# Top 5 Numpy Excersices for Beginners (Python Solutions)

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## Top 5 Numpy Excersices for Beginners

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 5 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`

## 5 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]]```