Split values into columns of a pandas DataFrame

Split values into columns of a pandas DataFrame

Setup

# Import libraries
import pandas as pd
import numpy as np

Split string and expand result in columns

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

	name
0	John A. Doe
1	Aby K. Parker
2	Bob T. Morris
3	Alice D. Allen

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

	0	1	2
0	John	A.	Doe
1	Aby	K.	Parker
2	Bob	T.	Morris
3	Alice	D.	Allen

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

Split lists into columns

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

	0
0	[1, 2, 3, 2, 7]
1	[5, 6, 5, 8, 3]
2	[4, 0, 2, 5, 3]
3	[0, 4, 3, 0, 0]
4	[3, 0, 1, 4, 8]

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

	0	1
0	1	7
1	5	3
2	4	3
3	0	0
4	3	8