Last Updated on August 22, 2023 by Editorial Team
Author(s): Dr. Mandar Karhade, MD. PhD.
Originally published on Towards AI.
This tutorial focuses on speeding up string manipulations.
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I got bored and decided to benchmark string manipulation methods and how they affect the performance of a pandas data frame. As it is well-known, Pandas data frames act weirdly once they grow beyond a certain limit. Mostly dependent on the memory pressure and also some overhead when elements from various rows are to be manipulated at once.
So, here is the experiment.
I created a data frame using Faker. The base data is the fake data of 100,000 rows.
!pip install fakerimport pandas as pdimport numpy as npdef gen_data(x): from faker… Read the full blog for free on Medium.
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Published via Towards AI