Cut Pandas DataFrame Memory Usage by 90% with Simple Type Conversions

This tutorial shows how to dramatically reduce pandas DataFrame memory consumption—by up to 90%—by inspecting internal storage, downcasting numeric columns, converting object columns to categoricals, and specifying optimal dtypes while reading CSV data, all demonstrated on a large MLB game logs dataset.

Memory Optimizationcategoricaldataframe

0 likes · 18 min read

Cut Pandas DataFrame Memory Usage by 90% with Simple Type Conversions

Python Programming Learning Circle

Aug 12, 2021 · Big Data

Optimizing pandas DataFrames to Reduce Memory Usage by Up to 90%

This tutorial demonstrates how to analyze pandas memory consumption, downcast numeric columns, convert object columns to categoricals, and specify optimal dtypes when reading CSV files, achieving a reduction of nearly 90% in DataFrame memory usage while preserving full analytical capabilities.

Memory OptimizationPythonbig-data

0 likes · 17 min read

Optimizing pandas DataFrames to Reduce Memory Usage by Up to 90%

Python Programming Learning Circle

Oct 29, 2020 · Fundamentals

Optimizing Pandas Memory Usage for Baseball Game Data

This article demonstrates how to reduce pandas DataFrame memory consumption by selecting appropriate column data types, downcasting numeric types, converting object columns to categorical, and specifying optimal dtypes during CSV import, using a 130‑year baseball dataset as a practical example.

Memory Optimizationcategoricaldataframe

0 likes · 12 min read

Optimizing Pandas Memory Usage for Baseball Game Data