How to drop rows in DataFrame by index labels in Python ?
In this article we are going to learn how to delete single or multiple rows from a Dataframe.
For this we are going to use the drop( ) function.
Syntax - DataFrame.drop( labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise' )
Where, the function accepts name/series of names in the label and deletes the rows or columns it points to. The axis is used to alter between rows and columns, 0 means rows and 1 means columns(default value is 0).
Also we have to pass inplace = True if we want the modified values to be updated in our dataframe object, as the drop( ) function returns the modified values into a new Dataframe object. To explain this properly we will be using inplace = True in all our programs.
We are going to use the following dataset as example
Name Age     Location Country a    Jill  16    Tokyo  Japan b  Phoebe  38 New York    USA c   Kirti  39 New York    USA d   Veena  40    Delhi  India e    John  54   Mumbai  India f Michael  21    Tokyo  Japan
Deleting a single Row in DataFrame by Row Index Label :
To delete a single row by the label we can just pass the label into the function.
Here let’s try to delete‘b’ row
#program :
import numpy as np
import pandas as pd
#Examole data
students = [('Jill',  16, 'Tokyo',    'Japan'),
('Phoebe', 38, 'New York', 'USA'),
('Kirti', 39, 'New York', 'USA'),
('Veena', 40, 'Delhi',    'India'),
('John',  54, 'Mumbai',   'India'),
("Michael",21, 'Tokyo',    'Japan')]
#Creating an object of dataframe class
dfObj = pd.DataFrame(students, columns = ['Name' , 'Age', 'Location' , 'Country'], index=['a', 'b', 'c' , 'd' , 'e' , 'f'])
#Deleting 'b' row
dfObj.drop('b',inplace=True)
print(dfObj)
Output :
Name Age     Location Country a    Jill  16    Tokyo  Japan c   Kirti  39 New York    USA d   Veena  40    Delhi  India e    John  54   Mumbai  India f Michael  21    Tokyo  Japan
Deleting Multiple Rows in DataFrame by Index Labels :
To delete multiple rows by their labels we can just pass the labels into the function inside a square bracket [ ].
Here let’s try to delete 'a' and 'b' row.
#program :
import numpy as np
import pandas as pd
#Examole data
students = [('Jill',  16, 'Tokyo',    'Japan'),
('Phoebe', 38, 'New York', 'USA'),
('Kirti', 39, 'New York', 'USA'),
('Veena', 40, 'Delhi',    'India'),
('John',  54, 'Mumbai',   'India'),
("Michael",21, 'Tokyo',    'Japan')]
#Creating an object of dataframe class
dfObj = pd.DataFrame(students, columns = ['Name' , 'Age', 'Location' , 'Country'], index=['a', 'b', 'c' , 'd' , 'e' , 'f'])
#Deleting 'a' and 'b' row
dfObj.drop(['a','b'],inplace=True)
print(dfObj)
Output :   Name    Age     Location    Country c   Kirti     39   New York   USA d   Veena   40   Delhi       India e    John     54   Mumbai     India f   Michael   21    Tokyo       Japan
Deleting Multiple Rows by Index Position in DataFrame :
To delete multiple rows we know the index position, however, the function drop( ) doesn’t take indices as parameters. So we create the list of labels and pass them into the drop( ) function. Let’s try deleting the same rows again but by index.
#program :
import numpy as np
import pandas as pd
#Examole data
students = [('Jill',  16, 'Tokyo',    'Japan'),
('Phoebe', 38, 'New York', 'USA'),
('Kirti', 39, 'New York', 'USA'),
('Veena', 40, 'Delhi',    'India'),
('John',  54, 'Mumbai',   'India'),
("Michael",21, 'Tokyo',    'Japan')]
#Creating an object of dataframe class
dfObj = pd.DataFrame(students, columns = ['Name' , 'Age', 'Location' , 'Country'], index=['a', 'b', 'c' , 'd' , 'e' , 'f'])
#Deleting 1st and 2nd row
dfObj.drop([dfObj.index[0] , dfObj.index[1]],inplace=True)
print(dfObj)
Output :   Name   Age     Location   Country c   Kirti     39   New York    USA d   Veena   40    Delhi      India e    John     54   Mumbai    India f   Michael   21    Tokyo     Japan
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