Computer Science 5 Marks Each

(a) Choose the right statement to get the given output:

(i) Df1.mean()

(ii) Df1.mean(axis=1)

(iii) Df1.average()

(iv) Df1.median()

(b) Write the command to get the given output:

(i) Df1.sort(by=‘City’)

(ii) Df1.sort_values(‘City’)

(iii) Df1.sort_values(by=‘City’)

(iv) Df1.sort_values(by==‘City’)

(c) Choose the right statement to get given output:

(i) Df1.desc()

(ii) Df1.statistics()

(iii) Df1.desctibe()

(iv) Df1.showall()

(d) Chose the right function to fill in given statement to make the city as index value:

Df1._________________(‘City’,inplace=True)

(i) Df1.set_index(‘City’,inplace=True)

(ii) Df1.index('City',inplace=True)

(iii) Df1.new_index(‘City ‘,inplace=True)

(iv) Df1.reset_index(‘City’,inplace=True)

(e) Which Pandas command is used to rename the columns & index name of the above dataframe

(i) Df1.renamecolumns()

(ii) Df1.Rename()

(iii) Df1.rename()

(iv) Df1.indexrename()

(a) Select the command from the given options that will give the following output:-

(i) print(emp.max)

(ii) print(emp.max(axis=1))

(iii) print(emp.max,axis=1)

(iv) print(emp.max())

(b) A manager wants to know the Favourite colour of the employee with the employee code 103. Help him to identify the correct set of statements from the given options:

(i) df1=emp[emp[‘Ecode’]==103]

print(df1)

(ii) df1=emp['Ecode'==103]

print(df1)

(iii) df1=emp[emp.Ecode=103]

print(df1)

(iv) df1=emp[emp.Ecode==103]

print(df1)

(c) Which of the following statement will give the names of the employees whose salary is more than 40000.

(i) print(emp.max())

(ii) print(emp[emp["Salary"]>40000])

(iii) print(emp["Salary"]>40000)

(iv) print(emp.max()>40000)

(d) Which of the following command will list only the columns Ename and Salary using loc:

(i) print(emp.loc[:,[0,2]]

(ii) print(emp.loc[:,["Ename","Salary"]])

(iii) print(emp.loc(:["Ename","Salary"]))

(iv) print(emp.loc[["Ename","Salary"]])

(e) Mr. Singh, the manager wants to add a new column, the Rank with the values ‘IV’, ‘II’, ‘III’, ‘IV’, ‘I’, to the data frame Help him to identify the right command from the followings to do so :

(i) emp.column=[‘IV’, ‘II’, ‘III’, ‘IV’, ‘I’ ]

(ii) emp.iloc["Rank"] =[‘IV’, ‘II’, ‘III’, ‘IV’, ‘I’]

(iii) emp["Rank"] =[‘IV’, ‘II’, ‘III’, ‘IV’, ‘I’ ]

(iv) None of the above

Answer the following questions:

(i) To create the data frame for the above dataset.

(ii) To join the data frames.

(iii) To count the the rows in new data frame.

(iv) To reset the index.

(i) To print the maximum salary of the Total Salary column.

(ii) To print the data in ascending order of Total Salary.

(iii) To print the data in descending order of Total Salary.

(iv) To print 'sum','mean','max','min','count','median','var'fro the columns 'SALARY','tax','Total Salary'.

(i) To print the complete dataframe Name wise.

(ii) To print the dataframe Name wise, Rep waise and Manage wise.

(iii) To print the data frame Manager and Rep wise.

(iv) To print the data frame Manager and Rep price wise

(v) To print the sum of the price, manager and rep wise.

(vi) To print the mean and count of the price which belong to each manager and rep.

(vii) To print the sum of the price which belong to each manager and rep.

(viii) To print the sum of the price which belong to each manager and rep along with Product belongs to them. Fill the NaN with 0.

(i) To count the rows in the dataset

(ii) What was the longest phone call / data entry?

(iii) How many seconds of phone calls are recorded in total?

(iv) How many entries are there for each month?

(v) To print the group key

(vi) To count the group keys

(vii) Get the first entry for each month

(viii) Get the sum of the durations per month

(ix) Get the number of dates / entries in each month

(x) What is the sum of durations, for calls only, to each network

(xi) How many calls, sms, and data entries are in each month?

(xii) How many calls, texts, and data are sent per month, split by network_type?

(xiii) Group the data frame by month and item and extract a number of stats from each group

(xiv) Group the data frame by month and item and extract a number of stats from each group

DataFrame: dfzoo

(i) Counting all the animals

(ii) Count the number of animals in zoo

(iii) To print the sum of the water need of an animals.

(iv) To print the sum of all values.

(v) To print the sum of only numeric values.

(vi) To print the minimum values of water need.

(vii) To print the mean values of water need.

(viii) To print the median values of water need.

(ix) To print the animal wise means value.

(x) To print the mean value water need of an each animal.