DataFrame (2 Marks Questions)

1. Consider DataFrame df as shown below :



	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

• Write command to calculate minimum value for each of the row from subset of dataframe that contains age, weight, height, runsscored
• Write command to calculate mean for last 3 rows.




Consider the DataFrame df from above question and write the command for the following: 




• Find the name of player with minimum age
• Add a new column BMI to df with value of BMI calculated as (BMI = weigth/height²).




Consider the DataFrame:

dfd = pd.DataFrame({'Name':['Saourabh','Ram','Shyam'],'Percentage':[80,85,82]})
Write the commands for (The output should display a single value): 
i) Count the number of Names in dfd
ii) Find the minimum percentage Marks 




What is use of reindex_like() function explain with example and Output.




What will be the output of the following python code:

import pandas as pd
import numpy as np
d = {'Student':['Ali','Ali','Tom','Tom'],\
    'House':['Red',Red,'Blue',Blue’],\
    'Points':[50,70,60,80]}
df =pd.DataFrame(d)
df1 = df.pivot_table(index='Student',columns='House',values=’Points’,aggfunc=np.sum)
print(df1)

2. Write a python statement to fill in the blanks so that the given output of is achieved:

   import pandas as pd
   import numpy as np
   d = {'Rollno':[101,102,103,104],
        'ECO':[70,80,50,80],'BST':[60,50,60,90]}
   df = pd.DataFrame(d)
   df1 = ___________________________
   print(df1)
   
   Output:
   Rollno         410
   ECO            280
   BST            260
   dtype:    int64
   



3. Write a python code to create a dataframe with appropriate headings from the list given below :
   ['S101', 'Amy', 70], ['S102', 'Bandhi', 69], ['S104', 'Cathy', 75], ['S105','Gundaho', 82]
4. How does dataframe specify indexes to its data rows?
5. if a datframe has following values the what will be the output of different statements:

   	Max speed	shield
   Viper	1	2
   cobra	4	5
   Sidevinder	7	8

• df.loc['viper']
• df.loc['cobra':'viper', 'max_speed']