Merge branch 'master' into ix_-preserve-type

1 files changed, 35 insertions, 35 deletions

diff --git a/numpy/doc/basics.py b/numpy/doc/basics.py
index dac236644..4d3ab046e 100644
--- a/numpy/doc/basics.py
+++ b/numpy/doc/basics.py
@@ -9,36 +9,36 @@ Array types and conversions between types
 NumPy supports a much greater variety of numerical types than Python does.
 This section shows which are available, and how to modify an array's data-type.
 
-==========  ==========================================================
-Data type   Description
-==========  ==========================================================
-bool_       Boolean (True or False) stored as a byte
-int_        Default integer type (same as C ``long``; normally either
-            ``int64`` or ``int32``)
-intc        Identical to C ``int`` (normally ``int32`` or ``int64``)
-intp        Integer used for indexing (same as C ``ssize_t``; normally
-            either ``int32`` or ``int64``)
-int8        Byte (-128 to 127)
-int16       Integer (-32768 to 32767)
-int32       Integer (-2147483648 to 2147483647)
-int64       Integer (-9223372036854775808 to 9223372036854775807)
-uint8       Unsigned integer (0 to 255)
-uint16      Unsigned integer (0 to 65535)
-uint32      Unsigned integer (0 to 4294967295)
-uint64      Unsigned integer (0 to 18446744073709551615)
-float_      Shorthand for ``float64``.
-float16     Half precision float: sign bit, 5 bits exponent,
-            10 bits mantissa
-float32     Single precision float: sign bit, 8 bits exponent,
-            23 bits mantissa
-float64     Double precision float: sign bit, 11 bits exponent,
-            52 bits mantissa
-complex_    Shorthand for ``complex128``.
-complex64   Complex number, represented by two 32-bit floats (real
-            and imaginary components)
-complex128  Complex number, represented by two 64-bit floats (real
-            and imaginary components)
-==========  ==========================================================
+============ ==========================================================
+Data type    Description
+============ ==========================================================
+``bool_``    Boolean (True or False) stored as a byte
+``int_``     Default integer type (same as C ``long``; normally either
+             ``int64`` or ``int32``)
+intc         Identical to C ``int`` (normally ``int32`` or ``int64``)
+intp         Integer used for indexing (same as C ``ssize_t``; normally
+             either ``int32`` or ``int64``)
+int8         Byte (-128 to 127)
+int16        Integer (-32768 to 32767)
+int32        Integer (-2147483648 to 2147483647)
+int64        Integer (-9223372036854775808 to 9223372036854775807)
+uint8        Unsigned integer (0 to 255)
+uint16       Unsigned integer (0 to 65535)
+uint32       Unsigned integer (0 to 4294967295)
+uint64       Unsigned integer (0 to 18446744073709551615)
+``float_``   Shorthand for ``float64``.
+float16      Half precision float: sign bit, 5 bits exponent,
+             10 bits mantissa
+float32      Single precision float: sign bit, 8 bits exponent,
+             23 bits mantissa
+float64      Double precision float: sign bit, 11 bits exponent,
+             52 bits mantissa
+``complex_`` Shorthand for ``complex128``.
+complex64    Complex number, represented by two 32-bit floats (real
+             and imaginary components)
+complex128   Complex number, represented by two 64-bit floats (real
+             and imaginary components)
+============ ==========================================================
 
 Additionally to ``intc`` the platform dependent C integer types ``short``,
 ``long``, ``longlong`` and their unsigned versions are defined.
@@ -114,10 +114,10 @@ properties of the type, such as whether it is an integer::
     >>> d
     dtype('int32')
 
-    >>> np.issubdtype(d, int)
+    >>> np.issubdtype(d, np.integer)
     True
 
-    >>> np.issubdtype(d, float)
+    >>> np.issubdtype(d, np.floating)
     False
 
 
@@ -155,11 +155,11 @@ with 80-bit precision, and while most C compilers provide this as their
 ``long double`` identical to ``double`` (64 bits). NumPy makes the
 compiler's ``long double`` available as ``np.longdouble`` (and
 ``np.clongdouble`` for the complex numbers). You can find out what your
-numpy provides with``np.finfo(np.longdouble)``.
+numpy provides with ``np.finfo(np.longdouble)``.
 
 NumPy does not provide a dtype with more precision than C
-``long double``s; in particular, the 128-bit IEEE quad precision
-data type (FORTRAN's ``REAL*16``) is not available.
+``long double``\\s; in particular, the 128-bit IEEE quad precision
+data type (FORTRAN's ``REAL*16``\\) is not available.
 
 For efficient memory alignment, ``np.longdouble`` is usually stored
 padded with zero bits, either to 96 or 128 bits. Which is more efficient