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
(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue, *,
dtype=None)
| 2756 | |
| 2757 | @array_function_dispatch(_corrcoef_dispatcher) |
| 2758 | def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue, *, |
| 2759 | dtype=None): |
| 2760 | """ |
| 2761 | Return Pearson product-moment correlation coefficients. |
| 2762 | |
| 2763 | Please refer to the documentation for `cov` for more detail. The |
| 2764 | relationship between the correlation coefficient matrix, `R`, and the |
| 2765 | covariance matrix, `C`, is |
| 2766 | |
| 2767 | .. math:: R_{ij} = \\frac{ C_{ij} } { \\sqrt{ C_{ii} C_{jj} } } |
| 2768 | |
| 2769 | The values of `R` are between -1 and 1, inclusive. |
| 2770 | |
| 2771 | Parameters |
| 2772 | ---------- |
| 2773 | x : array_like |
| 2774 | A 1-D or 2-D array containing multiple variables and observations. |
| 2775 | Each row of `x` represents a variable, and each column a single |
| 2776 | observation of all those variables. Also see `rowvar` below. |
| 2777 | y : array_like, optional |
| 2778 | An additional set of variables and observations. `y` has the same |
| 2779 | shape as `x`. |
| 2780 | rowvar : bool, optional |
| 2781 | If `rowvar` is True (default), then each row represents a |
| 2782 | variable, with observations in the columns. Otherwise, the relationship |
| 2783 | is transposed: each column represents a variable, while the rows |
| 2784 | contain observations. |
| 2785 | bias : _NoValue, optional |
| 2786 | Has no effect, do not use. |
| 2787 | |
| 2788 | .. deprecated:: 1.10.0 |
| 2789 | ddof : _NoValue, optional |
| 2790 | Has no effect, do not use. |
| 2791 | |
| 2792 | .. deprecated:: 1.10.0 |
| 2793 | dtype : data-type, optional |
| 2794 | Data-type of the result. By default, the return data-type will have |
| 2795 | at least `numpy.float64` precision. |
| 2796 | |
| 2797 | .. versionadded:: 1.20 |
| 2798 | |
| 2799 | Returns |
| 2800 | ------- |
| 2801 | R : ndarray |
| 2802 | The correlation coefficient matrix of the variables. |
| 2803 | |
| 2804 | See Also |
| 2805 | -------- |
| 2806 | cov : Covariance matrix |
| 2807 | |
| 2808 | Notes |
| 2809 | ----- |
| 2810 | Due to floating point rounding the resulting array may not be Hermitian, |
| 2811 | the diagonal elements may not be 1, and the elements may not satisfy the |
| 2812 | inequality abs(a) <= 1. The real and imaginary parts are clipped to the |
| 2813 | interval [-1, 1] in an attempt to improve on that situation but is not |
| 2814 | much help in the complex case. |
| 2815 |