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Expert-level Python programming with NumPy tutorials. Apply NumPy concepts to develop real-time projects & applications.

What you will learn

### NumPy – Linear Algebra

Description

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.

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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

1. POWERFUL N-DIMENSIONAL ARRAYS
• Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.
2. NUMERICAL COMPUTING TOOLS
• NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.
3. INTEROPERABLE
• NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.
4. PERFORMANT
• The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.
5. EASY TO USE
• NumPyâ€™s high level syntax makes it accessible and productive for programmers from any background or experience level.
6. 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 âˆ’

• 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

1. INTRODUCTION TO NUMPY
2. NUMPY TUTORIAL BASICS
3. NUMPY ATTRIBUTES AND FUNCTIONS
4. CREATING ARRAYS FROM EXISTING DATA
5. CREATING ARRAYS FROM RANGES
6. INDEXING AND SLICING IN NUMPY
8. APPEND AND RESIZE FUNCTIONS
11. NDITER FUNCTION
12. ARRAY MANIPULATION FUNCTIONS
13. NUMPY UNIQUE()
14. NUMPY DELETE()
15. NUMPY INSERT FUNCTION
16. NUMPY RAVEL AND SWAPAXES()
17. SPLIT FUNCTION
18. HSPLIT FUNCTION
19. VSPLIT FUNCTION
20. LEFTSHIFT AND RIGHTSHIFT FUNCTIONS
21. NUMPY TRIGONOMETRIC FUNCTIONS
22. NUMPY ROUND FUNCTIONS
23. NUMPY ARITHMATIC FUNCTIONS
24. NUMPY POWER AND RECIPROCAL FUNCTIONS
25. NUMPY MOD FUNCTION
26. NUMPY IMAG() AND REAL() FUNCTIONS
27. NUMPY CONCATENATE()
28. NUMPY STATISTICAL FUNCTIONS
29. STATISTICAL FUNCTIONS
30. NUMPY AVERAGE FUNCTION
31. NUMPY SEARCH SORT FUNCTIONS
32. SORT FUNCTION
33. NUMPY SORT FUNCTION
34. NUMPY ARGSORT()
35. NONZERO AND WHERE FUNCTIONS
36. EXTRACT FUNCTION
37. NUMPY ARGMAX ARGMIN()
38. BYTESWAP COPIES AND VIEWS
39. NUMPY STRING FUNCTIONS
40. NUMPY CENTER FUNCTION
41. CAPITALIZE AND CENTER()
42. NUMPY TITLE FUNCTION
43. STRING FUNCTIONS
44. NUMPY MATRIX LIBRARY
45. NUMPY JOIN ARRAYS
46. LINEAR ALGEBRA
47. RANDOM MODULE
48. SECRETS MODULE
49. RANDOM MODULE UNIFORM FUNCTION
50. RANDOM MODULE GENERATE NUMBER EXCEPT K
51. SECRETSMODULE GENERATE TOKENS
52. RANDOM MODULE GENERATE BINARY STRING
53. NUMPY MODULE REVISE
54. NUMPY INDEXING
55. NUMPY BASIC OPERATIONS
56. NUMPY UNARY OPERATORS
57. BINARY OPERATORS IN NUMPY
58. NUMPY UNIVERSAL FUNCTIONS
59. NUMPY FILTER ARRAYS
60. NUMPY MODULE PROJECTS
English
language

Content

### Introduction to NumPy

Introduction to NumPy

### NumPy Tutorial Basics

NumPy Tutorial Basics

### NumPy Attributes and Functions

NumPy Attributes and Functions

### Creating Arrays

Creating Arrays from Existing Data
Creating Array from Ranges

### Indexing and Slicing in NumPy

Indexing and Slicing in NumPy

### Append and Resize Functions

Append and Resize Functions

Nditer Function

### NumPy Functions

Array Manipulation Functions
NumPy Unique()
NumPy Delete() – part 1
NumPy Delete() – part 2
NumPy Insert Function
Numpy RAVEL() SWAPAXES()
SPLIT Function
HSPLIT()
VSPLIT()
LEFT Shift and RIGHT Shift Functions
NumPy Trigonometric Functions
NumPy Round Functions
NumPy Arithmetic Functions
NumPy Power and Reciprocal Functions
NumPy Power and Mod Functions
NumPy IMAG() REAL()

### NumPy CONCATENATE()

NumPy CONCATENATE()

### NumPy Statistical Functions

NumPy Statistical Functions – AMIN and AMAX
Statistical Functions – MEAN, MEDIAN, PTP()
NumPy AVERAGE Function

### NumPy Search and Sort

NumPy Sort Function
NumPy ARGSORT()
Nonzero Where
Extract
ARGMAX() and ARGMIN()

### Byteswap Copies and Views

Byteswap Copies and Views

### String Functions in NumPy

STRFUNCTIONS in NumPy
String Function in NumPy ADD() and MULTIPLY()
NumPy CENTER()
CAPITALIZE() CENTER() in NumPy
String Functions 1
String Functions 2

### NumPy Matrix Library

NumPy Matrix Library

### NumPy Joining Arrays

NumPy Joining Arrays

### Linear Algebra

Linear Algebra – part 1
Linear Algebra – part 2
Linear Algebra – part 3
Linear Algebra – part 4
Linear Algebra – part 5
Linear Algebra – part 6
Linear Algebra – part 7

### Random Module and Secrets Module

Random Module – part 1
Random Module – part 2
Random Module – part 3
Random Module – part 4
Random Module – part 5
Random Module – part 6
Random Module – part 7
Random Module – part 8
Random Module – part 9
Random Module – part 10
Random Module – part 11
Random Module – part 12
Random Module – part 13
Random Module – part 14
Random Module – part 15
Random Module – part 16
Random Module – part 17
Random Module – part 18
Random Module – part 19
Secrets Module – part 1
Secrets Module – part 2
Random Module Uniform Function
Random Module Generate Number Except K
Secrets Module Generate Tokens
Random Module Generate Binary String

### NumPy Module Revision

NumPy Module Revision – part 1
NumPy Module Revision – part 2
NumPy Indexing Revision

### NumPy Operators and Operations

NumPy Basic Operations
Unary Operators in NumPy
Binary Operators in NumPy
NumPy Universal Operators

### NumPy Filter Arrays

NumPy Filter Arrays

### NumPy Module Projects

NumPy Module Projects – part 1
NumPy Module Projects – part 2
NumPy Module Projects – part 3
NumPy Module Projects – part 4
NumPy Module Projects – part 5
NumPy Module Projects – part 6