Introduction To Numpy

In this Blog, I will be writing about all the basic stuff you need to know about numpy such as what is numpy, why we use numpy, why numpy is more useful than lists in python, getting started with numpy etc.

What is NumPy?

NumPy, which stands for Numerical Python, is a library consisting of multidimensional array objects and a collection of routines for processing those arrays. Using NumPy, mathematical and logical operations on arrays can be performed. This tutorial explains the basics of NumPy such as its architecture and environment. It also discusses the various array functions, types of indexing, etc.

Why Use NumPy?

n Python we have lists that serve the purpose of arrays, but they are slow to process.

NumPy aims to provide an array object that is up to 50x faster than traditional Python lists.

The array object in NumPy is called ndarray, it provides a lot of supporting functions that make working with ndarray very easy.

Arrays are very frequently used in data science, where speed and resources are very important.

Why is NumPy Faster Than Lists?

NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently.

This behavior is called locality of reference in computer science.

This is the main reason why NumPy is faster than lists. Also it is optimized to work with latest CPU architectures.

Before getting started first we need to install NumPy and in our systems. Make sure you already have python installed.

Installing NumPy

To download and install NumPy in your local environment. You first need to open cmd (command Prompt) and type the following command.

pip install numpy

After installing this library you need to import this library in your work space.

Importing NumPy

To import NumPy in your workspace refer to the below code.

import numpy as np

Now you are ready to get started with NumPy

Getting Started with NumPy

Arrays in NumPy

1. Creating array using NumPy

NumPy is used to work with arrays. The array object in NumPy is called ndarray.

We can create a NumPy ndarray object by using the array() function.

Example:

import numpy as np

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

print(arr)

print(type(arr))

type(): This built-in Python function tells us the type of the object passed to it. Like in above code it shows that arr is numpy.ndarray type.

2. NumPy Array Indexing

Array indexing is the same as accessing an array element.

You can access an array element by referring to its index number.

The indexes in NumPy arrays start with 0, meaning that the first element has index 0, and the second has index 1 etc.

Example

Get third and fourth elements from the following array and add them:

import numpy as np

arr = np.array([1, 2, 3, 4])

print(arr[2] + arr[3])

Access 2-D Arrays

To access elements from 2-D arrays we can use comma separated integers representing the dimension and the index of the element.

For Example:

Access the 5th element on 2nd dim:

import numpy as np

arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])

print(‘5th element on 2nd dim: ‘, arr[1, 4])

3. NumPy Array Slicing

Slicing in python means taking elements from one given index to another given index.

We pass slice instead of index like this: [start:end].

We can also define the step, like this: [start:end:step].

If we don’t pass start its considered 0

If we don’t pass end its considered length of array in that dimension

If we don’t pass step its considered 1.

Example

Slice elements from index 1 to index 5 from the following array:

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6, 7])

print(arr[1:5])

Negative Slicing

Use the minus operator to refer to an index from the end:

Example:

Slice from the index 3 from the end to index 1 from the end:

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6, 7])

print(arr[-3:-1])

4. NumPy Array Shape

NumPy arrays have an attribute called shape that returns a tuple with each index having the number of corresponding elements.

Example

Print the shape of a 2-D array:

import numpy as np

arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])

print(arr.shape)

5. NumPy Array Reshaping

Reshaping means changing the shape of an array.

The shape of an array is the number of elements in each dimension.

By reshaping we can add or remove dimensions or change number of elements in each dimension.

Reshape From 1-D to 2-D

Example

Convert the following 1-D array with 12 elements into a 2-D array.

The outermost dimension will have 4 arrays, each with 3 elements:

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])

newarr = arr.reshape(4, 3)

print(newarr)

Reshape From 1-D to 3-D

Example

Convert the following 1-D array with 12 elements into a 3-D array.

The outermost dimension will have 2 arrays that contains 3 arrays, each with 2 elements:

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])

newarr = arr.reshape(2, 3, 2)

print(newarr)

Note: You don’t need to import NumPy library each time on same workspace. This is just for reference purpose.

There are plenty more operations that can be performed using NumPy library. For more details you can always visit following links:

https://www.w3schools.com/python/numpy_intro.asp#:~:text=NumPy%20is%20a%20Python%20library%20used%20for%20working%20with%20arrays.&text=NumPy%20was%20created%20in%202005,NumPy%20stands%20for%20Numerical%20Python.

NumPy v1.19 Manual

Here’s my Jupyter Notebook, you can check for references.

Shag10/Machine-Learning

So I am ending my Blog here. Thank you for reading till the end. I hope you will find something useful here. Any critics, suggestions, how can I improve my Blog writing is highly appreciated. Please do comment if any.