Python NumPy Tutorial for Data Science

In this NumPy Tutorial, we will learn what is NumPy. We will learn about its uses, installation, operations and many other features.

So let’s start!!!

NumPy Introduction

NumPy stands for ‘Numerical Python’. It is a package in Python to work with arrays. It is a basic scientific library. Its most important feature is the n-dimensional array object. It has uses in statistical functions, linear algebra, arithmetic operations, bitwise operations, etc.

We perform all the operations on the array elements. We can initialize these arrays in several ways.

Prerequisite to Learn NumPy

The two basic prerequisites for NumPy are Python and Mathematics. We need to know the python basics to work with the NumPy module.

The functions available in NumPy are built on python language. We can hence combine the knowledge of python arrays and list for array initialization and operations.

NumPy Installation

We can install Python NumPy by going to the command prompt and typing a simple command pip install NumPy. Then go to the IDE and use the import command import NumPy as np.

We can now access all the functionalities of the NumPy module.

Uses of NumPy

NumPy is one of the most useful external libraries available in Python. It has a wide variety of functions to work with arrays and a powerful multi-dimensional array object. It has operations that are applicable to a vast range of platforms.

Numpy can be put to use for storing, manipulation, and deletion of array elements. We can use it for sorting, indexing, and stacking of the array elements. It has modules regarding various operations:

• Arithmetic operations
• Statistical Operations
• Bitwise Operators
• Linear Algebra
• Copying and viewing arrays
• Stacking
• Searching, Sorting, and counting, etc.
• Mathematical Operations
• Broadcasting
• Matplotlib for graphical representations
• Matrix Operations, etc.

NumPy vs. Python arrays

The NumPy library is a great alternative to python arrays. The difference is that the NumPy arrays are homogeneous that makes it easier to work with. We can initialize the array elements in many ways, one being which is through the python lists.

The NumPy arrays are convenient as they have the following three features–

• Less Memory Requirement
• Faster Processing
• Convenience of use

Data types in NumPy

Numpy supports more data types as compared to Python. These data types are instances of dtype objects. Some of the scalar data types are given in the table below.

Sr.No.	Data Types	Description
1.	bool_	Boolean True/False
2.	int_	Integer type
3.	intc	Same as C int
4.	intp	An integer used for indexing
5.	int8	Byte(-128 to 127)
6.	int16	Integer(-32768 to 32767)
7.	int32	Integer(-2147483648 to 2147483647)
8.	int64	Integer (-9223372036854775808 to 9223372036854775807)
9.	uint8	Unsigned integer(0 to 225)
10.	unit16	Unsigned integer(0 to 65535)
11.	unit32	Unsigned Integer(0 to 4294967295)
12.	unit64	Unsigned Integer(0 to 18446744073709551615)
13.	float_	Shorthand for float64
14.	float16	Half precision float
15.	float32	Single precision float
16.	float64	Double precision float
17.	complex_	Shorthand for comples128
18.	complex64	Two 32bit float complex number
19.	complex128	Two 64 bit float complex number

NumPy Operations

NumPy consists of a wide range of functions to work with arrays.

1. Numpy ndim

It is the function which determines the dimensions of the input array

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

Output

2. Numpy itemsize()

We use this function to determine the size of the array elements.

import numpy as np
a = np.array([(1,1)])
print(a.itemsize)

Output

3. Numpy dtype()

We use this function to determine the data type of the array elements.

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

Output

4. Numpy reshape()

We use this function to reassign the array a new shape.

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

Output

5. Numpy slicing()

It is for extracting a particular set of elements from the array.

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

Output

6. Numpy linspace()

This is for array generation of evenly spread elements.

import numpy as np
a=np.linspace(1,5,10)
print(a)

Output

7. Numpy min() / Numpy max()

We can find the minimum and maximum values from the array.

import numpy as np
 
arr= np.array([10,20,30])
print(arr.min())
print(arr.max())

Output

8. Numpy sum()

This is to return the sum of all the array elements

import numpy as np
 
arr= np.array([10,50,100])
print(arr.sum())

Output

9. Numpy sqrt()/ Numpy std()

We can determine the square root and standard deviation of the array elements.

import numpy as np
a=np.array([(1,2,3),(4,5,6)])
print(np.sqrt(a))
print(np.std(a))

Output

10. +,-,/, *

We can determine the sum, difference, division, and multiplication of the array elements with the use of these operators.

import numpy as np
x= np.array([(1,1,1),(2,2,2)])
y= np.array([(3,3,3),(4,4,4)])
print(x+y)
print(x-y)
print(x*y)
print(x/y)

Output

11. Numpy hstack/ Numpy vstack()

These are stacking functions, we can perform horizontal and vertical stacking of arrays.

import numpy as np
x= np.array([(1,1,1),(2,2,2)])
y= np.array([(3,3,3),(4,4,4)])
print(np.vstack((x,y)))
print(np.hstack((x,y)))

Output

12. Numpy ravel()

This function concerts the entire array into a single column.

import numpy as np
arr= np.array([(1,1,1),(2,2,2)])
print(arr.ravel())

Output

There are a few special functions available in NumPy. We can plot the sine, cos, and tan curves using the matplotlib module. It is an alternative to other plotting software like MatLab.

It is a great alternative when working with graphical representations.

import numpy as np
import matplotlib.pyplot as plt
arr1= np.arange(0,2*np.pi,0.5)
arr2=np.sin(arr1)
arr3=np.cos(arr1)
arr4=np.tan(arr1)
plt.plot(arr1,arr2)
plt.plot(arr1,arr3)
plt.plot(arr1,arr4)
plt.show()

Summary

Here we come to the end of Numpy Tutorial.

NumPy is the basic library for mathematical operations in Machine Learning. It has a vast range of functions to manipulate arrays and matrices. It is a very convenient and user-friendly library. It has made to work with an array element a much easier task.

Working with arrays has turned out to be a much faster and time-efficient process. It is very easy to install and implement.

There are many modules in NumPy that are specific to various complex functions. It can also extend functionalities by combining with other python libraries.