CLN: reduce overhead in setup for categoricals benchmarks in asv (#24913)

Author: qwhelan

asv_bench/benchmarks/categoricals.py

@@ -223,12 +223,19 @@ class CategoricalSlicing(object):
 
  def setup(self, index):
  N = 10**6
- values = list('a' * N + 'b' * N + 'c' * N)
- indices = {
- 'monotonic_incr': pd.Categorical(values),
- 'monotonic_decr': pd.Categorical(reversed(values)),
- 'non_monotonic': pd.Categorical(list('abc' * N))}
- self.data = indices[index]
+ categories = ['a', 'b', 'c']
+ values = [0] * N + [1] * N + [2] * N
+ if index == 'monotonic_incr':
+ self.data = pd.Categorical.from_codes(values,
+ categories=categories)
+ elif index == 'monotonic_decr':
+ self.data = pd.Categorical.from_codes(list(reversed(values)),
+ categories=categories)
+ elif index == 'non_monotonic':
+ self.data = pd.Categorical.from_codes([0, 1, 2] * N,
+ categories=categories)
+ else:
+ raise ValueError('Invalid index param: {}'.format(index))
 
  self.scalar = 10000
  self.list = list(range(10000))