This post contains Multiple Choice Questions with answers for Data handling class 12 MCQ. This is based on syllabus designed by CBSE for Informatics Practices Class 12. Data handling majorly covers three topics:
Python Series
Python Dataframe
Plotting with pyplot
Python pandas is the library which is mainly used for data handling.
Contents
Data handling class 12 mcq Set-4 (Q-76 to 100)
- What will be shape of given dataframe?
5 4 3 2 9
6 4 6 3 7
a. (5,2)
b. (2,5)
c. (10,)
d. (2,)
Show Answer
b. (2,5)/p>
- Which of the following is correct statement?
a. inplace argument of rename function is set to False, than original dataframe is changed with new index/columns.
b. del statement of dataframe can be used to delete rows.
c. When a dataframe object is created, all the columns are sorted automatically.
d. While specifying your own index sequence in DataFrame() function, Python doesn’t care about length of index.
Show Answer
c. When a dataframe object is created, all the columns are sorted automatically.
- Which of the following is correct syntax of using DataFrame() for defining a dataframe?
a. pandas.DataFrame(Data, index, columns, dtype, copy)
b. pandas.DataFrame(Data, index, columns, dtype)
c. pandas.DataFrame(Data, index, columns)
d. b and c
e. All of above
Show Answer
e. All of the above
- Find the output of the following code:
df = pd.DataFrame({‘soap’: pd.Series([23,45,34],[‘A’,’B’,’C’]), ‘salt’ : pd.Series([11,23],[‘A’,’B’]), ‘sugar’:pd.Series([20,54],[‘C’,’D’])})
print(df)
a. soap salt sugar
A 23.0 11.0 NaN
B 45.0 23.0 NaN
C 34.0 NaN 20.0
D NaN NaN 54.0
b. A B C D
soap 23.0 45.0 34.0 NaN
salt 11.0 23.0 NaN NaN
sugar NaN NaN 20.0 54.0
Show Answer
a. soap salt sugar A 23.0 11.0 NaN B 45.0 23.0 NaN C 34.0 NaN 20.0 D NaN NaN 54.0
- What will be output of
print(df.loc[:])
a. Display ‘Error’
b. Display all rows
c. Display all columns
d. Display all rows and columns
Show Answer
b. Display all rows
- Identify the correct statement:
a. A dataframe can only store homogeneous elements
b. empty of dataframe counts NaN or NA values
c. The index of dataframe can be number, letter or string.
d. Size of dataframe returns total no of rows.
Show Answer
c. The index of dataframe can be number, letter or string.
- To access the a value of dataframe using row labels we can use-
a. at
b. loc
c. iat
d. iloc
Show Answer
a. at
- given a dataframe ‘df’ as shown below:
rollno name marks
0 1 ram NaN
1 2 sham 45
2 3 hari 56
3 4 krishna 49
What will output of following code?
df.count()
a) rollno 4 b) 12 c) 4 d) rollno 4 name 4 name 4 marks 3 marks 4
Show Answer
a) rollno 4 name 4 marks 3
- Considering following dataframe ‘class’ and state which of the following is not True?
boys girls subject awards class1 24 16 5 8 class2 20 20 5 10 class3 18 33 6 13 class4 21 19 6 9
a. class.loc[‘class1’, : ]
b. class.loc[ : , ‘boys’]
c. class.loc[‘class1’]
d. class.loc[‘boys’]
Show Answer
d. class.loc[‘boys’]
- Which is true
a. Pandas supports non-unique index values.
b. Pandas supports label based indexing
c. Pandas used range method for implicit indexing.
d. All are true
Show Answer
d. All are true
- A dataframe can be thought of as dictionary of list/series.
a. True
b. False
Show Answer
a. True
- Which of the following is not parameter of append() method used to merge two dataframes?
a. Axis
b. Sort
c. Verify_intigrity
d. Ignore_index
Show Answer
a. axis
- When a dataframe is created using dictionary of any sequence such as series or list, the resulting index or row labels are ________ of all indexes or labels.
a. Union
b. Intersection
c. Product
d. Sum
Show Answer
a. union
- For a dataframe df[:] = 0 will
a. Create result set with elements having data equal to 0
b. Assign 0 to all its elements
c. Display value of first row and column
d. Assign value of first row and column
Show Answer
a. Create result set with elements having data equal to 0
- For a given dataframe
coachid name rank A 1 Jack 12 B 2 Kim 3 C 3 Rajan 4 D 4 Juber 11 E 5 Hanuma 2
When Raja is trying to rename indexes using following code
df.rename(index=[‘a’,’b’,’c’])
he is getting error. Help him to select correct command to rename columns
a. df.rename(index=[‘a’,’b’,’c’], column = {})
b. df.rename(index={‘A’:‘a’,’B’:’b’,’C’:’c’})
c. df.rename(index=[‘A’=‘a’,’B’=’b’,’C’=’c’])
d. df.rename(index=(‘a’,’b’,’c’))
Show Answer
b. df.rename(index={‘A’:‘a’,’B’:’b’,’C’:’c’})
- find output of given code
d1 = {‘name’: [‘jai’,’veer’,’shera’], ‘code’ : np.array([102,448,504]), ‘gender’:’m’}
df6 = pd.DataFrame(d1, index = d1[‘code’])
print(df6))
a. name code gender 102 jai 102 m 448 veer 448 m 504 shera 504 m b. 102 448 504 name jai veer shera code 102 448 504 gender m m m
Show Answer
name code gender 102 jai 102 m 448 veer 448 m 504 shera 504 m
- In boolean Indexing we can filter data in ______ ways.
a. 1
b. 2
c. 3
d. many
Show Answer
b. 2
- To merge two dataframes df1 and df2 with no duplicate row labels, which command should be executed?
a. df1.append(df2, ignore_index=True)
b. df1.append(df2,ignore_index= False)
c. df1.append(df2, verify_intigrity = True)
d. df1.append(df2, verify_intigrity = False)
Show Answer
c. df1.append(df2, verify_intigrity = True)
- For a dataframe with columns eno, ename and salary, shilpa want to add a new column hra which will store 20% of salary. Help her to do this.
a. df[‘hra’] = df[‘salary]*0.2
b. df.hra = df.salary * 0.2
c. df[‘hra’ = df[‘salary’]*0.2]
d. None
Show Answer
a. df[‘hra’] = df[‘salary]*0.2
- The term used to represent row labels in a dataframe is
a. index
b. column
c. row
d. field
Show Answer
a. index
- (a) A dataframe can be created using 2D dictionary whether its inner dictionary have matching keys or not.
(b) NaN values are automatically filled for values of non-matching keys of inner keys of dictionary used in creating dataframe.
a. (A) is False but (R) is True
b. (A) is True but (R) is False
c. Both are True but (R) is not correct explanation of (A)
d. Both are True and (R) is correct explanation of (A)
Show Answer
c. Both are True but (R) is not correct explanation of (A)
- Consider the following dataframe df created
bcode bname rate 0 101 c++ 270 1 102 java 430 2 103 python NaN 4 104 dbms 380 5 105 mysql 320
Sujeet found that rate of python is missing. So he thought to fill it with 500rs. what command he should write to do so?
a. df.replacena(500)
b. df.fillna = 500
c. df.fillna(500)
d. df.replacena = 500
Show Answer
c. df.fillna(500)
- Consider the following dataframe df created
schools hospitals gym jaipur 120 30 5 raipur 130 34 4 nagpur 158 57 12 kanpur 144 48 9
find the sum of all the columns as given below:
jaipur 155
raipur 168
nagpur 227
kanpur 201
a. df.sum()
b. df.sum(axis = 0)
c. df.sum(axis = 1)
d. df.sum
Show Answer
c. df.sum(axis = 1)
- Anil has created a dataframe df which he wants to manage according to given questions.
EmpNo Name Salary 0 E01 raj 1000 1 E02 Sohail 1200 3 E03 Sameer 1800 4 E04 Kunal 1600 5 E05 John 1400
He wants to arrange and display data based on Name in descending order. What code he should write?
a. df.sort_values(by = ‘name’, ascending = False)
b. df.sort_values(by = ‘name’, asc = False)
c. df.sort_values(ascending = False)
d. df.sort_values(‘name’,asc = False)
Show Answer
a. df.sort_values(by = ‘name’, ascending = False)
- Anil has created a dataframe df which he wants to manage according to given questions.
EmpNo Name Salary 0 E01 raj 1000 1 E02 Sohail 1200 3 E03 Sameer 1800 4 E04 Kunal 1600 5 E05 John 1400
He wants to find all the records with salary less than 1500. He should write?
a. df.salary(data <=1500)
b. df[df.salary <=1500]
c. df[‘salary’<=1500]
d. None of the above
Show Answer
b. df[df.salary <=1500]