import pandas as pd 

immg = pd.read_csv("./data/raw/immg.csv", header = 0)
black = pd.read_csv("./data/raw/black.csv", header = 0)
white = pd.read_csv("./data/raw/white.csv", header = 0)
high_edu = pd.read_csv("./data/raw/high_edu.csv", header = 0)
low_edu = pd.read_csv("./data/raw/low_edu.csv", header = 0)
un_rate = pd.read_csv("./data/raw/un_rate.csv", header = 0)

data = [('Native', un_rate), ('African American', black), ('White', white), ('US Citizens with High Education Level', high_edu), ('US Citizens with Low Education Level', low_edu)]

immg['Growth'] = immg['2010'] - immg['2000']
changes = immg.copy()
del changes['2000']
del changes['2010']
for indivial_group in data:
    name = indivial_group[0]
    group = indivial_group[1]
    group['Growth'] = group['2010'] - group['2000']
    changes[name] = group['Growth']
changes.to_csv('./data/cleaned/changes.csv', index = False)

changes.head()

immg.to_csv('./data/cleaned/immg.csv', index = False)
black.to_csv('./data/cleaned/black.csv', index = False)
white.to_csv('./data/cleaned/white.csv', index = False)
high_edu.to_csv('./data/cleaned/high_edu.csv', index = False)
low_edu.to_csv('./data/cleaned/low_edu.csv', index = False)
un_rate.to_csv('./data/cleaned/un_rate.csv', index = False)