Pandas provide data analysts a way to delete and filter data frame using .drop() method. Rows can be removed using index label or column name using this method.
Syntax:
DataFrame.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors=’raise’)Parameters:
labels: String or list of strings referring row or column name.
axis: int or string value, 0 ‘index’ for Rows and 1 ‘columns’ for Columns.
index or columns: Single label or list. index or columns are an alternative to axis and cannot be used together.
level: Used to specify level in case data frame is having multiple level index.
inplace: Makes changes in original Data Frame if True.
errors: Ignores error if any value from the list doesn’t exists and drops rest of the values when errors = ‘ignore’Return type: Dataframe with dropped values
Now, Let’s create a sample dataframe
# import pandas library import pandas as pd # dictionary with list object in values details = { 'Name' : ['Ankit', 'Aishwarya', 'Shaurya','Shivangi'], 'Age' : [23, 21, 22,21], 'University' : ['BHU', 'JNU', 'DU', 'BHU'], } # creating a Dataframe object df = pd.DataFrame(details,columns = ['Name','Age','University'], index = ['a', 'b', 'c', 'd']) df |
Output:
Example #1: Delete a single Row in DataFrame by Row Index Label
# import pandas library import pandas as pd # dictionary with list object in values details = { 'Name' : ['Ankit', 'Aishwarya', 'Shaurya', 'Shivangi'], 'Age' : [23, 21, 22, 21], 'University' : ['BHU', 'JNU', 'DU', 'BHU'], } # creating a Dataframe object df = pd.DataFrame(details, columns = ['Name', 'Age', 'University'], index = ['a', 'b', 'c', 'd']) # return a new dataframe by dropping a # row 'c' from dataframe update_df = df.drop('c') update_df |
Output :
Example #2: Delete Multiple Rows in DataFrame by Index Labels
# import pandas library import pandas as pd # dictionary with list object in values details = { 'Name' : ['Ankit', 'Aishwarya', 'Shaurya', 'Shivangi'], 'Age' : [23, 21, 22, 21], 'University' : ['BHU', 'JNU', 'DU', 'BHU'], } # creating a Dataframe object df = pd.DataFrame(details, columns = ['Name', 'Age', 'University'], index = ['a', 'b', 'c', 'd']) # return a new dataframe by dropping a row # 'b' & 'c' from dataframe update_df = df.drop(['b', 'c']) update_df |
Output :
Example #3: Delete a Multiple Rows by Index Position in DataFrame
# import pandas library import pandas as pd # dictionary with list object in values details = { 'Name' : ['Ankit', 'Aishwarya', 'Shaurya', 'Shivangi'], 'Age' : [23, 21, 22, 21], 'University' : ['BHU', 'JNU', 'DU', 'BHU'], } # creating a Dataframe object df = pd.DataFrame(details, columns = ['Name', 'Age', 'University'], index = ['a', 'b', 'c', 'd']) # return a new dataframe by dropping a row # 'b' & 'c' from dataframe using their # respective index position update_df = df.drop([df.index[1], df.index[2]]) update_df |
Output :
Example #4: Delete rows from dataFrame in Place
# import pandas library import pandas as pd # dictionary with list object in values details = { 'Name' : ['Ankit', 'Aishwarya', 'Shaurya', 'Shivangi'], 'Age' : [23, 21, 22, 21], 'University' : ['BHU', 'JNU', 'DU', 'BHU'], } # creating a Dataframe object df = pd.DataFrame(details, columns = ['Name', 'Age', 'University'], index = ['a', 'b', 'c', 'd']) # droppping a row 'c' & 'd' from actual dataframe df.drop(['c', 'd'], inplace = True ) df |
Output :
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