diff options
Diffstat (limited to 'numpy/add_newdocs.py')
| -rw-r--r-- | numpy/add_newdocs.py | 28 |
1 files changed, 14 insertions, 14 deletions
diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py index 6f3a10de4..a88a782b4 100644 --- a/numpy/add_newdocs.py +++ b/numpy/add_newdocs.py @@ -3797,7 +3797,7 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('put', add_newdoc('numpy.core.multiarray', 'copyto', """ - copyto(dst, src, casting='same_kind', where=None, preservena=False) + copyto(dst, src, casting='same_kind', where=None) Copies values from one array to another, broadcasting as necessary. @@ -3825,9 +3825,6 @@ add_newdoc('numpy.core.multiarray', 'copyto', A boolean array which is broadcasted to match the dimensions of `dst`, and selects elements to copy from `src` to `dst` wherever it contains the value True. - preservena : bool, optional - If set to True, leaves any NA values in `dst` untouched. This - is similar to the "hard mask" feature in numpy.ma. """) @@ -3842,11 +3839,6 @@ add_newdoc('numpy.core.multiarray', 'putmask', If `values` is not the same size as `a` and `mask` then it will repeat. This gives behavior different from ``a[mask] = values``. - .. note:: The `putmask` functionality is also provided by `copyto`, which - can be significantly faster and in addition is NA-aware - (`preservena` keyword). Replacing `putmask` with - ``np.copyto(a, values, where=mask)`` is recommended. - Parameters ---------- a : array_like @@ -4845,14 +4837,15 @@ add_newdoc('numpy.lib._compiled_base', 'digitize', Parameters ---------- x : array_like - Input array to be binned. It has to be 1-dimensional. + Input array to be binned. Prior to Numpy 1.10.0, this array had to + be 1-dimensional, but can now have any shape. bins : array_like Array of bins. It has to be 1-dimensional and monotonic. right : bool, optional Indicating whether the intervals include the right or the left bin edge. Default behavior is (right==False) indicating that the interval - does not include the right edge. The left bin and is open in this - case. Ie., bins[i-1] <= x < bins[i] is the default behavior for + does not include the right edge. The left bin end is open in this + case, i.e., bins[i-1] <= x < bins[i] is the default behavior for monotonically increasing bins. Returns @@ -4863,7 +4856,7 @@ add_newdoc('numpy.lib._compiled_base', 'digitize', Raises ------ ValueError - If the input is not 1-dimensional, or if `bins` is not monotonic. + If `bins` is not monotonic. TypeError If the type of the input is complex. @@ -4877,6 +4870,13 @@ add_newdoc('numpy.lib._compiled_base', 'digitize', attempting to index `bins` with the indices that `digitize` returns will result in an IndexError. + .. versionadded:: 1.10.0 + + `np.digitize` is implemented in terms of `np.searchsorted`. This means + that a binary search is used to bin the values, which scales much better + for larger number of bins than the previous linear search. It also removes + the requirement for the input array to be 1-dimensional. + Examples -------- >>> x = np.array([0.2, 6.4, 3.0, 1.6]) @@ -4893,7 +4893,7 @@ add_newdoc('numpy.lib._compiled_base', 'digitize', 1.0 <= 1.6 < 2.5 >>> x = np.array([1.2, 10.0, 12.4, 15.5, 20.]) - >>> bins = np.array([0,5,10,15,20]) + >>> bins = np.array([0, 5, 10, 15, 20]) >>> np.digitize(x,bins,right=True) array([1, 2, 3, 4, 4]) >>> np.digitize(x,bins,right=False) |