Split a text column into two columns in Pandas DataFrame

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

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

Method 1: Using str.split()

Output

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

Output

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

Output

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.

Related Articles:

• Split a String into columns using regex in pandas DataFrame
• Python | Pandas Reverse split strings into two List/Columns using str.rsplit()