Select multiple columns by index or name in Pandas

In today’s post we’ll learn to slice and subset multiple columns from a Pandas dataframe. We’ll look into three methods for accomplishing our task:

Slicing columns using the “brackets notation”
Using the loc indexer
Using the iloc indexer

Data Preparation

Go ahead and import the pandas library into your Python development environment and create your DataFrame.

import pandas as pd

'read the data from a csv file
data = pd.read_csv('hr.csv')

'Preview the DataFrame
print(data)

Here’s our DataFrame:

	language	avg_salary	candidates
1	C#	85.0	82.0
2	Java	72.0	78.0
3	JavaScript	81.0	78.0
4	Ruby	82.0	87.0

Select multiple columns in a Python DataFrame

Using the brackets notation:

When using this technique we’ll subset the DataFrame using a list containing two or more column labels.

cols = ['language', 'candidates']
data[cols]

Here’s the result:

	language	candidates
1	C#	82.0
2	Java	78.0
3	JavaScript	78.0
4	Ruby	87.0

Using the loc indexer:

We can achieve the same result by passing the column labels as a parameter to the loc indexer. Here’s the code:


cols = ['language', 'candidates']
data.loc[:,cols]

Using the iloc indexer

We use the iloc indexer to slice one or several distinct columns ranges out of a DataFrame by index.

First let’s take a look at the DF columns index.

data.columns

Here’s our DataFrame index:

Index(['language', 'avg_salary', 'candidates'], dtype='object')

We are interested in the first two columns, so we’ll slice the index accordingly:

data.iloc[:,0:2]

# alternatively, we can use the following code:

data.iloc[:,range(2)]

Select Python columns by condition

You might want to subset your data according to specific logic related to the column labels or index.

Here’s a quick example that allows to achieve the same data subset we seen before:

filt = data.columns.str.contains('language')  | data.columns.str.contains('avg')

#pass the boolean filter to a loc indexer
data.loc[:,filt]

Drop multiple columns

A somewhat related topic is how to delete specific columns from your DataFrame. We have obviously covered it in a previous tutorial. You can find it here.

Additional learning: