如何修改 Pandas 中的日期时间索引格式(UTC)?-Python问题

How to modify Datetime index format (UTC) in Pandas?(如何修改 Pandas 中的日期时间索引格式(UTC)?)

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问题描述

我有一个看起来像这样的 df:

I have a df that looks like this:

2015-01-29 08:30:00-05:00  199425  199950  199375  199825                  
2015-01-29 08:45:00-05:00  199825  199850  199650  199800                 
2015-01-29 09:00:00-05:00  199825  199900  199450  199625

如何删除 -05:00 使其看起来像这样?:

How can I remove the -05:00 so It looks like this?:

2015-01-29 08:30:00  199425  199950  199375  199825                  
2015-01-29 08:45:00  199825  199850  199650  199800                 
2015-01-29 09:00:00  199825  199900  199450  199625

澄清一下，时间没问题，我不需要对此做任何转换，修改的只是格式，(-05:00)

Just to clarify, the time is fine, I don't need to do any transformation on that, the modification is just the format, (-05:00)

更新:

为了更清楚.-5:00 来自应用此程序

For further clarity. The -5:00 comes out of applying this procedure

eastern = pytz.timezone('US/Eastern')
df.index = df.index.tz_localize(pytz.utc).tz_convert(eastern)

谢谢

推荐答案

这是 2015 年 1 月的一个老问题.但是由于还没有答案(尽管有很多评论)，所以这里是 2019 年 10 月的答案.原文提问者可能已经找到了答案，但只是作为未来的参考.

This is an old question from Jan 2015. But since there is no answer yet (although lots of comments), here is an answer in Oct 2019. The original questioner probably found an answer already but just as a reference for the future.

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html

https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior

import pandas as pd

# create dataframe
df = pd.DataFrame({
    'date_original': ['2015-01-29 08:30:00-05:00', '2015-01-29 08:45:00-05:00', '2015-01-29 09:00:00-05:00'],
    'measurement': [199425, 199825, 199825]
})

# make sure to convert date column to datetime, not string
df['date_original'] = pd.to_datetime(df['date_original'])

print('Original dataframe:')
print(df)
print()

# remove the suffix from the date
df['date_transform'] = pd.to_datetime(df['date_original']).dt.strftime('%Y-%m-%d %H:%M:%S')

print('Transformed dataframe:')
print(df)
print()
df

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