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Diffstat (limited to 'numpy/doc/basics.py')
| -rw-r--r-- | numpy/doc/basics.py | 29 |
1 files changed, 18 insertions, 11 deletions
diff --git a/numpy/doc/basics.py b/numpy/doc/basics.py index bb16c3a91..75f0995d8 100644 --- a/numpy/doc/basics.py +++ b/numpy/doc/basics.py @@ -9,11 +9,15 @@ 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 Platform integer (normally either ``int32`` or ``int64``) +========== ========================================================== +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) @@ -22,19 +26,22 @@ 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``. +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``. +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. Numpy numerical types are instances of ``dtype`` (data-type) objects, each having unique characteristics. Once you have imported NumPy using @@ -43,7 +50,7 @@ having unique characteristics. Once you have imported NumPy using >>> import numpy as np -the dtypes are available as ``np.bool``, ``np.float32``, etc. +the dtypes are available as ``np.bool_``, ``np.float32``, etc. Advanced types, not listed in the table above, are explored in section :ref:`structured_arrays`. @@ -90,9 +97,9 @@ the type itself as a function. For example: :: array([0, 1, 2], dtype=int8) Note that, above, we use the *Python* float object as a dtype. NumPy knows -that ``int`` refers to ``np.int``, ``bool`` means ``np.bool`` and -that ``float`` is ``np.float``. The other data-types do not have Python -equivalents. +that ``int`` refers to ``np.int_``, ``bool`` means ``np.bool_``, +that ``float`` is ``np.float_`` and ``complex`` is ``np.complex_``. +The other data-types do not have Python equivalents. To determine the type of an array, look at the dtype attribute:: |