Pandas List Unpacking To Multiple Columns New update

In Pandas, you can use list unpacking to split a single column into multiple columns. Here’s an example:

Suppose you have a Pandas DataFrame with a column called “Name” that contains full names in the format “First Last”. You want to split this column into two separate columns, one for first name and one for last name.

You can do this using the str.split() method and list unpacking like this:

python
import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'Name': ['John Smith', 'Jane Doe']})

# Split the Name column into first and last name columns
df[['First Name', 'Last Name']] = df['Name'].str.split(expand=True)

# Drop the original Name column
df.drop('Name', axis=1, inplace=True)

# View the resulting DataFrame
print(df)

Output:

sql
  First Name Last Name
0       John     Smith
1       Jane       Doe

Here, the str.split() method splits the “Name” column into two columns, with expand=True specifying that the result should be a DataFrame rather than a Series. The resulting DataFrame is then assigned to two new columns, “First Name” and “Last Name”, using list unpacking.

Note that the original “Name” column is dropped using the drop() method with axis=1 (i.e., along columns) and inplace=True (i.e., modifying the DataFrame in place) to create the final result.