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| author | Jarrod Millman <millman@berkeley.edu> | 2010-02-17 23:53:04 +0000 |
|---|---|---|
| committer | Jarrod Millman <millman@berkeley.edu> | 2010-02-17 23:53:04 +0000 |
| commit | e2bb09430d90c73a7be6e47ea8c4528f094f693f (patch) | |
| tree | 3ded297a6cbe634446d6a54afc4e95c8c71553e6 /numpy/doc/basics.py | |
| parent | dcc721a5bddde3afd4ce47d7a7b76ec6c7102b92 (diff) | |
| download | numpy-e2bb09430d90c73a7be6e47ea8c4528f094f693f.tar.gz | |
more docstring updates from pydoc website (thanks to everyone who contributed!)
Diffstat (limited to 'numpy/doc/basics.py')
| -rw-r--r-- | numpy/doc/basics.py | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/numpy/doc/basics.py b/numpy/doc/basics.py index 5e7291957..ea651bbc7 100644 --- a/numpy/doc/basics.py +++ b/numpy/doc/basics.py @@ -44,7 +44,7 @@ having unique characteristics. Once you have imported NumPy using the dtypes are available as ``np.bool``, ``np.float32``, etc. Advanced types, not listed in the table above, are explored in -section `link_here`. +section :ref:`structured_arrays`. There are 5 basic numerical types representing booleans (bool), integers (int), unsigned integers (uint) floating point (float) and complex. Those with numbers @@ -98,8 +98,8 @@ To determine the type of an array, look at the dtype attribute:: dtype('uint8') dtype objects also contain information about the type, such as its bit-width -and its byte-order. See xxx for details. The data type can also be used -indirectly to query properties of the type, such as whether it is an integer:: +and its byte-order. The data type can also be used indirectly to query +properties of the type, such as whether it is an integer:: >>> d = np.dtype(int) >>> d |