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Ways to filter Pandas DataFrame by column values
  • Last Updated : 01 Oct, 2020

In this post, we will see different ways to filter Pandas Dataframe by column values. First, Let’s create a Dataframe:

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# importing pandas 
import pandas as pd 
    
# declare a dictionary
record =
  
 'Name' : ['Ankit', 'Swapnil', 'Aishwarya'
          'Priyanka', 'Shivangi', 'Shaurya' ],
    
 'Age' : [22, 20, 21, 19, 18, 22], 
    
 'Stream' : ['Math', 'Commerce', 'Science'
            'Math', 'Math', 'Science'], 
    
 'Percentage' : [90, 90, 96, 75, 70, 80] } 
    
# create a dataframe 
dataframe = pd.DataFrame(record,
                         columns = ['Name', 'Age'
                                    'Stream', 'Percentage']) 
# show the Dataframe
print("Given Dataframe :\n", dataframe)

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Output:

Dataframe

Method 1: Selecting rows of Pandas Dataframe based on particular column value using ‘>’, ‘=’, ‘=’, ‘<=’, ‘!=’ operator.



Example 1: Selecting all the rows from the given Dataframe in which ‘Percentage’ is greater than 75 using [ ].

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# selecting rows based on condition 
rslt_df = dataframe[dataframe['Percentage'] > 70
    
print('\nResult dataframe :\n', rslt_df)

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Output:

output dataframe

Example 2: Selecting all the rows from the given Dataframe in which ‘Percentage’ is greater than 70 using loc[ ]

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# selecting rows based on condition 
rslt_df = dataframe.loc[dataframe['Percentage'] > 70
    
print('\nResult dataframe :\n'
      rslt_df)

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Output:

output dataframe-1



Method 2: Selecting those rows of Pandas Dataframe whose column value is present in the list using isin() method of the dataframe.

Example 1: Selecting all the rows from the given dataframe in which ‘Stream’ is present in the options list using [ ].

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options = ['Science', 'Commerce'
    
# selecting rows based on condition 
rslt_df = dataframe[dataframe['Stream'].isin(options)] 
    
print('\nResult dataframe :\n',
      rslt_df)

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Output:

output dataframe-2

Example 2: Selecting all the rows from the given dataframe in which ‘Stream’ is present in the options list using loc[ ].

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options = ['Science', 'Commerce'
    
# selecting rows based on condition 
rslt_df = dataframe.loc[dataframe['Stream'].isin(options)] 
    
print('\nResult dataframe :\n'
      rslt_df)

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Output:

output dataframe-3

Method 3: Selecting rows of  Pandas Dataframe based on multiple column conditions using ‘&’ operator. 

Example1: Selecting all the rows from the given Dataframe in which ‘Age’ is equal to 22 and ‘Stream’ is present in the options list using [ ].

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options = ['Commerce' ,'Science'
    
# selecting rows based on condition 
rslt_df = dataframe[(dataframe['Age'] == 22) & 
          dataframe['Stream'].isin(options)] 
    
print('\nResult dataframe :\n',
      rslt_df)

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Output:

output dataframe-4

Example 2: Selecting all the rows from the given Dataframe in which ‘Age’ is equal to 22 and ‘Stream’ is present in the options list using loc[ ].

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options = ['Commerce', 'Science'
   
# selecting rows based on condition 
rslt_df = dataframe.loc[(dataframe['Age'] == 22) & 
              dataframe['Stream'].isin(options)] 
    
print('\nResult dataframe :\n',
      rslt_df)

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Output:

output dataframe-5

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