In this article we will discuss how to drop columns from a DataFrame object.

DataFrame provides a member function drop() i.e.

DataFrame.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')

It accepts a single Label Name or list of Labels and deletes the corresponding columns or rows (based on axis) with that label.
It considers the Labels as column names to be deleted, if axis == 1 or columns == True.

By default it doesn’t modify the existing DataFrame, instead it returns a new dataframe. If we want to delete the rows or columns from DataFrame in place then we need to pass another attribute i.e. inplace=True

Let’s understand by examples,

Create a DataFrame object,

# List of Tuples
students = [ ('jack', 34, 'Sydeny' , 'Australia') ,
             ('Riti', 30, 'Delhi' , 'India' ) ,
             ('Vikas', 31, 'Mumbai' , 'India' ) ,
             ('Neelu', 32, 'Bangalore' , 'India' ) ,
             ('John', 16, 'New York' , 'US') ,
             ('Mike', 17, 'las vegas' , 'US')  ]


#Create a DataFrame object
dfObj = pd.DataFrame(students, columns = ['Name' , 'Age', 'City' , 'Country'], index=['a', 'b', 'c' , 'd' , 'e' , 'f'])

Delete a Single column in DataFrame by Column Name

Contents of DataFrame object dfObj is,

Original DataFrame pointed by dfObj

let’s delete a column ‘Age’ from the above dataframe object,

modDfObj = dfObj.drop('Age' , axis='columns')

Contents of the new DataFrame object modDfObj is,

Column Age Deleted from DataFrame

Drop Multiple Columns by Label Names in DataFrame

To drop multiple columns from a DataFrame Object we can pass a list of column names to the drop() function.

For example, drop the columns ‘Age’ & ‘Name’ from the dataframe object dfObj i.e.

modDfObj = dfObj.drop(['Age' , 'Name'] , axis='columns')

Contents of the new DataFrame object modDfObj is,

Columns Age & Name deleted

 

Drop Columns by Index Position in DataFrame

To drop columns by index position, we first need to find out column names from index position and then pass list of column names to drop().

For example delete columns at index position 0 & 1 from dataframe object dfObj i.e.

# Delete columns at index 1 & 2
modDfObj = dfObj.drop([dfObj.columns[1] , dfObj.columns[2]] ,  axis='columns')

Contents of the new DataFrame object modDfObj is,

Columns Age & Name deleted

Drop Columns in Place

Delete columns ‘Age’ & ‘Name’ from dataFrame dfObj in Place by passing inplace=True in drop() function i.e.

dfObj.drop(['Age' , 'Name'] , axis='columns', inplace=True)

It will update the contents of dfObj i.e. columns ‘Age’ & ‘Name’ will be deleted from dfObj.

Drop Column If Exists

Before delete a column using drop() always check if column exists or not otherwise drop() will throw a KeyError i.e.

# Check if Dataframe has a column with Label name 'City'
if 'City' in dfObj.columns :
    dfObj.drop(['City'] , axis='columns', inplace=True)
else :
    print('Column Name not found')

Complete example is as follows,
import pandas as pd

def main():
    
    # List of Tuples
    students = [ ('jack', 34, 'Sydeny' , 'Australia') ,
                 ('Riti', 30, 'Delhi' , 'India' ) ,
                 ('Vikas', 31, 'Mumbai' , 'India' ) ,
                 ('Neelu', 32, 'Bangalore' , 'India' ) ,
                 ('John', 16, 'New York' , 'US') ,
                 ('Mike', 17, 'las vegas' , 'US')  ]
    
    
    #Create a DataFrame object
    dfObj = pd.DataFrame(students, columns = ['Name' , 'Age', 'City' , 'Country'], index=['a', 'b', 'c' , 'd' , 'e' , 'f']) 
    
    print("Original DataFrame" , dfObj, sep='\n')
    
    '''
    Delete a Single column in dataFrame by Column Name
    '''
    
    print("**** Delete column 'Age' in DataFrame object ****")
    
    modDfObj = dfObj.drop('Age' , axis='columns')
    
    print("New DataFrame" , modDfObj, sep='\n')
    
    '''
    Delete multiple columns in dataFrame by Column Names
    '''
    
    print("**** Delete columns 'Age' & 'Name' from DataFrame")
    
    modDfObj = dfObj.drop(['Age' , 'Name'] , axis='columns')
    
    print("New Dataframe" , modDfObj, sep='\n')
    
    '''
    Delete multiple columns in dataFrame by Column Names
    '''
    
    print("**** Delete columns at Index Position 1 & 2 in DataFrame")
     
    # Delete columns at index 1 & 2
    modDfObj = dfObj.drop([dfObj.columns[1] , dfObj.columns[2]] ,  axis='columns')
    
    print("New DataFrame with Deleted columns at Index position 1 and 2" , modDfObj, sep='\n')
   
    '''
    Delete multiple columns from dataFrame in Place
    '''
    
    print("Original Dataframe" , dfObj, sep='\n')
    
    print("**** Delete columns 'Age' & 'Name' from dataFrame in Place")
    
    dfObj.drop(['Age' , 'Name'] , axis='columns', inplace=True)
    
    print("Modified DataFrame in place" , dfObj, sep='\n')
    
    '''
    Delete column if exist
    '''
    
    #Create a DataFrame object
    dfObj = pd.DataFrame(students, columns = ['Name' , 'Age', 'City' , 'Country'], index=['a', 'b', 'c' , 'd' , 'e' , 'f'])  
    
    print("Original DataFrame" , dfObj, sep='\n')
    
    print(dfObj.columns)
    
    # Check if Dataframe has a column with Label name 'City'
    if 'City' in dfObj.columns :
        dfObj.drop(['City'] , axis='columns', inplace=True)
    else :
        print('Column Name not found')    
        
    
    print("Modified DataFrame" , dfObj, sep='\n')
    
if __name__ == '__main__':
    main()


Output:
Original DataFrame
    Name  Age       City    Country
a   jack   34     Sydeny  Australia
b   Riti   30      Delhi      India
c  Vikas   31     Mumbai      India
d  Neelu   32  Bangalore      India
e   John   16   New York         US
f   Mike   17  las vegas         US
**** Delete column 'Age' in DataFrame object ****
New DataFrame
    Name       City    Country
a   jack     Sydeny  Australia
b   Riti      Delhi      India
c  Vikas     Mumbai      India
d  Neelu  Bangalore      India
e   John   New York         US
f   Mike  las vegas         US
**** Delete columns 'Age' & 'Name' from DataFrame
New Dataframe
        City    Country
a     Sydeny  Australia
b      Delhi      India
c     Mumbai      India
d  Bangalore      India
e   New York         US
f  las vegas         US
**** Delete columns at Index Position 1 & 2 in DataFrame
New DataFrame with Deleted columns at Index position 1 and 2
    Name    Country
a   jack  Australia
b   Riti      India
c  Vikas      India
d  Neelu      India
e   John         US
f   Mike         US
Original Dataframe
    Name  Age       City    Country
a   jack   34     Sydeny  Australia
b   Riti   30      Delhi      India
c  Vikas   31     Mumbai      India
d  Neelu   32  Bangalore      India
e   John   16   New York         US
f   Mike   17  las vegas         US
**** Delete columns 'Age' & 'Name' from dataFrame in Place
Modified DataFrame in place
        City    Country
a     Sydeny  Australia
b      Delhi      India
c     Mumbai      India
d  Bangalore      India
e   New York         US
f  las vegas         US
Original DataFrame
    Name  Age       City    Country
a   jack   34     Sydeny  Australia
b   Riti   30      Delhi      India
c  Vikas   31     Mumbai      India
d  Neelu   32  Bangalore      India
e   John   16   New York         US
f   Mike   17  las vegas         US
Index(['Name', 'Age', 'City', 'Country'], dtype='object')
Modified DataFrame
    Name  Age    Country
a   jack   34  Australia
b   Riti   30      India
c  Vikas   31      India
d  Neelu   32      India
e   John   16         US
f   Mike   17         US

 

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