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Item Purchased     Bread
              Cost                22.5
              dtype: object
         >>> print('\nPurchase 2:\n',purchase2)
        output:
              Purchase 2:
              Name                   Nikita
              Items Purchased    Vegetables
              Cost                     90.0
              dtype: object

         >>> print('\nPurchase 3:\n',purchase3)
        output:
              Purchase 3:
              Name              Vinod
              Item Purchased     Milk
              Cost               75.0
              dtype: object
        We make use of the above data in the form of three series (purchase1, purchase2, purchase3) to create a
        DataFrame.

         >>> import pandas as pd
         >>> purchasesDF = pd.DataFrame([purchase1, purchase2, purchase3])
         >>> print(purchasesDF)
                   Name Item Purchased  Cost Items Purchased
              0  Ashish          Bread  22.5             NaN
              1  Nikita            NaN  90.0      Vegetables
              2   Vinod           Milk  75.0             NaN
        Note that row labels in the series (purchase1, purchase2, purchase3) now serve as column labels.

        2.2.3 Creating DataFrame using Lists

        Pandas also allows us to create a DataFrame using a list of lists. Each sublist in the list includes the values in a
        row of the DataFrame. For example, let us create a nested list comprising information about three purchases made
        at the grocery store. To create the DataFrame purchasesDF, we pass the list purchases as an input argument
        to pd.DataFrame:
         >>> import pandas as pd
         >>>  purchases = [['Ashish', 'Bread', 22.50], ['Nikita', 'Vegetables', 90.0], ['Vinod',
         ... 'Milk', 75.0]]
         >>> purchasesDF = pd.DataFrame(purchases)
         >>> print(purchasesDF)
        output:
                      0           1     2
              0  Ashish       Bread  22.5
              1  Nikita  Vegetables  90.0
              2   Vinod        Milk  75.0
        Note  that  default  row  and  column  indexes  also  known  as  row  and  column  labels  begin  with  0.  However,  use  of
        meaningful column names, makes it easier to comprehend the data stored in a DataFrame. Pandas allows us to name
        the columns explicitly using the keyword argument columns as shown below:

         >>> import pandas as pd
         >>>  purchases = [['Ashish', 'Bread', 22.50], ['Nikita', 'Vegetables', 90.0], ['Vinod',
         ... 'Milk', 75.0]]
                purchasesDF = pd.DataFrame(purchases, columns = ['CustomerName', 'ItemPurchased',
         ... 'Cost'])
         >>> print(purchasesDF)


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