pandas 将具有unix时间戳(以毫秒为单位)的行转换为日期时间 [英] Pandas converting row with unix timestamp (in milliseconds) to datetime

问题描述

我需要处理大量CSV文件，其中时间戳始终是一个字符串，代表以毫秒为单位的unix时间戳.我找不到有效修改这些列的方法.

I need to process a huge amount of CSV files where the time stamp is always a string representing the unix timestamp in milliseconds. I could not find a method yet to modify these columns efficiently.

这是我想出的，但是，这当然只重复了该列，而我不得不以某种方式将其放回原始数据集中.我确定创建DataFrame

This is what I came up with, however this of course duplicates only the column and I have to somehow put it back to the original dataset. I'm sure it can be done when creating the DataFrame?

import sys
if sys.version_info[0] < 3:
    from StringIO import StringIO
else:
    from io import StringIO
import pandas as pd

data = 'RUN,UNIXTIME,VALUE\n1,1447160702320,10\n2,1447160702364,20\n3,1447160722364,42'

df = pd.read_csv(StringIO(data))

convert = lambda x: datetime.datetime.fromtimestamp(x / 1e3)
converted_df = df['UNIXTIME'].apply(convert)

这将选择"UNIXTIME"列并将其更改为

This will pick the column 'UNIXTIME' and change it from

0    1447160702320
1    1447160702364
2    1447160722364
Name: UNIXTIME, dtype: int64

进入此

0   2015-11-10 14:05:02.320
1   2015-11-10 14:05:02.364
2   2015-11-10 14:05:22.364
Name: UNIXTIME, dtype: datetime64[ns]

但是，我想使用pd.apply()之类的方法来获取转换后的列返回的整个数据集，或者像我已经写的那样，仅在从CSV生成DataFrame时创建日期时间.

However, I would like to use something like pd.apply() to get the whole dataset returned with the converted column or as I already wrote, simply create datetimes when generating the DataFrame from CSV.

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