Learn about some of the many functions available in the NumPy package such as sin, cos and others. You can see how these are universal functions (ufuncs) that work both on scalars and arrays. Learn how to create your own ufunc using the “vectorize” decora
- [Instructor] We're not going to cover…all of NumPy's functions.…As you start working with NumPy,…you'll find the ones useful for you…and commit them to memory.…However, there's one aspect of NumPy functions…that I'd like to discuss.…Let's start with a small example.…We're doing np.sin of np.pi divided by two,…and we get one.…Sin works as expected.…Remember that NumPy expects degrees and radians.…And you might wonder why NumPy bothers…to define a function that's already defined in Python.…The reason is as follows.…Let's create a vector,…np.arange…from minus 3 to 3.…
And now we can call NumPy.sin on the vector itself…and get the results.…A lot of NumPy functions are universal functions…or ufuncs for short.…It means they can work both on scalars,…plain numbers and the rest.…This is very handy and let's us work…with the same function regardless of the input type.…What happens if you'd like to have your own function?…Do you need to check the type of the input…every time a function is called?…Well, you can.…But NumPy provides us with an easier way.…
- 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
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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