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output:
                       Product      Price
                  0    Bread           20
                  1    Milk            60
                  2    Biscuit         20
                  3    Bourn-Vita      70
                  4    Soap            40
                  Method loc: Access elements in a Pandas DataFrame using label-based indexing.
                  Method iloc: Access elements in a Pandas DataFrame using integer positional indexing.

                  The keyword argument index_col: Set a column as the row labels.
                  Method pd.set_index(): Set a column as the index after reading a .csv file into a DataFrame.




                     Consider the following DataFrame storing employee details:
                       ID        Name         Department      Salary
                       E1        Sid          Admin           90000
                       E2        Ram          Accounts        1000000
                       E3        Sita         Admin           90000
                       E4        Shyam        Accounts        1000000
                        (i)  Retrieve the contents of column ID and Department.
                        (ii)  Determine the salary of second employee.
                        (iii)  Set column ID as the index labels.



            2.6.5 Boolean Indexing
            Boolean indexing is a powerful technique for filtering data from a Pandas DataFrame based on some condition.

            To Retrieve Certain Rows

            With boolean indexing, you can filter a DataFrame to include only rows that satisfy a certain condition. To use Boolean
            indexing, we first mark the rows of our interest as True and the other rows as False, based on some condition. This
            process of marking the rows as True and False is known as masking, and the Boolean DataFrame of True and
            False is known as a mask. A boolean mask is a Pandas series of the same length as the original DataFrame. Below, we
            create a mask for marking the rows in the groceryDF DataFrame, for which the price of a product is more than 50:

             >>> import pandas as pd
             >>> groceryDF = pd.read_csv('Grocery.csv')
             >>> mask = groceryDF['Price'] > 50
             >>> print(mask)
             >>> type(mask)
            output:
                  0    False
                  1     True
                  2    False
                  3     True
                  4    False
                  5    False
                  6     True
                  7    False
                  Name: Price, dtype: bool
                  pandas.core.series.Series




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