pandas python datetime快速提取小时分钟
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/36771245/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
python datetime extract hour minute fast
提问by Tom Dawn
I have a 878000*1 dataframe, where the 1 column is date in several years. I have the following code to create new columns and store year,month,day,hour,min,week in different new columns:
我有一个 878000*1 的数据框，其中 1 列是几年内的日期。我有以下代码来创建新列并在不同的新列中存储年、月、日、小时、分钟、周：
for i in train.index:
train['Year'][i] = train.Dates[i].year
train['Month'][i] = train.Dates[i].month
train['Day'][i] = train.Dates[i].day
train['Hour'][i] = train.Dates[i].hour
train['Min'][i] = train.Dates[i].minute
train['Week'][i] = train.Dates[i].isocalendar()[1]
However this is really slow, my computer has been working overnight for this simple command and it still not finished. I am wondering if there is some faster way that I can use to extract these information without waiting for so long?
然而这真的很慢，我的电脑已经为这个简单的命令工作了一夜，但它仍然没有完成。我想知道是否有一些更快的方法可以用来提取这些信息而无需等待这么长时间？
回答by MaxU
first of all you don't want to do it in loop, you should work with vectorized data:
首先，您不想在循环中执行此操作，您应该使用矢量化数据：
train['Year'] = train.Dates.dt.year
train['Month'] = train.Dates.dt.month
...
...
回答by piRSquared
Setup
设置
In [15]: train = pd.DataFrame(pd.date_range('2015-12-31', '2016-12-31'), columns=['Dates'])
In [16]: train.head()
Out[16]:
Dates
0 2015-12-31
1 2016-01-01
2 2016-01-02
3 2016-01-03
4 2016-01-04
Solution
解决方案
In [17]: fields = ['Year', 'Month', 'Day', 'Hour', 'Min', 'Week']
In [18]: f = lambda x: pd.Series([x[0].year, x[0].month,
x[0].day, x[0].hour,
x[0].minute, x[0].isocalendar()[1]],
index=fields)
In [19]: train.apply(f, axis=1)
Looks like
好像
Out[19]:
Year Month Day Hour Min Week
0 2015 12 31 0 0 53
1 2016 1 1 0 0 53
2 2016 1 2 0 0 53
3 2016 1 3 0 0 53
4 2016 1 4 0 0 1
5 2016 1 5 0 0 1
6 2016 1 6 0 0 1
7 2016 1 7 0 0 1
8 2016 1 8 0 0 1
9 2016 1 9 0 0 1
10 2016 1 10 0 0 1
11 2016 1 11 0 0 2
12 2016 1 12 0 0 2
13 2016 1 13 0 0 2
14 2016 1 14 0 0 2
15 2016 1 15 0 0 2
16 2016 1 16 0 0 2
17 2016 1 17 0 0 2
18 2016 1 18 0 0 3
19 2016 1 19 0 0 3
20 2016 1 20 0 0 3
21 2016 1 21 0 0 3
22 2016 1 22 0 0 3
23 2016 1 23 0 0 3
24 2016 1 24 0 0 3
25 2016 1 25 0 0 4
26 2016 1 26 0 0 4
27 2016 1 27 0 0 4
28 2016 1 28 0 0 4
29 2016 1 29 0 0 4
.. ... ... ... ... ... ...
337 2016 12 2 0 0 48
338 2016 12 3 0 0 48
339 2016 12 4 0 0 48
340 2016 12 5 0 0 49
341 2016 12 6 0 0 49
342 2016 12 7 0 0 49
343 2016 12 8 0 0 49
344 2016 12 9 0 0 49
345 2016 12 10 0 0 49
346 2016 12 11 0 0 49
347 2016 12 12 0 0 50
348 2016 12 13 0 0 50
349 2016 12 14 0 0 50
350 2016 12 15 0 0 50
351 2016 12 16 0 0 50
352 2016 12 17 0 0 50
353 2016 12 18 0 0 50
354 2016 12 19 0 0 51
355 2016 12 20 0 0 51
356 2016 12 21 0 0 51
357 2016 12 22 0 0 51
358 2016 12 23 0 0 51
359 2016 12 24 0 0 51
360 2016 12 25 0 0 51
361 2016 12 26 0 0 52
362 2016 12 27 0 0 52
363 2016 12 28 0 0 52
364 2016 12 29 0 0 52
365 2016 12 30 0 0 52
366 2016 12 31 0 0 52
