Python for statement Creating recurring data frames by year

path = r'C:\Users\admin\Desktop\Python\Legal East Code_Full Material.xlsx'
code_df=pd.read_excel(path)

x=input ('Enter the address you want to find:') #Enter it in Korean to find the code to substitute it

code_select_df = code_df[code_df['waste' == 'exist']

code_sample_df = code_select_df[code_select_df['legal name'] == x]

code_sample_df["Court Code"]

code = code_sample_df["Court Code"]

x=str(cod.iloc[0])

x=x[0:5] #Only 5 digits out of the long legal code are required

y=int(input('Enter base year:'))) 


df_base=pd.DataFrame (columns=[year], 'middle-aged housing value', 'young housing price', 'middle-aged housing price'])

for j in range(0,10):

    k=y-j


    price_df=get_year_realprice('x','str(k)')
    #Enter middle-aged housing value calculation year


    temp=price_df["dedicated area"]
    price_df['exclusive area']=pd.to_numerical(temp)

    temp = price_df ['transaction amount'].apply(lambda x: x.replace(',', '') )
    price_df['transaction amount'] = pd.to_numerical(temp)

    price_df['fair unit price'] = price_df['transaction amount'] / price_df['exclusive area'] / 3.3

    temp=price_df['architectural year']
    price_df['construction year']=pd.to_numerical(temp)


    price_new_df = price_df[price_df['building year'] >=int(k)-4]
    #Based on young housing = within 5 years of completion 

    price_select_df = price_df[price_df['architectural year'] >= int(k)-9]
    price_old_df = price_select_df[price_select_df['building year']<= int(k)-5]
    #Based on middle-aged housing = within 6-10 years of completion

    old_price = np.mean (price_old_df['per-price'])
    new_price = np.mean (price_new_df['per-price'])
    mah_value=(old_price/new_price)

    df_base=df_base.append(pd.DataFrame([str(k), mah_value, new_price, old_price]), columns=['year', 'middle-aged housing value', 'young housing price', 'middle-aged housing price'], and ignore_index=true)
df_base

def get_realprice(code, date):

    url = 'http://openapi.molit.go.kr:8081/OpenAPI_ToolInstallPackage/service/rest/RTMSOBJSvc/getRTMSDataSvcAptTrade'
    key = 'NfQ7K6WgSudMMtb8YT5AC3%2FrDhHvg%2BZupj%2Fy4fze4XxJ61XctYdvZEc5wNFrqargp5yLGQwRw7RY16eCAebIbg%3D%3D'

    real_url = url +'?'+ 'LAWD_CD={}&DEAL_YMD={}&serviceKey={}'.format(code, date, key)


    data = requests.get(real_url)
    data_bs = bs4.BeautifulSoup(data.text, 'lxml-xml')
    item_list = data_bs.find_all('item')

    Transaction amount list = []
    Year of Architecture List = []
    Year List = []
    Wallist = []
    Ilist = []
    List of legal dongs = []
    Apartment List = []
    Dedicated Area List = []
    Layer List = []
    Landlord list = []
    Regional Code List = []

    for item in item_list:
        Transaction amount list.append(item.find('transaction amount')).text)
        A list of architectural years.text)
        Year list.append(item.find('year')).text)
        Wallist.append(item.find('month').text)
        Illist.append(item.find('work')).text)
        The list of legal dongs.append(item.find('legal dong')).text)
        The apartment list.append.text)
        Dedicated area list.append(item.find('dedicated area')).text)
        a floor listappend(item.find('layer')).text)
        a lot number listappend(item.find('lot number').text)
        Regional code list.append(item.find('regional code').text)

    result_df = pd.DataFrame({'transaction amount'):transaction amount list,
                 'Architectural Year': Architectural Year List 'Year': Year List, Month': Wallist, Day': Ilist,
                 Beopjeong-dong': Beopjeong-dong list, 'apartment': apartment list, 'exclusive area': exclusive area list,
                 'Layer':Layer list, 'Location number':Location list, 'Region code':Region code list})
    return result_df

def get_year_realprice(code, year):

    year_list = []
    for i in range(1, 13):
        if i < 10:
            temp_date = year + '0' + str(i)
        else:
            temp_date = year + str(i)

        year_list.append(get_realprice(code, temp_date))

    year_df = pd.concat(year_list)
    year_df = year_df.reset_index()
    del year_df['index']
    del year_df ["Court Rocks"]
    del year_df ["Apartment"]
    del year_df ["Layer"]
    "Del year_df" ["Zone"Zone"
    del year_df ["work"]
    delyear_df["Region Code"]

    return year_df