How to drop rows in Pandas DataFrame by index labels?

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:

pandas-drop-row-1

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 :

drop-rows-2

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 :

pandas-drop-row-3

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 :

pandas-drop-row

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 :

pandas-drop-row-6

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