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3.  Concatenate the original DataFrame with the DataFrame constructed in step 2 using Pandas method pd.concat().

          groceryDF = pd.read_csv('Grocery.csv')
           newPurchase = pd.DataFrame({'Product':['Jeans', 'Chocolate'], 'Category':['Clothes',
           'Food'], 'Price':[400, 50], 'Quantity':[2, 4] })
          pd.concat([groceryDF, newPurchase])
        output:
                     Product   Category         Price      Quantity
              0        Bread       Food            20             2
              1         Milk       Food            60             5
              2      Biscuit       Food            20             2
              3   Bourn-Vita       Food            70             1
              4         Soap    Hygiene            40             4
              5        Brush    Hygiene            30             2
              6    Detergent  Household            80             1
              7      Tissues    Hygiene            30             5
              0        Jeans    Clothes           400             2
              1    Chocolate       Food            50             4
        Note that the new rows are stored at index 0 and 1. If you want the the newly added rows to be indexed in sequence,
        we set the keyword argument ignore_index=True, as shown below:

         >>> groceryDF = pd.read_csv('Grocery.csv')
         >>> newPurchase = pd.DataFrame({'Product':['Jeans', 'Chocolate'], 'Category':['Clothes',
              'Food'], 'Price':[400, 50], 'Quantity':[2, 4] })
         >>> pd.concat([groceryDF, newPurchase], ignore_index=True)
        output:
                       Product      Category        Price      Quantity
              0          Bread           Food          20               2
              1           Milk           Food          60               5
              2        Biscuit           Food          20               2
              3    Bourn-Vita            Food          70               1
              4           Soap        Hygiene          40               4
              5          Brush        Hygiene          30               2
              6     Detergent      Household           80               1
              7        Tissues        Hygiene          30               5
              8          Jeans        Clothes         400               2
              9     Chocolate            Food          50               4
        Merging DataFrames

        To illustrate merging of the DataFrames. We create two DataFrames of purchases made on two days (to be called day1
        and day2) and then concatenate them, as follows:
        1.   We construct the first DataFrame groceryDF1 by reading the data of purchases on day1 from the csv file
           Grocery1.csv.
        2.   We construct the second DataFrame groceryDF1 by reading the data of purchases on day2 from the csv file
           Grocery2.csv.
        3.  Concatenate the DataFrames groceryDF1 and groceryDF2.

         >>> groceryDF1 = pd.read_csv('Grocery1.csv')
         >>> print('Day 1 Purchases: ')
         >>> print(groceryDF1.head())
         >>> groceryDF2 = pd.read_csv('Grocery2.csv')
         >>> print('\nDay 2 Purchases: ')
         >>> print(groceryDF2.head())
              Day 1 Purchases:
                    Product Category  Price  Quantity
              0       Bread     Food     20         2
              1        Milk     Food     60         5

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