使用 python pandas 组合日期和时间列 [英] Combine Date and Time columns using python pandas

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

推荐答案

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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