# Create empty DataFrame with three columns, will include index
rates_df = pd.DataFrame( columns=['country_code', 'year', 'rate'])
rates_df.head()

#Itterate over each row
for index, row in new_world_labor_rates_df.iterrows():
    # print each row
    #print(index, row[1])

    # declare variables and assign empty lists
    country = row[0]
    rate2010 = row[1]
    rate2011 = row[2]
    rate2012 = row[3]
    rate2013 = row[4]
    rate2014 = row[5]
    print(country)

    # append the data as a dictionary to the DataFrame
    # data is stored in the variables by using the for loop to itterate through the rows and then appending
    # the correct column to the right variable

    # these variables are then matched back to the DataFrame, by creating key value pairs (dictionaries)
    rates_df= rates_df.append({'country_code':country, 'year':'2010', 'rate':rate2010 }, ignore_index=True)
    rates_df= rates_df.append({'country_code':country, 'year':'2011', 'rate':rate2011 }, ignore_index=True)
    rates_df= rates_df.append({'country_code':country, 'year':'2012', 'rate':rate2012 }, ignore_index=True)
    rates_df= rates_df.append({'country_code':country, 'year':'2013', 'rate':rate2013 }, ignore_index=True)
    rates_df= rates_df.append({'country_code':country, 'year':'2014', 'rate':rate2014 }, ignore_index=True)