How to Apply a Function to Multiple Pandas Dataframe Columns and Create a New Column?

Applying a Function to Multiple Columns of a Pandas Dataframe

The situation is as follows: a function and a dataframe are defined, and the goal is to apply the function to two specific columns of the dataframe to generate a new column. However, an attempt to use the apply method with the function results in an error.

To address this issue, there are multiple approaches:

Lambda Expression with Column Names

A concise and readable solution is to use a lambda expression within the apply method:

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

This approach directly utilizes the column names instead of numerical indices, making it less prone to errors.

Example with Example Data

Consider the example data:

df = pd.DataFrame({'ID':['1', '2', '3'], 'col_1': [0, 2, 3], 'col_2':[1, 4, 5]})
mylist = ['a', 'b', 'c', 'd', 'e', 'f']

Running the previous code will generate a new column, col_3, containing the desired result:

  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]

Square Brackets for Non-Standard Column Names

If the column names contain spaces or match existing dataframe attributes, square brackets can be used:

df['col_3'] = df.apply(lambda x: f(x['col 1'], x['col 2']), axis=1)

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