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Introduction to NumPy

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New Way of Thinking

9:58 with Craig Dennis

Let's take a look at a detailed example that uses the new array programming paradigm. We'll have NumPy do some linear algebra for us!

Code

orders = np.array([
    [2, 0, 0, 0],
    [4, 1, 2, 2],
    [0, 1, 0, 1],
    [6, 0, 1, 2]
])

totals = np.array([3, 20.50, 10, 14.25])

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My Notes from Manipulation 

## Array Manipulation
* The documentation on [Array Manipulation](https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html) is a good one to keep bookmarked.
* `ndarray.reshape` creates a view with a new shape
  * You can use `-1` as a value to infer the missing dimension
* `ndarray.ravel` returns a single dimensional view of the array.
* `ndarray.flatten` can be used to make a single dimensional copy.
* `np.lookfor` is great for searching docstrings from within a notebook.

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    You can create, inspect, and modify them. 0:00
    You can manipulate them into new shapes, and 0:01
    you can filter out the values you don't want. 0:03
    And now, I'd like to take some to show off some common use cases for 0:05
    these arrays that we've been getting to know. 0:08
    But before we dive in here, 0:11
    I'd like to take a second to discuss a phenomenon known as imposter syndrome. 0:12
    Imposter syndrome happens when you doubt your skills, and 0:19
    you feel like you really aren't a pro. 0:21
    You feel like a fraud. 0:23
    You feel like everyone else knows more than you. 0:24
    It's an unfortunate and quite common feeling, and 0:27
    I want you to know that you aren't alone in experiencing it. 0:29

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