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The Ultimate Beginners Guide To Python Numpy
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.71 GB | Duration: 7h 46m

Master everything you need to know about NumPy for numerical analysis and scientific calculations! Solved exercises!

[b]What you'll learn[/b]

Strengthen your Python knowledge and enhance your programming skills to work with NumPy arrays in your projects.

Explore the powerful nature of NumPy arrays and their essential attributes for efficient data manipulation.

Create, populate, and navigate NumPy arrays, extracting and modifying data seamlessly.

Use methods and functions to perform complex operations on your arrays, optimizing your code and leveraging the full potential of NumPy.

Dive into fundamental data science concepts, learning to manipulate and analyze data effectively.

[b]Requirements[/b]

Basic programming logic and Python programming, although it's possible to follow the course without deep knowledge in these areas.

[b]Description[/b]

This course is designed for Python developers who want to explore the powerful features of the NumPy library. Through hands-on lessons, you will acquire the skills needed to work with multidimensional arrays, perform complex scientific calculations, and manipulate data efficiently.We will cover the following topics:ndarrays (the fundamental class of NumPy) and their attributes:Create and manipulate multidimensional arrays with the `ndarray` class Explore the essential attributes of `ndarrays` Learn array indexing and slicing techniques, and value assignment Understand the different ways to create populated arrays ndarray methods:Extract attributes and perform mathematical operations on arrays Use `ndarray` methods to efficiently manipulate data Array manipulation:Use array manipulation functions to modify and transform data Combine arrays in different ways to create more complex datasets Learn how to transpose, reorder, and invert arrays Explore advanced indexing techniques to extract specific information from arrays Powerful NumPy functions for analysis:Use linear algebra functions to solve systems of equations, compute inverse matrices, and more Apply statistical functions to analyze data, calculate measures of central tendency and dispersion Master NumPy universal functions to perform mathematical operations on arrays And more:Generate random numbers with different probability distributions Discover useful NumPy constants for scientific calculations Save and load arrays for data persistence By the end of this course, you will confidently use the NumPy library for numerical analysis in Python, work efficiently with multidimensional arrays, perform complex scientific calculations on arrays with precision and speed, manipulate data efficiently to extract valuable insights, and integrate the NumPy library into your existing Python development projects. With over 7 hours of step-by-step videos and solved exercises at the end of each section!

Overview

Section 1: Introduction

Lecture 1 Course content

Lecture 2 Course materials

Section 2: Numpy and lists

Lecture 3 Creating lists and arrays

Lecture 4 Data types

Lecture 5 Vectorization

Lecture 6 Broadcasting

Lecture 7 HOMEWORK

Lecture 8 Solution

Section 3: ndarray and its attributes

Lecture 9 Array from data

Lecture 10 Array with 1 and 2 dimensions

Lecture 11 Array with n dimensions

Lecture 12 Array attributes

Lecture 13 HOMEWORK

Lecture 14 Solution

Section 4: Filled arrays

Lecture 15 Filled arrays 1

Lecture 16 Filled arrays 2

Lecture 17 Filled arrays 3

Lecture 18 HOMEWORK

Lecture 19 Solution

Section 5: Indexing, slicing, assigning

Lecture 20 Indexing

Lecture 21 Slicing

Lecture 22 Assigning

Lecture 23 HOMEWORK

Lecture 24 Solution

Section 6: Methods

Lecture 25 Conversion methods

Lecture 26 Shape manipulation methods

Lecture 27 Item selection

Lecture 28 HOMEWORK

Lecture 29 Solution

Lecture 30 Calculation methods 1

Lecture 31 Calculation methods 2

Lecture 32 Calculation methods 3

Lecture 33 Calculation methods 4

Lecture 34 HOMEWORK

Lecture 35 Solution

Section 7: Random numbers

Lecture 36 Simple random numbers

Lecture 37 Permutations

Lecture 38 Distributions 1

Lecture 39 Distributions 2

Lecture 40 HOMEWORK

Lecture 41 Solution

Section 8: Manipulation functions

Lecture 42 Grouping and splitting arrays

Lecture 43 Adding axes

Lecture 44 Reordering elements

Lecture 45 Adding or removing elements

Lecture 46 Unique values

Lecture 47 HOMEWORK

Lecture 48 Solution

Section 9: Indexing functions

Lecture 49 Indexing functions 1

Lecture 50 Indexing functions 2

Lecture 51 Indexing functions 3

Lecture 52 HOMEWORK

Lecture 53 Solution

Section 10: Universal functions

Lecture 54 Mathematical functions 1

Lecture 55 Mathematical functions 2

Lecture 56 Trigonometric functions

Lecture 57 Comparison functions

Lecture 58 Float functions

Lecture 59 HOMEWORK

Lecture 60 Solution

Section 11: Linear algebra

Lecture 61 Basic operations

Lecture 62 Matrix and vector products 1

Lecture 63 Matrix and vector products 2

Lecture 64 HOMEWORK

Lecture 65 Solution

Section 12: Statistic

Lecture 66 Basic intuition

Lecture 67 Statistic 1

Lecture 68 Statistic 2

Lecture 69 HOMEWORK

Lecture 70 Solution

Section 13: Saving and loading arrays

Lecture 71 Saving and loading arrays

Lecture 72 HOMEWORK

Lecture 73 Solution

Section 14: Final remarks

Lecture 74 Final remarks

Lecture 75 BONUS

Python developers interested in learning how to optimize operations involving vector and matrix calculations.,Data Science students seeking essential knowledge in one of the key libraries for data manipulation and analysis in Python.,Data Science professionals looking to deepen their understanding of the main concepts and functionalities of one of the most commonly used libraries in their daily work.

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