Converting Zip Codes into Cities in Pandas Column Using .replace()

Overview

When working with geospatial data, it’s often necessary to convert zip codes into corresponding city names. In this article, we’ll explore how to achieve this conversion using the pandas library and the uszipcode module.

Background

The uszipcode module provides a convenient way to look up city names by their associated zip codes. This module can be used in conjunction with pandas DataFrames to perform geospatial data processing.

In this article, we’ll focus on converting zip code values in a pandas column using the .replace() method. We’ll explore why the original approach failed and how to improve it.

The Original Approach

Let’s examine the original code:

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())

The code attempts to convert each zip code in the zcode DataFrame into a corresponding city name using the uszipcode module. The original approach involves defining a function zco() that iterates over each zip code value and calls itself recursively.

However, this approach has two major issues:

1. Incorrect Usage of .apply(): The apply() method is used incorrectly in this context. It doesn’t support passing a callable that takes multiple arguments; instead, it applies the function to every element in the Series (in this case, the zcode['Postal_Code'] column).
2. Recursive Function Call: The zco() function calls itself recursively without any base case, leading to an infinite loop and potentially causing a stack overflow.

Improved Approach

To address these issues, let’s modify our approach:

import pandas as pd

# Define the uszipcode module for city name lookup
search = pd.read_csv('uszip.csv', on_bad_lines='skip')

def zco(x):
    """Converts a zip code to its corresponding city name."""
    # Get the city name from the uszipcode module
    city = search.loc[search['ZIP'].astype(int) == x, 'City'].iloc[0]
    return city if not pd.isnull(city) else x  # Replace with original value if city is None

# Apply the zco function to the Postal_Code column
zcode['Postal_Code'] = zcode['Postal_Code'].fillna(0).astype(int).astype(str).apply(zco)

Here are the key changes:

• We use the read_csv() function from pandas to load the uszipcode module data.
• The zco() function now takes a single argument, x, which represents the zip code value.
• Inside zco(), we look up the corresponding city name in the uszipcode module using the search DataFrame. If the city is not found (i.e., it’s None), we return the original value.

Alternative Approach: Using .transform()

Alternatively, you can use the .transform() method to achieve similar results:

zcode['Postal_Code'] = zcode['Postal_Code'].fillna(0).astype(int).astype(str).transform(lambda x: search.loc[search['ZIP'].astype(int) == int(x), 'City'].iloc[0] if not pd.isnull(search.loc[search['ZIP'].astype(int) == int(x), 'City'].iloc[0]) else int(x))

In this example, we use a lambda function to perform the city name lookup. If the city is not found (i.e., it’s None), we return the original value.

Conclusion

Converting zip codes into cities in pandas columns can be achieved using the .replace() method or alternative approaches like .transform(). The key takeaway is to use a single-argument callable function that takes the zip code value and returns its corresponding city name. This approach ensures efficient data processing and accurate results.

By understanding how to correctly apply the apply() and .transform() methods, you can leverage pandas and other libraries to perform complex geospatial data analysis tasks with ease.

Last modified on 2025-02-26