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BUG: drop_duplicates drops non-duplicate rows in the presence of integer columns #11403

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Merged
merged 2 commits into from
Oct 24, 2015
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BUG: drop_duplicates drops non-duplicate rows in the presence of integer columns #11403

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BUG: drop_duplicates drops non-duplicate rows in the presence of inte…
evanpw Oct 23, 2015
b710728
Revert "PERF: perf improvements in drop_duplicates for integer dtyped…
evanpw Oct 23, 2015
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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v0.17.1.txt
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Expand Up @@ -92,7 +92,7 @@ Bug Fixes
- Bug in ``pivot_table`` with ``margins=True`` when indexes are of ``Categorical`` dtype (:issue:`10993`)
- Bug in ``DataFrame.plot`` cannot use hex strings colors (:issue:`10299`)


- Bug in ``DataFrame.drop_duplicates`` (regression from 0.16.2) causing some non-duplicate rows containing integer values to be dropped (:issue:`11376`)


- Bug in ``pd.eval`` where unary ops in a list error (:issue:`11235`)
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8 changes: 1 addition & 7 deletions pandas/core/frame.py
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Expand Up @@ -2994,13 +2994,7 @@ def duplicated(self, subset=None, keep='first'):
from pandas.hashtable import duplicated_int64, _SIZE_HINT_LIMIT

def f(vals):

# if we have integers we can directly index with these
if com.is_integer_dtype(vals):
from pandas.core.nanops import unique1d
labels, shape = vals, unique1d(vals)
else:
labels, shape = factorize(vals, size_hint=min(len(self), _SIZE_HINT_LIMIT))
labels, shape = factorize(vals, size_hint=min(len(self), _SIZE_HINT_LIMIT))
return labels.astype('i8',copy=False), len(shape)

if subset is None:
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19 changes: 19 additions & 0 deletions pandas/tests/test_frame.py
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Expand Up @@ -8380,6 +8380,25 @@ def test_drop_duplicates(self):
expected = df.iloc[[-2,-1]]
assert_frame_equal(result, expected)

# GH 11376
df = pd.DataFrame({'x': [7, 6, 3, 3, 4, 8, 0],
'y': [0, 6, 5, 5, 9, 1, 2]})
expected = df.loc[df.index != 3]
assert_frame_equal(df.drop_duplicates(), expected)

df = pd.DataFrame([[1 , 0], [0, 2]])
assert_frame_equal(df.drop_duplicates(), df)

df = pd.DataFrame([[-2, 0], [0, -4]])
assert_frame_equal(df.drop_duplicates(), df)

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can u add the op example here as well
do we have sufficient coverage for various dtypes?

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I see tests for strings, integers, and floats. This particular problem happens only for integers, though.

x = np.iinfo(np.int64).max / 3 * 2
df = pd.DataFrame([[-x, x], [0, x + 4]])
assert_frame_equal(df.drop_duplicates(), df)

df = pd.DataFrame([[-x, x], [x, x + 4]])
assert_frame_equal(df.drop_duplicates(), df)

def test_drop_duplicates_for_take_all(self):
df = DataFrame({'AAA': ['foo', 'bar', 'baz', 'bar',
'foo', 'bar', 'qux', 'foo'],
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