Split values into columns of a pandas DataFrame

Setup

# Import libraries
import pandas as pd
import numpy as np

Apply function str.split() to the column, with parameter expand=True

# Create sample DataFrame
df = pd.DataFrame({'name':['John A. Doe', 'Aby K. Parker', 'Bob T. Morris', 'Alice D. Allen']})
df

# Split string and create multiple columns
df['name'].str.split(expand=True)

If you want to extract only the nth element in the resulting list, you can simply use .str[n]:

# Just take the last value of the list of splitted string
df['name'].str.split().str[-1]

0       Doe
1    Parker
2    Morris
3     Allen
Name: name, dtype: object

If you have list-like values in a column, and want to select some values and expand them into columns, specify result_type='expand' to apply()

# Create sample DataFrame
df2 = pd.Series([np.random.randint(0, 9, size=5) for i in range(5)]).to_frame()
df2

Extract some values without expansion:

# Extract some values without expansion
df2.apply(lambda x: (x[0][0], x[0][-1]), axis=1)

0    (1, 7)
1    (5, 3)
2    (4, 3)
3    (0, 0)
4    (3, 8)
dtype: object

Expand result into columns:

# Expand result to columns
df2.apply(lambda x: [x[0][0], x[0][-1]], result_type='expand', axis=1)