Python 3.x 如何使用Pandas查找固定时间段之间的平均值和标准差_Python 3.x_Pandas_Dataframe

Python 3.x 如何使用Pandas查找固定时间段之间的平均值和标准差

python-3.x pandas dataframe

Python 3.x 如何使用Pandas查找固定时间段之间的平均值和标准差,python-3.x,pandas,dataframe,Python 3.x,Pandas,Dataframe,我的数据集df如下所示： DateTimeVal Open 2017-01-01 17:00:00 5.1532 2017-01-01 17:01:00 5.3522 2017-01-01 17:02:00 5.4535 2017-01-01 17:03:00 5.3567 2017-01-01 17:04:00 5.1512 .... 它是一个基于minutediff的数据集在我的计算中，一天（24小时）被定

我的数据集

df

如下所示：

DateTimeVal            Open 
2017-01-01 17:00:00    5.1532    
2017-01-01 17:01:00    5.3522 
2017-01-01 17:02:00    5.4535    
2017-01-01 17:03:00    5.3567    
2017-01-01 17:04:00    5.1512 
....

它是一个基于

minute

diff

的数据集

在我的计算中，一天（

24小时

）被定义为：

17:00:00

周日

至

16:59:00

周一

等其他日期

我想做的是找到从

17:00:00

周日到

16:59:00

周一的

24小时

的

AVG

和

STD

，等等

我做了什么？

我做了

滚动

来查找

平均值

，但它只在

天

进行，而不在

时间范围内

# day avg
# 7 day rolling avg

df = (
df.assign(DAY_AVG=df.rolling(window=1*24*60)['Open'].mean()) 
df.assign(7DAY_AVG=df.rolling(window=7*24*60)['Open'].mean())
.groupby(df['DateTimeVal'].dt.date) 
.last() )

我需要以下两方面的帮助：

如何查找固定时间段之间的
```
AVG
```
和
```
STD
```

我如何找到

7D滚动和14天滚动的固定时间段之间的AVG
和STD

使用重新采样和基：
#Create empty dataframe for 2 days
df = pd.DataFrame(index = pd.date_range('2017-07-01', periods=48, freq='1H'))

#Set value equal to 1 from 17:00 to 16:59 next day
df.loc['2017-07-01 17:00:00': '2017-07-02 16:59:59', 'Value'] = 1

print(df)

输出：
                     Value
2017-07-01 00:00:00    NaN
2017-07-01 01:00:00    NaN
2017-07-01 02:00:00    NaN
2017-07-01 03:00:00    NaN
2017-07-01 04:00:00    NaN
2017-07-01 05:00:00    NaN
2017-07-01 06:00:00    NaN
2017-07-01 07:00:00    NaN
2017-07-01 08:00:00    NaN
2017-07-01 09:00:00    NaN
2017-07-01 10:00:00    NaN
2017-07-01 11:00:00    NaN
2017-07-01 12:00:00    NaN
2017-07-01 13:00:00    NaN
2017-07-01 14:00:00    NaN
2017-07-01 15:00:00    NaN
2017-07-01 16:00:00    NaN
2017-07-01 17:00:00    1.0
2017-07-01 18:00:00    1.0
2017-07-01 19:00:00    1.0
2017-07-01 20:00:00    1.0
2017-07-01 21:00:00    1.0
2017-07-01 22:00:00    1.0
2017-07-01 23:00:00    1.0
2017-07-02 00:00:00    1.0
2017-07-02 01:00:00    1.0
2017-07-02 02:00:00    1.0
2017-07-02 03:00:00    1.0
2017-07-02 04:00:00    1.0
2017-07-02 05:00:00    1.0
2017-07-02 06:00:00    1.0
2017-07-02 07:00:00    1.0
2017-07-02 08:00:00    1.0
2017-07-02 09:00:00    1.0
2017-07-02 10:00:00    1.0
2017-07-02 11:00:00    1.0
2017-07-02 12:00:00    1.0
2017-07-02 13:00:00    1.0
2017-07-02 14:00:00    1.0
2017-07-02 15:00:00    1.0
2017-07-02 16:00:00    1.0
2017-07-02 17:00:00    NaN
2017-07-02 18:00:00    NaN
2017-07-02 19:00:00    NaN
2017-07-02 20:00:00    NaN
2017-07-02 21:00:00    NaN
2017-07-02 22:00:00    NaN
2017-07-02 23:00:00    NaN

                     Value
2017-06-30 17:00:00    0.0
2017-07-01 17:00:00   24.0
2017-07-02 17:00:00    0.0

                    Value          
                      sum      mean
2018-09-30 17:00:00   120  0.117647
2018-10-01 17:00:00  1440  1.000000
2018-10-02 17:00:00   120  0.285036

现在使用，resample
和base=17

df.resample('24H', base=17).sum()

输出：
                     Value
2017-07-01 00:00:00    NaN
2017-07-01 01:00:00    NaN
2017-07-01 02:00:00    NaN
2017-07-01 03:00:00    NaN
2017-07-01 04:00:00    NaN
2017-07-01 05:00:00    NaN
2017-07-01 06:00:00    NaN
2017-07-01 07:00:00    NaN
2017-07-01 08:00:00    NaN
2017-07-01 09:00:00    NaN
2017-07-01 10:00:00    NaN
2017-07-01 11:00:00    NaN
2017-07-01 12:00:00    NaN
2017-07-01 13:00:00    NaN
2017-07-01 14:00:00    NaN
2017-07-01 15:00:00    NaN
2017-07-01 16:00:00    NaN
2017-07-01 17:00:00    1.0
2017-07-01 18:00:00    1.0
2017-07-01 19:00:00    1.0
2017-07-01 20:00:00    1.0
2017-07-01 21:00:00    1.0
2017-07-01 22:00:00    1.0
2017-07-01 23:00:00    1.0
2017-07-02 00:00:00    1.0
2017-07-02 01:00:00    1.0
2017-07-02 02:00:00    1.0
2017-07-02 03:00:00    1.0
2017-07-02 04:00:00    1.0
2017-07-02 05:00:00    1.0
2017-07-02 06:00:00    1.0
2017-07-02 07:00:00    1.0
2017-07-02 08:00:00    1.0
2017-07-02 09:00:00    1.0
2017-07-02 10:00:00    1.0
2017-07-02 11:00:00    1.0
2017-07-02 12:00:00    1.0
2017-07-02 13:00:00    1.0
2017-07-02 14:00:00    1.0
2017-07-02 15:00:00    1.0
2017-07-02 16:00:00    1.0
2017-07-02 17:00:00    NaN
2017-07-02 18:00:00    NaN
2017-07-02 19:00:00    NaN
2017-07-02 20:00:00    NaN
2017-07-02 21:00:00    NaN
2017-07-02 22:00:00    NaN
2017-07-02 23:00:00    NaN

                     Value
2017-06-30 17:00:00    0.0
2017-07-01 17:00:00   24.0
2017-07-02 17:00:00    0.0

                    Value          
                      sum      mean
2018-09-30 17:00:00   120  0.117647
2018-10-01 17:00:00  1440  1.000000
2018-10-02 17:00:00   120  0.285036


分钟采样更新：
df = pd.DataFrame({'Value': 0}, index = pd.date_range('2018-10-01', '2018-10-03', freq='1T'))

df.loc['2018-10-01 15:00:00':'2018-10-02 18:59:50', 'Value'] = 1

df.resample('24H', base=17).agg(['sum','mean'])

输出：
                     Value
2017-07-01 00:00:00    NaN
2017-07-01 01:00:00    NaN
2017-07-01 02:00:00    NaN
2017-07-01 03:00:00    NaN
2017-07-01 04:00:00    NaN
2017-07-01 05:00:00    NaN
2017-07-01 06:00:00    NaN
2017-07-01 07:00:00    NaN
2017-07-01 08:00:00    NaN
2017-07-01 09:00:00    NaN
2017-07-01 10:00:00    NaN
2017-07-01 11:00:00    NaN
2017-07-01 12:00:00    NaN
2017-07-01 13:00:00    NaN
2017-07-01 14:00:00    NaN
2017-07-01 15:00:00    NaN
2017-07-01 16:00:00    NaN
2017-07-01 17:00:00    1.0
2017-07-01 18:00:00    1.0
2017-07-01 19:00:00    1.0
2017-07-01 20:00:00    1.0
2017-07-01 21:00:00    1.0
2017-07-01 22:00:00    1.0
2017-07-01 23:00:00    1.0
2017-07-02 00:00:00    1.0
2017-07-02 01:00:00    1.0
2017-07-02 02:00:00    1.0
2017-07-02 03:00:00    1.0
2017-07-02 04:00:00    1.0
2017-07-02 05:00:00    1.0
2017-07-02 06:00:00    1.0
2017-07-02 07:00:00    1.0
2017-07-02 08:00:00    1.0
2017-07-02 09:00:00    1.0
2017-07-02 10:00:00    1.0
2017-07-02 11:00:00    1.0
2017-07-02 12:00:00    1.0
2017-07-02 13:00:00    1.0
2017-07-02 14:00:00    1.0
2017-07-02 15:00:00    1.0
2017-07-02 16:00:00    1.0
2017-07-02 17:00:00    NaN
2017-07-02 18:00:00    NaN
2017-07-02 19:00:00    NaN
2017-07-02 20:00:00    NaN
2017-07-02 21:00:00    NaN
2017-07-02 22:00:00    NaN
2017-07-02 23:00:00    NaN

                     Value
2017-06-30 17:00:00    0.0
2017-07-01 17:00:00   24.0
2017-07-02 17:00:00    0.0

                    Value          
                      sum      mean
2018-09-30 17:00:00   120  0.117647
2018-10-01 17:00:00  1440  1.000000
2018-10-02 17:00:00   120  0.285036

IIUC，您可以使用带有base
参数的resample
。感谢您撰写答案。一个澄清，我的数据集是minute
based@floss这个答案对你有帮助吗？你介意接受吗？