Learn how to use Numba JIT compiler to speed your Python and NumPy code. You can also take a look at Cython for speeding up code and integration with code written in C as shared libraries.
- [Instructor] As we've seen most of the time,…NumPy and Pandas provide us with enough speed for our needs.…Especially when we avoid using for loops,…and use vectorized operation or ufuncs.…However, there are cases when we can vectorize our code…or that we'd like to squeeze even more performance…for our code.…The common approach in Python is to write…some performance sensitive code in C,…and use it from Python.…However, before going that route,…let me show you two other approaches…that are less painful.…We're going to start with Numba.…
Numba is a JIT or just-in-time compiler for Python,…which means it analyzes the behavior…of the code at run time,…and then produces optimized code using the LLVM toolchain.…The first time our function is called, it will be in Python.…But after the first time, the optimized code…that Numba generated is going to get called.…Let's see it in action.…We open a new notebook.…Let's call this one speed.…Let's write a function with a for loop.…So def polyn of n,…total equals to zero,…then for i in range of n,…
- Working with Jupyter notebooks
- Using code cells
- Extensions to the Python language
- Markdown cells
- Editing notebooks
- NumPy basics
- Broadcasting, array operations, and ufuncs
- Folium and Geo
- Machine learning with scikit-learn
- Plotting with matplotlib and bokeh
- Branching into Numba, Cython, deep learning, and NLP
Skill Level Intermediate
NumPy Data Science Essential Trainingwith Charles Kelly3h 54m Intermediate
1. Scientific Python Overview
2. The Jupyter Notebook
3. NumPy Basics
Manage environments5m 11s
6. Folium and Geo
7. NY Taxi Data
10. Other Packages
11. Development Process
Next steps1m 33s
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