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>>> import pandas as pd
             >>>  accountsData = {'Date': ['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04',
             ... '2022-01-05'],'Sales': [10000, 20000, 15000, 12000, 13000],
             ... 'Expenses': [5000, 7500, 6000, 8000, 9000]}
            Now we convert the above dictionary to a DataFrame using the method pd.DataFrame():

             >>> accountsDF = pd.DataFrame(accountsData)
             >>> print(accountsDF)
            output:
                          Date  Sales  Expenses
                 0  2022-01-01  10000      5000
                 1  2022-01-02  20000      7500
                 2  2022-01-03  15000      6000
                 3  2022-01-04  12000      8000
                 4  2022-01-05  13000      9000
            Note that the keys of the dictionary act as column labels. For each key (acting as a label), the values in the list appear
            in the corresponding column in the DataFrame. Thus, the resulting DataFrame has three columns: Date, Sales, and
            Expenses as shown above. Note that row indexes are labeled as 0, 1, 2, ... .
            Let us consider the example of a grocery shop. The shop owner wants to keep track of the following details of the
            purchases  made  by  the  cutomers:  customer's  name,  item  purchased,  and  the  cost  of  purchase.  Currently,  this
            information is stored in the dictionary purchases. To facilitate further analysis, we transform this dictionary into the
            Pandas DataFrame (puchasesDF):
             >>> import pandas as pd
             >>> purchases = {'CustomerName':['Ashish', 'Nikita', 'Vinod'],
                              'ItemPuchased':['Bread', 'Vegetables', 'Milk'],
                              'Cost':[22.50, 90.00, 75.00]}
             >>> purchasesDF = pd.DataFrame(purchases)
             >>> print(purchasesDF)
            output:
                   CustomerName ItemPuchased  Cost
                 0       Ashish        Bread  22.5
                 1       Nikita   Vegetables  90.0
                 2        Vinod         Milk  75.0
            Note that the text in the columns- CustomerName and ItemPurchased is right-aligned. This is the default style in
            Pandas.

            2.2.2 Creating DataFrame using Series

            While constructing the purchasesDF, we assumed that the information is available in the form of a dictionary. Now
            let us suppose that the information about each purchase is available in the form of a series as shown below:
             >>> import pandas as pd
             >>> purchase1 = pd.Series({'Name': 'Ashish',
                                         'Item Purchased': 'Bread',
                                         'Cost': 22.50})
             >>> purchase2 = pd.Series({'Name': 'Nikita',
                                         'Items Purchased': 'Vegetables',
                                         'Cost': 90.00})
             >>> purchase3 = pd.Series({'Name': 'Vinod',
                                         'Item Purchased': 'Milk',
                                         'Cost': 75.00})
             >>> print('\nPurchase 1:\n',purchase1)
            output:
                 Purchase 1:
                 Name              Ashish

                                                                             Data Handling using Pandas DataFrame  31
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