Maximizing Python Speed with Numpy Vectorization (Part 1)
Why do people say Python is slow? How do you analyze a Python algorithm to find room for improvement?
We will walk you through the steps of how to think about optimizing a time series clustering algorithm using numpy vectorization techniques.
In Part 1 of this series, Sean will explain why numpy is fast and dive into the code that reduces the benchmark from 6 minutes to less than 10 seconds.
0:48 Why is SQL slow for this?
1:45 The essence of the problem
3:03 Agglomerative clustering
4:31 Why list of lists is slow?
5:04 What does contiguous mean?
7:05 How does vectorization help us?
MUSIC
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