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Diffstat (limited to 'numpy/doc/basics.py')
| -rw-r--r-- | numpy/doc/basics.py | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/numpy/doc/basics.py b/numpy/doc/basics.py index 745bff15a..dac236644 100644 --- a/numpy/doc/basics.py +++ b/numpy/doc/basics.py @@ -6,7 +6,7 @@ Array basics Array types and conversions between types ========================================= -Numpy supports a much greater variety of numerical types than Python does. +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. ========== ========================================================== @@ -43,7 +43,7 @@ complex128 Complex number, represented by two 64-bit floats (real 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 +NumPy numerical types are instances of ``dtype`` (data-type) objects, each having unique characteristics. Once you have imported NumPy using :: @@ -124,7 +124,7 @@ properties of the type, such as whether it is an integer:: Array Scalars ============= -Numpy generally returns elements of arrays as array scalars (a scalar +NumPy generally returns elements of arrays as array scalars (a scalar with an associated dtype). Array scalars differ from Python scalars, but for the most part they can be used interchangeably (the primary exception is for versions of Python older than v2.x, where integer array @@ -152,12 +152,12 @@ is possible in numpy depends on the hardware and on the development environment: specifically, x86 machines provide hardware floating-point with 80-bit precision, and while most C compilers provide this as their ``long double`` type, MSVC (standard for Windows builds) makes -``long double`` identical to ``double`` (64 bits). Numpy makes the +``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 does not provide a dtype with more precision than C +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. |