A warm welcome to the Python NumPy Programming and Project Development using NumPy course by Uplatz.

NumPy stands for Numerical Python and it is a core scientific computing library in Python. NumPy provides efficient multi-dimensional array objects and various operations to work with these array objects.

NumPy is a Python library used for working with arrays. It also has functions for working in domain of linear algebra, fourier transform, and matrices. NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely. NumPy is written partially in Python, but most of the parts that require fast computation are written in C or C++.

Purpose of using NumPy

In 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.

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.

NumPy is essentially 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.

NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:

Extract, Transform, Load: Pandas, Intake, PyJanitor

Exploratory analysis: Jupyter, Seaborn, Matplotlib, Altair

Model and evaluate: scikit-learn, statsmodels, PyMC3, spaCy

Report in a dashboard: Dash, Panel, Voila

Features of NumPy

POWERFUL N-DIMENSIONAL ARRAYS

Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.

NUMERICAL COMPUTING TOOLS

NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.

INTEROPERABLE

NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.

PERFORMANT

The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.

EASY TO USE

NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.

OPEN SOURCE

Distributed under a liberal BSD license, NumPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.

Using NumPy, a developer can perform the following operations −

Mathematical and logical operations on arrays.

Fourier transforms and routines for shape manipulation.

Operations related to linear algebra. NumPy has in-built functions for linear algebra and random number generation.

Uplatz provides this in-depth training on Python programming using NumPy. This NumPy course explains the concepts & structure of NumPy including its architecture and environment. The course discusses the various array functions, types of indexing, etc. and moves on to using NumPy for creating and managing multi-dimensional arrays with functions and operations. This Python NumPy course also discusses the practical implementation of NumPy to develop prediction models & projects.

NumPy Python Programming and Project Development using NumPy - Course Syllabus

INTRODUCTION TO NUMPY

NUMPY TUTORIAL BASICS

NUMPY ATTRIBUTES AND FUNCTIONS

CREATING ARRAYS FROM EXISTING DATA

CREATING ARRAYS FROM RANGES

INDEXING AND SLICING IN NUMPY

ADVANCED SLICING IN NUMPY

APPEND AND RESIZE FUNCTIONS

NDITER AND BROADCASTING

NUMPY BROADCASTING

NDITER FUNCTION

ARRAY MANIPULATION FUNCTIONS

NUMPY UNIQUE()

NUMPY DELETE()

NUMPY INSERT FUNCTION

NUMPY RAVEL AND SWAPAXES()

SPLIT FUNCTION

HSPLIT FUNCTION

VSPLIT FUNCTION

LEFTSHIFT AND RIGHTSHIFT FUNCTIONS

NUMPY TRIGONOMETRIC FUNCTIONS

NUMPY ROUND FUNCTIONS

NUMPY ARITHMATIC FUNCTIONS

NUMPY POWER AND RECIPROCAL FUNCTIONS

NUMPY MOD FUNCTION

NUMPY IMAG() AND REAL() FUNCTIONS

NUMPY CONCATENATE()

NUMPY STATISTICAL FUNCTIONS

STATISTICAL FUNCTIONS

NUMPY AVERAGE FUNCTION

NUMPY SEARCH SORT FUNCTIONS

SORT FUNCTION

NUMPY SORT FUNCTION

NUMPY ARGSORT()

NONZERO AND WHERE FUNCTIONS

EXTRACT FUNCTION

NUMPY ARGMAX ARGMIN()

BYTESWAP COPIES AND VIEWS

NUMPY STRING FUNCTIONS

NUMPY CENTER FUNCTION

CAPITALIZE AND CENTER()

NUMPY TITLE FUNCTION

STRING FUNCTIONS

NUMPY MATRIX LIBRARY

NUMPY JOIN ARRAYS

LINEAR ALGEBRA

RANDOM MODULE

SECRETS MODULE

RANDOM MODULE UNIFORM FUNCTION

RANDOM MODULE GENERATE NUMBER EXCEPT K

SECRETSMODULE GENERATE TOKENS

RANDOM MODULE GENERATE BINARY STRING

NUMPY MODULE REVISE

NUMPY INDEXING

NUMPY BASIC OPERATIONS

NUMPY UNARY OPERATORS

BINARY OPERATORS IN NUMPY

NUMPY UNIVERSAL FUNCTIONS

NUMPY FILTER ARRAYS

NUMPY MODULE PROJECTS