Source code for pandas_ta.statistics.variance

# -*- coding: utf-8 -*-
from pandas_ta import Imports
from pandas_ta.utils import get_offset, verify_series

[docs]def variance(close, length=None, ddof=None, talib=None, offset=None, **kwargs): """Indicator: Variance""" # Validate Arguments length = int(length) if length and length > 1 else 30 ddof = int(ddof) if isinstance(ddof, int) and ddof >= 0 and ddof < length else 1 min_periods = int(kwargs["min_periods"]) if "min_periods" in kwargs and kwargs["min_periods"] is not None else length close = verify_series(close, max(length, min_periods)) offset = get_offset(offset) mode_tal = bool(talib) if isinstance(talib, bool) else True if close is None: return # Calculate Result if Imports["talib"] and mode_tal: from talib import VAR variance = VAR(close, length) else: variance = close.rolling(length, min_periods=min_periods).var(ddof) # Offset if offset != 0: variance = variance.shift(offset) # Handle fills if "fillna" in kwargs: variance.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: variance.fillna(method=kwargs["fill_method"], inplace=True) # Name & Category = f"VAR_{length}" variance.category = "statistics" return variance
variance.__doc__ = \ """Rolling Variance Sources: Calculation: Default Inputs: length=30 VARIANCE = close.rolling(length).var() Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 30 ddof (int): Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. Default: 0 talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib version. Default: True offset (int): How many periods to offset the result. Default: 0 Kwargs: fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method Returns: pd.Series: New feature generated. """