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| -rw-r--r-- | doc/source/reference/arrays.dtypes.rst | 19 |
1 files changed, 10 insertions, 9 deletions
diff --git a/doc/source/reference/arrays.dtypes.rst b/doc/source/reference/arrays.dtypes.rst index 58b8080f4..e97ee3c3a 100644 --- a/doc/source/reference/arrays.dtypes.rst +++ b/doc/source/reference/arrays.dtypes.rst @@ -87,18 +87,18 @@ Sub-arrays always have a C-contiguous memory layout. >>> dt = np.dtype([('name', np.unicode_, 16), ('grades', np.float64, (2,))]) >>> dt['name'] - dtype('|U16') + dtype('<U16') >>> dt['grades'] - dtype(('float64',(2,))) + dtype(('<f8', (2,))) Items of an array of this data type are wrapped in an :ref:`array scalar <arrays.scalars>` type that also has two fields: >>> x = np.array([('Sarah', (8.0, 7.0)), ('John', (6.0, 7.0))], dtype=dt) >>> x[1] - ('John', [6.0, 7.0]) + ('John', [6., 7.]) >>> x[1]['grades'] - array([ 6., 7.]) + array([6., 7.]) >>> type(x[1]) <class 'numpy.void'> >>> type(x[1]['grades']) @@ -304,7 +304,7 @@ Type strings .. admonition:: Example >>> dt = np.dtype('uint32') # 32-bit unsigned integer - >>> dt = np.dtype('Float64') # 64-bit floating-point number + >>> dt = np.dtype('float64') # 64-bit floating-point number .. index:: triple: dtype; construction; from tuple @@ -328,13 +328,14 @@ Type strings The first argument is any object that can be converted into a fixed-size data-type object. The second argument is the desired shape of this type. If the shape parameter is 1, then the - data-type object is equivalent to fixed dtype. If *shape* is a - tuple, then the new dtype defines a sub-array of the given shape. + data-type object used to be equivalent to fixed dtype. This behaviour is + deprecated since NumPy 1.17 and will raise an error in the future. + If *shape* is a tuple, then the new dtype defines a sub-array of the given + shape. .. admonition:: Example >>> dt = np.dtype((np.int32, (2,2))) # 2 x 2 integer sub-array - >>> dt = np.dtype(('U10', 1)) # 10-character string >>> dt = np.dtype(('i4, (2,3)f8, f4', (2,3))) # 2 x 3 structured sub-array .. index:: @@ -441,7 +442,7 @@ Type strings byte position 0), ``col2`` (32-bit float at byte position 10), and ``col3`` (integers at byte position 14): - >>> dt = np.dtype({'col1': ('U10', 0), 'col2': (float32, 10), + >>> dt = np.dtype({'col1': ('U10', 0), 'col2': (np.float32, 10), ... 'col3': (int, 14)}) ``(base_dtype, new_dtype)`` |