按时间过滤 pandas 数据框 [英] filter pandas dataframe by time

问题描述

我有一个熊猫数据框，我想在大于或小于12pm的时间上对其进行子集化.首先，我将我的字符串datetime转换为pandas中的datetime [64] ns对象.

I have a pandas dataframe which I want to subset on time greater or less than 12pm. First i convert my string datetime to datetime[64]ns object in pandas.

segments_data['time'] = pd.to_datetime((segments_data['time']))

然后我将时间，日期，月份，年份和时间分开像下面这样的一周中的一天.

Then I separate time,date,month,year & dayofweek like below.

import datetime as dt

segments_data['date'] = segments_data.time.dt.date
segments_data['year'] = segments_data.time.dt.year
segments_data['month'] = segments_data.time.dt.month
segments_data['dayofweek'] = segments_data.time.dt.dayofweek
segments_data['time'] = segments_data.time.dt.time

我的时间栏如下所示.

My time column looks like following.

segments_data['time']
Out[1906]: 
  07:43:00
  07:52:00
  08:00:00
  08:42:00
  09:18:00
  09:18:00
  09:18:00
  09:23:00
  12:32:00
  12:43:00
  12:55:00
  Name: time, dtype: object

现在，我想对时间大于12pm且时间小于12pm的数据帧进行子集处理.

Now I want to subset dataframe with time greater than 12pm and time less than 12pm.

segments_data.time[segments_data['time'] < 12:00:00]

它不起作用，因为time是string object.

推荐答案

将一列保留为原始日期时间，将其命名为ts:

Leave a column as the raw datetime, call it ts:

segments_data['ts'] = pd.to_datetime((segments_data['time']))

接下来，您可以将日期时间转换为H:M:S字符串，并使用between(start,end)似乎有效:

Next you can cast the datetime to an H:M:S string and use between(start,end) seems to work:

In [227]:
segments_data=pd.DataFrame(x,columns=['ts'])
segments_data.ts = pd.to_datetime(segments_data.ts)
segments_data
Out[227]:
ts
0   2016-01-28 07:43:00
1   2016-01-28 07:52:00
2   2016-01-28 08:00:00
3   2016-01-28 08:42:00
4   2016-01-28 09:18:00
5   2016-01-28 09:18:00
6   2016-01-28 09:18:00
7   2016-01-28 09:23:00
8   2016-01-28 12:32:00
9   2016-01-28 12:43:00
10  2016-01-28 12:55:00

In [228]:    
 segments_data[segments_data.ts.dt.strftime('%H:%M:%S').between('00:00:00','12:00:00')]
Out[228]:
ts
0   2016-01-28 07:43:00
1   2016-01-28 07:52:00
2   2016-01-28 08:00:00
3   2016-01-28 08:42:00
4   2016-01-28 09:18:00
5   2016-01-28 09:18:00
6   2016-01-28 09:18:00
7   2016-01-28 09:23:00

这篇关于按时间过滤 pandas 数据框的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！