# 55 most important Python Numpy mcq questions and answers

## Python Numpy mcq questions and answers

55 most important Python Numpy mcq questions and answers are given below. These Numpy multiple choice questions include both theory and coding questions based on Numpy. We are also planning to add Numpy mcq questions and answers pdf download link. NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. We have added mcq on Pandas and Python mcq questions and PySpark.

Q.1. NumPY stands for?
A : Numbering Python
B : Number In Python
C : Numerical Python
D : None Of the above

C : Numerical Python.

Q.2. NumPy is often used along with packages like?
A : Node.js
B : Matplotlib
C : SciPy
D : Both B and C

NumPy is often used along with Matplotlib and SciPy packages.

Q.3. The most important object defined in NumPy is an N-dimensional array type called?
A : ndarray
B : narray
C : nd_array
D : darray

A: ndarray. The most important object defined in NumPy is an N-dimensional array type called ndarray.

4. What will be output for the following code?

import numpy as np
a = np.array([1,2,3])
print a
Q.

A : [[1, 2, 3]]
B : 
C : [1, 2, 3]
D : Error

C: [1, 2, 3].

Explanation:

The code creates a one-dimensional NumPy array using the np.array() function with the input [1,2,3].

Then, the print statement is used to output the value of the array “a”.

Since “a” is a one-dimensional array, the output will be [1, 2, 3].

5. What will be output for the following code?

import numpy as np
a = np.array([1, 2, 3], dtype = complex)
print a
Q.

A : [[ 1.+0.j, 2.+0.j, 3.+0.j]]
B : [ 1.+0.j]
C : Error
D : [ 1.+0.j, 2.+0.j, 3.+0.j]

[[ 1.+0.j, 2.+0.j, 3.+0.j]]

D: [ 1.+0.j, 2.+0.j, 3.+0.j].

Explanation:

The code creates a one-dimensional NumPy array using the np.array() function with the input [1, 2, 3] and data type complex.

Then, the print statement is used to output the value of the array “a”.

Since “a” is a one-dimensional array of complex numbers, the output will be [ 1.+0.j, 2.+0.j, 3.+0.j], where the “j” indicates that the numbers are complex.

Q.6. Which of the following statement is false?
A : ndarray is also known as the axis array.
B : ndarray.dataitemSize is the buffer containing the actual elements of the array.
C : NumPy main object is the homogeneous multidimensional array
D : In Numpy, dimensions are called axes

ndarray is also known as the axis array.

Q.7. If a dimension is given as ____ in a reshaping operation, the other dimensions are automatically calculated.
A : Zero
B : One
C : Negative one
D : Infinite

C: Negative one.

Q.8. Which of the following sets the size of the buffer used in ufuncs?
A : bufsize(size)
B : setsize(size)
C : setbufsize(size)
D : size(size)

C: setbufsize(size).

9. What will be output for the following code?

import numpy as np
dt = dt = np.dtype(‘i4’)
print dt
Q.

A : int32
B : int64
C : int128
D : int16

A: int32.

Q.10. Each built-in data type has a character code that uniquely identifies it.What is meaning of code “M”?
A : timedelta
B : datetime
C : objects
D : Unicode

B: datetime.

Q.11. Numpy developed by?
A : Guido van Rosum
B : Travis Oliphant
C : Wes McKinney
D : Jim Hugunin

B : Travis Oliphant

Q.12. Which of the following Numpy operation are correct?
A : Mathematical and logical operations on arrays.
B : Fourier transforms and routines for shape manipulation.
C : Operations related to linear algebra.
D : All of the above

A: All of the above are correct. Numpy provides a wide range of mathematical, logical, and linear algebra operations, as well as Fourier transforms and routines for shape manipulation.

Q.13. The basic ndarray is created using?
A : numpy.array(object, dtype = None, copy = True, subok = False, ndmin = 0)
B : numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)
C : numpy_array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)
D : numpy.array(object, dtype = None, copy = True, order = None, ndmin = 0)

numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)

14. What will be output for the following code?

import numpy as np
a = np.array([1, 2, 3,4,5], ndmin = 2)
print a
Q.

A : [[1, 2, 3, 4, 5]]
B : [1, 2, 3, 4, 5]
C : Error
D : Null

A : [[1, 2, 3, 4, 5]]

Q.15. What is the syntax for dtype object?
A : numpy.dtype(object, align, copy, subok)
B : numpy.dtype(object, align, copy)
C : numpy.dtype(object, align, copy, ndmin)
D : numpy_dtype(object, align, copy)

numpy.dtype(object, align, copy)

Q.16. Which of the following function stacks 1D arrays as columns into a 2D array?
A : row_stack
B : column_stack
C : com_stack
D : All of the above

B : column_stack

Q.17. Which of the following statement is true?
A : Some ufuncs can take output arguments.
B : Broadcasting is used throughout NumPy to decide how to handle disparately shaped arrays
C : Many of the built-in functions are implemented in compiled C code
D : The array object returned by __array_prepare__ is passed to the ufunc for computation.

The array object returned by __array_prepare__ is passed to the ufunc for computation.

Q.18. Which of the following set the floating-point error callback function or log object?
A : settercall
B : setterstack
C : setter
D : callstack

C : setter

19. What will be output for the following code?

import numpy as np
dt = np.dtype([(‘age’,np.int8)])
a = np.array([(10,),(20,),(30,)], dtype = dt)
print a[‘age’]
Q.

A : [[10 20 30]]
B : [10 20 30]
C : 
D : Error

B : [10 20 30]

Q.20. What is the range of uint32 data type?
A : (-2147483648 to 2147483647)
B : (-32768 to 32767)
C : (0 to 65535)
D : (0 to 4294967295)

D : (0 to 4294967295)

## numPY and Pandas mcq questions

Q.21. What will be printed?

import numpy as np
a = np.array([1,2,3,5,8])
b = np.array([0,3,4,2,1])
c = a + b
c = c*a
print (c)

A : 7
B : 12
C : 10
D : 21

D : 21

Q.22. What will be output for the following code?

import numpy as np
ary = np.array([1,2,3,5,8])
ary = ary + 1
print (ary)

A : 0
B : 1
C : 2
D : 3

D : 3

Q.23. What will be output for the following code?

import numpy as np
a = np.array([[1,2,3],[0,1,4]])
print (a.size)

A : 1
B : 5
C : 6
D : 4

C : 6

Q.24. What will be output for the following code?

import numpy as np
a = np.array([1,2,3,5,8])
print (a.ndim)

A : 0
B : 1
C : 2
D : 3

B : 1

Q.25. What will be output for the following code?

import numpy as np
a = np.array([[1,2,3],[0,1,4]])
b = np.zeros((2,3), dtype=np.int16)
c = np.ones((2,3), dtype=np.int16)
d = a + b + c
print (d[1,2] )

A : 5
B : 7
C : 3
D : 4

A : 5

Q.26. What is the correct code to install numpy in the linux system containing python3?

A : pip numpy install python3
B : pip3 install numpy
C : pip install numpy
D : python3 pip3 numpy install

B : pip3 install numpy

Q.27. What is the correct code to install numpy in the windows system containing python3?

A : pip3 install numpy
B : pip install numpy
C : python3 install numpy
D : none of above

B : pip install numpy

Q.28. fetch numpy as np
np.array(list)
Is it true to import numpy module like this?

A : Yes, true
B : Syntax Error

A : Yes, true

Q.29. import numpy as np
np.array(list)
Is it true to import numpy module like this?

A : Yes, true
B : Not, true
C : Syntax Error
D : All of above

A : Yes, true

Q.30. How to import numpy module?

A : from numpy import *
B : import numpy
C : import numpy as my_numpy
D : import numpy as np
E : all of above

D : import numpy as np

Q.31. What is zero() function in numpy use to?

A : make a matrix with first column 0
B : make a matrix with all elements 0
C : make a matrix with diagonal elements 0
D : All of the above

make a matrix with all elements 0

Q.32. What does it do?
array.min()

A : finds the maximum number in numpy array
B : finds the minimum number in numpy array
C : makes operation of minus if x < 100 D : answers B & C

finds the minimum number in numpy array

Q.33. Is the following statement true?
numpy array can be converted to the list in python3?

A : false
B : true
C : can’t say
D : None of the above

B : true

Q.34. Choose the true properties of nd-array as.

A : fast and flexible container for large datasets in python
B : Homogeneous data i.e. all of the elements must be the same type
C : None of the above
D : Both A & B

D : Both A & B

Q.35. Regarding creating ndarray, choose the build in functions in numpy.

A : np.array()
B : np.zeros()
C : np.empty()
D : np.arange()
E : All of the above

E : All of the above

Q.36. What are the attributes of numpy array?

A : shape, dtype, ndim
B : objects, type, list
C : objects, non vectorization
D : Unicode and shape

A: sum(), any(), np.type() are not all relevant methods for boolean in numpy array.

Q.37. Methods for boolean in numpy array.
Choose the relevant from the following options.

A : sum(), any(), np.type()
B : sum(), any(), all(), np.type()
C : objects(), any()
D : sum(), any(), all()

B: sum(), any(), all(), np.type() are relevant methods for boolean in numpy array.

Q.38. What is Fortran order in numpy?

A : reshaping regarding row major order
B : reshaping regarding column major order
C : converting to 1D array
D : All of the above

reshaping regarding column major order

Q.39. Correct syntax of the reshape() function in Numpy array python is
A : array.reshape(shape)
B : reshape(shape,array)
C : reshape(array,shape)
D : reshape(shape)

A : array.reshape(shape)

Q.40. How we can change the shape of the Numpy array in python?
A : By Shape()
B : By reshape()
C : By ord()
D : By change()

B : By reshape()

Q.41. How we can convert the Numpy array to the list in python?
A : list(array)
B : list.array
C : array.list
D : None of the above

A: The correct answer is A, i.e., list(array). To convert a Numpy array to a list in Python, we can use the built-in list() function by passing the Numpy array as an argument.

Q.42. How we can find the type of numpy array in python ?
A : dtype
B : type
C : typei
D : itype

A: The answer is A: dtype. We can use the dtype attribute of numpy array to find its data type.

Q.43. How we install Numpy in the system ?
A : install numpy
B : pip install python numpy
C : pip install numpy
D : pip install numpy python

A: The answer is C: pip install numpy. We can use pip, the package installer for Python, to install Numpy in our system.

Q.44. It is possible to convert the Numpy array to list in python ?
A : Yes
B : No
C : Sometimes
D : None of the above

A: The answer is A: Yes. We can use the tolist() method of numpy array to convert it into a Python list.

Q.45. Minimum number of argument to pass in full() function in Numpy array ?
A : 0
B : 1
C : 2
D : 3

A: The answer is B: 1. We need to pass at least one argument, which is the shape of the Numpy array, to the full() function.

Q..46 Numpy in the Python provides the
A : Function
B : Lambda function
C : Type casting
D : Array

A: The answer is D: Array. Numpy is a Python library that provides support for multi-dimensional arrays and matrices, along with a large collection of mathematical functions to operate on these arrays.

Q.47. Numpy.array(list), what it does ?
A : It convert array to list
B : It convert list to array
C : It convert array to array
D : Error

A: The answer is B: It converts list to array. The numpy.array() function is used to create a new Numpy array from a Python list.

Q.48. Shape() function in Numpy array is used to
A : Find the shape of the array
B : Change the shape of the array
C : Both of the above
D : None of the above

Find the shape of the array

Q.49. What is the use of the size attribute in Numpy array in python ?
A : It find the direction
B : It find the number of items
C : It find the shape
D : All of the above

It find the number of items

Q.50. what is the use of the zeros() function in Numpy array in python ?
A : To make a Matrix with all element 0
B : To make a Matrix with all diagonal element 0
C : To make a Matrix with first row 0
D : None of the above

To make a Matrix with all element 0

Q.51. Which of the following argument we need to pass in reshape() function?
A : Array
B : Shape
C : only array
D : Both array and shape

B: Shape. In the reshape() function of Numpy, we need to pass the new shape of the array as an argument.

Q.52. Which of the following counts the number of elements in Numpy array ?
A : count()
B : return()
C : shape()
D : size()

D: size(). The size() function in Numpy returns the number of elements in the array.

Q.53. Which of the following find the maximum number in the Numpy array ?
A : max(array)
B : array.max()
C : array(max)
D : None of the above

B: array.max(). The max() function in Numpy can be called directly on the array as a method to find the maximum element in the array.

Q.54. Which of the following is correct way to import the Numpy module in your program ?
A : import numpy
B : import numpy as np
C : from numpy import *
D : All of the above

B: import numpy as np. This is the standard and recommended way of importing the Numpy module in Python. Option A is also valid but it requires the use of the full module name whenever a Numpy function is called. Option C is not recommended as it can lead to namespace pollution.

Q.55. Which of the following is not valid to import the numpy module ?
A : import numpy as np
B : import numpy as n
C : import numpy as p
D : None of the above

C : import numpy as p is not a valid way to import the numpy module.

Q.56. Which of the following is the essential argument to pass in full() function of Numpy array ?
A : shape
B : value
C : Both of the above
D : None of the above

A : shape is the essential argument to pass in full() function of Numpy array.

Q.57. Which of the following is used to convert the list data type to the Numpy array ?
A : array(list)
B : array.list
C : Numpy.array(list)
D : None of the above

A : array(list) is used to convert the list data type to the Numpy array.

Q.58. Which of the following keyword is used to access the numpy module in python ?
A : access
B : import
C : fetch
D : from

D : from is used to access the numpy module in python. For example: from numpy import *