我创建了一个每小时日期数据框，现在我想创建一个列来标记每行（小时）是否处于夏令时。例如，在夏季，该标志应 == 1，而在冬季，该标志应 == 0。
# Localized dates dataframe
dates = pd.DataFrame(data=pd.date_range('2018-1-1', '2019-1-1', freq='h', tz='America/Denver'), columns=['date_time'])
# My failed attempt to create the flag column
dates['dst_flag'] = np.where(dates['date_time'].dt.daylight_saving_time == True, 1, 0)
评论中有一个很好的链接，至少可以让您手动执行此操作。 AFAIK，没有矢量化的方法可以做到这一点。
import pandas as pd
import numpy as np
from pytz import timezone
# Generate data (as opposed to index)
date_range = pd.to_datetime(pd.date_range('1/1/2018', '1/1/2019', freq='h', tz='America/Denver'))
date_range = [date for date in date_range]
# Localized dates dataframe
df = pd.DataFrame(data=date_range, columns=['date_time'])
# Map transition times to year for some efficiency gain
tz = timezone('America/Denver')
transition_times = tz._utc_transition_times[1:]
transition_times = [t.astimezone(tz) for t in transition_times]
transition_times_by_year = {}
for start_time, stop_time in zip(transition_times[::2], transition_times[1::2]):
year = start_time.year
transition_times_by_year[year] = [start_time, stop_time]
# If the date is in DST, mark true, else false
def mark_dst(dates):
for date in dates:
start_dst, stop_dst = transition_times_by_year[date.year]
yield start_dst <= date <= stop_dst
df['dst_flag'] = [dst_flag for dst_flag in mark_dst(df['date_time'])]
# Do a quick sanity check to make sure we did this correctly for year 2018
dst_start = df[df['dst_flag'] == True]['date_time'][0] # First dst time 2018
dst_end = df[df['dst_flag'] == True]['date_time'][-1] # Last dst time 2018
print(dst_start)
print(dst_end)
此输出：
2018-03-11 07:00:00-06:00
2018-11-04 06:00:00-07:00
这“可能”是正确的。我没有手动进行 UTC 转换或任何检查时间是否与给定时区完全正确的操作。您至少可以通过快速谷歌搜索来验证日期是否正确。 一些陷阱：
pd.date_range生成一个
index，而不是数据。我稍微更改了您的原始代码，使其成为数据而不是索引。我假设您已经有了数据。
tz._utc_transition_times的结构有些愚蠢。这是开始/停止 utc DST 转换时间，但早期日期中有一些愚蠢的东西。不过从1965年开始应该就不错了。如果您的日期早于该日期，请将
tz._utc_transition_times[1:]tz._utc_transition_timestz._utc_transition_times是“Python 私有”。它可能会在没有警告或通知的情况下进行更改，并且可能适用于未来或过去的
pytzpytz
其背后的基本思想是找出您所在时区的当前时间与 UTC 时间之间的差异。在冬季，时差将比 UTC 晚一小时。无论差异是什么，请添加获得标志 1 或 0 所需的内容。
在丹佛，夏季月份为 UTC-6，冬季月份为 UTC-7。因此，如果您采用丹佛的 tz 感知时间与 UTC 时间之间的差值，然后加 7，您将获得夏季月份的值为 1，冬季月份的值为 0。
import pandas as pd
start = pd.to_datetime('2020-10-30')
end = pd.to_datetime('2020-11-02')
dates = pd.date_range(start=start, end=end, freq='h', tz='America/Denver')
df1 = pd.DataFrame({'dst_flag': 1, 'date1': dates.tz_localize(None)}, index=dates)
# add extra day on each end so that there are no nan's after the join
dates = pd.to_datetime(pd.date_range(start=start - pd.to_timedelta(1, 'd'), end=end + pd.to_timedelta(1, 'd'), freq='h'), utc=True)
df2 = pd.DataFrame({'date2': dates.tz_localize(None)}, index=dates)
out = df1.join(df2)
out['dst_flag'] = (out['date1'] - out['date2']) / pd.to_timedelta(1, unit='h') + 7
out.drop(columns=['date1', 'date2'], inplace=True)
import pandas as pd
import pytz
# Create dates table and flag Daylight Saving Time dates
dates = pd.DataFrame(data=pd.date_range('2018-1-1', '2018-12-31-23', freq='h'), columns=['date_time'])
# Create a list of start and end dates for DST in each year, in UTC time
dst_changes_utc = pytz.timezone('America/Denver')._utc_transition_times[1:]
# Convert to local times from UTC times and then remove timezone information
dst_changes = [pd.Timestamp(i).tz_localize('UTC').tz_convert('America/Denver').tz_localize(None) for i in dst_changes_utc]
flag_list = []
for index, row in dates['date_time'].iteritems():
# Isolate the start and end dates for DST in each year
dst_dates_in_year = [date for date in dst_changes if date.year == row.year]
spring = dst_dates_in_year[0]
fall = dst_dates_in_year[1]
if (row >= spring) & (row < fall):
flag = 1
else:
flag = 0
flag_list.append(flag)
print(flag_list)
dates['dst_flag'] = flag_list
del(flag_list)
# Localized dates dataframe
df = pd.DataFrame(data=pd.date_range('2018-1-1', '2019-1-1', freq='h', tz='America/Denver'), columns=['date_time'])
df['utc_offset'] = df['date_time'].dt.strftime('%z').str[0:3].astype(float)
df['utc_offset_shifted'] = df['utc_offset'].shift(-1)
df['dst'] = df['utc_offset'] - df['utc_offset_shifted']
df_dst = df[(df['dst'] != 0) & (df['dst'])]
df_dst = df_dst.drop(['utc_offset', 'utc_offset_shifted'], axis=1).reset_index(drop=True)
print(df_dst)
此输出：
date_time dst
0 2018-03-11 01:00:00-07:00 -1.0
1 2018-11-04 01:00:00-06:00 1.0
dates['dst_flag'] = dates['date_time'].apply(lambda x: x.tzname() == 'CEST')
这会将CET
中的所有时间标记为 False，将 CEST 中的所有时间标记为 True。我不确定我是否想在一个巨大的柱子上这样做。
datetime转换为 python
to_pydatetime()datetime.dst()timedelta#time_delta should normally be 0.0 or 3600.0 seconds.
time_delta = [x.dst().total_seconds() for x in dates['date_time'].dt.to_pydatetime()]
dates['dst_flag'] = np.where(np.array(time_delta) < 0.5, 0, 1)
这一行应该可以让您一次性完成所有操作，而无需进行不方便的转换等：
dates = pd.DataFrame(data=pd.date_range('2018-1-1', '2019-1-1', freq='h', tz='America/Denver'), columns=['date_time'])
dates["dst_flag"]=dates.date_time.map(lambda date: int(date.dst() > pd.Timedelta(0)))