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PERF: Improve drop_duplicates for bool columns (#12963) #15738

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PERF: Improve drop_duplicates for bool columns (#12963) #15738

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5 changes: 5 additions & 0 deletions asv_bench/benchmarks/reindex.py
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Original file line number Diff line number Diff line change
Expand Up @@ -132,6 +132,9 @@ def setup(self):
self.K = 10000
self.key1 = np.random.randint(0, self.K, size=self.N)
self.df_int = DataFrame({'key1': self.key1})
self.df_bool = DataFrame({i: np.random.randint(0, 2, size=self.K,
dtype=bool)
for i in range(10)})

def time_frame_drop_dups(self):
self.df.drop_duplicates(['key1', 'key2'])
Expand All @@ -154,6 +157,8 @@ def time_series_drop_dups_string(self):
def time_frame_drop_dups_int(self):
self.df_int.drop_duplicates()

def time_frame_drop_dups_bool(self):
self.df_bool.drop_duplicates()

#----------------------------------------------------------------------
# blog "pandas escaped the zoo"
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.20.0.txt
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Expand Up @@ -788,6 +788,7 @@ Performance Improvements
- Improved performance of ``.rank()`` for categorical data (:issue:`15498`)
- Improved performance when using ``.unstack()`` (:issue:`15503`)
- Improved performance of merge/join on ``category`` columns (:issue:`10409`)
- Improved performance of ``drop_duplicates()`` on ``bool`` columns (:issue:`12963`)


.. _whatsnew_0200.bug_fixes:
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5 changes: 5 additions & 0 deletions pandas/core/algorithms.py
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Expand Up @@ -19,6 +19,7 @@
is_period_dtype,
is_period_arraylike,
is_float_dtype,
is_bool_dtype,
needs_i8_conversion,
is_categorical,
is_datetime64_dtype,
Expand Down Expand Up @@ -341,6 +342,10 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None):
# numpy dtype
dtype = values.dtype
vals = values.view(np.int64)
elif is_bool_dtype(values):
dtype = bool
# transform to int dtype to avoid object path
vals = np.asarray(values).view('uint8')
else:
vals = np.asarray(values)

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