str.split() for splitting in Pandas

Introduction

We use str.split() to break text into smaller parts. This helps us work with each piece separately.

You have a column of full names and want to separate first and last names.

You want to split a list of tags stored as one string into individual tags.

You need to extract parts of an address stored in one column.

You want to convert a string of numbers separated by commas into separate columns.

Syntax

Pandas

Series.str.split(pat=None, n=-1, expand=False)

pat is the character or pattern to split on. If None, splits on whitespace.

n limits the number of splits. Default -1 means no limit.

expand=True returns a DataFrame with split parts as columns.

Examples

Splits on whitespace by default.

Pandas

df['col'].str.split()

Splits the string at each comma.

Pandas

df['col'].str.split(',')

Splits only once at the first dash.

Pandas

df['col'].str.split('-', n=1)

Splits on commas and returns a DataFrame with each part as a separate column.

Pandas

df['col'].str.split(',', expand=True)

Sample Program

This code splits the full_name column into two columns: first_name and last_name.

Pandas

import pandas as pd

data = {'full_name': ['Alice Smith', 'Bob Johnson', 'Charlie Lee']}
df = pd.DataFrame(data)

# Split full_name into first and last name
split_names = df['full_name'].str.split(' ', expand=True)
split_names.columns = ['first_name', 'last_name']

print(split_names)

Important Notes

If you use expand=False (default), the result is a Series of lists.

Use expand=True to get a DataFrame with separate columns for each split part.

If the number of splits varies, some columns may have missing values (NaN).

Summary

str.split() breaks text into parts based on a separator.

Use expand=True to get multiple columns from one text column.

This helps analyze or clean text data by working with smaller pieces.