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download_wb.py
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download_wb.py
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# coding: utf-8
import pandas as pd
import wbdata
import os
# Access WB Data
countries = {"Burkina Faso":"BF", "Congo, Dem. Rep.":"CD", "Ethiopia":"ET", "Kenya":"KE", "Nigeria":"NG",
"Senegal":"SN", "Tanzania":"TZ", "Uganda":"UG", "South Africa":"ZA", "Zambia":"ZM"}
country_code = list({v for (k,v) in countries.items()})
country_name = list({k for (k,v) in countries.items()})
def collect():
# generate a dict from the indicators file
takwimu_indicators = pd.read_csv('key/takwimu_indicators.csv',
index_col=0, squeeze=True).to_dict()
# Gather indicator data on the selected countries
data = wbdata.get_dataframe(takwimu_indicators,
country=country_code, convert_date=False)
return data.to_csv('data/takwimu_worldbank_data.csv')
# def process_wb_data():
# for x in country_name:
# name = x+'.csv'
# directory = 'data/'+x
# if not os.path.exists(directory):
# os.makedirs(directory)
# data.loc[x].to_csv(directory+'/'+name)
# Structure into Hurumap format
data = pd.read_csv('data/takwimu_worldbank_data.csv')
columns = ['geography','date','male','female']
melted_columns = ['geography','geo_version','gender','total']
# population
def population():
df = data[['country', 'date','Population Male', 'PopulationFemale' ]].dropna(axis=0)
df = df[df['date']== df['date'].max()] # most recent values
df.columns = columns
df = df.melt(id_vars=['geography','date'], value_vars=['male','female'],
var_name='gender', value_name='total').sort_values('geography')
df = df.rename(columns={"date": "geo_version"})
population = df[melted_columns]
return population
# basic services
def basic_services():
df = data[['country', 'date','access to basic services - Electricity','access to basic services - Water' ]].dropna(axis=0)
df = df[df['date']== df['date'].max()]
df.columns = ['geography','date','electricity','water']
df = df.melt(id_vars=['geography','date'], value_vars=['electricity','water'],
var_name='service', value_name='total').sort_values('geography')
df = df.rename(columns={"date": "geo_version"})
basic_services = df[['geography','geo_version','service','total']]
return basic_services
# youth unemployment
def youth_unemployment():
df = data[['country', 'date','Youth unemployment-Male','Youth unemployment - Female' ]].dropna(axis=0)
df = df[df['date']== df['date'].max()] # most recent values
df.columns = columns
df = df.melt(id_vars=['geography','date'], value_vars=['male','female'],
var_name='gender', value_name='total').sort_values('geography')
df = df.rename(columns={"date": "geo_version"})
youth_unemployment = df[melted_columns]
return youth_unemployment
# Life expectancy
def life_expectancy():
df = data[['country','date','Life expectancy-Male','Life expectancy-Female']].dropna(axis=0)
df = df[df['date']== df['date'].max()] # most recent values
df.columns = columns
df = df.melt(id_vars=['geography','date'], value_vars=['male','female'],
var_name='gender', value_name='total').sort_values('geography')
df = df.rename(columns={"date": "geo_version"})
life_expectancy = df[melted_columns]
return life_expectancy
# infant & Under-5 motality (per 1000)
def infant_under_5_mortality():
df = data[['country', 'date','Infant Mortality','Under 5 Mortality rates']].dropna(axis=0)
df = df[df['date']== df['date'].max()] # most recent values
df.columns = columns
df = df.melt(id_vars=['geography','date'], value_vars=['male','female'],
var_name='gender', value_name='total').sort_values('geography')
df = df.rename(columns={"date": "geo_version"})
infant_under_5_mortality = df[melted_columns]
return infant_under_5_mortality
# Prevalence of HIV
def hiv_prevalence():
df = data[['country', 'date','Prevalence of HIV, male (% ages 15-24)','Prevalence of HIV, female (% ages 15-24)']].dropna(axis=0)
df = df[df['date']== df['date'].max()] # most recent values
df.columns = columns
df = df.melt(id_vars=['geography','date'], value_vars=['male','female'],
var_name='gender', value_name='total').sort_values('geography')
df = df.rename(columns={"date": "geo_version"})
hiv_prevalence = df[melted_columns]
return hiv_prevalence
# Primary completion rate
def primary_completion():
df = data[['country', 'date','Primary completion rate, male (%)','Primary completion rate, female (%)']].dropna(axis=0)
df = df[df['date']== df['date'].max()] # most recent values
df.columns = columns
df = df.melt(id_vars=['geography','date'], value_vars=['male','female'],
var_name='gender', value_name='total').sort_values('geography')
df = df.rename(columns={"date": "geo_version"})
primary_completion = df[melted_columns]
return primary_completion
# Employment to population ratio
def employment_to_population():
df = data[['country', 'date','Employment to population ratio male (%)','Employment to population ratio female (%)']].dropna(axis=0)
df = df[df['date']== df['date'].max()] # most recent values
df.columns = columns
df = df.melt(id_vars=['geography','date'], value_vars=['male','female'],
var_name='gender', value_name='total').sort_values('geography')
df = df.rename(columns={"date": "geo_version"})
employment_to_population = df[melted_columns]
return employment_to_population
# Physicians ,Nurses and Mid wives per 1000
def health_staff():
df = data[['country', 'date','Physicians per 1000','Nurses and Mid wives']].dropna(axis=0)
df = df[df['date']== df['date'].max()] # most recent values
df.columns = ['geography','date','physician','nurses_and_midwives' ]
df = df.melt(id_vars=['geography','date'], value_vars=['physicians','nurses_and_midwives'],
var_name='health_staff', value_name='total').sort_values('geography')
df = df.rename(columns={"date": "geo_version"})
health_staff = df[['geography','geo_version','health_staff','total']]
return health_staff
# Account ownership
def acc_ownership():
df = data[['country', 'date','Account ownership,male (% of population ages 15+)','Account ownership,female (% of population ages 15+)']].dropna(axis=0)
df = df[df['date']== df['date'].max()] # most recent values
df.columns = columns
df = df.melt(id_vars=['geography','date'], value_vars=['male','female'],
var_name='gender', value_name='total').sort_values('geography')
df = df.rename(columns={"date": "geo_version"})
acc_ownership = df[melted_columns]
return acc_ownership
# School enrollment, primary
def primary_school_enrollment():
df = data[['country', 'date','School enrollment, primary, male (% gross)','School enrollment, primary, female (% gross)']].dropna(axis=0)
df = df[df['date']== df['date'].max()] # most recent values
df.columns = columns
df = df.melt(id_vars=['geography','date'], value_vars=['male','female'],
var_name='gender', value_name='total').sort_values('geography')
df = df.rename(columns={"date": "geo_version"})
primary_school_enrollment = df[melted_columns]
return primary_school_enrollment
# Secondary school enrolment
def secondary_school_enrollment():
df = data[['country', 'date','Secondary school enrolment - Male (% gross)','Secondary school enrolment - Female (% gross)']].dropna(axis=0)
df = df[df['date']== df['date'].max()] # most recent values
df.columns = columns
df = df.melt(id_vars=['geography','date'], value_vars=['male','female'],
var_name='gender', value_name='total').sort_values('geography')
df = df.rename(columns={"date": "geo_version"})
secondary_school_enrollment = df[melted_columns]
return secondary_school_enrollment
# Literacy rate
def literacy_rate():
df = data[['country', 'date','Literacy rate - Male','Literacy rate - Female']].dropna(axis=0)
df = df[df['date']== df['date'].max()] # most recent values
df.columns = columns
df = df.melt(id_vars=['geography','date'], value_vars=['male','female'],
var_name='gender', value_name='total').sort_values('geography')
df = df.rename(columns={"date": "geo_version"})
literacy_rate = df[melted_columns]
return literacy_rate
# export datasets to csv
def save_to_csv():
population().to_csv('data/population.csv',index=False)
basic_services().to_csv('data/basic_services.csv',index=False)
youth_unemployment().to_csv('data/youth_unemployment.csv',index=False)
life_expectancy().to_csv('data/life_expectancy.csv',index=False)
infant_under_5_mortality().to_csv('data/infant_under_5_mortality.csv',index=False)
hiv_prevalence().to_csv('data/hiv_prevalence.csv',index=False)
primary_completion().to_csv('data/primary_completion.csv',index=False)
employment_to_population().to_csv('data/employment_to_population.csv',index=False)
health_staff().to_csv('data/health_staff.csv',index=False)
acc_ownership().to_csv('data/acc_ownership.csv',index=False)
primary_school_enrollment().to_csv('data/primary_school_enrollment.csv',index=False)
secondary_school_enrollment().to_csv('data/secondary_school_enrollment.csv',index=False)
literacy_rate().to_csv('data/literacy_rate.csv',index=False)
if __name__ == "__main__":
save_to_csv()