Solution: Merging Datasets

## Importing libraries
import pandas as pd
## Loading data
data = pd.read_csv('../data/PovStatsData.csv')
country = pd.read_csv('../data/PovStatsCountry.csv', na_values='', keep_default_na=False)
data = data.drop('Unnamed: 50', axis=1)
# Melting DataFrames
data_melt = pd.melt(data, id_vars=id_vars, var_name='year').dropna(subset=['value'])
data_melt['year'] = data_melt['year'].astype(int)
# Creating the is_country column
country['is_country'] = country['Region'].notna()
## pivoting melted DataFrame
data_pivot = data_melt.pivot(index=['Country Name', 'Country Code', 'year'], 
                             columns='Indicator Name',
                             values='value').reset_index()
# code from below
poverty = pd.merge(data_pivot, country, left_on='Country Code', right_on='Country Code', how='left')
# "High Income" is NA so we fill it with False values, as it is not a country
poverty['is_country'] = poverty['is_country'].fillna(False)
print(poverty.head())