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https://issues.apache.org/jira/browse/BEAM-12169?focusedWorklogId=722799&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-722799
]
ASF GitHub Bot logged work on BEAM-12169:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 08/Feb/22 12:52
Start Date: 08/Feb/22 12:52
Worklog Time Spent: 10m
Work Description: 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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Issue Time Tracking
-------------------
Worklog Id: (was: 722799)
Time Spent: 2h 10m (was: 2h)
> Allow non-deferred column operations on categorical columns
> -----------------------------------------------------------
>
> Key: BEAM-12169
> URL: https://issues.apache.org/jira/browse/BEAM-12169
> Project: Beam
> Issue Type: Improvement
> Components: dsl-dataframe, sdk-py-core
> Reporter: Brian Hulette
> Assignee: Andy Ye
> Priority: P3
> Labels: dataframe-api
> Time Spent: 2h 10m
> Remaining Estimate: 0h
>
> There are several operations that we currently disallow because they produce
> a variable set of columns in the output based on the data
> (non-deferred-columns). However, for some dtypes (categorical, boolean) we
> can easily enumerate all the possible values that will be seen at execution
> time, so we can predict the columns that will be seen.
> Note we still can't implement these operations 100% correctly, as pandas will
> typically only create columns for the values that are _observed_, while we'd
> have to create a column for every possible value.
> We should allow these operations in these special cases.
> Operations in this category:
> - DataFrame.unstack (can work if unstacked level is a categorical or boolean
> column)
> - Series.str.get_dummies
> - Series.str.split
> - Series.str.rsplit
> - DataFrame.pivot
> - DataFrame.pivot_table
> - len(GroupBy) and ngroups
> ** if groupers are all categorical _and_ observed=False or all boolean
> ** Note these two may not actually be equivalent in all cases:
> [https://github.com/pandas-dev/pandas/issues/26326]
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