[
https://issues.apache.org/jira/browse/BEAM-12169?focusedWorklogId=726295&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-726295
]
ASF GitHub Bot logged work on BEAM-12169:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 14/Feb/22 13:24
Start Date: 14/Feb/22 13:24
Worklog Time Spent: 10m
Work Description: yeandy commented on a change in pull request #16677:
URL: https://github.com/apache/beam/pull/16677#discussion_r805836633
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -4721,13 +4721,82 @@ def repeat(self, repeats):
pd.core.strings.StringMethods, 'get_dummies',
reason='non-deferred-columns')
- split = frame_base.wont_implement_method(
- pd.core.strings.StringMethods, 'split',
- reason='non-deferred-columns')
+ def _split_helper(
+ self, rsplit=False, pat=None, expand=False, regex=None, **kwargs):
+ if not expand:
+ # Not creating separate columns
+ proxy = self._expr.proxy()
+ func = lambda s: pd.concat([proxy,
+ (s.str.split(pat=pat, expand=expand, regex=regex, **kwargs)
+ if not rsplit else s.str.rsplit(pat=pat, expand=expand, **kwargs))]
+ )
+ else:
+ # Creating separate columns, so must be more strict on dtype
+ dtype = self._expr.proxy().dtype
+ if not isinstance(dtype, pd.CategoricalDtype):
+ method_name = 'rsplit' if rsplit else 'split'
+ raise frame_base.WontImplementError(
+ method_name + "() of non-categorical type is not supported because
"
+ "the type of the output column depends on the data. Please use "
+ "pd.CategoricalDtype with explicit categories.",
+ reason="non-deferred-columns")
- rsplit = frame_base.wont_implement_method(
- pd.core.strings.StringMethods, 'rsplit',
- reason='non-deferred-columns')
+ if regex is False or (
+ regex is None and isinstance(pat, str) and len(pat) == 1):
+ # Treat pat as literal string
+ split_cats = [
+ cat.split(
+ sep=kwargs.get('pat'),
+ maxsplit=kwargs.get('n', -1)
+ ) for cat in dtype.categories
+ ]
+ else:
+ # Treat pat as regex
+ split_cats = [
+ re.split(
+ pattern=pat,
+ string=cat,
+ maxsplit=kwargs.get('n', 0)
+ ) for cat in dtype.categories
+ ]
+
+ max_splits = len(max(split_cats, key=len))
+ proxy = pd.DataFrame(columns=range(max_splits))
+
+ func = lambda s: pd.concat([proxy,
+ (s.str.split(pat=pat, expand=expand, regex=regex, **kwargs)
+ if not rsplit else s.str.rsplit(pat=pat, expand=expand, **kwargs))]
+ ).replace(np.nan, value=None)
Review comment:
If an entry in a series is `np.nan`, and then is converted to dtype
CategoricalDtype, then pandas
[behavior](https://pandas.pydata.org/docs/reference/api/pandas.Series.str.split.html)
is to propogate the NaN. Example:
```
>>>s = pd.Series(
[
"this is a regular sentence",
"https://docs.python.org/3/tutorial/index.html";,
np.nan
]
)
>>>s.str.split(expand=True)
0 1 2 3
4
0 this is a regular
sentence
1 https://docs.python.org/3/tutorial/index.html None None None
None
2 NaN NaN NaN NaN
NaN
```
In row 1, where which the string does not get split, in order to propagate
`None` into other columns, I do `.replace(np.nan, value=None)`. However this
makes row 2 be all `None` instead of `NaN`.
Is there a way to only choose specific rows to be `NaN` and not `None`?
i.e. use `s.isna()` to find those indices?
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Issue Time Tracking
-------------------
Worklog Id: (was: 726295)
Time Spent: 4h (was: 3h 50m)
> 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: 4h
> 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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