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

Last Updated : 02 Jul, 2020

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.

Python3

# 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.

Python3

# 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.

Python3

# 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.

Python3

# 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