Page 219 - AI Ver 3.0 Class 11
P. 219

# Define a dictionary containing employee data
                     data = {'Name':['Adit', 'Ekam', 'Sakshi', 'Anu'],

                              'Age':[27, 24, 25, 30],
                              'Address':['Delhi', 'Kanpur', 'Meerut', 'Indore'],
                             'Qualification':['M.Sc.', 'MA', 'MCA', 'Ph.D.']}

                     # Convert the dictionary into DataFrame
                     df = pd.DataFrame(data)


                     # Add a new column 'Salary' to the DataFrame
                     df['Salary'] = [50000, 45000, 55000, 60000]

                     # Deleting a Row
                     df.drop(index=1, inplace=True)  # Deleting the row with index 1 (Ekam)
                     print("\nDataFrame after deleting a row:")

                     print(df)
                     # Deleting a Column

                     df.drop(columns='Address', inplace=True)  # Deleting the 'Address' column

                     print("DataFrame after deleting a column:")
                     print(df)
                 Output:

                    DataFrame after deleting a row:
                           Name   Age   Address  Qualification          Salary
                    0      Adit    27     Delhi               M.Sc.    50000
                    2    Sakshi    25    Meerut                 MCA     55000

                    3       Anu    30    Indore               Ph.D.    60000

                 DataFrame after deleting a column:
                           Name   Age   Qualification  Salary

                    0      Adit    27                M.Sc.    50000
                    2    Sakshi    25                  MCA    55000
                    3       Anu    30                Ph.D.    60000
                 To delete multiple columns with specific labels, you can modify the drop method to include a list of column labels.
                 DataFrame.drop() method can also be used to remove the duplicate rows.

                     # Deleting Columns with specific labels
                     labels_to_delete = ['Age', 'Address'] #specify in a list

                     df.drop(columns=labels_to_delete, inplace=True)









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