Pandas - Split strings into two List/Columns using str.split()

Pandas str.split() method is used for manipulating strings in a DataFrame. This method allows you to split strings based on a specified delimiter and create new columns or lists within a Series.

In this guide, we'll explore how to use the str.split() method with examples, making it easier to handle string data in your Pandas DataFrame.

Pandas str.split() Method Syntax

The str.split() method in Pandas is used to split strings in a column based on a given separator. The split data can then be:

• Stored as a list in a Pandas Series.
• Expanded into multiple columns for better organization.

Unlike Python’s built-in split() method, which works on individual strings, Pandas’ str.split() can be applied to an entire Series. To use this method, you need to prefix it with .str, differentiating it from Python’s default split() function.

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

Parameters: 

• pat: String value, separator or delimiter to separate string at. 
• n: Numbers of max separations to make in a single string, default is -1 which means all. 
• expand: Boolean value, returns a data frame with different value in different columns if True. Else it returns a series with list of strings. 

Splitting Strings into a List

The split function is applied to the Team column, splitting each string at the first occurrence of "t" (as n=1). Since expand=False, it returns a Series of lists instead of a DataFrame. Only the first "t" in each string is used for splitting.

Dataset Link: nba.csv

Output👁 Image

Splitting Strings into Separate Columns

You can split strings into multiple columns by setting expand=True. For instance, let’s split full names into first and last names.

In this example,

• The Name column is split at spaces (pat=" ").
• Setting expand=True ensures the results are split into two new columns: First Name and Last Name.
• The old Name column is removed using the .drop() method.

Output:

As shown in the output image, a new data frame was returned by the split() function and it was used to create two new columns ( First Name and Last Name) in the data frame.

Data frame with Added columns👁 out3-15

Using apply() with str.split()

For more customized splitting, you can combine apply() with str.split() to split strings dynamically and create new columns.

• The apply() function is used with a lambda function for custom splitting.
• The result is expanded into two columns, Team_Part1 and Team_Part2.

Output:

👁 out3-15

Key Points to Remember

• Use expand=True when splitting strings into separate columns.
• Use expand=False to store the result as lists in a Series.
• Combining apply() with str.split() provides flexibility for dynamic splitting operations.