Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Understanding the NumPy Mindset • Numerical Python

Name: Understanding the NumPy Mindset • Numerical Python
Rating: 4.5 (13 reviews)

A concise course to master the NumPy fundamentals

Created byStephen Gruppetta

Last updated 4/2024

English

English [Auto],

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

Course content

7 sections • 27 lectures • 3h 33m total length

What's This Course About?2:46
Explore NumPy, the numerical Python library underpinning quantitative data and data science modules, and understand the main data type ndarray and why a new data type is needed beyond lists.
Let's Start With Lists. Yes, Lists5:39
The Lesser-Known Array7:55
Explore the standard module's array type, converting lists to typed arrays and examining type codes, memory efficiency, and data-type constraints compared to lists as we prepare to learn numpy.

Installing NumPy2:55
You can use tools in your IDE, or go to the Terminal and type:
python -m pip install numpy
If you're using a virtual environment, make sure you've activated the virtual environment before installing.
Finally, Say Hello to `ndarray`9:22
Looking Ahead to Some Other `ndarray` Features7:28
Explore how the numpy ndarray extends lists with multi-dimensional data, and inspect shape and dimensions. Learn to convert data types with astype and perform axis-based max operations.

Performing Operations Element-by-Element in NumPy • Vectorisation8:18
Comparing Loops With Lists, List Comprehensions, and NumPy Vectorisation10:34
Let's Race • Timing The Three Versions9:38
The lecture compares a classic loop, a list comprehension, and numpy for multiplying data by ten, timing each with timeit, and highlights numpy's speed through vectorization.
A Glimpse At NumPy's Documentation3:44
Explore the numpy documentation to master function signatures, default values, and vectorization, and learn how to use numpy.max and ndarray.max with practical examples.

More Than One Dimension6:14
Explore how numpy arrays extend lists to two dimensions, using indexing and slices to access rows, columns, and subarrays, and compare dimensionality and shapes.
Creating Arrays of Random Numbers • The Five-Player Three-Round Game8:14
Broadcasting • A Brief Introduction10:11
Explore NumPy broadcasting by aligning shapes, using 1D and 2D corrections on a 5x3 data array, and applying transpose to broadcast across rows.
Another Glimpse At NumPy's Documentation10:43
Explore how to generate random integers with numpy's generator, customize with low, high, size, and dtype, and create zero and one-filled arrays, preparing for 3D arrays and basic image creation.
Let's Add One More Dimension • 3D Arrays9:53
Explore how to use numpy to create and manipulate 3d arrays (100 by 100 by 3) that represent images, including red, green, and blue channels, and visualize them with matplotlib.

Boolean Operations on NumPy Arrays6:13
Explore how numpy arrays enable vectorized, elementwise operations—multiplication, addition, and exponentiation—producing new arrays and boolean results from comparisons like greater than and equals.
Boolean Indexing and Filtering8:38
There's Lots More in NumPy's API12:45
Explore the basics of NumPy, focusing on broadcasting rules, vectorized operations, and handling arrays of numbers and strings with practical API insights.
Views and Copies • A Brief Introduction10:43
Explore views and copies in numpy, comparing numpy arrays to lists, showing how slicing and advanced indexing can share or copy data, and how the base attribute reveals views.

Reading The Data From a CSV File to a NumPy Array13:46
Learn to read a CSV into a numpy array using loadtxt, handle comma delimiters and missing values, inspect a 7750 by 14 dataset of year, day, temperatures from 1772.
Remove What We Don't Need From The Array6:46
Strip the first two columns to keep only temperatures. Filter out the minus nine nine nines markers with boolean masking and flatten to a 1-D array.
Finding the Minimum, Maximum, and Mean Temperatures4:41
Explore vectorized numpy operations to convert tenths of a degree to degrees Celsius and compute the min, max, and mean temperatures, with dtype as float and formatted output using f-strings.
Grouping Temperatures Using a Histogram6:40
Explore grouping temperatures with numpy histogram, using data arrays and the default ten bins to obtain the histogram and bin edges, and prepare for plotting with matplotlib.
Grouping Temperatures Using a Histogram • Plotting the Data5:10
Plot a histogram of temperatures using NumPy and Matplotlib, showing how bin edges, histogram data, and broadcasting issues affect the visualization.

From an Equation on Paper to An Equation on Computer • `np.arange()` and `np.lin10:44
Understand how numpy translates paper equations into code by sampling x values with np.arange and np.linspace, enabling plot-ready expressions like y = sin(x) - a over x.
Plotting The Equation10:43
Utilize NumPy vectorization to compute y = sin(x - a)/x for x from -10 to 10, and plot an animation showing how y changes as a varies with matplotlib.
Final Words2:57
Finally, explore the NumPy api reference and discover tools that save you from building from scratch. Embrace vectorized programming with the ndarray mindset.

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 NumPy
Understanding the NumPy mindset

This course covers the following topics:

NumPy's main data structure, the ndarray
Vectorisation in NumPy
Arrays in higher dimensions
The basics of broadcasting
Filtering NumPy arrays using Boolean operations and Boolean indexing
Reading external data using NumPy
Representing equations with NumPy

And of course, throughout the whole course you'll get familiar with the NumPy mindset.

---

About me, your instructor

I'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!

Who this course is for:

Python programmers who are keen to learn about Python's key numerical programming package: NumPy

Understanding the NumPy Mindset • Numerical Python

What you'll learn

Explore related topics

Course content

On the Road to NumPy3 lectures • 16min

Understanding NumPy's `ndarray` Data Type3 lectures • 20min

Vectorisation • A Fancy Word For NumPy's "Superpower"4 lectures • 32min

Multiple Dimensions in NumPy Arrays5 lectures • 45min

Boolean Operations, Boolean Indexing, Filtering, And More4 lectures • 38min

The Met Office Temperature Dataset5 lectures • 37min

Representing Equations Using NumPy3 lectures • 24min

Requirements

Description

Who this course is for: