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Fonction corrcoef - module numpy

Signature de la fonction corrcoef

def corrcoef(x, y=None, rowvar=True, bias=<no value>, ddof=<no value>, *, dtype=None) 

Description

corrcoef.__doc__

    Return Pearson product-moment correlation coefficients.

    Please refer to the documentation for `cov` for more detail.  The
    relationship between the correlation coefficient matrix, `R`, and the
    covariance matrix, `C`, is

    .. math:: R_{ij} = \frac{ C_{ij} } { \sqrt{ C_{ii} * C_{jj} } }

    The values of `R` are between -1 and 1, inclusive.

    Parameters
    ----------
    x : array_like
        A 1-D or 2-D array containing multiple variables and observations.
        Each row of `x` represents a variable, and each column a single
        observation of all those variables. Also see `rowvar` below.
    y : array_like, optional
        An additional set of variables and observations. `y` has the same
        shape as `x`.
    rowvar : bool, optional
        If `rowvar` is True (default), then each row represents a
        variable, with observations in the columns. Otherwise, the relationship
        is transposed: each column represents a variable, while the rows
        contain observations.
    bias : _NoValue, optional
        Has no effect, do not use.

        .. deprecated:: 1.10.0
    ddof : _NoValue, optional
        Has no effect, do not use.

        .. deprecated:: 1.10.0
    dtype : data-type, optional
        Data-type of the result. By default, the return data-type will have
        at least `numpy.float64` precision.

        .. versionadded:: 1.20

    Returns
    -------
    R : ndarray
        The correlation coefficient matrix of the variables.

    See Also
    --------
    cov : Covariance matrix

    Notes
    -----
    Due to floating point rounding the resulting array may not be Hermitian,
    the diagonal elements may not be 1, and the elements may not satisfy the
    inequality abs(a) <= 1. The real and imaginary parts are clipped to the
    interval [-1,  1] in an attempt to improve on that situation but is not
    much help in the complex case.

    This function accepts but discards arguments `bias` and `ddof`.  This is
    for backwards compatibility with previous versions of this function.  These
    arguments had no effect on the return values of the function and can be
    safely ignored in this and previous versions of numpy.

    Examples
    --------
    In this example we generate two random arrays, ``xarr`` and ``yarr``, and
    compute the row-wise and column-wise Pearson correlation coefficients,
    ``R``. Since ``rowvar`` is  true by  default, we first find the row-wise
    Pearson correlation coefficients between the variables of ``xarr``.

    >>> import numpy as np
    >>> rng = np.random.default_rng(seed=42)
    >>> xarr = rng.random((3, 3))
    >>> xarr
    array([[0.77395605, 0.43887844, 0.85859792],
           [0.69736803, 0.09417735, 0.97562235],
           [0.7611397 , 0.78606431, 0.12811363]])
    >>> R1 = np.corrcoef(xarr)
    >>> R1
    array([[ 1.        ,  0.99256089, -0.68080986],
           [ 0.99256089,  1.        , -0.76492172],
           [-0.68080986, -0.76492172,  1.        ]])

    If we add another set of variables and observations ``yarr``, we can
    compute the row-wise Pearson correlation coefficients between the
    variables in ``xarr`` and ``yarr``.

    >>> yarr = rng.random((3, 3))
    >>> yarr
    array([[0.45038594, 0.37079802, 0.92676499],
           [0.64386512, 0.82276161, 0.4434142 ],
           [0.22723872, 0.55458479, 0.06381726]])
    >>> R2 = np.corrcoef(xarr, yarr)
    >>> R2
    array([[ 1.        ,  0.99256089, -0.68080986,  0.75008178, -0.934284  ,
            -0.99004057],
           [ 0.99256089,  1.        , -0.76492172,  0.82502011, -0.97074098,
            -0.99981569],
           [-0.68080986, -0.76492172,  1.        , -0.99507202,  0.89721355,
             0.77714685],
           [ 0.75008178,  0.82502011, -0.99507202,  1.        , -0.93657855,
            -0.83571711],
           [-0.934284  , -0.97074098,  0.89721355, -0.93657855,  1.        ,
             0.97517215],
           [-0.99004057, -0.99981569,  0.77714685, -0.83571711,  0.97517215,
             1.        ]])

    Finally if we use the option ``rowvar=False``, the columns are now
    being treated as the variables and we will find the column-wise Pearson
    correlation coefficients between variables in ``xarr`` and ``yarr``.

    >>> R3 = np.corrcoef(xarr, yarr, rowvar=False)
    >>> R3
    array([[ 1.        ,  0.77598074, -0.47458546, -0.75078643, -0.9665554 ,
             0.22423734],
           [ 0.77598074,  1.        , -0.92346708, -0.99923895, -0.58826587,
            -0.44069024],
           [-0.47458546, -0.92346708,  1.        ,  0.93773029,  0.23297648,
             0.75137473],
           [-0.75078643, -0.99923895,  0.93773029,  1.        ,  0.55627469,
             0.47536961],
           [-0.9665554 , -0.58826587,  0.23297648,  0.55627469,  1.        ,
            -0.46666491],
           [ 0.22423734, -0.44069024,  0.75137473,  0.47536961, -0.46666491,
             1.        ]])