If you’re looking to read more on NumPy indexing, grab some coffee and head to the Indexing section in the NumPy docs. While you will use some indexing in practice here, NumPy’s complete indexing schematics, which extend Python’s slicing syntax, are their own beast.
Np treat array as element how to#
In this tutorial, you’ll see step by step how to take advantage of vectorization and broadcasting, so that you can use NumPy to its full capacity. When it comes to computation, there are really three concepts that lend NumPy its power: Why does speed matter? The reason that microperformance is worth monitoring is that small differences in runtime become amplified with repeated function calls: an incremental 50 μs of overhead, repeated over 1 million function calls, translates to 50 seconds of incremental runtime.
The runtime of an operation taking 50 microseconds (50 μs) falls under the realm of microperformance, which can loosely be defined as operations with a runtime between 1 microsecond and 1 millisecond. Granted, few people would categorize something that takes 50 microseconds (fifty millionths of a second) as “slow.” However, computers might beg to differ. One option suited for fast numerical operations is NumPy, which deservedly bills itself as the fundamental package for scientific computing with Python. Fortunately, there are a handful of ways to speed up operation runtime in Python without sacrificing ease of use. It is sometimes said that Python, compared to low-level languages such as C++, improves development time at the expense of runtime.
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