How Can I Apply a Custom Function to Multiple Pandas DataFrame Columns to Create a New Column?

Applying a Function to Multiple Columns in a Pandas DataFrame

Consider the following situation where you have a DataFrame containing multiple columns and a custom function you want to apply to those columns element-wise to create a new column.

Problem:

Suppose you have a DataFrame with three columns:

df = pd.DataFrame({'ID':['1','2','3'], 'col_1': [0,2,3], 'col_2':[1,4,5]})

And a function:

def get_sublist(sta, end):
    return mylist[sta:end+1]

Your goal is to apply get_sublist to the 'col_1' and 'col_2' columns of df to obtain a new column 'col_3' that contains the corresponding sublists of 'mylist'.

Solution:

To achieve this, you can use the following Pandas operation:

df['col_3'] = df.apply(lambda x: get_sublist(x.col_1, x.col_2), axis=1)

Here's a breakdown of the code:

• df.apply(): Applies the given function element-wise to each row of the DataFrame.
• lambda x: An anonymous function that accepts a single argument x, which represents a row of the DataFrame.
• x: The DataFrame row object that contains the values of the 'col_1' and 'col_2' columns for the current iteration.
• get_sublist(x.col_1, x.col_2): Calls the get_sublist function with the 'col_1' and 'col_2' values from the current row.
• axis=1: Specifies that the function should be applied across each row of the DataFrame, resulting in a new column.

Using this method, the resulting DataFrame will have the following structure:

  ID  col_1  col_2            col_3
0  1      0      1       ['a', 'b']
1  2      2      4  ['c', 'd', 'e']
2  3      3      5  ['d', 'e', 'f']

Note that this solution allows you to pass any custom function to the apply() method and apply it to multiple columns simultaneously. By accessing the column values through x.col_name, you can safely use column names even if they contain spaces or have the same name as existing DataFrame attributes.

The above is the detailed content of How Can I Apply a Custom Function to Multiple Pandas DataFrame Columns to Create a New Column?. For more information, please follow other related articles on the PHP Chinese website!