8000 BUG: Group-by numeric type-coercion with datetime by gwpdt · Pull Request #15680 · pandas-dev/pandas · GitHub
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BUG: Group-by numeric type-coercion with datetime #15680

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BUG: Group-by numeric type-coercion with datetime #15680

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46d12c2
BUG: Group-by numeric type-coericion with datetime
gwpdt Mar 14, 2017
c8844e0
CLN: PEP8 (whitespace fixes)
gwpdt Mar 14, 2017
0a15674
CLN: move import, add whatsnew entry
gwpdt Mar 14, 2017
e1ed104
TST: Rename and expand test_numeric_coercion
gwpdt Mar 16, 2017
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CLN: move import, add whatsnew entry
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@gwpdt
gwpdt committed Mar 14, 2017
commit 0a156745e39595fe125081282da2acc7f28cec13
1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.20.0.txt
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Expand Up @@ -886,3 +886,4 @@ Bug Fixes
- Bug in ``pd.melt()`` where passing a tuple value for ``value_vars`` caused a ``TypeError`` (:issue:`15348`)
- Bug in ``.eval()`` which caused multiline evals to fail with local variables not on the first line (:issue:`15342`)
- Bug in ``pd.read_msgpack`` which did not allow to load dataframe with an index of type ``CategoricalIndex`` (:issue:`15487`)
- Bug in ``groupby.apply()`` coercing ``object`` series to numeric types, when not all values were numeric (:issue:`15680`)
4 changes: 2 additions & 2 deletions pandas/core/groupby.py
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Expand Up @@ -11,7 +11,6 @@
callable, map
)

import pandas as pd
from pandas import compat
from pandas.compat.numpy import function as nv
from pandas.compat.numpy import _np_version_under1p8
Expand Down Expand Up @@ -3426,6 +3425,7 @@ def _decide_output_index(self, output, labels):

def _wrap_applied_output(self, keys, values, not_indexed_same=False):
from pandas.core.index import _all_indexes_same
from pandas.tools.util import to_numeric

if len(keys) == 0:
return DataFrame(index=keys)
Expand Down Expand Up @@ -3569,7 +3569,7 @@ def first_non_None_value(values):
so = self._selected_obj
if (so.ndim == 2 and so.dtypes.apply(is_datetimelike).any()):
result = result.apply(
lambda x: pd.to_numeric(x, errors='ignore'))
lambda x: to_numeric(x, errors='ignore'))
date_cols = self._selected_obj.select_dtypes(
include=['datetime', 'timedelta']).columns
date_cols = date_cols.intersection(result.columns)
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0