diff options
Diffstat (limited to 'numpy/add_newdocs.py')
| -rw-r--r-- | numpy/add_newdocs.py | 133 |
1 files changed, 4 insertions, 129 deletions
diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py index 6c6488547..b8223f7c6 100644 --- a/numpy/add_newdocs.py +++ b/numpy/add_newdocs.py @@ -204,8 +204,6 @@ add_newdoc('numpy.core', 'nditer', copies those elements indicated by this mask. * 'writemasked' indicates that only elements where the chosen 'arraymask' operand is True will be written to. - * 'use_maskna' indicates that this operand should be treated - like an NA-masked array. op_dtypes : dtype or tuple of dtype(s), optional The required data type(s) of the operands. If copying or buffering is enabled, the data will be converted to/from their original types. @@ -640,7 +638,7 @@ add_newdoc('numpy.core', 'broadcast', ('reset', add_newdoc('numpy.core.multiarray', 'array', """ - array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0, maskna=None, ownmaskna=False) + array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0) Create an array. @@ -676,25 +674,6 @@ add_newdoc('numpy.core.multiarray', 'array', Specifies the minimum number of dimensions that the resulting array should have. Ones will be pre-pended to the shape as needed to meet this requirement. - maskna : bool or None, optional - If this is set to True, it forces the array to have an NA mask. - If the input is an array without a mask, this means a view with - an NA mask is created. If the input is an array with a mask, the - mask is preserved as-is. - - If this is set to False, it forces the array to not have an NA - mask. If the input is an array with a mask, and has no NA values, - it will create a copy of the input without an NA mask. - - .. versionadded:: 1.7.0 - - ownmaskna : bool, optional - If this is set to True, forces the array to have a mask which - it owns. It may still return a view of the data from the input, - but the result will always own its own mask. - - .. versionadded:: 1.7.0 - Returns ------- @@ -705,11 +684,6 @@ add_newdoc('numpy.core.multiarray', 'array', -------- empty, empty_like, zeros, zeros_like, ones, ones_like, fill - Notes - ----- - The `maskna` and `ownmaskna` keywords are *experimental* in the 1.7 - release; their behavior may change in future versions. - Examples -------- >>> np.array([1, 2, 3]) @@ -769,8 +743,6 @@ add_newdoc('numpy.core.multiarray', 'empty', order : {'C', 'F'}, optional Whether to store multi-dimensional data in C (row-major) or Fortran (column-major) order in memory. - maskna : boolean - If this is true, the returned array will have an NA mask. See Also -------- @@ -919,35 +891,6 @@ add_newdoc('numpy.core.multiarray', 'zeros', """) -add_newdoc('numpy.core.multiarray', 'isna', - """ - isna(a) - - Returns an array with True for each element of *a* that is NA. - - Parameters - ---------- - a : array_like - The array for which to check for NA. - - Returns - ------- - result : bool or array of bool - Number of non-zero values in the array. - - Examples - -------- - >>> np.isna(np.NA) - True - >>> np.isna(1.5) - False - >>> np.isna(np.nan) - False - >>> a = np.array([0, np.NA, 3.5, np.NA]) - >>> np.isna(a) - array([False, True, False, True], dtype=bool) - """) - add_newdoc('numpy.core.multiarray', 'count_nonzero', """ count_nonzero(a) @@ -962,9 +905,6 @@ add_newdoc('numpy.core.multiarray', 'count_nonzero', Axis or axes along which a reduction is performed. The default (`axis` = None) is perform a reduction over all the dimensions of the input array. - skipna : bool, optional - If this is set to True, any NA elements in the array are skipped - instead of propagating. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, @@ -992,60 +932,6 @@ add_newdoc('numpy.core.multiarray', 'count_nonzero', [3]]) """) -add_newdoc('numpy.core.multiarray', 'count_reduce_items', - """ - count_reduce_items(arr, axis=None, skipna=False, keepdims=False) - - Counts the number of items a reduction with the same `axis` - and `skipna` parameter values would use. The purpose of this - function is for the creation of reduction operations - which use the item count, such as :func:`mean`. - - When `skipna` is False or `arr` doesn't have an NA mask, - the result is simply the product of the reduction axis - sizes, returned as a single scalar. - - Parameters - ---------- - arr : array_like - The array for which to count the reduce items. - axis : None or int or tuple of ints, optional - Axis or axes along which a reduction is performed. - The default (`axis` = None) is perform a reduction over all - the dimensions of the input array. - skipna : bool, optional - If this is set to True, any NA elements in the array are not - counted. The only time this function does any actual counting - instead of a cheap multiply of a few sizes is when `skipna` is - true and `arr` has an NA mask. - keepdims : bool, optional - If this is set to True, the axes which are reduced are left - in the result as dimensions with size one. With this option, - the result will broadcast correctly against the original `arr`. - - Returns - ------- - count : intp or array of intp - Number of items that would be used in a reduction with the - same `axis` and `skipna` parameter values. - - Examples - -------- - >>> a = np.array([[1,np.NA,1], [1,1,np.NA]]) - - >>> np.count_reduce_items(a) - 6 - >>> np.count_reduce_items(a, skipna=True) - 4 - >>> np.sum(a, skipna=True) - 4 - - >>> np.count_reduce_items(a, axis=0, skipna=True) - array([2, 1, 1]) - >>> np.sum(a, axis=0, skipna=True) - array([2, 1, 1]) - """) - add_newdoc('numpy.core.multiarray','set_typeDict', """set_typeDict(dict) @@ -1409,7 +1295,7 @@ add_newdoc('numpy.core.multiarray','correlate', add_newdoc('numpy.core.multiarray', 'arange', """ - arange([start,] stop[, step,], dtype=None, maskna=False) + arange([start,] stop[, step,], dtype=None) Return evenly spaced values within a given interval. @@ -1438,8 +1324,6 @@ add_newdoc('numpy.core.multiarray', 'arange', dtype : dtype The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. - maskna : boolean - If this is true, the returned array will have an NA mask. Returns ------- @@ -3320,7 +3204,7 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('conjugate', add_newdoc('numpy.core.multiarray', 'ndarray', ('copy', """ - a.copy(order='C', maskna=None) + a.copy(order='C') Return a copy of the array. @@ -3331,10 +3215,6 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('copy', 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous, 'C' otherwise. 'K' means match the layout of `a` as closely as possible. - maskna : bool, optional - If specifies, forces the copy to have or to not have an - NA mask. This is a way to remove an NA mask from an array - while making a copy. See also -------- @@ -5470,7 +5350,7 @@ add_newdoc('numpy.core', 'ufunc', ('types', add_newdoc('numpy.core', 'ufunc', ('reduce', """ - reduce(a, axis=0, dtype=None, out=None, skipna=False, keepdims=False) + reduce(a, axis=0, dtype=None, out=None, keepdims=False) Reduces `a`'s dimension by one, by applying ufunc along one axis. @@ -5515,11 +5395,6 @@ add_newdoc('numpy.core', 'ufunc', ('reduce', out : ndarray, optional A location into which the result is stored. If not provided, a freshly-allocated array is returned. - skipna : bool, optional - If this is set to True, the reduction is done as if any NA elements - were not counted in the array. The default, False, causes the - NA values to propagate, so if any element in a set of elements - being reduced is NA, the result will be NA. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, |