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CLN: remove unreachable code in pandas/core/groupby/generic.py::DataFrameGroupBy::_transform_general #32583

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CLN: remove unreachable code in pandas/core/groupby/generic.py::DataFrameGroupBy::_transform_general #32583

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6e51dc0
remove unreachable code
Mar 10, 2020
b221318
Merge remote-tracking branch 'upstream/master' into clean-up-_transfo…
MarcoGorelli Mar 14, 2020
500d5f0
revert elif -> if after return
MarcoGorelli Mar 14, 2020
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261 changes: 125 additions & 136 deletions pandas/core/groupby/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1202,158 +1202,147 @@ def first_not_none(values):
# GH9684. If all values are None, then this will throw an error.
# We'd prefer it return an empty dataframe.
return DataFrame()

elif isinstance(v, DataFrame):
return self._concat_objects(keys, values, not_indexed_same=not_indexed_same)
elif self.grouper.groupings is not None:
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Little hard to tell from the diff - is removing this branch the only change?

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@MarcoGorelli MarcoGorelli Mar 14, 2020 •
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I also changed the elifs after the return statements to if. This was only on line 1206 (new version). However, I've reverted that change.

Then, I removed

elif self.grouper.groupings is not None:

so I could shift the block after it back by 4 spaces.

Shall I only remove the last branch if that makes it easier to review?

if len(self.grouper.groupings) > 1:
key_index = self.grouper.result_index

else:
ping = self.grouper.groupings[0]
if len(keys) == ping.ngroups:
key_index = ping.group_index
key_index.name = key_names[0]

key_lookup = Index(keys)
indexer = key_lookup.get_indexer(key_index)
if len(self.grouper.groupings) > 1:
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is this possible to be None?

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@MarcoGorelli MarcoGorelli Mar 14, 2020 •
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For BaseGrouper, .groupings needs to be a list, so in that case, no, as inside its __init__ method there is

        self._groupings: List[grouper.Grouping] = list(groupings)

and .groupings simply returns ._groupings.

BinGrouper inherits from BaseGrouper, but changes its .groupings definition to

    @property
    def groupings(self) -> "List[grouper.Grouping]":
        return [
            grouper.Grouping(lvl, lvl, in_axis=False, level=None, name=name)
            for lvl, name in zip(self.levels, self.names)
        ]

So, it still cannot be None.

I'm still making sense of all this group-by code though, so I'll still look to see if there's anything I've missed

key_index = self.grouper.result_index
else:
ping = self.grouper.groupings[0]
if len(keys) == ping.ngroups:
key_index = ping.group_index
key_index.name = key_names[0]

# reorder the values
values = [values[i] for i in indexer]
else:
key_lookup = Index(keys)
indexer = key_lookup.get_indexer(key_index)

key_index = Index(keys, name=key_names[0])
# reorder the values
values = [values[i] for i in indexer]
else:

# don't use the key indexer
if not self.as_index:
key_index = None
key_index = Index(keys, name=key_names[0])

# make Nones an empty object
v = first_not_none(values)
if v is None:
return DataFrame()
elif isinstance(v, NDFrame):
# don't use the key indexer
if not self.as_index:
key_index = None

# this is to silence a DeprecationWarning
# TODO: Remove when default dtype of empty Series is object
kwargs = v._construct_axes_dict()
if v._constructor is Series:
backup = create_series_with_explicit_dtype(
**kwargs, dtype_if_empty=object
)
else:
backup = v._constructor(**kwargs)

values = [x if (x is not None) else backup for x in values]

v = values[0]

if isinstance(v, (np.ndarray, Index, Series)):
if isinstance(v, Series):
applied_index = self._selected_obj._get_axis(self.axis)
all_indexed_same = all_indexes_same([x.index for x in values])
singular_series = len(values) == 1 and applied_index.nlevels == 1

# GH3596
# provide a reduction (Frame -> Series) if groups are
# unique
if self.squeeze:
# assign the name to this series
if singular_series:
values[0].name = keys[0]

# GH2893
# we have series in the values array, we want to
# produce a series:
# if any of the sub-series are not indexed the same
# OR we don't have a multi-index and we have only a
# single values
return self._concat_objects(
keys, values, not_indexed_same=not_indexed_same
)

# still a series
# path added as of GH 5545
elif all_indexed_same:
from pandas.core.reshape.concat import concat

return concat(values)

if not all_indexed_same:
# GH 8467
return self._concat_objects(keys, values, not_indexed_same=True)

if self.axis == 0 and isinstance(v, ABCSeries):
# GH6124 if the list of Series have a consistent name,
# then propagate that name to the result.
index = v.index.copy()
if index.name is None:
# Only propagate the series name to the result
# if all series have a consistent name. If the
# series do not have a consistent name, do
# nothing.
names = {v.name for v in values}
if len(names) == 1:
index.name = list(names)[0]

# normally use vstack as its faster than concat
# and if we have mi-columns
if (
isinstance(v.index, MultiIndex)
or key_index is None
or isinstance(key_index, MultiIndex)
):
stacked_values = np.vstack([np.asarray(v) for v in values])
result = DataFrame(
stacked_values, index=key_index, columns=index
# make Nones an empty object
v = first_not_none(values)
if v is None:
return DataFrame()
elif isinstance(v, NDFrame):

# this is to silence a DeprecationWarning
# TODO: Remove when default dtype of empty Series is object
kwargs = v._construct_axes_dict()
if v._constructor is Series:
backup = create_series_with_explicit_dtype(
**kwargs, dtype_if_empty=object
)
else:
backup = v._constructor(**kwargs)

values = [x if (x is not None) else backup for x in values]

v = values[0]

if isinstance(v, (np.ndarray, Index, Series)):
if isinstance(v, Series):
applied_index = self._selected_obj._get_axis(self.axis)
all_indexed_same = all_indexes_same([x.index for x in values])
singular_series = len(values) == 1 and applied_index.nlevels == 1

# GH3596
# provide a reduction (Frame -> Series) if groups are
# unique
if self.squeeze:
# assign the name to this series
if singular_series:
values[0].name = keys[0]

# GH2893
# we have series in the values array, we want to
# produce a series:
# if any of the sub-series are not indexed the same
# OR we don't have a multi-index and we have only a
# single values
return self._concat_objects(
keys, values, not_indexed_same=not_indexed_same
)
else:
# GH5788 instead of stacking; concat gets the
# dtypes correct

# still a series
# path added as of GH 5545
elif all_indexed_same:
from pandas.core.reshape.concat import concat

result = concat(
values,
keys=key_index,
names=key_index.names,
axis=self.axis,
).unstack()
result.columns = index
elif isinstance(v, ABCSeries):
return concat(values)

if not all_indexed_same:
# GH 8467
return self._concat_objects(keys, values, not_indexed_same=True)

if self.axis == 0 and isinstance(v, ABCSeries):
# GH6124 if the list of Series have a consistent name,
# then propagate that name to the result.
index = v.index.copy()
if index.name is None:
# Only propagate the series name to the result
# if all series have a consistent name. If the
# series do not have a consistent name, do
# nothing.
names = {v.name for v in values}
if len(names) == 1:
index.name = list(names)[0]

# normally use vstack as its faster than concat
# and if we have mi-columns
if (
isinstance(v.index, MultiIndex)
or key_index is None
or isinstance(key_index, MultiIndex)
):
stacked_values = np.vstack([np.asarray(v) for v in values])
result = DataFrame(
stacked_values.T, index=v.index, columns=key_index
)
result = DataFrame(stacked_values, index=key_index, columns=index)
else:
# GH#1738: values is list of arrays of unequal lengths
# fall through to the outer else clause
# TODO: sure this is right? we used to do this
# after raising AttributeError above
return Series(values, index=key_index, name=self._selection_name)

# if we have date/time like in the original, then coerce dates
# as we are stacking can easily have object dtypes here
so = self._selected_obj
if so.ndim == 2 and so.dtypes.apply(needs_i8_conversion).any():
result = _recast_datetimelike_result(result)
else:
result = result._convert(datetime=True)

return self._reindex_output(result)

# values are not series or array-like but scalars
# GH5788 instead of stacking; concat gets the
# dtypes correct
from pandas.core.reshape.concat import concat

result = concat(
values, keys=key_index, names=key_index.names, axis=self.axis,
).unstack()
result.columns = index
elif isinstance(v, ABCSeries):
stacked_values = np.vstack([np.asarray(v) for v in values])
result = DataFrame(stacked_values.T, index=v.index, columns=key_index)
else:
# only coerce dates if we find at least 1 datetime
should_coerce = any(isinstance(x, Timestamp) for x in values)
# self._selection_name not passed through to Series as the
# result should not take the name of original selection
# of columns
return Series(values, index=key_index)._convert(
datetime=True, coerce=should_coerce
)
# GH#1738: values is list of arrays of unequal lengths
# fall through to the outer else clause
# TODO: sure this is right? we used to do this
# after raising AttributeError above
return Series(values, index=key_index, name=self._selection_name)

# if we have date/time like in the original, then coerce dates
# as we are stacking can easily have object dtypes here
so = self._selected_obj
if so.ndim == 2 and so.dtypes.apply(needs_i8_conversion).any():
result = _recast_datetimelike_result(result)
else:
result = result._convert(datetime=True)

return self._reindex_output(result)

# values are not series or array-like but scalars
else:
# Handle cases like BinGrouper
return self._concat_objects(keys, values, not_indexed_same=not_indexed_same)
# only coerce dates if we find at least 1 datetime
should_coerce = any(isinstance(x, Timestamp) for x in values)
# self._selection_name not passed through to Series as the
# result should not take the name of original selection
# of columns
return Series(values, index=key_index)._convert(
datetime=True, coerce=should_coerce
)

def _transform_general(self, func, *args, **kwargs):
from pandas.core.reshape.concat import concat
Expand Down