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 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 - 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
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