Python 将时间戳列拆分为单独的日期和时间列
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Splitting timestamp column into separate date and time columns
提问by Tom
I have a pandas dataframe with over 1000 timestamps (below) that I would like to loop through:
我有一个包含超过 1000 个时间戳（如下）的 Pandas 数据框，我想循环遍历：
2016-02-22 14:59:44.561776
I'm having a hard time splitting this time stamp into 2 columns- 'date' and 'time'. The date format can stay the same, but the time needs to be converted to CST (including milliseconds).
我很难将这个时间戳分成 2 列——“日期”和“时间”。日期格式可以保持不变，但时间需要转换为CST（包括毫秒）。
Thanks for the help
谢谢您的帮助
采纳答案by Alexander
I'm not sure why you would want to do this in the first place, but if you really must...
我不确定你为什么首先要这样做，但如果你真的必须......
df = pd.DataFrame({'my_timestamp': pd.date_range('2016-1-1 15:00', periods=5)})
>>> df
my_timestamp
0 2016-01-01 15:00:00
1 2016-01-02 15:00:00
2 2016-01-03 15:00:00
3 2016-01-04 15:00:00
4 2016-01-05 15:00:00
df['new_date'] = [d.date() for d in df['my_timestamp']]
df['new_time'] = [d.time() for d in df['my_timestamp']]
>>> df
my_timestamp new_date new_time
0 2016-01-01 15:00:00 2016-01-01 15:00:00
1 2016-01-02 15:00:00 2016-01-02 15:00:00
2 2016-01-03 15:00:00 2016-01-03 15:00:00
3 2016-01-04 15:00:00 2016-01-04 15:00:00
4 2016-01-05 15:00:00 2016-01-05 15:00:00
The conversion to CST is more tricky. I assume that the current timestamps are 'unaware', i.e. they do not have a timezone attached? If not, how would you expect to convert them?
转换为 CST 更加棘手。我假设当前的时间戳是“不知道的”，即它们没有附加时区？如果没有，您希望如何转换它们？
For more details:
更多细节：
https://docs.python.org/2/library/datetime.html
https://docs.python.org/2/library/datetime.html
How to make an unaware datetime timezone aware in python
EDIT
编辑
An alternative method that only loops once across the timestamps instead of twice:
只在时间戳上循环一次而不是两次的替代方法：
new_dates, new_times = zip(*[(d.date(), d.time()) for d in df['my_timestamp']])
df = df.assign(new_date=new_dates, new_time=new_times)
回答by wrkyle
Try
尝试
s = '2016-02-22 14:59:44.561776'
date,time = s.split()
then convert time as needed.
然后根据需要转换时间。
If you want to further split the time,
如果你想进一步分割时间，
hour, minute, second = time.split(':')
回答by Nitin Sanghi
try this:
尝试这个：
def time_date(datetime_obj):
date_time = datetime_obj.split(' ')
time = date_time[1].split('.')
return date_time[0], time[0]
回答by Mathieu B
If your timestamps are already in pandas format (not string), then:
如果您的时间戳已经是 Pandas 格式（不是字符串），那么：
df["date"] = df["timestamp"].date
dt["time"] = dt["timestamp"].time
If your timestamp is a string, you can parse it using the datetime module:
如果您的时间戳是一个字符串，您可以使用 datetime 模块解析它：
from datetime import datetime
data1["timestamp"] = df["timestamp"].apply(lambda x: \
datetime.strptime(x,"%Y-%m-%d %H:%M:%S.%f"))
Source: http://pandas.pydata.org/pandas-docs/stable/timeseries.html
资料来源：http: //pandas.pydata.org/pandas-docs/stable/timeseries.html
回答by pp_
If your timestamp is a string, you can convert it to a datetimeobject:
如果您的时间戳是字符串，则可以将其转换为datetime对象：
from datetime import datetime
timestamp = '2016-02-22 14:59:44.561776'
dt = datetime.strptime(timestamp, '%Y-%m-%d %H:%M:%S.%f')
From then on you can bring it to whatever format you like.
从那时起，您可以将其转换为您喜欢的任何格式。
回答by Anton Protopopov
I think the most easiest way is to use dtattribute of pandas Series. For your case you need to use dt.dateand dt.time:
我认为最简单的方法是使用dtpandas Series 的属性。对于您的情况，您需要使用dt.date和dt.time：
df = pd.DataFrame({'full_date': pd.date_range('2016-1-1 10:00:00.123', periods=10, freq='5H')})
df['date'] = df['full_date'].dt.date
df['time'] = df['full_date'].dt.time
In [166]: df
Out[166]:
full_date date time
0 2016-01-01 10:00:00.123 2016-01-01 10:00:00.123000
1 2016-01-01 15:00:00.123 2016-01-01 15:00:00.123000
2 2016-01-01 20:00:00.123 2016-01-01 20:00:00.123000
3 2016-01-02 01:00:00.123 2016-01-02 01:00:00.123000
4 2016-01-02 06:00:00.123 2016-01-02 06:00:00.123000
5 2016-01-02 11:00:00.123 2016-01-02 11:00:00.123000
6 2016-01-02 16:00:00.123 2016-01-02 16:00:00.123000
7 2016-01-02 21:00:00.123 2016-01-02 21:00:00.123000
8 2016-01-03 02:00:00.123 2016-01-03 02:00:00.123000
9 2016-01-03 07:00:00.123 2016-01-03 07:00:00.123000
回答by Okroshiashvili
Had same problem and this worked for me.
有同样的问题，这对我有用。
Suppose the date column in your dataset is called "date"
假设数据集中的日期列称为“日期”
import pandas as pd
df = pd.read_csv(file_path)
df['Dates'] = pd.to_datetime(df['date']).dt.date
df['Time'] = pd.to_datetime(df['date']).dt.time
This will give you two columns "Dates" and "Time" with splited dates.
这将为您提供带有拆分日期的两列“日期”和“时间”。
