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Introduction to pandas

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Vectorization and Broadcasting Review

2:00 with Craig Dennis

Vectorization and Broadcasting are what makes NumPy so fast. Pandas' data structures have similar super powers!

Example Code

Vectorization in NumPy

Arrays provide a vectorized method named add which removes the need for you to loop through each value to add things together.

np.array([1, 2, 3]) + np.array([4, 5, 6])

array([5, 7, 9])
Broadcasting in NumPy

Scalar values can be broadcasted to values, it's as if there was an equal sized array of all 1's.

conference_counts = np.array([4, 5, 10, 8, 15])
# Broadcast a scalar value
conference_counts + 1

array([ 5,  6, 11,  9, 16])

In the next step we'll see how to use vectorization and broadcasting in Pandas.

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    NumPy is fast, 0:00
    much faster than we could ever achieve ourselves using just straight Python. 0:01
    One of the reasons for this is that it relies heavily on vectorization. 0:05
    It might have been a while since you heard that term, so 0:10
    I thought we'd take a moment to recall what that means. 0:12
    Vectorization allows us to avoid looping. 0:15
    It provides the ability to work on an entire set of values all at one. 0:18
    Because that abstraction is in place, 0:22
    optimizations are handled at a very low level. 0:24
    That is, if we remember to use them. 0:27
    Because Panda sits directly on top of NumPy, I wanna make sure that you 0:29
    don't forget about the vectorization superpowers that are available to you. 0:33
    Just like in NumPy, whenever you start to write a loop, you should pause and 0:38
    think about how you might solve the problem in a vectorized manner. 0:42
    A key feature that allow vectorization to happen more easily is broadcasting. 0:45
    Broadcasting enables you to use similar enough values in element to element based 0:50
    operations. 0:55
    You don't have to have the same number of elements, 0:56
    your intention can be figured out in context. 0:59
    For instance, let's assume that we work in the learning and 1:01
    development department of a business. 1:04
    And we wanna keep track of how many conferences our employees are attending. 1:06
    We have a single dimensional NumPy array, where each element represents the count of 1:10
    all conferences attended by specific employees. 1:14
    There's a conference that comes through our town, and so 1:17
    we send every single employee to it. 1:20
    What we really want is to increment each of these values by 1. 1:21
    But before we simply just loop through these, we should step back and 1:25
    lean on vectorization. 1:30
    We currently don't have an array the same size, full of 1s. 1:31
    We could create one, but that'd be some extra work. 1:34
    What we can do is just add 1, and 1:37
    our scalar value will be assumed to be the same size when the vectorization occurs. 1:39
    Is just as if we had an array of all 1s. 1:45
    Our 1 is broadcasted to all entries. 1:47
    The series object supports both vectorization and broadcasting. 1:50
    They are important tools to remember that you have at your disposal. 1:55
    Let's explore how they work. 1:58

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