Principle:TA Lib Ta lib python DataFrame Integration
| Knowledge Sources | |
|---|---|
| Domains | Data_Preparation, Technical_Analysis |
| Last Updated | 2026-02-09 22:00 GMT |
Overview
A transparent type conversion pattern that allows technical indicator functions to accept and return pandas or polars Series while internally computing on numpy arrays.
Description
Many data analysis workflows use pandas DataFrames or polars DataFrames as their primary data structure. The DataFrame Integration principle enables indicator functions to work seamlessly with these types without requiring manual conversion.
The pattern works bidirectionally:
- Input conversion: Detect pandas/polars Series, convert to float64 numpy arrays
- Computation: Call the underlying C function on numpy arrays
- Output conversion: Convert results back to the original type, preserving metadata (e.g., pandas index)
This is implemented as a decorator pattern that wraps all 161 indicator functions and all streaming functions.
Usage
Use this principle when building data analysis pipelines with pandas or polars. Pass DataFrame columns directly to TA-Lib functions without manual conversion.
Theoretical Basis
The decorator pattern follows a proxy architecture:
# Abstract decorator pattern
def type_transparent_wrapper(indicator_function):
def wrapper(*args, **kwargs):
original_type = detect_input_type(args, kwargs)
numpy_args = convert_to_numpy(args, kwargs)
result = indicator_function(*numpy_args)
return convert_to_original_type(result, original_type)
return wrapper
Key design decisions:
- Type detection: Check if any positional or keyword argument is a pandas/polars Series
- Mutual exclusion: Cannot mix pandas and polars in the same call (raises Exception)
- Streaming awareness: Scalar streaming results are returned as-is, not wrapped in Series
- Tuple handling: Multi-output functions wrap each element of the result tuple individually