CODEWITHSUNDEEP
pythondataframe
user90379
user90379

Reputation: 137

Condition for specific row of dataframe

These are the attributes of my data set.

the data set

My aim is to compute the average zip-code price for apartments in Paris (20 districts in total, column name is "Zipcode"). Because the original data set didn't have an avg_zip_price_app column, I had to create it. 

def get_avg_zip_appartment_price(df, zip):
    price = 0
    if np.where(df["Zipcode"] == zip): # this row's zipcode
        price = 12811 
    elif np.where(df["Zipcode"] == zip):
        price = 11623
    elif np.where(df["Zipcode"] == zip):
        price = 12345
    elif np.where(df["Zipcode"] == zip):
        price = 13197
    elif np.where(df["Zipcode"] == zip):
        price = 12335
    elif np.where(df["Zipcode"] == zip):
        price = 14420
    elif np.where(df["Zipcode"] == zip):
        price = 13899
    elif np.where(df["Zipcode"] == zip):
        price = 11673
    elif np.where(df["Zipcode"] == zip):
        price = 10932
    elif np.where(df["Zipcode"] == zip):
        price = 10301
    elif np.where(df["Zipcode"] == zip):
        price = 9244
    elif np.where(df["Zipcode"] == zip):
        price = 9146
    elif np.where(df["Zipcode"] == zip):
        price = 10032
    elif np.where(df["Zipcode"] == zip):
        price = 9951
    elif np.where(df["Zipcode"] == zip):
        price = 9350
    elif np.where(df["Zipcode"] == zip):
        price = 11079
    elif np.where(df["Zipcode"] == zip):
        price = 10687
    elif np.where(df["Zipcode"] == zip):
        price = 9664
    elif np.where(df["Zipcode"] == zip):
        price = 8385
    elif np.where(df["Zipcode"] == zip):
        price = 8744
    return price 

conditions = [
    (df['Zipcode'] == 75001),
    (df['Zipcode'] == 75002),
    (df['Zipcode'] == 75003),
    (df['Zipcode'] == 75004),
    (df['Zipcode'] == 75005),
    (df['Zipcode'] == 75006),
    (df['Zipcode'] == 75007),
    (df['Zipcode'] == 75008),
    (df['Zipcode'] == 75009),
    (df['Zipcode'] == 75010),
    (df['Zipcode'] == 75011),
    (df['Zipcode'] == 75012),
    (df['Zipcode'] == 75013),
    (df['Zipcode'] == 75014),
    (df['Zipcode'] == 75015),
    (df['Zipcode'] == 75016),
    (df['Zipcode'] == 75017),
    (df['Zipcode'] == 75018),
    (df['Zipcode'] == 75019),
    (df['Zipcode'] == 75020)
]
choices = [
    get_avg_zip_appartment_price(user_df, 75001), get_avg_zip_appartment_price(user_df, 75002),get_avg_zip_appartment_price(user_df, 75003),
    get_avg_zip_appartment_price(user_df, 75004), get_avg_zip_appartment_price(user_df, 75005),get_avg_zip_appartment_price(user_df, 75006),
    get_avg_zip_appartment_price(user_df, 75007),get_avg_zip_appartment_price(user_df, 75008),get_avg_zip_appartment_price(user_df, 75009),
    get_avg_zip_appartment_price(user_df, 75010),get_avg_zip_appartment_price(user_df, 75011),get_avg_zip_appartment_price(user_df, 75012),
    get_avg_zip_appartment_price(user_df, 75013),get_avg_zip_appartment_price(user_df, 75014),get_avg_zip_appartment_price(user_df, 75015),
    get_avg_zip_appartment_price(user_df, 75016),get_avg_zip_appartment_price(user_df, 75017),get_avg_zip_appartment_price(user_df, 75018),
    get_avg_zip_appartment_price(user_df, 75019),get_avg_zip_appartment_price(user_df, 75020)]
user_df['avg_zip_price_app'] = np.select(conditions, choices)
print(user_df.head())

But I always get the same value for each observation. Is it because the syntax in my get_avg_zip_appartment_price(df, zip) method for the row's condition is incorrect and therefore every time the method is called, it checks for the first row and it's true, so the price value is always the same for all rows? This is the result I get:

output of "print(user_df.head()"

Upvotes: 0

Views: 78

Answers (1)

avr_dude
avr_dude

Reputation: 242

The mistake in your code : 

np.where(df["Zipcode"] == zip) #This will return true whenever there is a zip entry in df.

If zip = -1, then get_avg_zip_appartment_price(df, zip) will return 0, since it will not match with any record in df.

You can use a dictionary key-value pairs to give prices to zip codes.

Upvotes: 2

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