How to select multiple columns in a pandas dataframe

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Let’s discuss all different ways of selecting multiple columns in a pandas DataFrame.

Method #1: Basic Method

Given a dictionary which contains Employee entity as keys and list of those entity as values.

# Import pandas package 
import pandas as pd 
  
# Define a dictionary containing employee data 
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], 
        'Age':[27, 24, 22, 32], 
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'], 
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} 
  
# Convert the dictionary into DataFrame  
df = pd.DataFrame(data) 
  
# select two columns 
df[['Name', 'Qualification']] 

Output:

Select Second to fourth column.

# Import pandas package 
import pandas as pd 
  
# Define a dictionary containing employee data 
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], 
        'Age':[27, 24, 22, 32], 
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'], 
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} 
  
# Convert the dictionary into DataFrame  
df = pd.DataFrame(data) 
  
# select all rows  
# and second to fourth column 
df[df.columns[1:4]] 

Output:

Method #2: Using loc[]

Example 1: Select two columns

# Import pandas package 
import pandas as pd 
  
# Define a dictionary containing employee data 
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], 
        'Age':[27, 24, 22, 32], 
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'], 
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} 
  
# Convert the dictionary into DataFrame  
df = pd.DataFrame(data) 
  
# select three rows and two columns 
df.loc[1:3, ['Name', 'Qualification']] 

Output:

Example 2: Select one to another columns. In our case we select column name “Name” to “Address”.

# Import pandas package 
import pandas as pd 
  
# Define a dictionary containing employee data 
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], 
        'Age':[27, 24, 22, 32], 
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'], 
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} 
  
# Convert the dictionary into DataFrame  
df = pd.DataFrame(data) 
  
# select two rows and  
# column "name" to "Address" 
# Means total three columns 
df.loc[0:1, 'Name':'Address'] 

Output:

Example 3: First filtering rows and selecting columns by label format and then Select all columns.

# Import pandas package 
import pandas as pd 
  
# Define a dictionary containing employee data 
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], 
        'Age':[27, 24, 22, 32], 
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'], 
        'Qualification':['Msc', 'MA', 'MCA', 'Phd'] 
       } 
# Convert the dictionary into DataFrame  
df = pd.DataFrame(data) 
  
# .loc DataFrame method 
# filtering rows and selecting columns by label 
# format 
# df.loc[rows, columns] 
# row 1, all columns 
df.loc[0, :] 

Output:

Method #3: Using iloc[]

Example 1: Select first two column.

# Import pandas package 
import pandas as pd 
  
# Define a dictionary containing employee data 
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], 
        'Age':[27, 24, 22, 32], 
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'], 
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} 
  
# Convert the dictionary into DataFrame  
df = pd.DataFrame(data) 
  
# Remember that Python does not 
# slice inclusive of the ending index. 
# select all rows  
# select first two column 
df.iloc[:, 0:2]  

Output:

Example 2: Select all or some columns, one to another using .iloc.

# Import pandas package 
import pandas as pd 
  
# Define a dictionary containing employee data 
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], 
        'Age':[27, 24, 22, 32], 
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'], 
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} 
  
# Convert the dictionary into DataFrame  
df = pd.DataFrame(data) 
  
# iloc[row slicing, column slicing] 
df.iloc [0:2, 1:3] 

Output:

Method #4: Using .ix

Select all or some columns, one to another using .ix.

# Import pandas package 
import pandas as pd 
  
# Define a dictionary containing employee data 
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], 
        'Age':[27, 24, 22, 32], 
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'], 
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} 
  
# Convert the dictionary into DataFrame  
df = pd.DataFrame(data) 
  
# select all rows and 0 to 2 columns  
print(df.ix[:, 0:2]) 

Output:

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