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.

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# 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']]

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Output:




Select Second to fourth column.













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# 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]] 



















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Output:




 


Method #2: Using loc[]


Example 1: Select two columns













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# 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']] 



















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Output:




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













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# 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'] 



















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Output:



          
          
          
          

            

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













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# 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, :] 



















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Output:




 


Method #3: Using iloc[]


Example 1:  Select first two column.













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# 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]  



















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Output:




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













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# 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] 



















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Output:




 


Method #4: Using .ix


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













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# 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]) 



















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Output:





          
          
          
          

            

    

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