diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 32 |
1 files changed, 18 insertions, 14 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index d01430c8c..ae1420b72 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -638,7 +638,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, >>> rng = np.random.RandomState(10) # deterministic random data >>> a = np.hstack((rng.normal(size=1000), ... rng.normal(loc=5, scale=2, size=1000))) - >>> plt.hist(a, bins='auto') # plt.hist passes it's arguments to np.histogram + >>> plt.hist(a, bins='auto') # plt.hist passes its arguments to np.histogram >>> plt.title("Histogram with 'auto' bins") >>> plt.show() @@ -1027,9 +1027,16 @@ def average(a, axis=None, weights=None, returned=False): a : array_like Array containing data to be averaged. If `a` is not an array, a conversion is attempted. - axis : int, optional - Axis along which to average `a`. If `None`, averaging is done over - the flattened array. + axis : None or int or tuple of ints, optional + Axis or axes along which to average `a`. The default, + axis=None, will average over all of the elements of the input array. + If axis is negative it counts from the last to the first axis. + + .. versionadded:: 1.7.0 + + If axis is a tuple of ints, averaging is performed on all of the axes + specified in the tuple instead of a single axis or all the axes as + before. weights : array_like, optional An array of weights associated with the values in `a`. Each value in `a` contributes to the average according to its associated weight. @@ -4498,24 +4505,21 @@ def meshgrid(*xi, **kwargs): "Valid values for `indexing` are 'xy' and 'ij'.") s0 = (1,) * ndim - output = [np.asanyarray(x).reshape(s0[:i] + (-1,) + s0[i + 1::]) + output = [np.asanyarray(x).reshape(s0[:i] + (-1,) + s0[i + 1:]) for i, x in enumerate(xi)] - shape = [x.size for x in output] - if indexing == 'xy' and ndim > 1: # switch first and second axis - output[0].shape = (1, -1) + (1,)*(ndim - 2) - output[1].shape = (-1, 1) + (1,)*(ndim - 2) - shape[0], shape[1] = shape[1], shape[0] + output[0].shape = (1, -1) + s0[2:] + output[1].shape = (-1, 1) + s0[2:] - if copy_: - output = [x.copy() for x in output] - - if not sparse and len(output) > 0: + if not sparse: # Return the full N-D matrix (not only the 1-D vector) output = np.broadcast_arrays(*output, subok=True) + if copy_: + output = [x.copy() for x in output] + return output |