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 :
