yeandy commented on a change in pull request #16706:
URL: https://github.com/apache/beam/pull/16706#discussion_r798115218
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File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -3986,29 +3996,78 @@ def apply(self, func, *args, **kwargs):
fn_input = project(self._ungrouped_with_index.proxy().reset_index(
grouping_columns, drop=True))
result = func(fn_input)
- if isinstance(result, pd.core.generic.NDFrame):
- if result.index is fn_input.index:
- proxy = result
+ def index_to_arrays(index):
+ return [index.get_level_values(level)
+ for level in range(index.nlevels)]
+
+
+ # By default do_apply will just call pandas apply()
+ # We override it below if necessary
+ do_apply = lambda gb: gb.apply(func, *args, **kwargs)
+
+ if (isinstance(result, pd.core.generic.NDFrame) and
+ result.index is fn_input.index):
+ # Special case where apply fn is a transform
+ # Note we trust that if the user fn produces a proxy with the identical
+ # index, it will produce results with identical indexes at execution
+ # time too
+ proxy = result
+ elif isinstance(result, pd.DataFrame):
+ # apply fn is not a transform, we need to make sure the original index
+ # values are prepended to the result's index
+ proxy = result[:0]
+
+ # First adjust proxy
+ proxy.index = pd.MultiIndex.from_arrays(
+ index_to_arrays(self._ungrouped.proxy().index) +
+ index_to_arrays(proxy.index),
+ names=self._ungrouped.proxy().index.names + proxy.index.names)
+
+
+ # Then override do_apply function
+ new_index_names = self._ungrouped.proxy().index.names
+ if len(new_index_names) > 1:
+ def add_key_index(key, df):
+ # df is a dataframe or Series representing the result of func for
+ # a single key
+ # key is a tuple with the MultiIndex values for this key
+ df.index = pd.MultiIndex.from_arrays(
+ [[key[i]] * len(df) for i in range(len(new_index_names))] +
+ index_to_arrays(df.index),
+ names=new_index_names + df.index.names)
+ return df
else:
- proxy = result[:0]
-
- def index_to_arrays(index):
- return [index.get_level_values(level)
- for level in range(index.nlevels)]
-
- # The final result will have the grouped indexes + the indexes from the
- # result
- proxy.index = pd.MultiIndex.from_arrays(
- index_to_arrays(self._ungrouped.proxy().index) +
- index_to_arrays(proxy.index),
- names=self._ungrouped.proxy().index.names + proxy.index.names)
+ def add_key_index(key, df):
+ # df is a dataframe or Series representing the result of func for
+ # a single key
+ df.index = pd.MultiIndex.from_arrays(
+ [[key] * len(df)] + index_to_arrays(df.index),
+ names=new_index_names + df.index.names)
+ return df
+
+
+ do_apply = lambda gb: pd.concat([
+ add_key_index(k, func(gb.get_group(k), *args, **kwargs))
+ for k in gb.groups.keys()])
+ elif isinstance(result, pd.Series):
+ if isinstance(fn_input, pd.DataFrame):
+ # DataFrameGroupBy
+ dtype = pd.Series([result]).dtype
+ proxy = pd.DataFrame(columns=result.index,
+ dtype=result.dtype,
+ index=self._ungrouped.proxy().index)
Review comment:
Thanks for the example! Now it better paints the picture on how we
construct the proxies. Figuring this out must require playing around with a lot
of different DF/Series examples 😆
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