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Introduction to NumPy

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Universal Functions

11:01 with Craig Dennis

Universal functions are vectorized functions that can work on all values in an array at once. This is part of the magic of how things work so fast in NumPy. Let's explore the common ones.

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My Notes for New Way of Thinking 

## Linear Algebra
* There is a module for linear algebra, [linalg](https://docs.scipy.org/doc/numpy/reference/routines.linalg.html)
* You can solve for a system of equations using the [solve function](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.solve.html#numpy.linalg.solve)
    * You can create a square 2 dimensional matrix and a constant row vector and solve for each variable column
    * You can double check the answer using the inner product or [dot](https://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html#numpy.dot).
* You can use the `@` to produce the dot product of two arrays.

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    It's not only faster, but we usually ended up writing fewer lines of code. 0:00
    Out of the box, there are quite a few NumPy functions that are available for 0:02
    you to use that are already vectorized. 0:05
    These are referred to as universal functions, or ufuncs. 0:07
    When there's one of these universal functions available for 0:11
    what you're trying to accomplish, you want to ensure that make use of it. 0:14
    Before you start writing a loop, look first to the ufuncs. 0:17
    Let's take a look at the more popular examples of these universal functions and 0:21
    then I'll show you where to learn more. 0:26
    Here are my notes from the linear algebra exercise that we just did. 0:28

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