diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 74 |
1 files changed, 70 insertions, 4 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 1053598b1..6ea9cc4de 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -431,10 +431,13 @@ def asarray_chkfinite(a, dtype=None, order=None): of lists and ndarrays. Success requires no NaNs or Infs. dtype : data-type, optional By default, the data-type is inferred from the input data. - order : {'C', 'F'}, optional - Whether to use row-major (C-style) or - column-major (Fortran-style) memory representation. - Defaults to 'C'. + order : {'C', 'F', 'A', 'K'}, optional + Memory layout. 'A' and 'K' depend on the order of input array a. + 'C' row-major (C-style), + 'F' column-major (Fortran-style) memory representation. + 'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise + 'K' (keep) preserve input order + Defaults to 'C'. Returns ------- @@ -2543,6 +2546,69 @@ def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue): 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. ]]) """ if bias is not np._NoValue or ddof is not np._NoValue: |