From Python to Numpy

Gain insights into utilizing NumPy for data manipulation and analytics. Learn about implementing concepts in both Python and NumPy through coding challenges and quizzes.

Intermediate

45 Lessons

Certificate of Completion

Gain insights into utilizing NumPy for data manipulation and analytics. Learn about implementing concepts in both Python and NumPy through coding challenges and quizzes.

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Explanations

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This course includes

81 Playgrounds

17 Quizzes

This course includes

81 Playgrounds

17 Quizzes

Course Overview

If you're looking to grow your career in machine learning or data science in this day and age, adding a powerful library to your skill set is an important place to start. In that vein, Python has become one of the most widely used tools in the industry for serious data analytics, and NumPy is probably the most widely used data analytics library. With NumPy, you can manipulate data involving multi-dimensional arrays and matrices (think linear algebra). Join us as we venture into the vast world of NumPy in t...Show More

Course Content

Introduction

Get familiar with NumPy fundamentals, including array creation, reshaping, indexing, and vectorization.

What is NumPy?

Creation in NumPy Reshaping in NumPy Indexing in NumPy Broadcasting in NumPy NumPy Vectorization Readability vs. Speed

Anatomy of an Array

Look at the foundational elements of NumPy arrays, memory layout, views, copies, and optimization.

Introduction Memory layout Views and Copies

Coding Example: How to find if one vector is view of the other?Solution Review

Code Vectorization

Examine methods to enhance Python code efficiency using NumPy for various simulations.

Introduction Uniform Vectorization Coding Example: Game of life (Python approach)Coding Example: Game of life (NumPy approach)Coding Example: Reaction-Diffusion Temporal Vectorization Coding Example: The Mandelbrot Set (Python approach)Coding Example: The Mandelbrot Set (NumPy approach)Coding Example: Minkowski-Bouligand Dimension Spatial Vectorization Coding Example: Implement the behavior of Boids (Python approach)Coding Example: Implement the behavior of Boids (NumPy approach)Conclusion

Problem Vectorization

Find out about problem vectorization to enhance algorithmic efficiency in Python and NumPy.

Introduction Coding Example: Find shortest path in a maze Coding Example: Find shortest path in a maze (Breadth-First approach)Coding Example: Find shortest path in a maze (Bellman-Ford approach)Coding Example: Fluid Dynamics Coding Example: Blue Noise Sampling Coding Example: Blue Noise Sampling using DART method Coding Example: Blue Noise Sampling using Bridson method Conclusion

Custom Vectorization

Dig into custom vectorization techniques using Typed Lists and memory-aware NumPy arrays.

Typed list Coding Example: Modifying the list Memory-aware Array: Glumpy Memory-aware Array: Array Subclass in GPUData class Conclusion

Beyond NumPy

4 Lessons

Implement optimized Python techniques and explore advanced libraries beyond NumPy for enhanced performance.

Conclusion

2 Lessons

Master the steps to effectively leverage NumPy for creative and efficient problem-solving.

Course Author

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Emma Bostian 🐞

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Evan Dunbar

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Anthony Walker

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Emma Bostian 🐞

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