pandas/core/window/ewm.py
def cov(
        self,
        other: Optional[Union[np.ndarray, FrameOrSeries]] = None,
        pairwise: Optional[bool] = None,
        bias: bool = False,
        **kwargs,
    ):
        """
        Exponential weighted sample covariance.

        Parameters
        ----------
        other : Series, DataFrame, or ndarray, optional
            If not supplied then will default to self and produce pairwise
            output.
        pairwise : bool, default None
            If False then only matching columns between self and other will be
            used and the output will be a DataFrame.
            If True then all pairwise combinations will be calculated and the
            output will be a MultiIndex DataFrame in the case of DataFrame
            inputs. In the case of missing elements, only complete pairwise
            observations will be used.
        bias : bool, default False
            Use a standard estimation bias correction.
        **kwargs
           Keyword arguments to be passed into func.
        """
        if other is None:
            other = self._selected_obj
            # only default unset
            pairwise = True if pairwise is None else pairwise
        other = self._shallow_copy(other)

        def _get_cov(X, Y):
            X = self._shallow_copy(X)
            Y = self._shallow_copy(Y)
            cov = window_aggregations.ewmcov(
                X._prep_values(),
                np.array([0], dtype=np.int64),
                np.array([0], dtype=np.int64),
                self.min_periods,
                Y._prep_values(),
                self.com,
                self.adjust,
                self.ignore_na,
                bias,
            )
            return wrap_result(X, cov)

        return flex_binary_moment(
            self._selected_obj, other._selected_obj, _get_cov, pairwise=bool(pairwise)
        )

        Exponential weighted sample covariance.

        Parameters
        ----------
        other : Series, DataFrame, or ndarray, optional
            If not supplied then will default to self and produce pairwise
            output.
        pairwise : bool, default None
            If False then only matching columns between self and other will be
            used and the output will be a DataFrame.
            If True then all pairwise combinations will be calculated and the
            output will be a MultiIndex DataFrame in the case of DataFrame
            inputs. In the case of missing elements, only complete pairwise
            observations will be used.
        bias : bool, default False
            Use a standard estimation bias correction.
        **kwargs
           Keyword arguments to be passed into func.
        
Exponential weighted sample covariance.
Parameters

other : Series, DataFrame, or ndarray, optional
If not supplied then will default to self and produce pairwise
output.
pairwise : bool, default None
If False then only matching columns between self and other will be
used and the output will be a DataFrame.
If True then all pairwise combinations will be calculated and the
output will be a MultiIndex DataFrame in the case of DataFrame
inputs. In the case of missing elements, only complete pairwise
observations will be used.
bias : bool, default False
Use a standard estimation bias correction.
kwargs
Keyword arguments to be passed into func.
Exponential weighted sample covariance . Parameters other : Series DataFrame or ndarray optional If not supplied then will default to self and produce pairwise output . pairwise : bool default None If False then only matching columns between self and other will be used and the output will be a DataFrame . If True then all pairwise combinations will be calculated and the output will be a MultiIndex DataFrame in the case of DataFrame inputs . In the case of missing elements only complete pairwise observations will be used . bias : bool default False Use a standard estimation bias correction . kwargs Keyword arguments to be passed into func .
def cov(
        self,
        other: Optional[Union[np.ndarray, FrameOrSeries]] = None,
        pairwise: Optional[bool] = None,
        bias: bool = False,
        **kwargs,
    ):
        if other is None:
            other = self._selected_obj
            pairwise = True if pairwise is None else pairwise
        other = self._shallow_copy(other)
        def _get_cov(X, Y):
            X = self._shallow_copy(X)
            Y = self._shallow_copy(Y)
            cov = window_aggregations.ewmcov(
                X._prep_values(),
                np.array([0], dtype=np.int64),
                np.array([0], dtype=np.int64),
                self.min_periods,
                Y._prep_values(),
                self.com,
                self.adjust,
                self.ignore_na,
                bias,
            )
            return wrap_result(X, cov)
        return flex_binary_moment(
            self._selected_obj, other._selected_obj, _get_cov, pairwise=bool(pairwise)
        )
def cov ( self , other : Optional [ Union [ np . ndarray , FrameOrSeries ] ] = None , pairwise : Optional [ bool ] = None , bias : bool = False , ** kwargs , ) : if other is None : other = self . _selected_obj pairwise = True if pairwise is None else pairwise other = self . _shallow_copy ( other ) def _get_cov ( X , Y ) : X = self . _shallow_copy ( X ) Y = self . _shallow_copy ( Y ) cov = window_aggregations . ewmcov ( X . _prep_values ( ) , np . array ( [ 0 ] , dtype = np . int64 ) , np . array ( [ 0 ] , dtype = np . int64 ) , self . min_periods , Y . _prep_values ( ) , self . com , self . adjust , self . ignore_na , bias , ) return wrap_result ( X , cov ) return flex_binary_moment ( self . _selected_obj , other . _selected_obj , _get_cov , pairwise = bool ( pairwise ) )
Exponential weighted sample covariance.
Exponential weighted sample covariance .
"""
        Exponential weighted sample covariance.

        Parameters
        ----------
        other : Series, DataFrame, or ndarray, optional
            If not supplied then will default to self and produce pairwise
            output.
        pairwise : bool, default None
            If False then only matching columns between self and other will be
            used and the output will be a DataFrame.
            If True then all pairwise combinations will be calculated and the
            output will be a MultiIndex DataFrame in the case of DataFrame
            inputs. In the case of missing elements, only complete pairwise
            observations will be used.
        bias : bool, default False
            Use a standard estimation bias correction.
        **kwargs
           Keyword arguments to be passed into func.
        """ # only default unset
self
other
Optional[Union[np.ndarray, FrameOrSeries]]
pairwise
Optional[bool]
bias
bool