Split a text column into two columns in Pandas DataFrame

Last Updated : 30 Oct, 2025

In a Pandas DataFrame, a single column may contain multiple pieces of information—like full names, addresses, or codes—that are easier to work with when separated into individual columns.

This article demonstrates how to efficiently split a text column into two or more columns using Pandas.

Sample DataFrame

Python 
import pandas as pd

df = pd.DataFrame({
    'Name': ['John Larter', 'Robert Junior', 'Jonny Depp'],
    'Age': [32, 34, 36]
})
print(df)

Output
            Name  Age
0    John Larter   32
1  Robert Junior   34
2     Jonny Depp   36

Method 1: Using str.split()

Python 
df[['First', 'Last']] = df.Name.str.split(expand=True)
print(df)

Output

s11

Explanation:

  • str.split(): splits each string into a list.
  • expand=True: converts the list into separate columns.
  • Column names First and Last are assigned explicitly.

Method 2: Using a Custom Delimiter

Python 
df[['First','Last']] = df.Name.str.split("_",expand=True)

Output

s12

Explanation:

  • df.Name: selects the Name column.
  • .str.split("_"): splits each string in the column at the underscore _.
  • expand=True: converts the resulting list into separate columns instead of a list.

Method 3: Using apply() with pd.Series

Python 
df[['First', 'Last']] = df.Name.apply(lambda x: pd.Series(str(x).split("_")))
print(df)

Output

s14

Explanation:

  • df.Name: accesses the Name column of the DataFrame.
  • apply(lambda x: ...): applies a function to each element of the column.
  • str(x).split("_"): converts the element to a string (if not already) and splits it at _.
  • pd.Series(...): converts the resulting list into a Series, so each part becomes a separate column.
Comment

Explore