diff options
Diffstat (limited to 'numpy/core/fromnumeric.py')
| -rw-r--r-- | numpy/core/fromnumeric.py | 85 |
1 files changed, 45 insertions, 40 deletions
diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py index 3389e7d66..140056432 100644 --- a/numpy/core/fromnumeric.py +++ b/numpy/core/fromnumeric.py @@ -25,7 +25,7 @@ __all__ = [ 'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip', 'compress', 'cumprod', 'cumproduct', 'cumsum', 'diagonal', 'mean', 'ndim', 'nonzero', 'partition', 'prod', 'product', 'ptp', 'put', - 'rank', 'ravel', 'repeat', 'reshape', 'resize', 'round_', + 'ravel', 'repeat', 'reshape', 'resize', 'round_', 'searchsorted', 'shape', 'size', 'sometrue', 'sort', 'squeeze', 'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var', ] @@ -380,6 +380,7 @@ def choose(a, choices, out=None, mode='raise'): See Also -------- ndarray.choose : equivalent method + numpy.take_along_axis : Preferable if `choices` is an array Notes ----- @@ -908,17 +909,17 @@ def sort(a, axis=-1, kind=None, order=None): .. versionadded:: 1.12.0 - quicksort has been changed to `introsort <https://en.wikipedia.org/wiki/Introsort>`_. + quicksort has been changed to `introsort <https://en.wikipedia.org/wiki/Introsort>`_. When sorting does not make enough progress it switches to - `heapsort <https://en.wikipedia.org/wiki/Heapsort>`_. + `heapsort <https://en.wikipedia.org/wiki/Heapsort>`_. This implementation makes quicksort O(n*log(n)) in the worst case. 'stable' automatically chooses the best stable sorting algorithm - for the data type being sorted. - It, along with 'mergesort' is currently mapped to - `timsort <https://en.wikipedia.org/wiki/Timsort>`_ - or `radix sort <https://en.wikipedia.org/wiki/Radix_sort>`_ - depending on the data type. + for the data type being sorted. + It, along with 'mergesort' is currently mapped to + `timsort <https://en.wikipedia.org/wiki/Timsort>`_ + or `radix sort <https://en.wikipedia.org/wiki/Radix_sort>`_ + depending on the data type. API forward compatibility currently limits the ability to select the implementation and it is hardwired for the different data types. @@ -2782,6 +2783,10 @@ def alen(a): 7 """ + # NumPy 1.18.0, 2019-08-02 + warnings.warn( + "`np.alen` is deprecated, use `len` instead", + DeprecationWarning, stacklevel=2) try: return len(a) except TypeError: @@ -3120,10 +3125,37 @@ def around(a, decimals=0, out=None): ----- For values exactly halfway between rounded decimal values, NumPy rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0, - -0.5 and 0.5 round to 0.0, etc. Results may also be surprising due - to the inexact representation of decimal fractions in the IEEE - floating point standard [1]_ and errors introduced when scaling - by powers of ten. + -0.5 and 0.5 round to 0.0, etc. + + ``np.around`` uses a fast but sometimes inexact algorithm to round + floating-point datatypes. For positive `decimals` it is equivalent to + ``np.true_divide(np.rint(a * 10**decimals), 10**decimals)``, which has + error due to the inexact representation of decimal fractions in the IEEE + floating point standard [1]_ and errors introduced when scaling by powers + of ten. For instance, note the extra "1" in the following: + + >>> np.round(56294995342131.5, 3) + 56294995342131.51 + + If your goal is to print such values with a fixed number of decimals, it is + preferable to use numpy's float printing routines to limit the number of + printed decimals: + + >>> np.format_float_positional(56294995342131.5, precision=3) + '56294995342131.5' + + The float printing routines use an accurate but much more computationally + demanding algorithm to compute the number of digits after the decimal + point. + + Alternatively, Python's builtin `round` function uses a more accurate + but slower algorithm for 64-bit floating point values: + + >>> round(56294995342131.5, 3) + 56294995342131.5 + >>> np.round(16.055, 2), round(16.055, 2) # equals 16.0549999999999997 + (16.06, 16.05) + References ---------- @@ -3414,7 +3446,7 @@ def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): instead of a single axis or all the axes as before. dtype : data-type, optional Type to use in computing the variance. For arrays of integer type - the default is `float32`; for arrays of float types it is the same as + the default is `float64`; for arrays of float types it is the same as the array type. out : ndarray, optional Alternate output array in which to place the result. It must have @@ -3573,30 +3605,3 @@ def alltrue(*args, **kwargs): numpy.all : Equivalent function; see for details. """ return all(*args, **kwargs) - - -@array_function_dispatch(_ndim_dispatcher) -def rank(a): - """ - Return the number of dimensions of an array. - - .. note:: - This function is deprecated in NumPy 1.9 to avoid confusion with - `numpy.linalg.matrix_rank`. The ``ndim`` attribute or function - should be used instead. - - See Also - -------- - ndim : equivalent non-deprecated function - - Notes - ----- - In the old Numeric package, `rank` was the term used for the number of - dimensions, but in NumPy `ndim` is used instead. - """ - # 2014-04-12, 1.9 - warnings.warn( - "`rank` is deprecated; use the `ndim` attribute or function instead. " - "To find the rank of a matrix see `numpy.linalg.matrix_rank`.", - VisibleDeprecationWarning, stacklevel=3) - return ndim(a) |