我有日期时间对象作为 pandas 数据框中的索引，我想在不使用 for 循环的情况下对其进行本地化。这是代码:(数据是数据框)
from pytz import timezone
utc = timezone('UTC')
utc_times = [utc.localize(entry) for entry in data.index]
cst_times = [entry.astimezone(timezone('US/Central')) for entry in utc_times]
data.index = cst_times
随着数据集的增长，速度会变慢。有什么办法可以加快速度吗？
最佳答案
如果您的索引是 DateTimeIndex，您应该能够执行以下操作:
import pandas as pd
times = pd.DatetimeIndex(start='2018-04-26 11:00:00', periods=50000, freq='1h')
data = pd.DataFrame(index=times)
utc_times = data.index.tz_localize('UTC')
cst_times = utc_times.tz_convert('US/Central')
data.index = cst_times
对于 50,000 倍的索引，此方法的速度快了 1000 倍以上。见下文:
%% time
# Original method
utc_times = [utc.localize(entry) for entry in data.index]
cst_times = [entry.astimezone(timezone('US/Central')) for entry in utc_times]
data.index = cst_times
CPU times: user 1.28 s, sys: 38.2 ms, total: 1.32 s
Wall time: 1.49 s
--
%%time
# New method
utc_times = data.index.tz_localize('UTC')
cst_times = utc_times.tz_convert('US/Central')
data.index = cst_times
CPU times: user 354 µs, sys: 9 µs, total: 363 µs
Wall time: 389 µs
关于python - 在Python中为大型数据集本地化时间的快速方法？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/50067808/