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Whenever possible switch to using categorical dtypes instead of object, at it allows to significantly reduce memory utilization when number of repeated values in a column is more than 50%.
Pay extra attention as comparison of dataframes (e.g. in tests) is sensible to dtypes (e.g. order of the values in category). Here is an article providing more information about internal structures of dataframes in pandas.
The text was updated successfully, but these errors were encountered:
khaeru
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Use categorical dtypes to optimize memory itilization
Return categorical dtypes from (JDBC)Backend to reduce memory usage
Nov 29, 2019
Whenever possible switch to using categorical dtypes instead of object, at it allows to significantly reduce memory utilization when number of repeated values in a column is more than 50%.
Pay extra attention as comparison of dataframes (e.g. in tests) is sensible to dtypes (e.g. order of the values in category).
Here is an article providing more information about internal structures of dataframes in pandas.
The text was updated successfully, but these errors were encountered: