convert_housing_data_to_quarters.py

# converting house data to quater -->
def convert_housing_data_to_quarters():
     
     states = {'OH': 'Ohio', 'KY': 'Kentucky', 'AS': 'American Samoa', 'NV': 'Nevada', 'WY': 'Wyoming', 'NA': 'National', 'AL': 'Alabama',
'MD': 'Maryland', 'AK': 'Alaska', 'UT': 'Utah', 'OR': 'Oregon', 'MT': 'Montana', 'IL': 'Illinois', 'TN': 'Tennessee', 'DC': 'District of Columbia', 'VT': 'Vermont', 'ID': 'Idaho', 'AR': 'Arkansas',
'ME': 'Maine', 'WA': 'Washington', 'HI': 'Hawaii', 'WI': 'Wisconsin', 'MI': 'Michigan', 'IN': 'Indiana', 'NJ': 'New Jersey', 'AZ': 'Arizona', 'GU': 'Guam', 'MS': 'Mississippi', 'PR': 'Puerto Rico',
'NC': 'North Carolina', 'TX': 'Texas', 'SD': 'South Dakota', 'MP': 'Northern Mariana Islands', 'IA': 'Iowa', 'MO': 'Missouri', 'CT': 'Connecticut', 'WV': 'West Virginia', 'SC': 'South Carolina',
'LA': 'Louisiana', 'KS': 'Kansas', 'NY': 'New York', 'NE': 'Nebraska', 'OK': 'Oklahoma', 'FL': 'Florida', 'CA': 'California', 'CO': 'Colorado', 'PA': 'Pennsylvania', 'DE': 'Delaware', 'NM': 'New Mexico', 
'RI': 'Rhode Island', 'MN': 'Minnesota', 'VI': 'Virgin Islands', 'NH': 'New Hampshire', 'MA': 'Massachusetts', 'GA': 'Georgia', 'ND': 'North Dakota', 'VA': 'Virginia'}
     
     df = pd.read_csv('City_Zhvi_AllHomes.csv',header=0)
     
     # years to keep -->
     def years_to_keep(list):
             for i in range(len(list)):
                 for j in list:
                     if re.match('^19',j):
                         list.remove(j)

             return list 
             
    #Removing Unecessary Columns First -->
     cols_to_keep = list(df.columns.values)
     cols_to_keep = years_to_keep(cols_to_keep)
     cols_to_keep = [ele for ele in cols_to_keep if ele not in {'Metro','CountyName','SizeRank'}]
     df = df[cols_to_keep]
     df['State'] = df['State'].replace(states)

    # Creating a new dataframe with --> 
     df_compiled = df.copy().set_index(['State', 'RegionName', 'RegionID']).stack(dropna=False)
     df_compiled = df_compiled.reset_index().rename(columns={'level_3': 'year_month', 0: 'gdp'})
     df_compiled.drop_duplicates(inplace=True)
        
    # Converting from monthly to quaterly format -->
     df_compiled['quarter'] = df_compiled['year_month'].apply(convert_to_qtr)
     df_compiled = df_compiled.drop('year_month', axis=1)    
    
    # return result dataframe -->
     result = df_compiled.pivot_table(values='gdp', index=['State', 'RegionName', 'RegionID'], columns='quarter', aggfunc=np.mean)
     result = result.reset_index()
     result = result.drop('RegionID', axis=1)
     result = result.set_index(['State', 'RegionName'])
     
     return result    
    
convert_housing_data_to_quarters()