將項目從熊貓系列轉換為日期時間

Question

我有一個Pandas系列（“ timeSeries”），其中包含一天中的某個時間。 一些項目為空白，一些為實際時間（08：00； 13：00），一些為時間指示（早晨，下午）。

由於我所在的時間是紐約，所以我想將時間格式的項目轉換為倫敦時間。 當我還添加了timedelta(hours=5)時，無法使用pd.to_datetime(timeSeries, error='ignore') timedelta(hours=5) 。 所以我試圖添加一個if條件，但是它不起作用。

樣本初始DataFrame：

dfNY = pd.DataFrame({'TimeSeries': [13:00, nan, 06:00, 'Morning', 'Afternoon', nan, nan, 01:30])

所需結果：

dfLondon = pd.DataFrame({'TimeSeries': [18:00, nan, 11:00, 'Morning', 'Afternoon', nan, nan, 06:30])

對我的代碼的任何幫助或簡化都將非常有用。

london = dt.datetime.now(timezone("America/New_York"))
newYork = dt.datetime.now(timezone("Europe/London"))
timeDiff = (london - dt.timedelta(hours = newYork.hour)).hour

for dayTime in timeSeries: 
     if dayTime == "%%:%%": 
        print(dayTime)
        dayTime = pd.to_datetime(dayTime) + dt.timedelta(hours=timeDiff)
return timeSeries

更新：在下面的注釋中使用pytz方法產生的時區偏離我的5分鍾。 我們該如何解決？

Answer 1

使用.dt訪問器 ，您可以將時區設置為您的值，然后使用tz.localize和tz_convert將其轉換為另一個時區。

import pandas as pd
import numpy as np

pd.options.display.max_columns = 5

df = pd.DataFrame({'TimeSeries': ["13:00", np.nan, "06:00", 'Morning', 'Afternoon', np.nan, np.nan, "01:30"]})

#   Convert your data to datetime, errors appears, but we do not care about them.
#   We also explicitly note that the datetime is a specific timezone.
df['TimeSeries_TZ'] = pd.to_datetime(df['TimeSeries'], errors='coerce', format='%H:%M')\
                     .dt.tz_localize('America/New_York')
print(df['TimeSeries_TZ'])
# 0   1900-01-01 13:00:00-04:56
# 1                         NaT
# 2   1900-01-01 06:00:00-04:56
# 3                         NaT
# 4                         NaT
# 5                         NaT
# 6                         NaT
# 7   1900-01-01 01:30:00-04:56

#   Then, we can use the datetime accessor to convert the timezone.
df['Converted_time'] = df['TimeSeries_TZ'].dt.tz_convert('Europe/London').dt.strftime('%H:%M')
print(df['Converted_time'])
# 0    17:55
# 1      NaT
# 2    10:55
# 3      NaT
# 4      NaT
# 5      NaT
# 6      NaT
# 7    06:25

#   If you want to convert the original result that CAN be converted, while keeping the values that
#   raised errors, you can copy the original data, and change the data that is not equal to the value
#   that means an error was raised, e.g : NaT (not a timestamp).
df['TimeSeries_result'] = df['TimeSeries'].copy()
df['TimeSeries_result'] = df['TimeSeries'].where(~df['Converted_time'].ne('NaT'), df['Converted_time'])


print(df[['TimeSeries', 'TimeSeries_result']])
#   TimeSeries TimeSeries_result
# 0      13:00             17:55
# 1        NaN               NaN
# 2      06:00             10:55
# 3    Morning           Morning
# 4  Afternoon         Afternoon
# 5        NaN               NaN
# 6        NaN               NaN
# 7      01:30             06:256          06:25             06:25