熊猫如何按时间段过滤DataFrame

Question

我有一个包含下表的文件：

    Name        AvailableDate            totalRemaining
0   X3321       2018-03-14 13:00:00      200
1   X3321       2018-03-14 14:00:00      200
2   X3321       2018-03-14 15:00:00      200
3   X3321       2018-03-14 16:00:00      200
4   X3321       2018-03-14 17:00:00      193

我想返回一个包含特定时间期间所有记录的DataFrame，而不管实际的日期如何。

我按照这里的例子：

filter pandas dataframe by time

但是当我执行以下操作时：

## setup
import pandas as pd
import numpy as np

### Step 2
### Check available slots
file2 = r'C:\Users\user\Desktop\Files\data.xlsx'

slots = pd.read_excel(file2,na_values='')

## filter the preferred ones
slots['nextAvailableDate'] = pd.to_datetime((slots['nextAvailableDate']))


slots['times'] = pd.to_datetime((slots['nextAvailableDate']))
slots = slots[slots['times'].between('21:00:00', '02:00:00')]

这将返回空 DataFrame 以及此解决方案：

slots = slots[slots['times'].dt.strftime('%H:%M:%S').between('21:00:00', '02:00:00')]

有没有办法在不单独创建时间列的情况下正确地做到这一点？请问我应该如何解决这个问题？

我的目标：

Name        AvailableDate            totalRemaining
0   X3321       2018-03-14 21:00:00      200
1   X3321       2018-03-14 22:00:00      200
2   X3321       2018-03-14 23:00:00      200
3   X3321       2018-03-14 00:00:00      200
4   X3321       2018-03-14 01:00:00      193

数据集中出现的每一天。

Answer 1

我认为需要between_time 与Datetimeindex 一起使用由set_index 创建的列，添加reset_index 和reindex 以获得相同的列顺序：

print (slots)
     Name        AvailableDate  totalRemaining
0   X3321  2018-03-14 21:00:00             200
1   X3321  2018-03-14 20:00:00             200
2   X3321  2018-03-14 22:00:00             200
3   X3321  2018-03-14 23:00:00             200
4   X3321  2018-03-14 00:00:00             200
5   X3321  2018-03-14 01:00:00             193
6   X3321  2018-03-14 13:00:00             200
7   X3321  2018-03-14 14:00:00             200
8   X3321  2018-03-14 15:00:00             200
9   X3321  2018-03-14 16:00:00             200
10  X3321  2018-03-14 17:00:00             193

slots['AvailableDate'] = pd.to_datetime(slots['AvailableDate'])

df = (slots.set_index('AvailableDate')
          .between_time('21:00:00', '02:00:00')
          .reset_index()
          .reindex(columns=df.columns))
print (df)
        AvailableDate   Name  totalRemaining
0 2018-03-14 21:00:00  X3321             200
1 2018-03-14 22:00:00  X3321             200
2 2018-03-14 23:00:00  X3321             200
3 2018-03-14 00:00:00  X3321             200
4 2018-03-14 01:00:00  X3321             193

Answer 2

您可以将pd.Series.between 与datetime 对象一起使用，如下所示。

from datetime import datetime

start = datetime.strptime('21:00:00', '%H:%M:%S').time()
end = datetime.strptime('02:00:00', '%H:%M:%S').time()

slots = slots[slots['times'].dt.time.between(start, end)]

使用示例

from datetime import datetime
import pandas as pd

slots = pd.DataFrame({'times': ['2018-03-08 05:00:00', '2018-03-08 07:00:00',
                                '2018-03-08 01:00:00', '2018-03-08 20:00:00',
                                '2018-03-08 22:00:00', '2018-03-08 23:00:00']})


slots['times'] = pd.to_datetime(slots['times'])

start = datetime.strptime('21:00:00', '%H:%M:%S').time()
end = datetime.strptime('23:30:00', '%H:%M:%S').time()

slots = slots[slots['times'].dt.time.between(start, end)]

#                 times
# 4 2018-03-08 22:00:00
# 5 2018-03-08 23:00:00