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NumPy is a computational library that helps in speeding up Vector Algebra operations that involve Vectors (Distance between points, Cosine Similarity) and Matrices. Specifically, it helps in constructing powerful n-dimensional arrays that works smoothly with distributed and GPU systems. It is a very handy library and extensively used in the domains of Data Analytics and Machine Learning. Other than Python, it can also be used in tandem with languages like C and Fortran. Being an Open Source Library under a liberal BSD license, it is developed and maintained publicly on GitHub.
Here are 20 Basic NumPy Exercises which every beginner must go through and acquainted with.
NumPy Installation in Python
In the command line (cmd) type the following command,
pip install numpy
20 NumPy Exercises for Beginners
Importing NumPy and printing version number
import numpy as np print(np.__version__)
Corresponding Output
1.19.2
EXERCISE 1 - Element-wise addition of 2 numpy arrays
Given are 2 similar dimensional numpy arrays, how to get a numpy array output in which every element is an element-wise sum of the 2 numpy arrays?
Sample Solution
a = np.array([[1,2,3], [4,5,6]]) b = np.array([[10,11,12], [13,14,15]]) c = a + b print(c)
Corresponding Output
[[11 13 15] [17 19 21]]
EXERCISE 2 - Multiplying a matrix (numpy array) by a scalar
Given a numpy array (matrix), how to get a numpy array output which is equal to the original matrix multiplied by a scalar?
Sample Solution
a = np.array([[1,2,3], [4,5,6]]) b = 2*a # multiplying the numpy array a(matrix) by 2 print(b)
Corresponding Output
[[ 2 4 6] [ 8 10 12]]
EXERCISE 3 - Identity Matrix
Create an identity matrix of dimension 4-by-4
Sample Solution
i = np.eye(4)
print(i)
Corresponding Output
[[1. 0. 0. 0.] [0. 1. 0. 0.] [0. 0. 1. 0.] [0. 0. 0. 1.]]
EXERCISE 4 - Array re-dimensioning
Convert a 1-D array to a 3-D array
Sample Solution
a = np.array([x for x in range(27)])
o = a.reshape((3,3,3))
print(o)
Corrresponding Output
[[[ 0 1 2] [ 3 4 5] [ 6 7 8]] [[ 9 10 11] [12 13 14] [15 16 17]] [[18 19 20] [21 22 23] [24 25 26]]]
EXERCISE 5 - Array datatype conversion
Convert all the elements of a numpy array from float to integer datatype
Sample Solution
a = np.array([[2.5, 3.8, 1.5], [4.7, 2.9, 1.56]]) o = a.astype('int') print(o)
Corresponding Output
[[2 3 1] [4 2 1]]
EXERCISE 6 - Obtaining Boolean Array from Binary Array
Convert a binary numpy array (containing only 0s and 1s) to a boolean numpy array
Sample Solution
a = np.array([[1, 0, 0], [1, 1, 1], [0, 0, 0]]) o = a.astype('bool') print(o)
Corresponding Output
[[ True False False] [ True True True] [False False False]]
EXERCISE 7 - Horizontal Stacking of Numpy Arrays
Stack 2 numpy arrays horizontally i.e., 2 arrays having the same 1st dimension (number of rows in 2D arrays)
Sample Solution
a1 = np.array([[1,2,3], [4,5,6]]) a2 = np.array([[7,8,9], [10,11,12]]) o = np.hstack((a1, a2)) print(o)
Corresponding Output
[[ 1 2 3 7 8 9] [ 4 5 6 10 11 12]]
EXERCISE 8 - Vertically Stacking of Numpy Arrays
Stack 2 numpy arrays vertically i.e., 2 arrays having the same last dimension (number of columns in 2D arrays)
Sample Solution
a1 = np.array([[1,2], [3,4], [5,6]]) a2 = np.array([[7,8], [9,10], [10,11]]) o = np.vstack((a1, a2)) print(o)
Corresponding Output
[[ 1 2] [ 3 4] [ 5 6] [ 7 8] [ 9 10] [10 11]]
EXERCISE 9 - Custom Sequence Generation
Generate a sequence of numbers in the form of a numpy array from 0 to 100 with gaps of 2 numbers, for example: 0, 2, 4 ....
Sample Solution
list_of_numbers = [x for x in range(0, 101, 2)] o = np.array(list_of_numbers) print(o)
Alternative Solution
o = np.arange(0, 101, 2) print(o)
Corresponding Output
[ 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100]
EXERCISE 10 - Getting the positions (indexes) where elements of 2 numpy arrays match
From 2 numpy arrays, extract the indexes in which the elements in the 2 arrays match
Sample Solution
a = np.array([1,2,3,4,5]) b = np.array([1,3,2,4,5]) print(np.where(a == b))
Corresponding Output
(array([0, 3, 4], dtype=int64),)
EXERCISE 11 - Generation of given count of equally spaced numbers within a specified range
Output a sequence of equally gapped 5 numbers in the range 0 to 100 (both inclusive)
Sample Solution
o = np.linspace(0, 100, 5) print(o)
Corresponding Output
[ 0. 25. 50. 75. 100.]
EXERCISE 12 - Matrix Generation with one particular value
Output a matrix (numpy array) of dimension 2-by-3 with each and every value equal to 5
Sample Solution
o = np.full((2, 3), 5) print(o)
Alternate Solution
a = np.ones((2, 3)) o = 5*a print(o)
Corresponding Output
[[5 5 5] [5 5 5]]
EXERCISE 13 - Array Generation by repeatition of a small array across each dimension
Output an array by repeating a smaller array of 2 dimensions, 10 times
Sample Solution
a = np.array([[1,2,3], [4,5,6]]) o = np.tile(a, 10)
print(o)
Corresponding Output
[[1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3] [4 5 6 4 5 6 4 5 6 4 5 6 4 5 6 4 5 6 4 5 6 4 5 6 4 5 6 4 5 6]]
EXERCISE 14 - Array Generation of random integers within a specified range
Output a 5-by-5 array of random integers between 0 (inclusive) and 10 (exclusive)
Sample Solution
np.random.seed(123) # setting the seed o = np.random.randint(0, 10, size = (5,5)) print(o)
Corresponding Output
[[2 2 6 1 3] [9 6 1 0 1] [9 0 0 9 3] [4 0 0 4 1] [7 3 2 4 7]]
EXERCISE 15 - Array Generation of random numbers following normal distribution
Output a 3-by-3 array of random numbers following normal distribution
Sample Solution
np.random.seed(123) # setting the seed o = np.random.normal(size = (3,3)) print(o)
Corresponding Output
[[-1.0856306 0.99734545 0.2829785 ] [-1.50629471 -0.57860025 1.65143654] [-2.42667924 -0.42891263 1.26593626]]
EXERCISE 16 - Matrix Multiplication
Given 2 numpy arrays as matrices, output the result of multiplying the 2 matrices (as a numpy array)
Sample Solution
a = np.array([[1,2,3], [4,5,6], [7,8,9]]) b = np.array([[2,3,4], [5,6,7], [8,9,10]]) o = a@b print(o)
Alternate Solution
a = np.array([[1,2,3], [4,5,6], [7,8,9]]) b = np.array([[2,3,4], [5,6,7], [8,9,10]]) o = np.matmul(a, b) print(o)
Corresponding Output
[[ 36 42 48] [ 81 96 111] [126 150 174]]
EXERCISE 17 - Matrix Transpose
Output the transpose of a matrix (as numpy array)
Sample Solution
a = np.array([[1,2,3], [4,5,6], [7,8,9]]) a_transpose = a.T print(a_transpose)
Corresponding Output
[[1 4 7] [2 5 8] [3 6 9]]
EXERCISE 18 - Sine of an Angle (in radians)
Calculate the sine of an array of angles (in radians) using NumPy
Sample Solution
angles = np.array([3.14, 3.14/2, 6.28]) sine_of_angles = np.sin(angles) print('Sine of the given array of angles = ', sine_of_angles)
Corresponding Output
Sine of the given array of angles = [ 0.00159265 0.99999968 -0.0031853 ]
EXERCISE 19 - Cosine Similarity
Calculate the cosine similarity of 2 vectors (as numpy arrays)
Sample Solution
angles = np.array([3.14, 3.14/2, 6.28]) cosine_of_angles = np.cos(angles) print('Cosine of the given array of angles = ', cosine_of_angles)
Corresponding Output
[-9.99998732e-01 7.96326711e-04 9.99994927e-01]
EXERCISE 20 - Generating the array element indexes such that the array elements appear in ascending order
Output the array element indexes such that the array elements appear in the ascending order
Sample Solution
array = np.array([10,1,5,2]) indexes = np.argsort(array) print(indexes)
Corresponding Output
[1 3 2 0]
Conclusion
These are the very basics of NumPy that every beginner should get their hands dirty with, in order to get started with Data Analytics and Machine Learning. In case you are stuck somewhere in any of the numpy exercises or need further clarification on a concept of data science or Python, FavTutor is always here to provide you with help from expert tutors 24/7.
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