Selecting Multiple Columns From a Pandas DataFrame

Discussing how to select multiple columns from a DataFrame in pandas

Photo by Aviv Ben Or on Unsplash

Introduction

Multiple column selection is one of the most common and simple tasks one can perform. In today’s short guide we will discuss about a few possible ways for selecting multiple columns from a pandas DataFrame. Specifically, we will explore how to do so

• using basing indexing
• with loc
• using iloc
• through the creation of a new DataFrame

Additionally, we will discuss when to use one method over the other, based on your specific use-case and whether you need to generate a view or a copy of the original DataFrame object.

First, let’s create an example DataFrame that we’ll reference throughout this article in order to demonstrate a few concepts.

import pandas pd

df = pd.DataFrame({
    'colA':[1, 2, 3], 
    'colB': ['a', 'b', 'c'],
    'colC': [True, False, True],
    'colD': [1.0, 2.0, 3.0],
})
print(df)
   colA colB   colC  colD
0     1    a   True   1.0
1     2    b  False   2.0
2     3    c   True   3.0

Using basic indexing