Python – Use .replace() to convert postal codes to cities in pandas columns

Use .replace() to convert postal codes to cities in pandas columns… here is a solution to the problem.

Use .replace() to convert postal codes to cities in pandas columns


have a list of zip codes and I’m trying to convert it to a city using the uszipcode module. My Data Frame:

index   Color   Postal_Code
0       blue    10006.0 
1       green   11415.0
2       red     10037.0

I wrote this code and updated the columns using .replace().

def zco():
    for x in zcode['Postal_Code']:
        x = int(x)                          #convert to int because value is float
        city = search.by_zipcode(x)['City'] #Module extracts the city name 
        if city == str(city):               #The module doesn't recognize some zipcodes, thus generating None.This will skip None values.
            str(x).replace(str(x), city)    #replace int value with city
        else: continue

zcode['Postal_Code'] = zcode['Postal_Code'].apply(zco())

But I get an error :

‘NoneType’ object is not callable

Why is that? Is there a better way to replace and update postal codes in columns?


The main problem is that instead of passing the correct callable to df.apply, you call the zco() > that returns None, and then pass that to the app.

In addition, zco must be a callable that can accept a single parameter, rather than traversing the entire column at once. df.apply has solved this problem.

You can convert postal codes faster with df.astype outside of zco:


Your ZCO definition can then be shortened to:

def zco(x):
    city = search.by_zipcode(x)['City']  
    return city if city else x  # if city is None for certain zipcodes, take advantage of the truthiness of None

Note that the definition of zco has changed significantly, accepting one parameter and operating on only one item at a time, rather than on the entire row.

Alternatively, you can use df.transform(callable, axis=1):


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