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Female;65-69;19773
                 Male;50-54;36249
                 Male;70-74;12639
                 Male;85-89;1718
                 Female;40-44;44528
                 Female;75-79;8407
                 Female;90-94;812
                 Male;5-9;63226
                 Male;20-24;66257
                 Male;45-49;42078
                 Male;60-64;25049
                 Male;0-4;59967
                 Male;10-14;65685
                 Male;15-19;67336
                 Male;35-39;54325
                 Male;40-44;47639
                 Male;65-69;19370
                 Male;90-94;568
                 Male;100+;15
                 Female;5-9;57728
                 Female;20-24;59916
                 Female;25-29;57608
                 Female;70-74;13496
                 Female;80-84;5115
                 Female;85-89;2401
               3.  Consider the following dictionary comprising students' details:
                 data = {'Name': ['Hetansh', 'Supriya', 'Mehak', 'Madhu', 'Rama'],
                         'RollNumber': [10, 11, 12, 13, 14],
                         'Grade': ['A', 'B', 'A', 'B', 'C'],
                     'Marks': [99, 82, 95, 80, 60]
                 }
                  Create a Pandas DataFrame studentDF using the above dictionary.
               4.  Consider the following Pandas DataFrame df:
                       Name         RollNumber         Grade         Marks
                 0     Hetansh     10                  A             99
                 1     Supriya     11                  B             82
                 2     Mehak        12                 A             95
                 3     Madhu        13                 B             80
                 4     Rama         14                 C             60
                  Determine the output of the following statements:
                  (i)  print(df.ndim)
                  (ii)  print(df.shape)
                  (iii)  print(df.index)
                  (iv)  print(df.columns)
                  (v)  print(df.head(5))
                  (vi)  print(df.tail(2))
                  (vii)  print(df.iloc[2])
                  (viii) print(df.loc[1,'Name'])
                  (ix)  print(df.set_index('RollNumber'))
                  (x)  print(df[df['Marks'] > 98])
                                                                             Data Handling using Pandas DataFrame  75
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