You can talk about the richness of the scientific Python ecosystem. Learn how to pick the right open source package for your project and some guidelines on how to evaluate it.
- [Instructor] There are dozens, or even hundreds…of open source packages for scientific…computation in Python.…People sometimes find this intimidating.…Which package should you choose?…Here are some guidelines that I use…when evaluating packages.…Ask around.…The community is helpful and people are knowledgeable…about which packages are best for your needs.…Most packages are hosted on GitHub.…Go over and check the activity, number of contributors,…number of stars, et cetera.…For example, if you head over to the Pandas GitHub page,…you will see that the last comment…was an hour ago, on the right.…
There are more than 700 people who contributed…to the project, here on the right.…And the project has more than 9,000 stars,…on the top and to the right.…These are signs of a good, healthy project.…Don't be intimidated by the number of issues.…Some of them are feature requests,…and as we say, packages that don't have bugs…are packages that no one used.…After you find the package you like,…go and evaluate it.…Use it on real data and see how it behaves,…
- 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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