Python-具有多个时区的pandas datetime列 [英] Python - pandas datetime column with multiple timezones

问题描述

我有一个包含多个用户和时区的数据框，例如：

I have a data frame with multiple users and timezones, like such:

cols = ['user', 'zone_name', 'utc_datetime']
data = [
    [1, 'Europe/Amsterdam', pd.to_datetime('2019-11-13 11:14:15')],
    [2, 'Europe/London', pd.to_datetime('2019-11-13 11:14:15')],
]

df = pd.DataFrame(data, columns=cols)

基于此其他帖子，我应用了以下更改以获取用户的本地日期时间：

Based on this other post, I apply the following change to get the user local datetime:

df['local_datetime'] = df.groupby('zone_name')[
    'utc_datetime'
].transform(lambda x: x.dt.tz_localize(x.name))

输出以下内容：

    user    zone_name     utc_datetime          local_datetime
    1   Europe/Amsterdam  2019-11-13 11:14:15   2019-11-13 11:14:15+01:00
    2   Europe/London     2019-11-13 11:14:15   2019-11-13 11:14:15+00:00

不过， local_datetime 列是对象并且我找不到一种方法来获取它，方法为 datetime64 [ns] 并采用以下格式（期望的输出）：

However, the local_datetime column is an object and I cannot find a way to get it as datetime64[ns] and in the following format (desired output):

    user    zone_name     utc_datetime          local_datetime
    1   Europe/Amsterdam  2019-11-13 11:14:15   2019-11-13 12:14:15
    2   Europe/London     2019-11-13 11:14:15   2019-11-13 11:14:15

推荐答案

我认为您需要 Series.dt.tz_convert 在lambda函数中：

I think you need Series.dt.tz_convert in lambda function:

df['local_datetime'] = (pd.to_datetime(df.groupby('zone_name')['utc_datetime']
    .transform(lambda x: x.dt.tz_localize('UTC').dt.tz_convert(x.name))
    .astype(str).str[:-6]))

print(df)
   user         zone_name        utc_datetime      local_datetime
0     1  Europe/Amsterdam 2019-11-13 11:14:15 2019-11-13 12:14:15
1     2     Europe/London 2019-11-13 11:14:15 2019-11-13 11:14:15

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