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1000x faster data manipulation: vectorizing with Pandas and Numpy
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Speaker: Nathan Cheever
The data transformation code you're writing is correct, but potentially
1000x slower than it needs to be! In this talk, we will go over multiple
ways to enhance a data transformation workflow with Pandas and Numpy by
showing how to replace slower, perhaps more familiar, ways of operating on
Pandas data frames with faster-vectorized solutions to common use cases
like:
* if-else logic in applied row-wise functions
* dictionary lookups with conditional logic
* Date comparisons and calculations
* Regex and string column manipulation
* and others! ...
without needing a beefier computer, writing Cython, or other libraries
outside the Pandas ecosystem.
The data transformation code you're writing is correct, but potentially
1000x slower than it needs to be! In this talk, we will go over multiple
ways to enhance a data transformation workflow with Pandas and Numpy by
showing how to replace slower, perhaps more familiar, ways of operating on
Pandas data frames with faster-vectorized solutions to common use cases
like:
* if-else logic in applied row-wise functions
* dictionary lookups with conditional logic
* Date comparisons and calculations
* Regex and string column manipulation
* and others! ...
without needing a beefier computer, writing Cython, or other libraries
outside the Pandas ecosystem.