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8.  We can use the following statements to retrieve those columns of DataFrame df whose names
              begining with letter 'A':                                                                 _________
              mask = df.columns.str.startswith('A')
              data = df.loc[:, mask]
           9.  In the method read_csv(), we set the input argument index_col=1 to set the first column as
              the row label.                                                                            _________
          10.  We can explicitly specify the column delimiter using either the delimiter or sep keyword
              argument while invoking the read_csv() function.                                          _________

        C.  Fill in the blanks.
           1.  We can use the _________ function to create a Pandas DataFrame using a dictionary.
           2.  To read a CSV file into the DataFrame, we can use _________ function.
           3.  To retrieve multiple columns from a DataFrame, we can provide a _________ containing the column names inside square
              brackets after the DataFrame.
           4.  The function _________ can be used to retrieve first few (say, n) rows of the DataFrame.
           5.  The keyword argument _________ of pd.read_csv() can be set to None to indicate that there is no header row
              comprising column names in the CSV file to be read.
           6.  To slice rows and columns in a DataFrame using label-based indexing, we can use the _________ attribute.
           7.  To set row labels in a DataFrame, we can use the _________ attribute of the DataFrame.
           8.  The _________ attribute of the DataFrame returns a tuple comprising the number of rows and columns.
           9.  The _________ attribute is used to retrieve the type of objects in various columns of a DataFrame.
          10.  The keyword argument _________ of the method pd.read_csv() can be used to specify the list of irrelevant rows.

        D.  Answer the following questions:
           1.  What is the difference between loc and iloc attribute of Pandas DataFrame?
         Ans.   The loc method is used to access elements in a Pandas DataFrame using label-based indexing, while the iloc method is used
              to access elements in a Pandas DataFrame using integer positional indexing.

           2.  Consider the following dictionary-based data:

              data = {
                  'Name': ['Rohan', 'Jasmine', 'Mohit', 'Anshika'],
                  'Age': [25, 30, 28, 32],
                  'Gender': ['M', 'F', 'M', 'M'],
                  'Height': [175, 160, 180, 165],
                  'Weight': [70, 55, 80, 60]
              }
              Write a statement to create a Pandas DataFrame df using the above dictionary. Also write a statement to display the
              contents of the dataframe.

         Ans.  df = pd.DataFrame(data)
              print(df)

           3.  Consider the following Pandas DataFrame df:
                     Name         Age        Gender       Height      Weight
               0     Rohan        25           M           175          70
               1     Jasmine      30           F           160          55
               2     Mohit        28           M           180          80
               3     Anshika      32           F           165          60




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