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NumPy Tutorial

Home Software Development Software Development Tutorials NumPy Tutorial

Basics

Introduction to NumPy

Install NumPy

NumPy Data Types

NumPy Functions

NumPy Histogram

numPy.where()

numpy.sort

NumPy floor()

Matrix in NumPy

NumPy Matrix Transpose

Matrix Multiplication in NumPy

NumPy Arrays

NumPy Array Functions

NumPy Ndarray

NumPy Array Append

NumPy empty array

NumPy ndarray tolist

numpy.linspace()

NumPy.argmax()

NumPy Linear Algebra

numpy.diff()

numpy.unique()

NumPy zeros

numpy.mean()

numpy.dot()

Numpy.argsort()

numpy.pad()

numpy.ravel()

NumPy stack

NumPy vstack

NumPy hstack

NumPy sum

NumPy cumsum

NumPy max

NumPy squeeze

NumPy median

NumPy Round

NumPy NaN

NumPy divide

NumPy square

NumPy ones

NumPy Meshgrid

NumPy exponential

NumPy percentile

NumPy fft

NumPy flatten

NumPy Concatenate

NumPy Outer

NumPy zip

NumPy Newaxis

NumPy Format

Numpy Eigenvalues

Numpy Random Seed ()

NumPy random choice

NumPy random normal

NumPy unravel_index

Numpy.eye()

NumPy append

NumPy searchsorted

NumPy shape

NumPy reshape

NumPy savetxt

NumPy genfromtxt

NumPy standard deviation

NumPy covariance

NumPy repeat

NumPy Tile

NumPy Broadcasting

NumPy axis

NumPy correlation

NumPy logspace()

NumPy Normal Distribution

NumPy Convolve

NumPy Indexing

NumPy norm

NumPy Inverse

NumPy linalg norm

NumPy nonzero

NumPy polyfit

NumPy 2D array

NumPy concatenate arrays

NumPy 3D array

NumPy Array Indexing

NumPy datetime64

NumPy Identity Matrix

NumPy for loop

NumPy Factorial

NumPy isclose

NumPy find

NumPy Vector

NumPy linear regression

NumPy interpolate

NumPy datetime

Numpy.clip()

Numpy.loadtxt()

NumPy Cross Product

NumPy arange

numpy.save()

NumPy Empty

NumPy Log

NumPy random

NumPy size

NumPy load

NumPy map

NumPy power

NumPy Vectorize

NumPy zeros_like

NumPy.array() in Python

NumPy Tutorial

NumPy is a package(library) for python, This package consists of Array, Objects, Multi-Dimensional array, and various functions to manipulate and handle these arrays. With the help of Its functions we can perform various mathematical and logical operations. suppose you have an array and you wanted to check the dimension of the array , dimension means n*m matrix. So with the help of the NumPy function called ndim, we can get it easily. suppose we have some array and we want to know the size of our array than we can use the size function of NumPy. Even if we want to know the data type of array attributes that we can know with a function called dtype.

Why do we need to learn NumPy?

In python, it has list which can handle array but still, we should learn NumPy because it has a better and optimized way to use Array. Generally an array can be of heterogeneous type in python , like [12,”ranjan”,”ajay”,78] . But if we want to have a homogeneous array than we should only use NumPy.Because many times in real application we store similar types of data in the one array.

There are several important reasons why we should learn NumPy they are listed below.

  • It takes very less memory to store data

suppose you have a very big size of the array and you want to store them in such a way that they take less memory than NumPy will do it very efficiently as they will take less memory to store. Because if any one of us is writing code for NLP or for algorithms than he/she will need to write code in a more optimized way to store data with taking less memory.

  • Very efficient to create n-dimensional arrays 

If some of you are trying to learn NLP, then you will see that the n-dimension matrix are used for storing data, So it will be a great option to use NumPy to store data as it saves memory.

Let me tell you one example,  if we have any  2 * 5 matrix array than we can convert it into  5 * 2 and if we have any  1 * 4 matrix array than we can convert it into  2 * 2. All of these can be simply done by using NumPy.reshape(...).

  • Searching any element in It is very faster

We will face situations where we need to find any specific element from arrays so to handle these things in a better way NumPy provides function they are where, nonzero and count_nonzero. These functions are optimized internally for faster search.

Applications of NumPy

Because of better array storage and great mathematical routines availability in Numpy, it has mostly used in NLP and scientific applications where more algorithms are used. Because in these cases we needed to calculate and manage very big floating and double numbers with exact precision.

Prerequisites

You should have a basic understanding of programming languages and a basic understanding of Python along with we should know arrays and operations of the array .

Target Audience

This is useful for python developers, especially those who are more keen on learning algorithms and complex data manipulations.

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