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>>> groceryDF['Total Price'] = Total
             >>> groceryDF.to_csv('GroceryV2.csv', header= True)
            Note that the function call groceryDF.to_csv('GroceryV2.csv', header= True) saves the file in the current
            directory. Alternatively, complete pathname may be specified. Further, if the keyword argument header = True is
            not included, the DataFrame contents will still be transferred to the file, but without the column names.


                  The DataFrame object can be saved as CSV (Comma-Separated Values) file using the to_csv() function of Pandas
                  DataFrame:
                   df.to_csv('output.csv', index=False)




             C T  04     Write  the  Pandas  statements  to  save  the  updated  employeeDF  DataFrame  to  csv  file
                         employeeDFupdated.csv.






            2.10 Grouping and Aggregation

            Often, we need to analyse data group wise. For example, we may like to examine the purchases in different categories.
            We may also want to aggregate the data group wise. For example, it would be interesting to know the sum or mean
            of the amount spent on purchases in each category. Similarly, one may like to know the count of purchases in each
            category. Before, proceeding further, let us compute the total price paid for each item and add the column Total
            Price to the DataFrame groceryDF, as shown below:
             >>> groceryDF = pd.read_csv('Grocery.csv')
             >>> # Multiplying Price and Quantity values to get Total Price for each item
             >>> TotalPrice = groceryDF['Price'] * groceryDF['Quantity']
             >>> # Adding a new Column to the Dataframe
             >>> groceryDF['Total Price'] = TotalPrice
             >>> print(groceryDF.head())
                       Product Category  Price  Quantity  Total Price
                 0       Bread     Food     20         2           40
                 1        Milk     Food     60         5          300
                 2     Biscuit     Food     20         2           40
                 3  Bourn-Vita     Food     70         1           70
                 4        Soap  Hygiene     40         4          160
            2.10.1 Grouping

            As mentioned above, sometimes, it is useful to organise the data in a DataFrame into groups based on specific criteria,
            such as values stored in one or more columns. To begin with, let us organise the data in the DataFrame groceryDF
            category wise, using the function groupby().
                 #Groupby in Pandas
                 #All products grouped according to the category
             >>> groceryGroupedDF = groceryDF.groupby('Category')
             >>> print(groceryGroupedDF)
             >>> <pandas.core.groupby.generic.DataFrameGroupBy object at 0x7e6816317100>
            Now, we have created a DataFrameGroupBy object that can be used to perform operations on each group. To begin
            with, we use the object groceryGroupedDF to perform iteration group-wise.
            Each group is described by a tuple comprising the value that defines the group and a DataFrame object comprising the
            associated part of the groceryGroupedDF, as shown below:

             >>> for record in groceryGroupedDF:
             >>>     print(type(record), len(record), record[0], type(record[1]))

                                                                             Data Handling using Pandas DataFrame  61
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