How to Drop rows in DataFrame by conditions on column values?
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 libraryimport pandas as pd # dictionary with list object in valuesdetails = { '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:
Example 1 : Delete rows based on condition on a column.
# import pandas libraryimport pandas as pd # dictionary with list object in valuesdetails = { '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 21index_names = df[ df['Age'] == 21 ].index # drop these row indexes# from dataFramedf.drop(index_names, inplace = True) df |
Output :
Example 2 : Delete rows based on multiple conditions on a column.
# import pandas libraryimport pandas as pd # dictionary with list object in valuesdetails = { '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 <= 23index_names = df[ (df['Age'] >= 21) & (df['Age'] <= 23)].index # drop these given row# indexes from dataFramedf.drop(index_names, inplace = True) df |
Output :
Example 3 : Delete rows based on multiple conditions on different columns.
# import pandas libraryimport pandas as pd # dictionary with list object in valuesdetails = { '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 BHUindex_names = df[ (df['Age'] >= 21) & (df['University'] == 'BHU')].index # drop these given row# indexes from dataFramedf.drop(index_names, inplace = True) df |
Output :
Last Updated on October 24, 2021 by admin