yeandy commented on a change in pull request #16615:
URL: https://github.com/apache/beam/pull/16615#discussion_r801597567
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -4625,9 +4625,48 @@ def repeat(self, repeats):
raise TypeError("str.repeat(repeats=) value must be an int or a "
f"DeferredSeries (encountered {type(repeats)}).")
- get_dummies = frame_base.wont_implement_method(
- pd.core.strings.StringMethods, 'get_dummies',
- reason='non-deferred-columns')
+ @frame_base.with_docs_from(pd.core.strings.StringMethods)
+ @frame_base.args_to_kwargs(pd.core.strings.StringMethods)
+ def get_dummies(self, **kwargs):
+ """
+ Series must be categorical type. Either cast to ``category`` to
+ infer categories, or preferred, cast to ``CategoricalDtype``
+ to ensure correct categories.
Review comment:
Ack on 1.
And for 2, when I was referring to "cast to `category`", I meant that one
could feasibly be doing the following
```
>>> s = pd.Series(['a', 'b', 'a', 'c', 'a', np.nan]).astype('category')
>>> s
0 a
1 b
2 a
3 c
4 a
5 NaN
dtype: category
Categories (3, object): ['a', 'b', 'c']
>>> s.dtype
CategoricalDtype(categories=['a', 'b', 'c'], ordered=False)
```
resulting in the inferred categorical types. This obviously can be fragile,
but nevertheless technically works, and results in the `dtype` to be
`CategoricalDtype`. If we explicitly define with `CategoricalDtype`, then we
may get:
```
>>> s = pd.Series(['a', 'b', 'a', 'c', 'a', np.nan],
dtype=pd.CategoricalDtype(categories=['b', 'a']))
>>> s
0 a
1 b
2 a
3 NaN
4 a
5 NaN
dtype: category
Categories (2, object): ['b', 'a']
>>> s.dtype
CategoricalDtype(categories=['b', 'a'], ordered=False)
```
Note that in the second example, I didn't pass in the category `c`, which
will result in the unknown category (`NaN`) in row 3. The onus for getting
these categories correct would be on the user.
I can definitely I can add a dedicated documentation section about this.
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