CBSE Class 11 & 12 Computer Science and Informatics Practices Python Materials, Video Lecture

1. Consider the DataFrame df below and write the command for the following:

2.  name age weight height runsscored 0 mayur 15 51 5.1 55 1 anil 16 48 5.2 25 2 viraj 17 49 5.1 71 3 viraj 17 51 5.3 53 4 mahesh 16 48 5.1 51 5 viraj 17 59 5.3 50

•  Find the minimum height of viraj using pivot table.
•  Print the name of players in the ascending order of their total run scored.

3. Consider the DataFrame df below and write the command for the following:
4.  name age weight height runsscored 0 mayur 15 51 5.1 55 1 anil 16 48 5.2 25 2 viraj 17 49 5.1 71 3 viraj 17 51 5.3 53 4 mahesh 16 48 5.1 51 5 viraj 17 59 5.3 50
• Increase weight of all the players with 10kg.
• Find all the players whose weight is more than 50kg.
• Find the players whose runscored is less than average of all the employees.

5. Consider DataFrame df as shown above: (command should be single line command)
• a) Write command to calculate minimum value for each of the row from subset of dataframe that contains age, weight, height, runsscored.
• b) Write command to calculate sum of score for rows from 2 to 4.
• c) Find the name of player with runscored is more than 70.
• d) Find the name of players whose weight is more than the average weight of the team.

6. Write the code in pandas to create the following dataframes :
```df1     df2
mark1 mark2   mark1 mark2
0 10  15   0  30   20
1 40  45    1  20   25
2 15  30   2  20   30
3 40  70   3  50   30
```
Write the commands to do the following operations on the dataframes given above :
• To add dataframes df1 and df2.
• To subtract df2 from df1
• To rename column mark1 as marks1in both the dataframes df1 and df2.
• To change index label of df1 from 0 to zero and from 1 to one.
7. Assume data frame DF that contains Data about Payment of parking area with points ‘A1’, ‘A2’, ‘A3’, ‘A4’, ‘A5’ S indexers shown below. give the output of any 4 questions from (i) to (v).
```      Car type   Car Name   Parking Slot  Payment
A1  Hatch Back      i10      Block A       2000
A2       Sedan     Ciaz      Block B       2300
A3         MPV    Omini      Block D       2500
A4         SUV  Scorpio      Block C       3000```
i) df.CarName ii) df['Status'] = ['Paid', 'Unpaid', 'Paid', 'Unpaid'] iii) df.loc['A1':'A3', "Payment"] iv) df.sort_values("Payment", ascending = False) v) df.drop('A4', axis=0)