# Python NumPy Programming and Project Development using NumPy

Python NumPy Programming and Project Development using NumPy, Expert-level Python programming with NumPy tutorials. Apply NumPy concepts to develop real-time projects & applications.

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

NumPy stands for **Num**erical **Py**thon 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