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| author | Pierre de Buyl <pdebuyl@pdebuyl.be> | 2016-09-06 14:42:08 +0200 |
|---|---|---|
| committer | Pierre de Buyl <pdebuyl@pdebuyl.be> | 2016-09-06 14:42:08 +0200 |
| commit | 2a55233b81a6ea18a57d1dd4f7bc5fff9f2fb681 (patch) | |
| tree | 1a4a81faf9e59d0cabf9cbace9dc967bad60caa1 /numpy/doc/subclassing.py | |
| parent | 773e3cad9a71cb9a7849d8e251fb8a99ab35d06b (diff) | |
| download | numpy-2a55233b81a6ea18a57d1dd4f7bc5fff9f2fb681.tar.gz | |
DOC: change Numpy to NumPy in dosctrings and comments
The strings in error messages were left untouched
Diffstat (limited to 'numpy/doc/subclassing.py')
| -rw-r--r-- | numpy/doc/subclassing.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/numpy/doc/subclassing.py b/numpy/doc/subclassing.py index 40522bb9c..9a339430b 100644 --- a/numpy/doc/subclassing.py +++ b/numpy/doc/subclassing.py @@ -97,7 +97,7 @@ Implications for subclassing If we subclass ndarray, we need to deal not only with explicit construction of our array type, but also :ref:`view-casting` or -:ref:`new-from-template`. Numpy has the machinery to do this, and this +:ref:`new-from-template`. NumPy has the machinery to do this, and this machinery that makes subclassing slightly non-standard. There are two aspects to the machinery that ndarray uses to support @@ -523,7 +523,7 @@ Extra gotchas - custom ``__del__`` methods and ndarray.base One of the problems that ndarray solves is keeping track of memory ownership of ndarrays and their views. Consider the case where we have created an ndarray, ``arr`` and have taken a slice with ``v = arr[1:]``. -The two objects are looking at the same memory. Numpy keeps track of +The two objects are looking at the same memory. NumPy keeps track of where the data came from for a particular array or view, with the ``base`` attribute: |