Deletion is one of the primary operations when it comes to data analysis. Very often we see that a particular attribute in the data frame is not at all useful for us while working on a specific analysis, rather having it may lead to problems and unnecessary change in the prediction. For example, if we want to analyze the students’ BMI of a particular school, then there is no need to have the religion column/attribute for the students, so we prefer to delete the column. Let us now see the syntax of deleting a column from a dataframe.
Syntax:
del df['column_name']
Let us now see few examples:
Example 1:
Python3
# importing the moduleimport pandas as pd# creating a DataFramemy_df = {'Name': ['Rutuja', 'Anuja'], 'ID': [1, 2], 'Age': [20, 19]}df = pd.DataFrame(my_df)display("Original DataFrame")display(df)# deleting a columndel df['Age']display("DataFrame after deletion")display(df) |
Output :
Note the column ‘Age” has been dropped.
Example 2:
Python3
# importing the moduleimport pandas as pd# creating a DataFramemy_df = {'Students': ['A', 'B', 'C', 'D'], 'BMI': [22.7, 18.0, 21.4, 24.1], 'Religion': ['Hindu', 'Islam', 'Christian', 'Sikh']}df = pd.DataFrame(my_df)display("Original DataFrame")display(df)# deleting a columndel df['Religion']display("DataFrame after deletion")display(df) |
Output :
Note that the unnecessary column, ‘Religion’ has been deleted successfully.
