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DOC: Recommend the use of np.ndim over np.isscalar, and explain the differences #11882

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Sep 8, 2018
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DOC: Recommend the use of np.ndim over np.isscalar, and explain the differences #11882

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36 changes: 36 additions & 0 deletions numpy/core/numeric.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1942,10 +1942,46 @@ def isscalar(num):
val : bool
True if `num` is a scalar type, False if it is not.

See Also
--------
ndim : Get the number of dimensions of an array

Notes
-----
In almost all cases ``np.ndim(x) == 0`` should be used instead of this
function, as that will also return true for 0d arrays. This is how
numpy overloads functions in the style of the ``dx`` arguments to `gradient`
and the ``bins`` argument to `histogram`. Some key differences:

+--------------------------------------+---------------+-------------------+
| x |``isscalar(x)``|``np.ndim(x) == 0``|
+======================================+===============+===================+
| PEP 3141 numeric objects (including | ``True`` | ``True`` |
| builtins) | | |
+--------------------------------------+---------------+-------------------+
| builtin string and buffer objects | ``True`` | ``True`` |
+--------------------------------------+---------------+-------------------+
| other builtin objects, like | ``False`` | ``True`` |
| `pathlib.Path`, `Exception`, | | |
| the result of `re.compile` | | |
+--------------------------------------+---------------+-------------------+
| third-party objects like | ``False`` | ``True`` |
| `matplotlib.figure.Figure` | | |
+--------------------------------------+---------------+-------------------+
| zero-dimensional numpy arrays | ``False`` | ``True`` |
+--------------------------------------+---------------+-------------------+
| other numpy arrays | ``False`` | ``False`` |
+--------------------------------------+---------------+-------------------+
| `list`, `tuple`, and other sequence | ``False`` | ``False`` |
| objects | | |
+--------------------------------------+---------------+-------------------+

Examples
--------
>>> np.isscalar(3.1)
True
>>> np.isscalar(np.array(3.1))
False
>>> np.isscalar([3.1])
False
>>> np.isscalar(False)
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