Class 12 IP Chapter 2 Python Pandas Solutions Sumita Arora New Syllabus / Uncategorized / By Neha Exercise 2.1 10. What does groupby( ) do ? ...click here for answer 1. Name some descriptive statistic functions used with dataframes. ...click here for answer 2. To calculate statistical values for each row, the axis argument’ should be _____ ? The axis ...click here for answer 3. What are quantile and quartiles ? ...click here for answer 4. What does quantile( ) do ? ...click here for answer 5. Name pivoting functions available for dataframes. ...click here for answer 6. What is the use of aggfunc argument in pivot_table( ) ? ...click here for answer 7. What does hist( ) do ? ...click here for answer 8. Name some function-application functions. ...click here for answer 9. When should pipe( ) be preferred over sandwiching of function call ? ...click here for answer Back Exercise Part A 1. What do quantile and var( ) functions do ? ...click here for answer 2. What is a quartile ? How is it different from quantile ? ...click here for answer 3. How do you create quantiles and quartiles in Python Pandas ? ...click here for answer 4. What is the use of pipe( ) ...click here for answer 5. What is Pivoting ? How is it useful ? ...click here for answer 6. Which pivoting function can work with duplicate values ? ...click here for answer 8. How useful is sorting and grouping ? ...click here for answer 7. What is the use of aggregation ? ...click here for answer 9. How is pivot_table( ) different from pivot( ) when both perform pivoting ? ...click here for answer 10. What is histogram ? How is it different from a bar chart ? ...click here for answer 11. How are apply( ) and applymap( ) functions similar and different ? ...click here for answer 12. What is the usage of creating groups ? ...click here for answer 13. How are agg( ) and transform( ) similar or different ? ...click here for answer 14. How is reindexing useful ? ...click here for answer 15. How are reindex( ) and reindex_like( ) similar and different ? ...click here for answer 16. What is the difference between reindex( ) and rename( ) functions ? ...click here for answer 17. How is reindexing and relabelling useful in dataframes ? ...click here for answer Back Exercise Part B 1. Consider dataframe wdf as shown below ...click here for answer 2. Explain what following commands are doing ? ...click here for answer 3. Write command to pipe functions sqrt( ), power( , 3), multiply 10) on dataframe wdf. ...click here for answer 4. Write command to apply( ) function np.sqrt on the dataframe wdf. ...click here for answer 5. Write command to applymap( ) function np.sqrt on the dataframe wdf. ...click here for answer 6. Both the above questions are producing the same result. Why ? ...click here for answer 7. Change the index of dataframe wdf so that its data is not lost and its row indexes change ‘R ow l’, ‘Row2’, ‘Row3’, and so on. ...click here for answer 9. Reindex dataframe wdf so that two new rows get added to it while the previous data is retained. Fill the new rows with value 10.0. ...click here for answer 10. Can you apply groupby( ) on above dataframe ndf ? Why why not ? ...click here for answer 11. Create a histogram using dataframe ndf. ...click here for answer 8. Reindex dataframe wdf so that two new columns get added to it while the previous data is retained. ...click here for answer