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c.  dfFilledNaN = df.fillna(76)
                    print("DataFrame after filling NaN with 76:")
                    print(dfFilledNaN)
                    print("\n")
                  d.  dfSetIndex = df.set_index("Name")
                    print("DataFrame with index set to Name:")
                    print(dfSetIndex)
                    print("\n")
                  e.  averageMarksPerStudent = df.groupby(["Name", "Degree"])["Score"].mean()
                    print("Name and degree-wise average marks of each student:")
                    print(averageMarksPerStudent)
                    print("\n")
                  f.  numStudentsInMBA = df[df["Degree"] == "MBA"].shape[0]
                    print("Number of students in MBA:", numStudentsInMBA)
                    print("\n")
                  g.  modeMarksBCA = df[df["Degree"] == "BCA"]["Score"].mode().values
                    print("Mode marks in BCA:", modeMarksBCA)

































                                                           Answers

              Multiple Choice Questions
              1. (a)     2. (b)      3. (a)     4. (c)     5. (a)      6. (c)     7. (a)     8. (a)
              9. (a)     10. (a)
              True or False
              1. (T)   2. (F)    3. (F)    4. (T)   5. (F)   6. (T)    7. (F)    8. (T)    9. (F)   10. (T)

              Fill in the blanks
              1. pd.DataFrame()            2. pd.read_csv()     3. list / array             4. df.head(n)
              5. header                    6. loc               7. index                    8. shape
              9. dtypes                    10. skiprows


                                                                             Data Handling using Pandas DataFrame  89
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