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DOC: Clarify sorting and order of categoricals and fix typo #47846

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DOC: Clarify sorting and order of categoricals and fix typo #47846

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DOC: Clarify sorting and order of categoricals and fix typo
StefRe Jul 25, 2022
35e2ca3
Merge branch 'main' into DOC/categorical
StefRe Oct 7, 2022
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14 changes: 9 additions & 5 deletions doc/source/user_guide/categorical.rst
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Expand Up @@ -439,15 +439,19 @@ Sorting and order
.. _categorical.sort:

If categorical data is ordered (``s.cat.ordered == True``), then the order of the categories has a
meaning and certain operations are possible. If the categorical is unordered, ``.min()/.max()`` will raise a ``TypeError``.
meaning and certain operations are possible. If the categorical is unordered, the data can still be sorted,
but ``.min()/.max()`` will raise a ``TypeError``.

.. ipython:: python

s = pd.Series(pd.Categorical(["a", "b", "c", "a"], ordered=False))
s.sort_values(inplace=True)
s = pd.Series(["a", "b", "c", "a"]).astype(CategoricalDtype(ordered=True))
s.sort_values(inplace=True)
s
s.sort_values(ascending=False)
try:
s.min()
except TypeError as e:
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It would better to demonstrate this with the :okexcept: directive

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Hm, if I look at examples where the :okexcept: directive is used, for instance here or here, I'm not sure if this is so good an idea in the given case, as it inserts quite a lengthy (~15 lines) traceback which, in my opinion, distracts from reading and makes the example less clear than with try / except.

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Could you show an image of how the docs would render if :okexcept: was used?

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see the links I provided in the reply:

.. ipython:: python
    :okexcept:

    if pd.Series([False, True, False]):
        print("I was true")

renders as
grafik

and

.. ipython:: python
   :okexcept:

   mi.levels[0].name = "name via level"

renders as
grafik

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Sorry to clarify, how would the docs you modified in this PR render with :okexcept:?

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@StefRe StefRe Aug 5, 2022 •
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.. ipython:: python
    :okexcept:

    s = pd.Series(pd.Categorical(["a", "b", "c", "a"], ordered=False))
    s.sort_values(ascending=False)
    s.min()

renders as

out

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I would still be partial to adding :okexcept: here. Though verbose, it shows the exact behavior that the user should see.

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I'm afraid I can't agree here: IMO the purpose here is just to demonstrate that an exception will be raised when trying to do min or max on a not ordered categorical series. If we then catch it or let it be thrown doesn't play a role for this purpose, so I'd prefer the visually less distracting option of catching it. This is the way it's done all the way in the given section of the user guide (9 times), why should we treat this 10th time differently? Maybe we can leave the try/except here just for uniformity's sake.

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Personally, I think those other sections should also use :okexcept: as well.

print(f"TypeError: {e}")
s = s.astype(CategoricalDtype(ordered=True))
s.sort_values()
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I think this will need to be s = s.sort_values()

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Of course you could write s = s.sort_values() followed by s to show the result but as we don't need the sorted series further down (I mean for the following code it doesn't play a role if s has been sorted or not) I think this could be shortened to just s.sort_values() which will output the returned sorted Series.

In principle, one could remove this line completely, because if we can sort an unordered categories series, we can sort an ordered categories series all the more (it's a leftover from the beginning, when sorting of an unordered categories series was not allowed, this was changed in 87fec5b).

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Sure removing this line would be better then

s.min(), s.max()

You can set categorical data to be ordered by using ``as_ordered()`` or unordered by using ``as_unordered()``. These will by
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2 changes: 1 addition & 1 deletion pandas/core/arrays/categorical.py
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Expand Up @@ -256,7 +256,7 @@ class Categorical(NDArrayBackedExtensionArray, PandasObject, ObjectStringArrayMi
"""
Represent a categorical variable in classic R / S-plus fashion.

`Categoricals` can only take on only a limited, and usually fixed, number
`Categoricals` can only take on a limited, and usually fixed, number
of possible values (`categories`). In contrast to statistical categorical
variables, a `Categorical` might have an order, but numerical operations
(additions, divisions, ...) are not possible.
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