ArjanCodes
17 minutes
Pandas Data Type Optimization in Python Using Categorical Types to Reduce Memory Usage
Pandas represents tabular data (a DataFrame, composed of one-dimensional Series columns) using NumPy arrays and dtypes plus its own extension types, and anything it cannot classify falls back to the generic object type. Choosing a data type that matches the actual data determines memory use and performance: the categorical type stores each distinct value once and replaces repeated strings with compact integer codes, so it pays off when a column's number of distinct values is far smaller than its number of records. This belongs to data analysis and performance engineering in Python, applying the general principles of type systems, dictionary (categorical) encoding, and memory-efficient data representation.