使用 python pandas 组合日期和时间列
问题描述
我有一个带有以下列的 pandas 数据框:
I have a pandas dataframe with the following columns:
data = {'Date': ['01-06-2013', '02-06-2013', '02-06-2013', '02-06-2013', '02-06-2013', '03-06-2013', '03-06-2013', '03-06-2013', '03-06-2013', '04-06-2013'],
'Time': ['23:00:00', '01:00:00', '21:00:00', '22:00:00', '23:00:00', '01:00:00', '21:00:00', '22:00:00', '23:00:00', '01:00:00']}
df = pd.DataFrame(data)
Date Time
0 01-06-2013 23:00:00
1 02-06-2013 01:00:00
2 02-06-2013 21:00:00
3 02-06-2013 22:00:00
4 02-06-2013 23:00:00
5 03-06-2013 01:00:00
6 03-06-2013 21:00:00
7 03-06-2013 22:00:00
8 03-06-2013 23:00:00
9 04-06-2013 01:00:00
如何合并数据['Date'] &data['Time'] 得到以下内容?有没有办法使用 pd.to_datetime?
How do I combine data['Date'] & data['Time'] to get the following? Is there a way of doing it using pd.to_datetime?
Date
01-06-2013 23:00:00
02-06-2013 01:00:00
02-06-2013 21:00:00
02-06-2013 22:00:00
02-06-2013 23:00:00
03-06-2013 01:00:00
03-06-2013 21:00:00
03-06-2013 22:00:00
03-06-2013 23:00:00
04-06-2013 01:00:00
解决方案
值得一提的是，您可能已经能够直接阅读此内容，例如如果您使用的是 read_csv使用 parse_dates=[['Date', 'Time']].
It's worth mentioning that you may have been able to read this in directly e.g. if you were using read_csv using parse_dates=[['Date', 'Time']].
假设这些只是字符串，您可以简单地将它们添加在一起(使用空格)，允许您使用 to_datetime，无需指定 format= 参数即可工作
Assuming these are just strings you could simply add them together (with a space), allowing you to use to_datetime, which works without specifying the format= parameter
In [11]: df['Date'] + ' ' + df['Time']
Out[11]:
0 01-06-2013 23:00:00
1 02-06-2013 01:00:00
2 02-06-2013 21:00:00
3 02-06-2013 22:00:00
4 02-06-2013 23:00:00
5 03-06-2013 01:00:00
6 03-06-2013 21:00:00
7 03-06-2013 22:00:00
8 03-06-2013 23:00:00
9 04-06-2013 01:00:00
dtype: object
In [12]: pd.to_datetime(df['Date'] + ' ' + df['Time'])
Out[12]:
0 2013-01-06 23:00:00
1 2013-02-06 01:00:00
2 2013-02-06 21:00:00
3 2013-02-06 22:00:00
4 2013-02-06 23:00:00
5 2013-03-06 01:00:00
6 2013-03-06 21:00:00
7 2013-03-06 22:00:00
8 2013-03-06 23:00:00
9 2013-04-06 01:00:00
dtype: datetime64[ns]
或者，不使用 + ' '，但必须使用 format= 参数.此外，pandas 擅长推断要转换为 datetime 的格式，但是，指定确切的格式更快.</p>
Alternatively, without the + ' ', but the format= parameter must be used. Additionally, pandas is good at inferring the format to be converted to a datetime, however, specifying the exact format is faster.
pd.to_datetime(df['Date'] + df['Time'], format='%m-%d-%Y%H:%M:%S')
注意:令人惊讶的是(对我而言)，这在将 NaN 转换为 NaT 时效果很好，但值得担心的是转换(可能使用 raise 参数).
# sample dataframe with 10000000 rows using df from the OP
df = pd.concat([df for _ in range(1000000)]).reset_index(drop=True)
%%timeit
pd.to_datetime(df['Date'] + ' ' + df['Time'])
[result]:
1.73 s ± 10.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%%timeit
pd.to_datetime(df['Date'] + df['Time'], format='%m-%d-%Y%H:%M:%S')
[result]:
1.33 s ± 9.88 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
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