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import pandas as pd
class IndexCache:
number = 1
repeat = (3, 100, 20)
params = [
[
"CategoricalIndex",
"DatetimeIndex",
"Float64Index",
"IntervalIndex",
"Int64Index",
"MultiIndex",
"PeriodIndex",
"RangeIndex",
"TimedeltaIndex",
"UInt64Index",
]
]
param_names = ["index_type"]
def setup(self, index_type):
N = 10**5
if index_type == "MultiIndex":
self.idx = pd.MultiIndex.from_product(
[pd.date_range("1/1/2000", freq="min", periods=N // 2), ["a", "b"]]
)
elif index_type == "DatetimeIndex":
self.idx = pd.date_range("1/1/2000", freq="min", periods=N)
elif index_type == "Int64Index":
self.idx = pd.Index(range(N), dtype="int64")
elif index_type == "PeriodIndex":
self.idx = pd.period_range("1/1/2000", freq="min", periods=N)
elif index_type == "RangeIndex":
self.idx = pd.RangeIndex(start=0, stop=N)
elif index_type == "IntervalIndex":
self.idx = pd.IntervalIndex.from_arrays(range(N), range(1, N + 1))
elif index_type == "TimedeltaIndex":
self.idx = pd.TimedeltaIndex(range(N))
elif index_type == "Float64Index":
self.idx = pd.Index(range(N), dtype="float64")
elif index_type == "UInt64Index":
self.idx = pd.Index(range(N), dtype="uint64")
elif index_type == "CategoricalIndex":
self.idx = pd.CategoricalIndex(range(N), range(N))
else:
raise ValueError
assert len(self.idx) == N
self.idx._cache = {}
def time_values(self, index_type):
self.idx._values
def time_shape(self, index_type):
self.idx.shape
def time_is_monotonic_decreasing(self, index_type):
self.idx.is_monotonic_decreasing
def time_is_monotonic_increasing(self, index_type):
self.idx.is_monotonic_increasing
def time_is_unique(self, index_type):
self.idx.is_unique
def time_engine(self, index_type):
self.idx._engine
def time_inferred_type(self, index_type):
self.idx.inferred_type
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