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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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5 changes: 4 additions & 1 deletion pandas/core/groupby.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,8 @@
zip, range, lzip,
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 @@ -3566,7 +3568,8 @@ def first_non_None_value(values):
# as we are stacking can easily have object dtypes here
so = self._selected_obj
if (so.ndim == 2 and so.dtypes.apply(is_datetimelike).any()):
result = result._convert(numeric=True)
result = result.apply(
lambda x: pd.to_numeric(x, errors='ignore'))
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don't import pd
import to_numeric from pandas.util.misc (inside the function)

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or u can import pd inside the function is ok too

date_cols = self._selected_obj.select_dtypes(
include=['datetime', 'timedelta']).columns
date_cols = date_cols.intersection(result.columns)
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10 changes: 10 additions & 0 deletions pandas/tests/groupby/test_groupby.py
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Expand Up @@ -4314,6 +4314,16 @@ def test_cummin_cummax(self):
expected = pd.Series([1, 2, 1], name='b')
tm.assert_series_equal(result, expected)

def test_numeric_coercion(self):
# GH 14423
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there are a couple of issues marked duplicate can u add there tests here as well (inside this test is ok)

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add a 1-liner explaining this (maybe also make the test name a bit more descriptive)

df = pd.DataFrame({'Number': [1, 2],
'Date': ["2017-03-02"] * 2,
'Str': ["foo", "inf"]})
expected = df.groupby(['Number']).apply(lambda x: x.iloc[0])
df.Date = pd.to_datetime(df.Date)
result = df.groupby(['Number']).apply(lambda x: x.iloc[0])
tm.assert_series_equal(result['Str'], expected['Str'])


def _check_groupby(df, result, keys, field, f=lambda x: x.sum()):
tups = lmap(tuple, df[keys].values)
Expand Down