Learn about NumPy and how it’s the base for most of the Python scientific packages. See some of the differences between NumPy numbers and Python numbers and how this makes NumPy very fast.
- [ Miki] Let's take a look at the building block…of most of the scientific Python packages,…which is NumPy, sometimes pronounced "numpee".…NumPy provides fast arrays, or matrices,…math functions, linear algebra,…randomization, and more.…You may ask,…"Why do we need matrices when we have Python lists?"…Since most Scientific computation…run on homogenous arrays or matrices,…there are better options for both speed…and memory consumption.…We'll open a new notebook from the homepage.…Go over to the new button on the right,…and click on Python three.…
Let's name this notebook NumPy.…Let's start with the Python Integer.…We'll take two and raise it to the power of 1,000.…So two to the power of 1,000, and we run it.…This means that the python integers…are not machine integers.…They take more memory and multiplication…can't be done in hardware.…NumPy provide integers that are machine integers.…They don't scale as much as you want,…but they are much faster.…Also, in scientific computation,…a 64 or 32-bit integer or float…can usually hold all the values that we need.…
- 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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