Understanding The Numpy Mindset • Numerical Python

[mitsumi]

Understanding The Numpy Mindset • Numerical Python

Published 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.43 GB | Duration: 3h 33m

A concise course to master the NumPy fundamentals

What you'll learn
Learn the fundamentals of Python's NumPy package
Understand the mindset needed to work with NumPy
Learn to create and use NumPy's ndarray object
Use NumPy in real-world examples
Requirements
You should be familiar with Python fundamentals such as control statements, defining functions, using data structures such as lists
Description
This is a concise course that covers the fundamentals of Python's NumPy package. Most students who learn NumPy for the first time say that it feels different from the core Python they learnt. And they're right.NumPy requires a different mindset. There's a reason why NumPy does things differently and if you understand why things are the way they are in NumPy, the rest of your NumPy journey will be easier.This course doesn't try to cover everything in the NumPy package. That's impossible and not desirable. Instead, I designed this course to be concise and to focus on what really matters:Understanding the core topics in NumPyUnderstanding the NumPy mindsetThis course covers the following topics:NumPy's main data structure, the ndarrayVectorisation in NumPyArrays in higher dimensionsThe basics of broadcastingFiltering NumPy arrays using Boolean operations and Boolean indexingReading external data using NumPyRepresenting equations with NumPyAnd of course, throughout the whole course you'll get familiar with the NumPy mindset.---About me, your instructorI've been teaching Python and NumPy for a decade. Before that, I worked as a physicist and used numerical and scientific programming in my research work for over a decade.My focus is on communicating clearly, in a friendly and relaxed manner. I'm the author of the The Python Coding Book (you can ask Google for a "python book" and you'll find this book as one of the first entries) and I have taught Python to individuals and corporations around the world.And I'm approachable. You can ask me questions and I'll always reply, whether here on social media or anywhere else you can find me!
Overview
Section 1: On the Road to NumPy
Lecture 1 What's This Course About?
Lecture 2 Let's Start With Lists. Yes, Lists
Lecture 3 The Lesser-Known Array
Section 2: Understanding NumPy's `ndarray` Data Type
Lecture 4 Installing NumPy
Lecture 5 Finally, Say Hello to `ndarray`
Lecture 6 Looking Ahead to Some Other `ndarray` Features
Section 3: Vectorisation • A Fancy Word For NumPy's "Superpower"
Lecture 7 Performing Operations Element-by-Element in NumPy • Vectorisation
Lecture 8 Comparing Loops With Lists, List Comprehensions, and NumPy Vectorisation
Lecture 9 Let's Race • Timing The Three Versions
Lecture 10 A Glimpse At NumPy's Documentation
Section 4: Multiple Dimensions in NumPy Arrays
Lecture 11 More Than One Dimension
Lecture 12 Creating Arrays of Random Numbers • The Five-Player Three-Round Game
Lecture 13 Broadcasting • A Brief Introduction
Lecture 14 Another Glimpse At NumPy's Documentation
Lecture 15 Let's Add One More Dimension • 3D Arrays
Section 5: Boolean Operations, Boolean Indexing, Filtering, And More
Lecture 16 Boolean Operations on NumPy Arrays
Lecture 17 Boolean Indexing and Filtering
Lecture 18 There's Lots More in NumPy's API
Lecture 19 Views and Copies • A Brief Introduction
Section 6: The Met Office Temperature Dataset
Lecture 20 Reading The Data From a CSV File to a NumPy Array
Lecture 21 Remove What We Don't Need From The Array
Lecture 22 Finding the Minimum, Maximum, and Mean Temperatures
Lecture 23 Grouping Temperatures Using a Histogram
Lecture 24 Grouping Temperatures Using a Histogram • Plotting the Data
Section 7: Representing Equations Using NumPy
Lecture 25 From an Equation on Paper to An Equation on Computer • `np.arange()` and `np.lin
Lecture 26 Plotting The Equation
Lecture 27 Final Words
Python programmers who are keen to learn about Python's key numerical programming package: NumPy

Screenshots

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