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asv_bench/benchmarks/tslibs
1 file changed +32
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+ try :
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+ from pandas ._libs .tslibs import normalize_i8_timestamps , is_date_array_normalized
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+ except ImportError :
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+ from pandas ._libs .tslibs .conversion import (
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+ normalize_i8_timestamps ,
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+ is_date_array_normalized ,
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+ )
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+
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+ import pandas as pd
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+
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+ from .tslib import _sizes , _tzs
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+
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+
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+ class Normalize :
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+ params = [
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+ _sizes ,
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+ _tzs ,
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+ ]
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+ param_names = ["size" , "tz" ]
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+
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+ def setup (self , size , tz ):
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+ # use an array that will have is_date_array_normalized give True,
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+ # so we do not short-circuit early.
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+ dti = pd .date_range ("2016-01-01" , periods = 10 , tz = tz ).repeat (size // 10 )
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+ self .i8data = dti .asi8
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+
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+ def time_normalize_i8_timestamps (self , size , tz ):
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+ normalize_i8_timestamps (self .i8data , tz )
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+
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+ def time_is_date_array_normalized (self , size , tz ):
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+ # TODO: cases with different levels of short-circuiting
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+ is_date_array_normalized (self .i8data , tz )
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