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How to Drop rows in DataFrame by conditions on column values?

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In this article, we are going to see several examples of how to drop rows from the dataframe based on certain conditions applied on a column. Pandas provide data analysts a way to delete and filter data frame using dataframe.drop() method. We can use this method to drop such rows that do not satisfy the given conditions. Let’s create a Pandas dataframe.
# import pandas library
import pandas as pd
  
# dictionary with list object in values
details = {
    'Name' : ['Ankit', 'Aishwarya', 'Shaurya',
              'Shivangi', 'Priya', 'Swapnil'],
    'Age' : [23, 21, 22, 21, 24, 25],
    'University' : ['BHU', 'JNU', 'DU', 'BHU'
                    'Geu', 'Geu'],
}
  
# creating a Dataframe object 
df = pd.DataFrame(details, columns = ['Name', 'Age',
                                      'University'],
                  index = ['a', 'b', 'c', 'd', 'e',
                           'f'])
  
df

                    
Output: python-pandas-drop-rows-1 Example 1 : Delete rows based on condition on a column.
# import pandas library
import pandas as pd
  
# dictionary with list object in values
details = {
    'Name' : ['Ankit', 'Aishwarya', 'Shaurya',
              'Shivangi', 'Priya', 'Swapnil'],
    'Age' : [23, 21, 22, 21, 24, 25],
    'University' : ['BHU', 'JNU', 'DU', 'BHU'
                    'Geu', 'Geu'],
}
  
# creating a Dataframe object 
df = pd.DataFrame(details, columns = ['Name', 'Age',
                                      'University'],
                  index = ['a', 'b', 'c', 'd', 'e', 'f'])
  
# get names of indexes for which
# column Age has value 21
index_names = df[ df['Age'] == 21 ].index
  
# drop these row indexes
# from dataFrame
df.drop(index_names, inplace = True)
  
df

                    
Output : python-pandas-drop-rows-2 Example 2 : Delete rows based on multiple conditions on a column.
# import pandas library
import pandas as pd
  
# dictionary with list object in values
details = {
    'Name' : ['Ankit', 'Aishwarya', 'Shaurya'
              'Shivangi', 'Priya', 'Swapnil'],
    'Age' : [23, 21, 22, 21, 24, 25],
    'University' : ['BHU', 'JNU', 'DU', 'BHU',
                    'Geu', 'Geu'],
}
  
# creating a Dataframe object 
df = pd.DataFrame(details, columns = ['Name', 'Age',
                                      'University'],
                  index = ['a', 'b', 'c', 'd', 'e', 'f'])
  
# get names of indexes for which column Age has value >= 21
# and <= 23
index_names = df[ (df['Age'] >= 21) & (df['Age'] <= 23)].index
  
# drop these given row
# indexes from dataFrame
df.drop(index_names, inplace = True)
  
df

                    
Output : python-pandas-drop-rows-3 Example 3 : Delete rows based on multiple conditions on different columns.
# import pandas library
import pandas as pd
  
# dictionary with list object in values
details = {
    'Name' : ['Ankit', 'Aishwarya', 'Shaurya',
              'Shivangi', 'Priya', 'Swapnil'],
    'Age' : [23, 21, 22, 21, 24, 25],
    'University' : ['BHU', 'JNU', 'DU', 'BHU'
                    'Geu', 'Geu'],
}
  
# creating a Dataframe object 
df = pd.DataFrame(details, columns = ['Name', 'Age',
                                      'University'],
                  index = ['a', 'b', 'c', 'd', 'e', 'f'])
  
# get names of indexes for which
# column Age has value >= 21
# and column University is BHU
index_names = df[ (df['Age'] >= 21) & (df['University'] == 'BHU')].index
  
# drop these given row
# indexes from dataFrame
df.drop(index_names, inplace = True)
  
df

                    
Output : python-pandas-drop-rwos-51

Last Updated : 02 Jul, 2020
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