Implementation:TA Lib Ta lib python Abstract Input Preparation

Knowledge Sources	ta-lib-python
Domains	Data_Preparation, Technical_Analysis
Last Updated	2026-02-09 22:00 GMT

Overview

Concrete pattern for preparing OHLCV input data as dict, pandas DataFrame, or polars DataFrame for the Abstract API's set_input_arrays method.

Description

The Abstract API validates and stores input data via the set_input_arrays() method (or by assignment to the input_arrays property). It checks that all required price series keys exist in the provided data structure. The accepted types are defined in __INPUT_ARRAYS_TYPES which includes dict, pandas.DataFrame, and polars.DataFrame (when available).

Usage

Prepare a dict or DataFrame with OHLCV columns before passing to any Abstract API Function object.

Code Reference

Source Location

• Repository: ta-lib-python
• File: talib/_abstract.pxi
• Lines: L21-69 (__INPUT_PRICE_SERIES_DEFAULTS and input type detection)

Signature

# Input data preparation patterns
# Pattern 1: Dict of numpy arrays
inputs = {
    'open': np.array([...], dtype=float),
    'high': np.array([...], dtype=float),
    'low': np.array([...], dtype=float),
    'close': np.array([...], dtype=float),
    'volume': np.array([...], dtype=float),
}

# Pattern 2: Pandas DataFrame
inputs = pd.DataFrame({
    'open': [...], 'high': [...], 'low': [...],
    'close': [...], 'volume': [...]
})

# Pattern 3: Polars DataFrame
inputs = pl.DataFrame({
    'open': [...], 'high': [...], 'low': [...],
    'close': [...], 'volume': [...]
})

Import

import numpy as np
from talib import abstract

I/O Contract

Inputs

Name	Type	Required	Description
input_arrays	dict / pd.DataFrame / pl.DataFrame	Yes	OHLCV data with named price series keys

Outputs

Name	Type	Description
validated_data	dict / pd.DataFrame / pl.DataFrame	Same structure, stored for indicator computation

Usage Examples

Dict Input

import numpy as np
from talib import abstract

inputs = {
    'open': np.random.random(100),
    'high': np.random.random(100) + 1,
    'low': np.random.random(100),
    'close': np.random.random(100) + 0.5,
    'volume': np.random.random(100) * 1000,
}

sma = abstract.Function('SMA')
sma.set_input_arrays(inputs)

DataFrame Input

import pandas as pd
from talib import abstract

df = pd.DataFrame({
    'open': np.random.random(100),
    'high': np.random.random(100) + 1,
    'low': np.random.random(100),
    'close': np.random.random(100) + 0.5,
    'volume': np.random.random(100) * 1000,
})

sma = abstract.Function('SMA')
result = sma.run(df)  # Returns pandas Series

Related Pages

Implements Principle

• Principle:TA_Lib_Ta_lib_python_Abstract_Data_Input