diff options
Diffstat (limited to 'numpy/add_newdocs.py')
| -rw-r--r-- | numpy/add_newdocs.py | 18 |
1 files changed, 7 insertions, 11 deletions
diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py index f2b7077c7..a534e852c 100644 --- a/numpy/add_newdocs.py +++ b/numpy/add_newdocs.py @@ -211,7 +211,7 @@ add_newdoc('numpy.core', 'nditer', 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. - order : {'C', 'F', 'A', or 'K'}, optional + order : {'C', 'F', 'A', 'K'}, optional Controls the iteration order. 'C' means C order, 'F' means Fortran order, 'A' means 'F' order if all the arrays are Fortran contiguous, 'C' order otherwise, and 'K' means as close to the @@ -1503,7 +1503,7 @@ add_newdoc('numpy.core.multiarray', 'lexsort', Parameters ---------- - keys : (k,N) array or tuple containing k (N,)-shaped sequences + keys : (k, N) array or tuple containing k (N,)-shaped sequences The `k` different "columns" to be sorted. The last column (or row if `keys` is a 2D array) is the primary sort key. axis : int, optional @@ -1598,7 +1598,6 @@ add_newdoc('numpy.core.multiarray', 'can_cast', Examples -------- - Basic examples >>> np.can_cast(np.int32, np.int64) @@ -1978,7 +1977,7 @@ add_newdoc('numpy.core', 'einsum', If provided, forces the calculation to use the data type specified. Note that you may have to also give a more liberal `casting` parameter to allow the conversions. - order : {'C', 'F', 'A', or 'K'}, optional + order : {'C', 'F', 'A', 'K'}, optional Controls the memory layout of the output. 'C' means it should be C contiguous. 'F' means it should be Fortran contiguous, 'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise. @@ -3047,7 +3046,7 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('astype', ---------- dtype : str or dtype Typecode or data-type to which the array is cast. - order : {'C', 'F', 'A', or 'K'}, optional + order : {'C', 'F', 'A', 'K'}, optional Controls the memory layout order of the result. 'C' means C order, 'F' means Fortran order, 'A' means 'F' order if all the arrays are Fortran contiguous, @@ -3083,7 +3082,7 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('astype', Raises ------ - ComplexWarning : + ComplexWarning When casting from complex to float or int. To avoid this, one should use ``a.real.astype(t)``. @@ -3110,12 +3109,12 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('byteswap', Parameters ---------- - inplace: bool, optional + inplace : bool, optional If ``True``, swap bytes in-place, default is ``False``. Returns ------- - out: ndarray + out : ndarray The byteswapped array. If `inplace` is ``True``, this is a view to self. @@ -5058,7 +5057,6 @@ add_newdoc('numpy.lib._compiled_base', 'add_newdoc_ufunc', Notes ----- - This method allocates memory for new_docstring on the heap. Technically this creates a mempory leak, since this memory will not be reclaimed until the end of the program @@ -5900,7 +5898,6 @@ add_newdoc('numpy.core.multiarray', 'dtype', ('fields', Examples -------- - >>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))]) >>> print dt.fields {'grades': (dtype(('float64',(2,))), 16), 'name': (dtype('|S16'), 0)} @@ -6008,7 +6005,6 @@ add_newdoc('numpy.core.multiarray', 'dtype', ('names', Examples -------- - >>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))]) >>> dt.names ('name', 'grades') |