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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