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BUG: Retain tz-aware dtypes with melt (#15785) #20292

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Merged
merged 4 commits into from
Mar 13, 2018
Merged

BUG: Retain tz-aware dtypes with melt (#15785) #20292

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be66786
BUG: Retain tz-aware dtypes with melt
mroeschke Mar 12, 2018
2d28685
clarify comment
mroeschke Mar 12, 2018
78a2d84
Reduce duplication and dict insertion order fix
mroeschke Mar 12, 2018
abe48c9
address review
mroeschke Mar 13, 2018
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.23.0.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -896,6 +896,7 @@ Timezones
- Bug in :func:`Timestamp.tz_localize` where localizing a timestamp near the minimum or maximum valid values could overflow and return a timestamp with an incorrect nanosecond value (:issue:`12677`)
- Bug when iterating over :class:`DatetimeIndex` that was localized with fixed timezone offset that rounded nanosecond precision to microseconds (:issue:`19603`)
- Bug in :func:`DataFrame.diff` that raised an ``IndexError`` with tz-aware values (:issue:`18578`)
- Bug in :func:`melt` that converted tz-aware dtypes to tz-naive (:issue:`15785`)

Offsets
^^^^^^^
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9 changes: 8 additions & 1 deletion pandas/core/reshape/melt.py
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Expand Up @@ -13,7 +13,9 @@

import re
from pandas.core.dtypes.missing import notna
from pandas.core.dtypes.common import is_extension_type
from pandas.core.tools.numeric import to_numeric
from pandas.core.reshape.concat import concat


@Appender(_shared_docs['melt'] %
Expand Down Expand Up @@ -70,7 +72,12 @@ def melt(frame, id_vars=None, value_vars=None, var_name=None,

mdata = {}
for col in id_vars:
mdata[col] = np.tile(frame.pop(col).values, K)
id_data = frame.pop(col)
if is_extension_type(id_data):
id_data = concat([id_data] * K, ignore_index=True)
else:
id_data = np.tile(id_data.values, K)
mdata[col] = id_data

mcolumns = id_vars + var_name + [value_name]

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21 changes: 21 additions & 0 deletions pandas/tests/reshape/test_melt.py
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Expand Up @@ -212,6 +212,27 @@ def test_multiindex(self):
res = self.df1.melt()
assert res.columns.tolist() == ['CAP', 'low', 'value']

@pytest.mark.parametrize("col", [
pd.Series(pd.date_range('2010', periods=5, tz='US/Pacific')),
pd.Series(["a", "b", "c", "a", "d"], dtype="category"),
pd.Series([0, 1, 0, 0, 0])])
def test_pandas_dtypes(self, col):
# GH 15785
df = DataFrame({'klass': range(5),
'col': col,
'attr1': [1, 0, 0, 0, 0],
'attr2': col})
expected_value = pd.concat([pd.Series([1, 0, 0, 0, 0]), col],
ignore_index=True)
result = melt(df, id_vars=['klass', 'col'], var_name='attribute',
value_name='value')
expected = DataFrame({0: list(range(5)) * 2,
1: pd.concat([col] * 2, ignore_index=True),
2: ['attr1'] * 5 + ['attr2'] * 5,
3: expected_value})
expected.columns = ['klass', 'col', 'attribute', 'value']
tm.assert_frame_equal(result, expected)


class TestLreshape(object):

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