In this article, we will discuss how to drop columns in Pandas Dataframe by label Names or by Index Positions. Drop columns from a DataFrame can be achieved in multiple ways.
Let’s create a simple dataframe with a dictionary of lists, say column names are: ‘Name’, ‘Age’, ‘Place’, ‘College’.
# import pandas library as pd import pandas as pd # List of Tuples students = [('Ankit', 22, 'Up', 'Geu'), ('Ankita', 31, 'Delhi', 'Gehu'), ('Rahul', 16, 'Tokyo', 'Abes'), ('Simran', 41, 'Delhi', 'Gehu'), ('Shaurya', 33, 'Delhi', 'Geu'), ('Harshita', 35, 'Mumbai', 'Bhu' ), ('Swapnil', 35, 'Mp', 'Geu'), ('Priya', 35, 'Uk', 'Geu'), ('Jeet', 35, 'Guj', 'Gehu'), ('Ananya', 35, 'Up', 'Bhu') ] # Create a DataFrame object from # list of tuples with columns # and indices. details = pd.DataFrame(students, columns =['Name', 'Age', 'Place', 'College'], index =['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']) # show the dataframe details |
Output :
Method 1: Drop Columns from a Dataframe using dataframe.drop() method.
Example 1: Remove specific single mention column.
# import pandas library as pd import pandas as pd # List of Tuples students = [('Ankit', 22, 'Up', 'Geu'), ('Ankita', 31, 'Delhi', 'Gehu'), ('Rahul', 16, 'Tokyo', 'Abes'), ('Simran', 41, 'Delhi', 'Gehu'), ('Shaurya', 33, 'Delhi', 'Geu'), ('Harshita', 35, 'Mumbai', 'Bhu' ), ('Swapnil', 35, 'Mp', 'Geu'), ('Priya', 35, 'Uk', 'Geu'), ('Jeet', 35, 'Guj', 'Gehu'), ('Ananya', 35, 'Up', 'Bhu') ] # Create a DataFrame object from # list of tuples with columns # and indices. details = pd.DataFrame(students, columns =['Name', 'Age', 'Place', 'College'], index =['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']) # Remove column name 'Age' rslt_df = details.drop(['Age'], axis = 1) # show the dataframe rslt_df |
Output :
Example 2 : Remove specific multiple mentions columns.
# import pandas library as pd import pandas as pd # List of Tuples students = [('Ankit', 22, 'Up', 'Geu'), ('Ankita', 31, 'Delhi', 'Gehu'), ('Rahul', 16, 'Tokyo', 'Abes'), ('Simran', 41, 'Delhi', 'Gehu'), ('Shaurya', 33, 'Delhi', 'Geu'), ('Harshita', 35, 'Mumbai', 'Bhu' ), ('Swapnil', 35, 'Mp', 'Geu'), ('Priya', 35, 'Uk', 'Geu'), ('Jeet', 35, 'Guj', 'Gehu'), ('Ananya', 35, 'Up', 'Bhu') ] # Create a DataFrame object from # list of tuples with columns # and indices. details = pd.DataFrame(students, columns =['Name', 'Age', 'Place', 'College'], index =['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']) # Remove two columns name is 'Age' and # 'College' rslt_df = details.drop(['Age', 'College'], axis = 1) # show the dataframe rslt_df |
Output :
Example 3: Remove columns as based on column index.
# import pandas library as pd import pandas as pd # List of Tuples students = [('Ankit', 22, 'Up', 'Geu'), ('Ankita', 31, 'Delhi', 'Gehu'), ('Rahul', 16, 'Tokyo', 'Abes'), ('Simran', 41, 'Delhi', 'Gehu'), ('Shaurya', 33, 'Delhi', 'Geu'), ('Harshita', 35, 'Mumbai', 'Bhu' ), ('Swapnil', 35, 'Mp', 'Geu'), ('Priya', 35, 'Uk', 'Geu'), ('Jeet', 35, 'Guj', 'Gehu'), ('Ananya', 35, 'Up', 'Bhu') ] # Create a DataFrame object from # list of tuples with columns # and indices. details = pd.DataFrame(students, columns =['Name', 'Age', 'Place', 'College'], index =['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']) # Remove three columns as index base # 0, 1, 2 rslt_df = details.drop(details.columns[[0, 1, 2]], axis = 1) # show the dataframe rslt_df |
Output :
Method 2: Drop Columns from a Dataframe using iloc[] and drop() method.
Example: Remove all columns between a specific column to another columns(exclude)
# import pandas library as pd import pandas as pd # List of Tuples students = [('Ankit', 22, 'Up', 'Geu'), ('Ankita', 31, 'Delhi', 'Gehu'), ('Rahul', 16, 'Tokyo', 'Abes'), ('Simran', 41, 'Delhi', 'Gehu'), ('Shaurya', 33, 'Delhi', 'Geu'), ('Harshita', 35, 'Mumbai', 'Bhu' ), ('Swapnil', 35, 'Mp', 'Geu'), ('Priya', 35, 'Uk', 'Geu'), ('Jeet', 35, 'Guj', 'Gehu'), ('Ananya', 35, 'Up', 'Bhu') ] # Create a DataFrame object from # list of tuples with columns # and indices. details = pd.DataFrame(students, columns =['Name', 'Age', 'Place', 'College'], index =['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']) # Remove all columns from column # index 1 to 3(exclude) rslt_df = details.drop(details.iloc[:, 1:3], axis = 1) # show the dataframe rslt_df |
Output :
Method 3: Drop Columns from a Dataframe using loc[] and drop() method.
Example: Remove all columns between a specific column name to another columns name.
# import pandas library as pd import pandas as pd # List of Tuples students = [('Ankit', 22, 'Up', 'Geu'), ('Ankita', 31, 'Delhi', 'Gehu'), ('Rahul', 16, 'Tokyo', 'Abes'), ('Simran', 41, 'Delhi', 'Gehu'), ('Shaurya', 33, 'Delhi', 'Geu'), ('Harshita', 35, 'Mumbai', 'Bhu' ), ('Swapnil', 35, 'Mp', 'Geu'), ('Priya', 35, 'Uk', 'Geu'), ('Jeet', 35, 'Guj', 'Gehu'), ('Ananya', 35, 'Up', 'Bhu') ] # Create a DataFrame object from # list of tuples with columns # and indices. details = pd.DataFrame(students, columns =['Name', 'Age', 'Place', 'College'], index =['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']) # Remove all columns from column name # 'Name' to 'College' rslt_df = details.drop(details.loc[:, 'Name':'College'].columns, axis = 1) # show the dataframe # only indexes print rslt_df |
Output :
Note: Different loc() and iloc() is iloc() exclude last column range element.
Method 4: Drop Columns from a Dataframe by iterative way.
Example: Remove specific column.
# import pandas library as pd import pandas as pd # List of Tuples students = [('Ankit', 22, 'Up', 'Geu'), ('Ankita', 31, 'Delhi', 'Gehu'), ('Rahul', 16, 'Tokyo', 'Abes'), ('Simran', 41, 'Delhi', 'Gehu'), ('Shaurya', 33, 'Delhi', 'Geu'), ('Harshita', 35, 'Mumbai', 'Bhu' ), ('Swapnil', 35, 'Mp', 'Geu'), ('Priya', 35, 'Uk', 'Geu'), ('Jeet', 35, 'Guj', 'Gehu'), ('Ananya', 35, 'Up', 'Bhu') ] # Create a DataFrame object from # list of tuples with columns # and indices. details = pd.DataFrame(students, columns =['Name', 'Age', 'Place', 'College'], index =['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']) # loop throughout all the columns for column in details.columns : if column == 'Age' : # delete the column del details[column] # show the dataframe details |
Output :
