702	2.2113	0.0988	0.1508	4.6343	0.0406	1.8481	0.0000	0.0564	0.8579	3.8558	43.7438	6.9	8.3	0.422100	0.011553	0.345620	0.064927	2.717390e+08	%
70	Iran (Islamic Republic of)	0.0007	0.5369	7.9295	0.0020	14.0141	0.4916	0.7813	7.9235	2.3965	3.5808	0.0007	0.1405	0.0540	0.4729	0.0559	3.2877	0.1299	2.1980	0.0000	0.6421	0.7706	12.5207	42.0701	25.5	4.9	1.734248	0.069414	1.482322	0.182512	8.415000e+07	%
71	Iraq	0.1915	0.0423	6.2809	0.0000	24.0855	1.2467	0.4450	6.4975	2.5171	1.9902	0.3975	0.0396	0.3037	0.4807	0.0304	1.0842	0.1783	3.3755	0.0000	0.1268	2.0139	4.9563	43.7165	27.4	29	1.579398	0.033004	1.508287	0.038107	3.972500e+07	%
72	Ireland	7.5703	0.8710	18.5712	0.0005	6.3057	0.4653	1.1942	3.3996	4.0102	11.9469	0.2951	0.0831	0.1733	0.1325	0.0362	3.5362	0.1855	4.0147	0.0000	0.0836	0.7497	4.9510	31.4242	26.9	<2.5	4.048531	0.073436	0.467000	3.508095	5.003000e+06	%
73	Israel	1.2876	0.1026	18.7463	0.0017	7.6277	0.5664	1.4696	6.3746	5.5003	10.9066	0.2602	0.2007	0.5233	0.3640	0.0278	1.9975	0.2619	1.7747	0.0000	0.2262	1.6743	8.8586	31.2472	26.7	<2.5	7.439052	0.055024	6.609635	0.774392	9.216000e+06	%
74	Italy	3.1892	0.2834	19.0329	0.0005	8.5417	0.6247	1.5816	6.0207	4.2963	12.1258	0.0403	0.1194	0.1836	0.2781	0.0064	1.9043	0.2946	1.7175	0.0000	0.4230	1.4224	6.9463	30.9671	22.9	<2.5	4.353685	0.150927	3.494529	0.708229	6.029600e+07	%
75	Jamaica	2.4636	0.2074	11.9958	0.0074	8.5031	0.1457	1.8692	8.0661	4.3372	5.2170	0.6952	0.2111	0.9990	0.2177	0.3752	4.5837	0.1677	4.6889	0.0000	0.0110	0.7584	6.4772	38.0023	24.4	8	0.599537	0.012709	0.435208	0.151620	2.809000e+06	%
76	Japan	4.0440	0.0751	15.3194	0.1088	12.1692	1.6960	3.9283	2.8618	4.2599	5.0630	0.2962	0.1883	0.7737	0.1287	0.1036	2.2072	0.3731	2.2055	0.2280	0.0812	1.3445	7.8669	34.6776	4.4	<2.5	0.320821	0.005059	0.284770	0.030991	1.259670e+08	%
77	Jordan	0.0846	0.1162	9.3186	0.0000	14.1380	0.3344	0.5985	4.6624	3.8651	4.2637	0.3579	0.1397	0.7280	0.6393	0.0469	2.1431	0.3742	4.7276	0.0000	0.1652	1.9861	10.6308	40.6798	33.4	12.2	3.126276	0.040912	2.991760	0.093604	1.067900e+07	%
78	Kazakhstan	2.1919	0.3611	19.1598	0.0000	6.5119	0.4584	0.2393	3.2145	3.4501	14.3591	0.2967	0.2914	0.1154	0.0255	0.0106	5.4755	0.1319	1.4560	0.0000	0.0845	1.1215	10.2055	30.8394	21.3	<2.5	1.299402	0.016651	1.137764	0.144987	1.873200e+07	%
79	Kenya	1.9664	0.1175	11.4394	0.0000	15.1221	0.1447	0.4329	4.6941	1.7336	8.7987	0.0250	0.2110	0.2686	1.7510	0.0174	7.5828	0.2230	1.8098	0.0000	0.0892	0.6689	4.3450	38.5589	6.0	29.4	0.189975	0.003318	0.157491	0.029166	5.352800e+07	%
80	Kiribati	0.2970	0.0486	8.8958	0.0000	8.2747	0.1640	6.1065	5.8907	2.3856	0.1561	0.5104	0.0350	9.9946	0.0032	0.0064	7.1830	0.0311	4.1373	0.0000	0.1951	0.4419	4.1389	41.1042	45.6	2.7	NaN	NaN	NaN	NaN	1.250000e+05	%
81	Korea, North	0.8981	0.0010	3.3933	0.0010	17.9378	0.4668	1.1568	6.1383	1.3872	0.3146	0.0690	0.0660	0.2090	1.1000	0.0000	6.2438	0.0142	0.3694	0.0000	0.0426	0.7783	12.8082	46.6046	7.1	47.8	NaN	NaN	NaN	NaN	2.577900e+07	%
82	Korea, South	4.8669	0.2553	11.8174	1.6794	11.7286	0.6414	3.4396	3.6993	4.4239	1.1394	0.0000	0.2378	0.5632	0.0720	0.0288	0.9799	0.0945	2.4835	0.0006	0.1664	1.1663	12.3349	38.1810	4.9	<2.5	0.156227	0.002841	0.136855	0.016531	5.178100e+07	%
83	Kuwait	0.0044	0.0022	10.7213	0.0022	11.4132	1.3306	0.8343	5.3058	4.8770	3.4788	0.8554	0.1969	0.2783	0.4353	0.2020	3.2296	0.2696	2.9163	0.0276	0.1512	1.1976	12.9953	39.2750	37.0	2.8	3.624728	0.020550	3.434087	0.170092	4.691000e+06	%
84	Kyrgyzstan	0.6313	0.1910	15.4588	0.0000	10.1059	0.2653	0.0700	2.2846	1.8707	12.8008	0.2076	0.2604	0.0098	0.3439	0.0197	5.8068	0.0608	3.2495	0.0000	0.1449	0.3550	11.3219	34.5412	15.4	7.1	1.288929	0.021580	1.236609	0.030740	6.594000e+06	%
85	Lao People's Democratic Republic	0.7883	0.0979	3.6579	0.0006	16.3943	0.1063	1.4213	8.8158	1.7111	0.1632	0.3404	0.1581	0.3584	0.1485	0.1317	3.4835	0.9869	1.1101	1.9254	0.0028	0.2149	11.6410	46.3416	4.5	16.5	0.000621	0.000000	0.000566	0.000055	7.242000e+06	%
86	Latvia	5.4573	1.1805	18.8216	0.0005	6.3952	0.6645	1.3226	2.5738	3.6559	11.8285	0.2596	0.1686	0.1107	0.0032	0.0122	6.5835	0.1963	2.6169	0.0000	0.0793	0.6990	6.1946	31.1757	25.7	<2.5	3.749737	0.069769	3.122976	0.556993	1.902000e+06	%
87	Lebanon	0.7164	0.0796	8.0679	0.0000	11.8253	0.1730	0.7091	6.9852	1.9469	5.0748	0.0747	0.0853	0.5052	0.5011	0.0406	3.2676	0.3647	5.1366	0.0000	0.5271	1.4807	10.5128	41.9252	31.3	11	4.656938	0.052190	2.947062	1.657685	6.825000e+06	%
88	Lesotho	3.4135	0.0233	6.0914	0.0000	23.4645	0.1204	0.2383	1.8065	2.5290	2.7893	0.3898	0.3924	0.0725	1.1603	0.1165	7.1935	0.0363	2.2079	0.0000	0.0052	0.6786	3.3721	43.8988	13.5	13.1	0.437908	0.008543	0.126331	0.303035	2.142000e+06	%
89	Liberia	1.0338	0.0264	3.7603	0.0000	16.1465	0.1598	0.7764	5.3606	2.4123	0.1545	0.2997	0.2311	0.4159	0.0977	0.0013	15.3608	0.0132	1.1236	1.1804	0.0779	1.9567	3.1728	46.2383	8.6	37.2	0.038671	0.001661	0.034994	0.002017	5.058000e+06	%
90	Lithuania	5.5419	0.5316	23.0490	0.0009	6.4732	0.7000	1.5431	2.4007	3.7899	16.3529	0.0126	0.1301	0.0867	0.4388	0.0546	2.2766	0.2164	4.5020	0.0000	0.0550	0.4924	4.4045	26.9471	28.4	<2.5	6.667072	0.104760	4.939621	1.622691	2.794000e+06	%
91	Luxembourg	8.0858	0.5443	14.6038	0.0051	5.1366	0.7875	1.6249	4.4841	4.0469	7.4929	0.3877	0.1036	0.1658	0.0399	0.0586	2.2067	1.2823	8.1121	0.0000	0.0308	0.5630	4.8536	35.3838	24.2	<2.5	8.150633	0.093354	7.661551	0.395728	6.320000e+05	%
92	Madagascar	0.7972	0.0415	4.9125	0.0024	19.9604	0.0770	0.6267	5.0701	1.4618	2.5031	0.0782	0.2002	0.1848	0.3708	0.0486	14.4675	0.2085	1.0922	0.5852	0.0261	0.3115	1.8871	45.0869	4.5	44.4	0.068849	0.001015	0.065779	0.002055	2.769100e+07	%
93	Malawi	1.1827	0.0814	2.7754	0.0000	10.6197	0.0829	0.6970	8.4723	1.4923	0.2524	0.0396	0.1695	0.5436	1.1841	0.0139	20.2901	0.0389	0.6412	0.0000	0.0161	0.2040	3.9786	47.2242	4.7	17.5	0.140486	0.004375	0.059320	0.076790	1.913000e+07	%
94	Malaysia	0.8948	0.0882	12.6354	0.0204	17.3088	1.6596	5.3482	4.1713	4.9092	0.5300	0.1541	0.0789	0.5801	0.2617	0.4502	1.6568	0.1012	3.9030	0.0000	0.0835	1.6336	6.1678	37.3632	15.3	2.5	0.728230	0.002614	0.570361	0.155255	3.278100e+07	%
95	Maldives	0.6859	0.0029	14.2704	0.0068	11.5385	1.1422	8.7959	6.0777	2.0051	2.3155	3.0315	0.0019	0.2082	0.0808	0.3434	1.6403	0.3960	2.9790	0.1002	0.7569	0.1819	7.7180	35.7208	7.9	10.3	3.078743	0.009982	2.690573	0.378189	5.410000e+05	%
96	Mali	2.8904	0.0444	10.1637	0.0000	19.8566	0.0573	0.7543	3.8991	1.9124	7.1206	0.1841	0.2746	0.4910	0.7955	0.0638	2.6772	0.0614	0.8811	0.0000	0.2374	0.5144	7.2943	39.8262	7.1	6.3	0.040294	0.001669	0.029895	0.008730	2.025100e+07	%
97	Malta	3.9051	0.2062	13.6092	0.0017	7.8996	0.6714	1.7922	4.6310	4.6096	6.2546	0.0315	0.0747	0.2899	0.2197	0.0331	2.0590	0.2101	5.2164	0.0000	0.1236	0.4860	11.2856	36.3899	31.0	<2.5	3.558621	0.053448	3.045211	0.459962	5.220000e+05	%
98	Mauritania	0.0055	0.1127	15.0193	0.0000	20.4873	0.2539	1.0025	1.2991	3.3994	9.8250	0.7519	0.4279	0.0799	1.0999	0.0066	1.0168	0.3677	4.3166	0.0000	0.0000	1.5716	3.9784	34.9779	11.3	10.4	0.361118	0.009140	0.343763	0.008215	4.650000e+06	%
99	Mauritius	3.0805	0.0074	9.2700	0.0102	17.1855	0.5910	2.1303	3.5680	4.6992	1.7166	1.0545	0.1173	0.4479	0.8560	0.1847	1.8348	0.1625	3.5680	0.0000	0.0369	2.0490	6.7122	40.7175	11.5	6.5	0.046166	0.000791	0.042688	0.002688	1.265000e+06	%
100	Mexico	4.4105	0.1672	15.1527	0.0201	11.4325	1.2967	0.9656	7.6794	4.3561	7.9917	0.0275	0.3552	0.1947	0.7071	0.1121	1.1644	0.0248	3.2266	0.0000	0.1121	0.6856	5.0713	34.8456	28.4	3.6	1.507199	0.129731	1.159805	0.217663	1.277920e+08	%
101	Mongolia	3.1430	0.3846	25.8904	0.0000	10.1415	0.3727	0.0440	1.0413	8.0929	15.8560	0.5962	1.1411	0.0266	0.0366	0.0037	2.6155	0.1401	1.4680	0.0000	0.0137	0.5550	4.3326	24.1046	19.6	13.4	0.060190	0.000060	0.043112	0.017019	3.361000e+06	%
102	Montenegro	2.2188	0.2067	22.8328	0.0014	5.0447	0.5542	0.6472	5.7297	3.3604	17.9487	0.8470	0.1132	0.0865	0.2113	0.0175	1.1955	0.2605	6.0418	0.0000	0.0948	0.4258	5.0001	27.1612	24.9	<2.5	10.408199	0.134405	9.039871	1.233923	6.220000e+05	%
103	Morocco	0.2870	0.1449	8.4754	0.0000	18.7029	0.4848	1.3901	7.2110	2.5074	3.7540	0.0079	0.1949	0.1892	0.3306	0.1185	3.4356	0.2042	2.6816	0.0000	0.2549	0.8168	7.2860	41.5224	25.6	3.4	1.321111	0.023312	1.262575	0.035225	3.595200e+07	%
104	Mozambique	1.2195	0.0297	4.0111	0.0000	13.1904	0.1932	1.1353	2.8312	0.9302	1.6385	0.1565	0.0852	0.1258	1.2809	0.0040	22.2864	0.1050	1.0649	0.0000	0.2011	0.7509	2.7738	45.9864	6.0	27.9	0.141539	0.001447	0.085959	0.054133	3.116600e+07	%
105	Myanmar	0.2195	0.1751	13.5188	0.0034	16.7608	0.4613	4.0424	4.0287	4.9753	3.6306	0.3391	0.2298	1.1003	1.2806	0.2170	1.4514	0.2298	2.8592	0.0000	0.2913	0.6322	7.0725	36.4807	5.7	10.6	NaN	NaN	NaN	NaN	5.470400e+07	%
106	Namibia	5.4716	0.1134	9.6094	0.0053	9.9189	0.1196	1.0164	3.1266	2.6844	5.4479	0.7228	0.2207	0.0818	0.7298	0.1266	12.8231	0.0888	3.9848	0.0009	0.0114	0.9531	2.3626	40.3800	15.0	27.3	1.371271	0.014601	1.312830	0.043841	2.541000e+06	%
107	Nepal	0.3318	0.0312	5.4355	0.0000	18.1511	0.1435	0.2050	4.7443	0.9817	3.9212	0.0594	0.1536	0.0594	0.9107	0.4195	6.0521	0.0319	0.5470	3.0677	0.0978	0.8209	9.2770	44.5576	3.8	8.7	0.906141	0.006784	0.892826	0.006531	2.999600e+07	%
108	Netherlands	4.3903	0.3343	24.2977	0.0111	4.9685	0.7470	1.1517	4.9230	4.0105	18.0051	0.0000	0.0397	0.1111	0.0852	0.0640	4.2639	0.2904	2.3446	0.0000	0.1587	0.8485	3.2587	25.6959	23.1	<2.5	5.815292	0.082830	0.076063	5.656398	1.746700e+07	%
109	New Caledonia	5.0363	0.0821	11.7818	0.0804	8.5840	0.7494	2.0361	5.5855	5.8687	2.7675	3.6589	0.1993	2.2961	0.1275	0.0488	2.5648	0.1745	3.1901	0.0000	0.0376	1.3157	5.5941	38.2208	NaN	7.1	NaN	NaN	NaN	NaN	2.950000e+05	%
110	New Zealand	5.2220	0.7446	17.5777	0.0170	7.7522	0.7067	1.6771	4.0202	6.8421	7.4627	0.0000	0.1275	0.3296	0.2414	0.0495	3.3719	0.2374	3.8188	0.0109	0.1499	0.5364	6.6889	32.4155	32.0	<2.5	0.046521	0.000501	0.044696	0.001323	4.987000e+06	%
111	Nicaragua	1.7194	0.0836	13.1538	0.0030	16.8052	0.5490	0.6557	3.8377	3.1044	8.5527	0.6658	0.2055	0.3354	1.3618	0.0060	1.9118	0.0050	5.7021	0.0000	0.0030	0.9227	3.5808	36.8356	21.8	17	0.095497	0.002577	0.064054	0.028866	6.596000e+06	%
112	Niger	0.0558	0.1185	7.2385	0.0000	22.2843	0.0239	0.2022	2.5503	1.2702	5.4224	0.0687	0.2022	1.4804	3.4838	0.0807	1.3489	0.0349	0.3666	0.0000	0.0000	0.3975	10.6117	42.7585	4.7	16.5	0.019009	0.000682	0.016330	0.001997	2.418900e+07	%
113	Nigeria	4.0037	0.0151	1.7391	0.0000	10.9220	0.1976	0.7239	4.4268	0.5675	0.1730	0.0437	0.0627	0.4763	0.8311	0.1715	19.5271	0.0064	0.6501	0.0000	0.0841	0.8573	6.2627	48.2585	7.8	13.4	0.067547	0.000799	0.054602	0.012146	2.061400e+08	%
114	North Macedonia	1.9045	0.3784	12.0639	0.0000	7.3937	0.2847	0.3507	5.0893	2.0683	8.8045	0.2959	0.1773	0.3253	0.2847	0.1288	3.6779	0.2474	2.9104	0.0000	0.1796	0.9505	14.5512	37.9330	23.9	3.2	4.548029	0.140192	4.025721	0.382115	2.080000e+06	%
115	Norway	3.7939	0.6119	21.1992	0.0005	5.9777	0.6194	2.7466	4.2801	3.6083	13.5227	3.6634	0.0893	0.3113	0.3204	0.0235	2.8097	0.4819	2.2652	0.0000	0.0990	0.8697	3.9131	28.7933	25.0	<2.5	1.200167	0.010804	0.334101	0.855263	5.387000e+06	%
116	Oman	0.2621	0.0571	13.5101	0.0000	9.6835	0.5741	2.0105	9.7870	3.0531	7.6265	0.0423	0.1888	0.1338	0.2776	0.2297	1.6984	0.1395	2.5903	0.0077	0.0521	0.6995	10.8902	36.4860	22.9	6.8	2.865288	0.032506	2.700318	0.132463	4.713000e+06	%
117	Pakistan	0.0085	0.0969	22.2758	0.0011	14.6224	0.3577	0.1831	2.4099	1.7957	19.6466	0.0117	0.1969	0.3577	0.6993	0.1618	1.8202	0.5258	2.6707	1.7915	0.0852	0.3417	2.2173	27.7226	7.8	20.3	0.250961	0.005416	0.230941	0.014604	2.209400e+08	%
118	Panama	6.6254	0.2467	12.3346	0.0031	14.2627	0.4604	1.0050	6.6783	4.9800	5.3530	0.7431	0.2873	0.4589	0.4129	0.0153	1.3758	0.1195	4.2277	0.0000	0.0360	0.7645	1.9480	37.6619	22.5	10	7.622321	0.127364	6.773850	0.721107	4.283000e+06	%
119	Paraguay	3.9095	0.2191	11.2654	0.0000	7.5639	0.9035	0.3119	5.4532	4.0434	5.3208	0.1788	0.4675	0.2745	0.6185	0.0082	12.5302	1.0112	2.4084	0.0000	0.0015	1.0733	3.7113	38.7260	19.0	10.7	1.896664	0.038691	1.562591	0.295382	7.255000e+06	%
120	Peru	3.8623	0.0316	8.6638	0.0014	11.8140	0.6431	1.7993	8.5783	1.7540	4.0729	0.0719	0.3629	0.2458	0.6367	0.0172	8.7924	0.2501	1.7339	0.0000	0.0230	0.5066	4.8044	41.3344	19.1	9.7	3.573742	0.127751	3.310514	0.135477	3.282400e+07	%
121	Philippines	1.9716	0.3074	6.7108	0.0000	20.0498	0.3527	2.4506	8.5466	3.2257	0.1045	0.1925	0.2717	0.5425	0.1106	0.0340	2.8773	0.5399	2.3774	0.0000	0.1838	0.4119	5.4481	43.2905	6.0	13.3	0.488699	0.010139	0.445757	0.032803	1.095810e+08	%
122	Poland	6.8201	0.9842	16.5590	0.0000	7.9033	0.3702	0.5845	3.0079	4.8500	9.7542	0.0000	0.0170	0.0798	0.1099	0.0202	5.5631	0.1509	2.4775	0.0000	0.0476	0.3959	6.8655	33.4390	25.6	<2.5	4.028175	0.101632	3.392335	0.534208	3.836800e+07	%
123	Portugal	4.7147	0.4077	18.6614	0.0005	6.2984	0.4053	2.7295	6.5126	4.5168	10.3936	0.1393	0.2079	0.0965	0.1618	0.0173	3.1060	0.2684	1.7316	0.0000	0.2319	0.7246	7.3366	31.3374	23.2	<2.5	7.429605	0.136070	5.846836	1.446699	1.025500e+07	%
124	Republic of Moldova	3.8296	0.4339	16.5343	0.0000	11.6462	0.6795	0.9910	3.4256	3.2622	10.9992	0.5083	0.1694	0.0616	0.0642	0.0068	3.9066	0.0830	2.6632	0.0000	0.0180	0.8250	6.4285	33.4640	20.1	NaN	4.646195	0.099859	4.333692	0.212645	3.535000e+06	%
125	Romania	4.7679	0.3474	15.9287	0.0000	8.0412	0.6848	0.2844	4.2115	3.0499	11.4018	0.2567	0.1594	0.0974	0.1045	0.1274	4.7130	0.1398	1.4126	0.0000	0.1093	0.7301	9.3641	34.0679	24.5	<2.5	3.863328	0.097755	3.583358	0.182215	1.924100e+07	%
126	Russian Federation	3.7425	0.2486	16.1525	0.0023	8.2803	0.9029	1.1312	3.1620	4.3180	9.3253	0.0118	0.2249	0.1319	0.1426	0.0118	6.3194	0.1809	4.6144	0.0000	0.0118	0.8663	6.3713	33.8472	25.7	<2.5	2.663104	0.051120	2.316142	0.295842	1.467330e+08	%
127	Rwanda	1.3631	0.0186	2.6366	0.0000	3.5380	0.0269	0.5279	15.7285	0.6257	1.3521	0.0283	0.0855	0.2233	2.5036	0.0179	20.4415	0.0110	0.5782	0.0000	0.0000	0.2109	2.7193	47.3634	4.8	36.8	0.126135	0.001675	0.093013	0.031447	1.295200e+07	%
128	Saint Kitts and Nevis	4.0867	0.1735	14.0252	0.0000	6.3135	0.3868	3.4174	7.5449	7.8207	1.8950	1.5442	0.3318	0.9470	0.5422	0.0901	2.7150	0.0749	6.4623	0.0000	0.0673	1.3850	4.2090	35.9677	23.1	NaN	0.074074	0.000000	0.066667	0.007407	5.400000e+04	%
129	Saint Lucia	7.8024	0.0252	14.8893	0.0009	7.2091	0.3326	3.0652	5.9453	8.1700	2.8881	1.6054	0.4063	0.6274	0.5663	0.1780	2.6427	0.0602	3.7043	0.0000	0.0575	0.6103	4.1169	35.0964	19.8	NaN	0.996154	0.009890	0.480769	0.505495	1.820000e+05	%
130	Saint Vincent and the Grenadines	2.5260	0.0220	11.0865	0.0000	9.0142	0.3412	1.3785	10.4710	6.9184	2.2593	1.9325	0.1672	0.1049	0.6452	0.2113	4.3734	0.2933	3.4592	0.0000	0.0357	0.7227	5.1371	38.9005	23.8	5.7	1.082883	0.002703	0.361261	0.718919	1.110000e+05	%
131	Samoa	1.4236	0.2262	11.1630	0.1236	3.6768	0.1314	2.7788	10.7845	5.7993	1.9647	0.7751	0.1398	10.5187	0.0000	0.0582	7.6944	0.1344	2.0811	0.0036	0.0108	0.5117	1.1656	38.8349	45.5	2.7	0.001000	0.000000	0.001000	0.000000	2.000000e+05	%
132	Sao Tome and Principe	1.5187	0.0220	4.1199	0.0000	7.3796	0.0562	2.3033	18.2736	1.2054	0.5152	0.4240	0.0187	9.9865	0.3109	0.0293	3.7154	0.0073	2.5939	0.0000	0.0024	0.7805	0.8570	45.8801	10.6	7	0.643333	0.008571	0.504762	0.130000	2.100000e+05	%
133	Saudi Arabia	0.0000	0.3917	12.9962	0.0000	16.3284	0.6003	1.0369	7.0413	4.9529	5.8598	0.6690	0.1547	0.1492	0.4768	0.2434	1.4542	0.2050	2.8571	0.0009	0.1135	1.7626	5.7024	37.0038	35.0	7.1	1.055795	0.018256	1.030898	0.006641	3.504100e+07	%
134	Senegal	0.3209	0.0353	5.3434	0.0000	25.9204	0.1878	2.0588	2.3456	1.6081	1.2496	0.0148	0.2060	0.7637	0.4712	0.0717	2.6177	0.1161	2.0941	0.0000	0.1047	1.9325	7.8951	44.6423	7.4	11.3	0.170145	0.004031	0.139107	0.027007	1.674400e+07	%
135	Serbia	4.3920	0.2524	16.7466	0.0000	9.7485	0.6322	0.4148	5.9753	3.5893	11.6190	0.0067	0.2389	0.1322	0.1805	0.0242	2.5450	0.2430	2.0255	0.0007	0.1268	0.4644	7.3941	33.2476	23.5	5.7	5.840908	0.059106	0.000000	5.781803	6.957000e+06	%
136	Sierra Leone	0.3009	0.0067	5.6877	0.0000	19.9349	0.1343	2.5044	3.2119	0.7837	2.1711	0.2504	0.0857	0.5999	0.8913	0.0638	12.4228	0.0762	0.6323	0.0000	0.1000	1.2760	4.5526	44.3138	7.5	25.6	0.047286	0.000990	0.030738	0.015557	7.977000e+06	%
137	Slovakia	6.1266	1.3559	18.9841	0.0000	5.4026	0.9390	0.7045	3.5463	4.3096	11.4696	0.0290	0.2055	0.2055	0.0973	0.0421	3.6814	0.2513	5.5166	0.0000	0.1118	1.4380	4.5601	31.0231	22.4	3.4	4.792640	0.094013	4.361424	0.337202	5.462000e+06	%
138	Slovenia	4.9933	1.1248	18.9196	0.0005	7.6345	0.5376	0.6515	6.4367	4.1610	12.2569	0.6133	0.1879	0.1285	0.1106	0.0561	2.6837	0.2615	2.4915	0.0000	0.1972	0.4739	5.0020	31.0774	22.5	<2.5	8.235901	0.171755	7.312934	0.751213	2.103000e+06	%
139	Solomon Islands	0.8298	0.1038	3.8504	0.0121	9.0233	0.0826	2.2840	3.2798	0.8897	0.4380	0.5585	0.0394	5.0334	0.7366	0.0364	24.2500	0.0856	0.8268	0.0000	0.0356	0.3327	1.1223	46.1492	20.5	8.9	0.002378	0.000000	0.001399	0.000979	7.150000e+05	%
140	South Africa	6.2658	0.0593	11.8433	0.0009	17.1035	0.5940	0.5365	1.9355	5.3933	4.8155	0.0009	0.4448	0.2462	0.1546	0.0539	2.8503	0.1321	4.4677	0.0000	0.0108	1.1726	3.7632	38.1554	27.0	6.2	2.471490	0.077447	2.248100	0.145943	5.962800e+07	%
141	Spain	5.3152	0.2357	18.3382	0.0011	6.0548	0.7321	2.2646	4.8332	5.3456	9.5991	0.0000	0.1605	0.2650	0.2666	0.0069	3.0975	0.2229	1.8482	0.0000	0.4287	1.5122	7.8139	31.6581	27.1	<2.5	6.176110	0.128867	0.315684	5.731559	4.763500e+07	%
142	Sri Lanka	0.6810	0.0147	5.5328	0.0039	20.7839	0.4178	3.0173	4.1171	0.8845	1.0948	0.0303	0.0998	7.1364	0.9608	0.5244	1.9793	0.2818	2.7209	0.0000	0.1350	0.2466	4.8753	44.4618	5.4	9	0.312604	0.001600	0.285335	0.025669	2.193700e+07	%
143	Sudan	0.0000	0.0958	13.1178	0.0000	12.9192	0.1176	0.1018	7.0919	2.1133	10.4116	0.0741	0.2796	0.4920	1.0631	0.0405	1.9167	0.1502	3.1231	0.7262	0.0000	0.9366	8.3477	36.8813	7.4	20.1	0.067582	0.004176	0.049702	0.013704	4.384900e+07	%
144	Suriname	4.1704	0.0438	6.7984	0.0000	13.9476	0.5407	1.4348	8.6872	3.6451	1.0789	1.2414	0.0576	0.6757	0.1719	0.0224	2.4553	0.0903	4.7051	0.3207	0.0155	1.3351	5.3705	43.1917	26.5	8.5	1.429752	0.026446	1.318017	0.085289	6.050000e+05	%
145	Sweden	4.4232	1.0442	24.0627	0.0000	6.1923	0.7266	1.7659	3.5405	4.1476	16.3687	0.4623	0.0086	0.1712	0.0937	0.0484	2.8343	0.5533	2.5211	0.0000	0.0996	0.3967	4.6110	25.9282	22.1	<2.5	5.666975	0.116749	0.000000	5.550226	1.037700e+07	%
146	Switzerland	4.9978	0.4802	22.4227	0.0022	5.9818	0.5377	0.9109	4.6041	3.6314	16.7060	0.0000	0.1549	0.1124	0.0995	0.0382	2.1542	0.3727	2.9737	0.0000	0.2377	1.0637	4.9478	27.5705	21.2	<2.5	6.160215	0.111258	3.678480	2.370477	8.634000e+06	%
147	Taiwan*	2.7663	0.1577	12.1312	0.0281	10.0808	1.0900	2.3182	8.3904	6.0558	2.3299	0.1874	0.1507	1.0736	0.2077	0.1023	0.8565	0.1374	2.3455	0.2826	0.4185	1.8271	9.1938	37.8684	NaN	3.5	0.003914	0.000038	0.003571	0.000305	2.361000e+07	%
148	Tajikistan	0.1423	0.0018	7.2363	0.0000	13.3691	0.2255	0.0342	3.7863	1.5221	5.2373	0.2089	0.2144	0.1349	0.2486	0.0305	2.9278	0.0555	1.6607	0.0000	0.0582	0.8419	19.2995	42.7642	12.6	NaN	0.141139	0.000955	0.140185	0.000000	9.429000e+06	%
149	Thailand	3.6223	0.0902	7.7828	0.0325	17.5571	1.1143	2.6297	8.3291	2.4675	1.3279	0.0090	0.1199	1.2775	0.2344	0.6626	1.8887	0.1262	4.0424	0.0000	0.0938	0.7816	3.5962	42.2145	10.8	7.8	0.035126	0.000119	0.025464	0.009544	6.653400e+07	%
150	Timor-Leste	0.4459	0.0897	5.4983	0.0000	26.3320	0.1025	1.0225	2.1027	4.0376	0.1307	0.8905	0.1153	0.5612	0.9098	0.0500	5.5022	1.0136	2.8203	0.0000	0.0013	0.6842	3.1867	44.5030	2.9	24.9	0.006070	0.000000	0.004628	0.001442	1.318000e+06	%
151	Togo	1.0144	0.0292	3.6702	0.0000	15.8144	0.1067	1.3178	0.9100	1.4133	0.7246	0.7448	0.0775	0.4718	1.7626	0.0595	20.0654	0.0843	1.8312	0.0000	0.0000	1.4099	2.1637	46.3287	7.1	16.1	0.065660	0.000954	0.054777	0.009929	8.279000e+06	%
152	Trinidad and Tobago	2.4881	0.2804	12.7835	0.0000	13.0903	0.4044	2.3299	4.9733	6.7063	2.8789	0.2413	0.1837	1.2016	0.8939	0.2305	3.5148	0.0899	5.6200	0.0000	0.0928	1.2563	3.5256	37.2145	19.7	5.5	0.556026	0.009861	0.529949	0.016216	1.369000e+06	%
153	Tunisia	0.7037	0.2083	9.6312	0.0000	12.0523	0.4508	0.7615	5.4307	1.6411	6.4775	0.0116	0.0926	0.2031	0.4311	0.1065	1.7724	0.1979	2.0832	0.0000	0.3489	1.2551	15.7731	40.3673	27.3	4.3	1.817216	0.060205	1.453304	0.303707	1.189600e+07	%
154	Turkey	0.6474	0.1459	12.3156	0.0000	9.7080	0.3956	0.2582	6.8321	2.0694	9.3619	0.0101	0.0857	0.2720	0.6820	0.1251	2.4947	0.2002	1.7004	0.0000	0.4664	1.0174	13.5284	37.6836	32.2	<2.5	3.015138	0.031868	2.881050	0.102220	8.373700e+07	%
155	Turkmenistan	0.5038	0.3373	16.0611	0.0000	14.8029	0.5139	0.2137	4.1441	4.3767	10.5606	0.2530	0.0589	0.0661	0.1032	0.0015	2.3000	0.0945	0.6746	0.0000	0.0283	0.5655	10.4014	33.9389	17.5	5.4	NaN	NaN	NaN	NaN	6.031000e+06	%
156	Uganda	6.1671	0.0352	5.6473	0.0000	6.7480	0.0815	1.0442	11.3112	0.8978	3.4755	0.0371	0.1130	0.6338	2.3942	0.0120	12.3610	0.0130	1.2601	0.0000	0.0000	0.7440	2.6703	44.3536	4.1	41	0.087058	0.000715	0.031370	0.054973	4.574100e+07	%
157	Ukraine	2.6250	0.1753	13.1214	0.0006	9.3613	0.8434	0.6821	2.9418	2.7425	8.5442	0.0070	0.1339	0.0175	0.1099	0.0111	7.6721	0.0883	3.8811	0.0000	0.0807	0.6470	9.4361	36.8777	26.1	3.5	3.075923	0.059206	2.653382	0.363335	4.177800e+07	%
158	United Arab Emirates	0.0000	0.1896	10.5491	0.0000	12.9060	0.5958	1.9682	8.1938	4.9400	2.6468	0.9471	0.2095	0.6133	2.1108	0.3194	0.8937	0.1649	3.6576	0.0000	0.5838	0.7949	8.2695	39.4461	29.9	2.6	3.307445	0.009348	3.079168	0.218930	9.778000e+06	%
159	United Kingdom	5.2632	0.2754	18.8798	0.0006	6.5412	0.6210	1.0911	4.9551	4.4181	12.3684	0.6658	0.1056	0.2240	0.2035	0.0653	5.1133	0.2461	2.4383	0.0000	0.0901	0.7372	4.5851	31.1116	29.5	<2.5	5.868483	0.167220	0.015161	5.686102	6.716000e+07	%
160	United Republic of Tanzania	4.5185	0.0403	4.8022	0.0000	10.4453	0.0300	0.5828	6.8147	0.7902	3.3606	0.0214	0.0000	1.2839	2.1761	0.0274	13.9370	0.0300	1.0345	0.0000	0.0420	0.9136	3.9520	45.1973	7.1	30.7	0.000852	0.000035	0.000306	0.000511	5.973400e+07	%
161	United States of America	4.8647	0.1681	21.2352	0.0020	5.5048	0.7860	1.1287	4.5432	6.2645	12.8657	0.0000	0.0197	0.3246	0.1893	0.0490	2.8390	0.2458	3.3200	0.0000	0.1772	0.9889	5.7249	28.7587	37.3	<2.5	8.159922	0.140103	0.000000	8.019819	3.298780e+08	%
162	Uruguay	3.5664	0.2100	17.8865	0.0000	10.2510	0.7764	0.6058	5.3746	3.7863	12.3906	0.3918	0.1168	0.2770	0.1936	0.0144	3.0440	0.6169	3.4102	0.0000	0.0131	1.0337	3.9333	32.1076	28.9	<2.5	1.269102	0.013622	1.089465	0.166015	3.531000e+06	%
163	Uzbekistan	0.6835	0.1353	14.4463	0.0000	10.5365	0.3566	0.1218	5.2150	1.8621	11.7952	0.0400	0.1748	0.0287	0.0022	0.0038	2.8891	0.0644	0.5796	0.0000	0.0649	0.6109	14.8354	35.5540	15.3	6.3	0.231302	0.001817	0.226751	0.002733	3.417400e+07	%
164	Vanuatu	0.2424	0.0793	6.6244	0.0000	6.4102	0.1875	2.1649	5.8103	2.7466	1.2692	0.2882	0.1776	12.1763	0.0008	0.0374	12.7275	0.0305	1.3645	0.0000	0.0038	0.3674	3.9175	43.3737	23.5	7.2	0.000312	0.000000	0.000312	0.000000	3.210000e+05	%
165	Venezuela (Bolivarian Republic of)	2.5952	0.0403	14.7565	0.0000	12.9253	0.3389	0.9456	7.6460	3.8328	9.3920	0.0702	0.2078	0.0281	0.3342	0.0009	2.5643	0.1479	3.4106	0.0000	0.0009	1.3734	4.1474	35.2416	25.2	21.2	0.452585	0.004287	0.424399	0.023899	2.864500e+07	%
166	Vietnam	1.4591	0.1640	8.5765	0.0042	16.8740	0.3077	2.6392	5.9029	4.4382	0.6069	0.0126	0.4149	0.8410	0.2032	0.2074	1.0596	0.2880	1.2846	0.8150	0.3070	0.2201	11.9508	41.4232	2.1	9.3	0.002063	0.000036	0.001526	0.000501	9.620900e+07	%
167	Yemen	0.0364	0.0446	5.7874	0.0000	27.2077	0.2579	0.5240	5.1344	2.7871	1.8911	0.2033	0.2827	0.0893	0.8645	0.0347	1.0794	0.2199	5.0468	0.0000	0.0017	1.0811	3.2135	44.2126	14.1	38.9	0.007131	0.002062	0.004788	0.000282	2.982600e+07	%
168	Zambia	5.7360	0.0829	6.0197	0.0000	21.1938	0.3399	1.6924	1.0183	1.8427	1.7570	0.2149	0.3048	1.8736	0.1756	0.0478	7.9649	0.0618	1.5632	0.0000	0.0014	0.6657	3.4649	43.9789	6.5	46.7	0.334133	0.004564	0.290524	0.039045	1.838400e+07	%
169	Zimbabwe	4.0552	0.0755	8.1489	0.0000	22.6240	0.2678	0.5518	2.2000	2.6142	4.4310	0.2012	0.2086	0.4498	0.4261	0.0252	2.9870	0.1494	4.6485	0.0000	0.0518	1.7103	2.3213	41.8526	12.3	51.3	0.232033	0.008854	0.190964	0.032214	1.486300e+07	%

	Country	Obesity	Confirmed	Deaths	Recovered	Active
0	Afghanistan	4.5	0.142134	0.006186	0.123374	0.012574
1	Albania	22.3	2.967301	0.050951	1.792636	1.123714
2	Algeria	26.6	0.244897	0.006558	0.167572	0.070767
3	Angola	6.8	0.061687	0.001461	0.056808	0.003419
4	Antigua and Barbuda	19.1	0.293878	0.007143	0.190816	0.095918
...	...	...	...	...	...	...
165	Venezuela (Bolivarian Republic of)	25.2	0.452585	0.004287	0.424399	0.023899
166	Vietnam	2.1	0.002063	0.000036	0.001526	0.000501
167	Yemen	14.1	0.007131	0.002062	0.004788	0.000282
168	Zambia	6.5	0.334133	0.004564	0.290524	0.039045
169	Zimbabwe	12.3	0.232033	0.008854	0.190964	0.032214

	Country	Undernourished	Confirmed	Deaths	Recovered	Active
0	Afghanistan	29.799999	0.142134	0.006186	0.123374	0.012574
1	Albania	6.200000	2.967301	0.050951	1.792636	1.123714
2	Algeria	3.900000	0.244897	0.006558	0.167572	0.070767
3	Angola	25.000000	0.061687	0.001461	0.056808	0.003419
5	Argentina	4.600000	4.356147	0.108227	3.905192	0.342729
...	...	...	...	...	...	...
165	Venezuela (Bolivarian Republic of)	21.200001	0.452585	0.004287	0.424399	0.023899
166	Vietnam	9.300000	0.002063	0.000036	0.001526	0.000501
167	Yemen	38.900002	0.007131	0.002062	0.004788	0.000282
168	Zambia	46.700001	0.334133	0.004564	0.290524	0.039045
169	Zimbabwe	51.299999	0.232033	0.008854	0.190964	0.032214

	Country	Vegetables	Confirmed	Deaths	Recovered	Active
0	Afghanistan	0.3593	0.142134	0.006186	0.123374	0.012574
1	Albania	0.6503	2.967301	0.050951	1.792636	1.123714
2	Algeria	0.5145	0.244897	0.006558	0.167572	0.070767
3	Angola	0.1231	0.061687	0.001461	0.056808	0.003419
4	Antigua and Barbuda	0.2469	0.293878	0.007143	0.190816	0.095918
...	...	...	...	...	...	...
165	Venezuela (Bolivarian Republic of)	0.1851	0.452585	0.004287	0.424399	0.023899
166	Vietnam	0.6373	0.002063	0.000036	0.001526	0.000501
167	Yemen	0.1667	0.007131	0.002062	0.004788	0.000282
168	Zambia	0.1567	0.334133	0.004564	0.290524	0.039045
169	Zimbabwe	0.0789	0.232033	0.008854	0.190964	0.032214

	Country	Region	Animal fats	Animal Products	Cereals - Excluding Beer	Eggs	Fish, Seafood	Fruits - Excluding Wine	...	Vegetable Oils	Fruits - Excluding Wine	Deaths	Confirmed	Obesity	Undernourished	Population	Recovered	Active	Unit (all except Population)
0	Afghanistan	Middle East, North Africa, Greater Arabia	6.2224	21.6397	8.0353	0.6859	0.0327	0.4246	...	17.0831	0.4246	0.006186	0.142134	4.5	29.8	38928000.0	0.123374	0.012574	%
1	Albania	Europe	3.4172	32.0002	2.6734	1.6448	0.1445	0.6418	...	9.2443	0.6418	0.050951	2.967301	22.3	6.2	2838000.0	1.792636	1.123714	%
2	Algeria	Middle East, North Africa, Greater Arabia	0.8972	14.4175	4.2035	1.2171	0.2008	0.5772	...	27.3606	0.5772	0.006558	0.244897	26.6	3.9	44357000.0	0.167572	0.070767	%
3	Angola	Sub-Saharan Africa	1.3130	15.3041	6.5545	0.1539	1.4155	0.3488	...	22.4638	0.3488	0.001461	0.061687	6.8	25	32522000.0	0.056808	0.003419	%
4	Antigua and Barbuda	Central America, Caribbean	4.6686	27.7033	3.2153	0.3872	1.5263	1.2177	...	14.4436	1.2177	0.007143	0.293878	19.1	NaN	98000.0	0.190816	0.095918	%

	Country	Continent	Region	Deaths	Obesity	Meat	Pulses	Vegetables	Fruits - Excluding Wine	Fruits - Excluding Wine	Undernourished	Population	Confirmed	Animal Products
0	Afghanistan	Asia	Middle East, North Africa, Greater Arabia	0.006186	4.5	6.1244	0.1960	0.3593	0.4246	0.4246	29.8	38928000.0	0.142134	21.6397
1	Albania	Europe	Europe	0.050951	22.3	8.7428	0.1148	0.6503	0.6418	0.6418	6.2	2838000.0	2.967301	32.0002
2	Algeria	Africa	Middle East, North Africa, Greater Arabia	0.006558	26.6	3.8961	0.2698	0.5145	0.5772	0.5772	3.9	44357000.0	0.244897	14.4175
3	Angola	Africa	Sub-Saharan Africa	0.001461	6.8	11.0268	0.3282	0.1231	0.3488	0.3488	25	32522000.0	0.061687	15.3041
4	Antigua and Barbuda	Americas	Central America, Caribbean	0.007143	19.1	14.3202	0.0673	0.2469	1.2177	1.2177	NaN	98000.0	0.293878	27.7033