# Class 12 Informatics Practices Data Handling using Python Pandas-I MCQs Set 2

### Class 12 Informatics Practices

#### Data Handling Using Pandas – I MCQs

##### Topics Covered : Series

21. A Series is a one-dimensional array containing a sequence of values of _________ data type

a. int

b. float

c. string

d. any (int, float, list, string)

Answer: d. any (int, float, list, string)

22. Series is having by default numeric data labels. The value of data labels is starting from _________ and ended at number of values – 1.

a. 0

b. 1

c. -1

d. n

23. The data label associated with a particular value is called its _______.

a. Item

b. Value

c. Column

d. Index

24. Can we assign values of other data types as index to a Series object.

a. Yes

b. No

c. Yes, but only numeric types

d. Yes, but only numeric and boolean types.

25. The given figure, is an example of _________.

a. Series

b. DataFrame

c. Array

d. List

26. A series can be created by using ___________.

a. Scalar Value

b. List

c. NumPy Array

d. Dictionary

e. All of these

27. In the given code , a series is created from ______ values.

>>> ser1 = pd.Series(20, index = range(4))

a. Scalar Value

b. List

c. NumPy Array

d. Dictionary

28. In Series, Index can be specified ____________.

a. explicitly

b. implicityly

c. Both a and b

d. None of these

Answer: c. Both a and b

29. If You do not explicitly specify an index for the data values, then by default indices range from ______ through _______.

a. 1 , N – 1

b. 0, N

c. 0, N – 1

d. 1, N

30. In the given code , Identify the value of Indices.

>>> ser1 = pd.Series(20, index = range(4))

a. 1, 2, 3, 4

b. 0, 1, 2, 3

c. 1, 2, 3

d. 0, 1, 2

Answer: b. 0, 1, 2, 3

31. Can you assign user-defined labels to the index and use them to access elements of a Series.

a. Yes

b. No

32. In the given code , a series is created from ______ values.

>>> ser1 = pd.Series([20, 16, 76, 34] )

a. Scalar Value

b. List

c. NumPy Array

d. Dictionary

33. Can you use strings or letters as Indices in Series?

a. Yes

b. No

34. Which argument can we use in the Series( ) method to specify the index values explicitly?

a. INDEX

b. Index

c. index

d. indices

35. Identify the data type of index in the given code?

>>> ser1 = pd.Series([2,6,4,8], index = [1,2,5,6])

a. int64

b. float64

c. string

d. object

36. Identify the data type of index in the given code?

>>> ser1 = pd.Series([2,6,4,8], index = [1.5, 2.6, 5.8, 6.4 ] )

a. int64

b. float64

c. string

d. object

37. Identify the data type of index in the given code?

>>> ser1 = pd.Series( [2, 6, 4, 8 ] , index = [True, False, False, True] )

a. integer

b. boolean

c. string

d. object

38. Identify the data type of index in the given code?

>>> ser1 = pd.Series( [2, 6, 4, 8 ] , index = [‘Ram’, ‘Amrit’, ‘Tanmay’, ‘Anjeev’ ] )

a. integer

b. boolean

c. string

d. object

39. Identify the data type of index in the given code?

>>> ser1 = pd.Series( [2, 6, 4, 8 ] , index = [ 1 , ‘A’, True, 5.9 ] )

a. integer

b. float

c. string

d. object

40. An element of Series can be access by _________.

a. Index

b. Value

c. Both a and b

d. None of these