How to slice Pandas dataframe columns?

In today’s quick tutorial we’ll learn how we can subset specific columns from a Pandas DataFrame with Python.

We’ll cover three cases:

Slicing DataFrames with the loc indexer.
Selecting columns using the brackets [] notation.
Sub-setting by position with the iloc indexer

Preparations

We’ll first go ahead and import the Pandas library

import pandas as pd

data = pd.read_csv('languages.csv)

Here’s our data:

	language	month	salary	num_candidates	days_to_hire
1	PHP	July	136.0	78.0	54.0
2	PHP	May	120.0	79.0	56.0
3	SQL	November	108.0	87.0	59.0
4	Scala	March	119.0	83.0	37.0

1. Slice column by name with the loc[] indexer

Let’s assume that we would like to pick only the month an num_candidates columns. We’ll use the loc indexer and pass the relevant rows and columns labels.

cols= ['month', 'num_candidates']
rows = 1,2,3,4
data.loc[rows,cols]

The output will be:

	month	num_candidates
1	July	78.0
2	May	79.0
3	November	87.0
4	March	83.0

Note that you can adjust the row range to subset only specific rows

# select columns by name/label
cols= ['month', 'num_candidates']
rows = 1,4
data.loc[rows,cols]

More about using the loc indexer to subset DataFrames with multiple conditions in this tutorial.

2. Slicing DataFrames with the brackets notation

This is probably the simple way to slice one or more columns from a DataFrame. You can filter specific rows by creating a more complex filter criteria (we’ll cover that in a different post).

# define list of columns to select
cols= ['month', 'num_candidates']

# apply the brackets notation
data[cols]

Here’s the output:

	month	num_candidates
1	July	78.0
2	May	79.0
3	November	87.0
4	March	83.0

3. Selecting columns with the iloc position indexer

Kindly note that iloc gets two parameters:

single or list of integer positions of the rows (starting from 0)
single or list of integer positions of the columns (starting from 0)

# select columns by position/number
rows= [0,3]
cols= [1, 3]
data.iloc[rows,cols]

Here’s the result:

	month	num_candidates
1	July	78.0
4	March	83.0