Understanding Zip Codes

Understanding Zip Codes with Python


Zip codes are a system of postal codes used in the United States to help organize and direct mail delivery. USPS zip codes consist of five digits and can include an optional four-digit suffix. While seemingly simple, zip codes can be used in various ways, including marketing, sales, research, and analysis. In this article, we will explore how to work with zip codes using Python programming.

Importing Required Modules

Before we start analyzing zip codes using Python, we need to import relevant modules.

import pandas as pd
import zipfile
import io

These modules are essential in processing zip codes stored in compressed files and transforming them into a useful format for Python processes.

Extracting Data

In many cases, zip code databases are stored in compressed format as .zip files or .gzip files. We will use the pandas module to extract data from these files.

#Extract a zip file of a list of zip codes

with zipfile.ZipFile('zip-codes.zip', 'r') as zip_ref:

#Read file contents
with open('zip-codes.txt','r') as file:
    content = file.read()

#Convert data to pandas dataframe
data = io.StringIO(content) 
zipcodes_df = pd.read_csv(data, sep='\t', header=None) 

#Rename dataframe columns
zipcodes_df.columns = ['zip','city','state','lat','long','timezone','dst']

In this example, we extract data from a compressed zip file named “zip-codes.zip” that contains zip codes for various cities across the United States. We read the file’s content with python’s built-in `open()` function and use the `read_csv()` function of pandas to convert data into a pandas dataframe.

We rename the column headers to better describe our data structures, like zip, city, state, lat, long, timezone, and dst.

Manipulating Data

With our data in a pandas dataframe, we can now manipulate it for our specific purposes.

For instance, suppose we want to find zip codes within a certain state. In that case, we can use the following code:

#Find all zip codes for the state of California
cali_zipcodes_df = zipcodes_df[zipcodes_df['state'] == 'CA']

In this example, we create a new dataframe called “cali_zipcodes_df,” which only contains rows of zip codes located in the state of California.

Another example of data manipulation is calculating the zip code distribution across different states.

#Calculate the distribution of zip codes across states
zipcodes_count_by_state = zipcodes_df.groupby(['state'])['zip'].count().reset_index() 

In this case, we group our original zip code dataframe by the state column and calculate the count of zip codes for each state. The `reset_index()` function is used to change the dataframe format from hierarchical to tabular.


Zip codes are an essential data point that can be used for many purposes, ranging from marketing campaigns to research analysis. We can use Python programming to extract and manipulate the data contained within this postal code system. With a minimal effort, we can extract the data in a structured format and use it for our specific purposes.

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