Classe « DataFrame »

Méthode pandas.DataFrame.cov

Signature de la méthode cov

def cov(self, min_periods: 'Optional[int]' = None, ddof: 'Optional[int]' = 1) -> 'DataFrame' 

Description

cov.__doc__

        Compute pairwise covariance of columns, excluding NA/null values.

        Compute the pairwise covariance among the series of a DataFrame.
        The returned data frame is the `covariance matrix
        <https://en.wikipedia.org/wiki/Covariance_matrix>`__ of the columns
        of the DataFrame.

        Both NA and null values are automatically excluded from the
        calculation. (See the note below about bias from missing values.)
        A threshold can be set for the minimum number of
        observations for each value created. Comparisons with observations
        below this threshold will be returned as ``NaN``.

        This method is generally used for the analysis of time series data to
        understand the relationship between different measures
        across time.

        Parameters
        ----------
        min_periods : int, optional
            Minimum number of observations required per pair of columns
            to have a valid result.

        ddof : int, default 1
            Delta degrees of freedom.  The divisor used in calculations
            is ``N - ddof``, where ``N`` represents the number of elements.

            .. versionadded:: 1.1.0

        Returns
        -------
        DataFrame
            The covariance matrix of the series of the DataFrame.

        See Also
        --------
        Series.cov : Compute covariance with another Series.
        core.window.ExponentialMovingWindow.cov: Exponential weighted sample covariance.
        core.window.Expanding.cov : Expanding sample covariance.
        core.window.Rolling.cov : Rolling sample covariance.

        Notes
        -----
        Returns the covariance matrix of the DataFrame's time series.
        The covariance is normalized by N-ddof.

        For DataFrames that have Series that are missing data (assuming that
        data is `missing at random
        <https://en.wikipedia.org/wiki/Missing_data#Missing_at_random>`__)
        the returned covariance matrix will be an unbiased estimate
        of the variance and covariance between the member Series.

        However, for many applications this estimate may not be acceptable
        because the estimate covariance matrix is not guaranteed to be positive
        semi-definite. This could lead to estimate correlations having
        absolute values which are greater than one, and/or a non-invertible
        covariance matrix. See `Estimation of covariance matrices
        <https://en.wikipedia.org/w/index.php?title=Estimation_of_covariance_
        matrices>`__ for more details.

        Examples
        --------
        >>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],
        ...                   columns=['dogs', 'cats'])
        >>> df.cov()
                  dogs      cats
        dogs  0.666667 -1.000000
        cats -1.000000  1.666667

        >>> np.random.seed(42)
        >>> df = pd.DataFrame(np.random.randn(1000, 5),
        ...                   columns=['a', 'b', 'c', 'd', 'e'])
        >>> df.cov()
                  a         b         c         d         e
        a  0.998438 -0.020161  0.059277 -0.008943  0.014144
        b -0.020161  1.059352 -0.008543 -0.024738  0.009826
        c  0.059277 -0.008543  1.010670 -0.001486 -0.000271
        d -0.008943 -0.024738 -0.001486  0.921297 -0.013692
        e  0.014144  0.009826 -0.000271 -0.013692  0.977795

        **Minimum number of periods**

        This method also supports an optional ``min_periods`` keyword
        that specifies the required minimum number of non-NA observations for
        each column pair in order to have a valid result:

        >>> np.random.seed(42)
        >>> df = pd.DataFrame(np.random.randn(20, 3),
        ...                   columns=['a', 'b', 'c'])
        >>> df.loc[df.index[:5], 'a'] = np.nan
        >>> df.loc[df.index[5:10], 'b'] = np.nan
        >>> df.cov(min_periods=12)
                  a         b         c
        a  0.316741       NaN -0.150812
        b       NaN  1.248003  0.191417
        c -0.150812  0.191417  0.895202