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7.  The read_csv() method in Pandas with the na_values option set to a specific value or a list of values
               may be used to read missing values as NaN (Not a Number).                                _________
           8.  Grouping and aggregation in Pandas DataFrame can be performed using the groupby() function
              along with aggregate functions like sum(), mean(), etc.                                   _________

        C.  Fill in the blanks.
           1.  The _______________ function in Pandas DataFrame provides statistical summary metrics such as count, mean, standard
              deviation, minimum, and quartiles.
           2.  The unique() method of Pandas DataFrame returns _______________ values found in a specified column.
           3.  To remove a column from a Pandas DataFrame, use the drop() method with the relevant column name and the axis
              argument set to _______________.
           4.  To rename a column named "OldCol" to "NewCol" in a DataFramedf, we can use the syntax df = df. _____________
              ({'OldCol': 'NewCol'}, axis=1).
           5.  _______________  keyword  argument  should  be  set  to  all  in  describe()  method  to  find  summary  statistics  for
              non-numeric columns.
           6.  Argument axis should be set to value _______________ to apply the operation row-wise across column.
           7.  Pandas allows us to set an attribute as an index using the method _______________.
           8.  Pandas method _______________ can be used to drop a column or row from the DataFrame.
           9.  In Pandas DataFrame, grouping and aggregation may be accomplished by using the _______________ method, followed by
              an aggregate function such as sum(), mean(), or count().
          10.  To write the contents of a DataFramedf to a CSV file named 'details.csv', we can use the syntax df. _____________
              ('details.csv').
        D.  Answer the following questions:
           1.  Consider the following four series, each of them comprising the details of a student:
              student1 = pd.Series(['Seema', 10, 'A', 99])

              student2 = pd.Series(['Supriya', 11, 'B', 82])
              student3 = pd.Series(['Mehak', 12, 'A', 95])
              student4 = pd.Series(['Madhu', 13, 'B', 80])
              Using the above four series, create a DataFrame with four columns namely, Name, RollNumber, Grade, and Marks.
           2.  Create a csv file with the following data and read the content in a Pandas DataFrame. Also, write statements to display the
              number of rows,  the number of columns, and  column labels:
               Gender;Age group;Population
              Male;25-29;63129
              Male;30-34;59247
              Male;55-59;30768
              Male;80-84;3944
              Female;0-4;55340
              Female;30-34;54832
              Female;45-49;39938
              Female;60-64;25038
              Female;100+;23
              Female;15-19;61074
              Female;95-99;176
              Male;75-79;7166
              Male;95-99;119
              Female;10-14;59621
              Female;35-39;50436
              Female;50-54;34835
              Female;55-59;29953


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