# NumPy - Python Package for Data MCQ solution | TCS Fresco Play

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NumPy python package for data

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1. Which of the following characters are used to represent an unordered list?

**All of those mentioned**

2. Which of the following is a Scientific distribution of Python, used for Data Science?

**All of those mentioned**

3. It is possible to embed code snippets in markdown. State True or False?

**TRUE**

4. A line magic methods starts with ______ ?

**%**

5. A cell magic methods starts with ______ ?

**%%**

6. Which language is used to write documentation in a Jupyter Notebook?

**MARKDOWN**

7. Which of the following magic method list the history of input commands ?

**%hist**

8. Which of the following is used to know about an object in Ipython?

**?**

9. Which of the following is used to run system commands from Ipython?

**!**

10. What is the basic element of a Jupyter Notebook ?

**Cell**

11. What is the input of a cell magic method ?

**Python code written in multiple lines of a single cell.**

12. How to write the word 'Python' in markdown, to emphasize it in bold ?

****Python****

13. Which of the following is Ture about Numpy array data elements?

**Data elements are of same type and of fixed size**

14. Which of the following attribute determine the number of dimensions of a ndarray?

**ndim**

15. Which of the following attribute returns total number of bytes consumed by a ndarray?

**nbytes**

16. What does flags attribute of an ndarray return?

**Memory layout of a ndarray**

17. Which of the following attribute returns the number of elements in each dimension of a multi dimensional array?

**num**

18. What is the output of the following code?

**8**

import numpy as np

x = np.array([[3.2, 7.8, 9.2],

[4.5, 9.1, 1.2]], dtype='int64')

print(x.itemsize)

19. What is the output of the following code?

**complex128**

import numpy as np

y = np.array([3+4j, 0.4+7.8j])

print(y.dtype)

20. Which of the following method is used to read data from a text file?

**loadtxt**

21. What is the output of the following code ?

**[[ 1. 0.] [ 0. 1.]]**

22. Which of the following expressions return an Error?

**2**

1. np.random.rand(3, 2)

2. np.random.random(3, 2)

import numpy as np

z = np.eye(2)

print(y)

23. What is the output of the following code ?

**[[0 0 0] [0 0 0]]**

import numpy as np

x = np.array([[-1,0,1], [-2, 0, 2]])

y = np.zeros_like(x)

print(y)

24. Which of the following numpy method is used to simulate a binomial distribution?

**binomial**

25. What is the output of the following code ?

**[ 1. 3.25 5.5 7.75 10. ] [1 6]**

import numpy as np

print(np.linspace(1, 10, 5))

print(np.arange(1, 10, 5))

26. What is the output of the following code ?

**(2,2)**

import nupy as np

print(np.array(([1, 2], (3,4))).shape)

27. Predict the number of rows and columns of array x defined below?

**4, 5**

import numpy as np

x = np.arange(20).reshape(4, 5)

28. What is the output of the following code ?

**[[0 1] [3 4]]**

import numpy as np

x = np.arange(6).reshape(2,3)

y = np.hsplit(x,(2,))

print(y[0])

29. Which of the following numpy method is used to join arrays horizontally ?

**hstack**

30. What is the shape of array x defined below?** **

**Results in Error while creating**

import numpy as np

x = np.arange(20).reshape(10, 10)

31. What is the output of the following code?

**[[0 1]]**

import numpy as np

x = np.arange(4).reshape(2,2)

y = np.vsplit(x,2)

print(y[0])

32. Array x is defined below. Determine the number of elements of it contains in second dimension?

**15**

import numpy as np

x = np.arange(90).reshape(3, 15, 2)

33. What is the output of the following code ?

**[[0 1 4 5] [2 3 6 7]]**

import numpy as np

x = np.arange(4).reshape(2,2)

y = np.arange(4, 8).reshape(2,2)

print(np.hstack((x,y)))

34. Which of the following numpy method is used to join arrays vertically ?

**vstack**

35. Which of the following method is used to convert the data type of an array?

**a****stype**

36. Which of the following method is used to check if a number is an infinite or not ?

**isinfinite**

37. What is the shape of Broadcasting array resulted from arrays with shapes (4, 1, 1,7) and (3, 1) ?

**(4, 1, 3, 7)**

38. What is the output of the following Code?

**FALSE**

import numpy as np

x = np.arange(4)

y = np.arange(4)

print(x == y)

39. What is the output of the following code ?

**[ 2. 7. 12. 17.]**

import numpy as np

x = np.arange(20).reshape(4,5)

print(x.mean(axis=1))

40. What is the output of the following Code?

**[****0 1 4 9]**

import numpy as np

x = np.arange(4)

print(x**2)

41. Is Broadcasting feasible between two arrays whose shapes are (5, 8, 2) and (2,) ?

**Yes**

42. Is Broadcasting feasible between two arrays whose shapes are (5, 8, 1) and (4, 2) ?

**No**

43. What is the ouput of the following code?

**FALSE**

import numpy as np

x = np.arange(4).reshape(2,2)

print(np.isfinite(x))

44. What is the output of the following code?

[**3 3 3 3]**

import numpy as np

print(np.repeat(3, 4))

45. What is the output of the following code ?

**[[0, 1], [2, 3]]**

import numpy as np

x = np.arange(4).reshape(2,2)

print(x.tolist())

46. What is the outout of the following code ?

**[[ 6 -6] [-6 6]]**

import numpy as np

x = np.array([[-2],

[2]])

y = np.array([[-3, 3]])

print(x.dot(y))

47. What is the outout of the following code ?

**[[-5 1] [-1 5]]**

import numpy as np

x = np.array([[-2],

[2]])

y = np.array([[-3, 3]])

print(x + y)

48. What is the output of the following code?

**[4 4 4 4 4]**

import numpy as np

x = np.arange(30).reshape(5,6)

print(x.argmax(axis=1))

49. What is the shape of Broadcasting array resulted from arrays with shapes (9, 2, 1, 5) and (3, 1) ?

**(9, 2, 3, 5)**

50. What is the output of the following code?

**[25 27]**

import numpy as np

x = np.arange(30).reshape(3,5,2)

print(x[-1, 2:-1, -1])

51. What is the output of the following code?

**[[0 1]]**

import numpy as np

x = np.array([[0, 1], [1, 1], [2, 2]])

y = x.sum(-1)

print(x[y < 2, :])

52. What is the output of the following code?

**[1 4 6]**

import numpy as np

x = np.array([[1, 2], [3, 4], [5, 6]])

print(x[[0, 1, 2], [0, 1, 1]])

53. What is the output of the following code?

**[1 2 4]**

import numpy as np

x = np.array([[0, 1], [1, 1], [2, 2]])

print(x.sum(-1))

54. What is the output of the following code?

**[11 15 19]**

import numpy as np

x = np.arange(30).reshape(3,5,2)

print(x[1,::2,1])

55. What is the output of the following code?

**[1 5 9]**

import numpy as np

x = np.arange(12).reshape(3,4)

print(x[:,1])

56. What is the output of the following code?

**[0 1 2 3]**

import numpy as np

x = np.arange(4)

print(x.flatten())

57. What is the output of the following code?

**[4 5 6 7]**

import numpy as np

x = np.arange(12).reshape(3,4)

print(x[-2])

58. What is the output of the following code?

**[14 15]**

import numpy as np

x = np.arange(30).reshape(3,5,2)

print(x[1][::2][1])

59. What is the output of the following code?

**[1 2 4]**

import numpy as np

x = np.array([[0, 1], [1, 1], [2, 2]])

print(x.sum(-1))

60. What is the output of the following code?

**(1, 4)**

import numpy as np

x = np.arange(12).reshape(3,4)

print(x[-1:,].shape)

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