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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!
What you'll learn
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.
Requirements
Basic programming logic and Python programming, although it's possible to follow the course without deep knowledge in these areas.
Description
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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