Using result_type with pandas apply function

I want to use apply on a pandas.DataFrame that I created, and return for each row a list of values, where each value is a column in itself.

I wrote the following code:

import pandas as pd

def get_list(row):
  return [i for i in range(5)]

df = pd.DataFrame(0, index=np.arange(100), columns=['col'])


df.apply(lambda row: get_list(row), axis=1, result_type='expand')

When I add result_type='expand' in order to change the returned array into separate columns I get the following error:

TypeError: ("<lambda>() got an unexpected keyword argument 'result_type'", 'occurred at index 0')

However if I drop the result_type field it runs fine (returns a column of arrays), what might be the problem?

• I’m using colab to run my code

Answer

This code works in pandas version 0.23.3, properly you just need to run pip install --upgrade pandas in your terminal.

Or

You can accomplish it without the result_type as follows:

def get_list(row):
    return pd.Series([i for i in range(5)])

df = pd.DataFrame(0, index=np.arange(100), columns=['col'])
pd.concat([df, df.apply(get_list, axis=1)], axis=1)

    col 0   1   2   3   4
0   0   0   1   2   3   4
1   0   0   1   2   3   4
2   0   0   1   2   3   4
3   0   0   1   2   3   4
4   0   0   1   2   3   4
...

BTW, you don’t need a lambda for it, you can just:

df.apply(get_list, axis=1, result_type='expand')

Update The result_type was announced in the release notes of pandas 0.23: https://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0230 so I am afraid you will have to update.