PERF: Period factorization (pandas-dev#14419)

Author: chris-b1

asv_bench/benchmarks/groupby.py

@@ -548,6 +548,32 @@ def time_groupby_sum(self):
  self.df.groupby(['a'])['b'].sum()
 
 
+class groupby_period(object):
+ # GH 14338
+ goal_time = 0.2
+
+ def make_grouper(self, N):
+ return pd.period_range('1900-01-01', freq='D', periods=N)
+
+ def setup(self):
+ N = 10000
+ self.grouper = self.make_grouper(N)
+ self.df = pd.DataFrame(np.random.randn(N, 2))
+
+ def time_groupby_sum(self):
+ self.df.groupby(self.grouper).sum()
+
+
+class groupby_datetime(groupby_period):
+ def make_grouper(self, N):
+ return pd.date_range('1900-01-01', freq='D', periods=N)
+
+
+class groupby_datetimetz(groupby_period):
+ def make_grouper(self, N):
+ return pd.date_range('1900-01-01', freq='D', periods=N,
+ tz='US/Central')
+
 #----------------------------------------------------------------------
 # Series.value_counts
 

doc/source/whatsnew/v0.19.1.txt

@@ -20,7 +20,7 @@ Highlights include:
 Performance Improvements
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-
+ - Fixed performance regression in factorization of ``Period`` data (:issue:`14338`)
 
 
 

pandas/core/algorithms.py

@@ -285,18 +285,27 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None):
  note: an array of Periods will ignore sort as it returns an always sorted
  PeriodIndex
  """
- from pandas import Index, Series, DatetimeIndex
-
- vals = np.asarray(values)
-
- # localize to UTC
- is_datetimetz_type = is_datetimetz(values)
- if is_datetimetz_type:
- values = DatetimeIndex(values)
- vals = values.asi8
+ from pandas import Index, Series, DatetimeIndex, PeriodIndex
+
+ # handling two possibilities here
+ # - for a numpy datetimelike simply view as i8 then cast back
+ # - for an extension datetimelike view as i8 then
+ # reconstruct from boxed values to transfer metadata
+ dtype = None
+ if needs_i8_conversion(values):
+ if is_period_dtype(values):
+ values = PeriodIndex(values)
+ vals = values.asi8
+ elif is_datetimetz(values):
+ values = DatetimeIndex(values)
+ vals = values.asi8
+ else:
+ # numpy dtype
+ dtype = values.dtype
+ vals = values.view(np.int64)
+ else:
+ vals = np.asarray(values)
 
- is_datetime = is_datetime64_dtype(vals)
- is_timedelta = is_timedelta64_dtype(vals)
  (hash_klass, vec_klass), vals = _get_data_algo(vals, _hashtables)
 
  table = hash_klass(size_hint or len(vals))
@@ -311,13 +320,9 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None):
  uniques, labels = safe_sort(uniques, labels, na_sentinel=na_sentinel,
  assume_unique=True)
 
- if is_datetimetz_type:
- # reset tz
- uniques = values._shallow_copy(uniques)
- elif is_datetime:
- uniques = uniques.astype('M8[ns]')
- elif is_timedelta:
- uniques = uniques.astype('m8[ns]')
+ if dtype is not None:
+ uniques = uniques.astype(dtype)
+
  if isinstance(values, Index):
  uniques = values._shallow_copy(uniques, name=None)
  elif isinstance(values, Series):