Question

我有时间序列＆＃34;半小时＆＃34;数据。我需要重新采样demand到＆＃34; 1天＆＃34;在重采样期间使用加权平均值（使用price）。

dft

                           demand  price
2012-01-01 00:00:00  30940.500000  42.18
2012-01-01 00:30:00  31189.166667  43.48
2012-01-01 01:00:00  30873.166667  42.28
2012-01-01 01:30:00  30110.833333  38.48
2012-01-01 02:00:00  29721.500000  37.28
2012-01-01 02:30:00  28970.000000  36.24
2012-01-01 03:00:00  27955.000000  32.16
                          ...    ...
2014-12-30 20:30:00  41685.500000  40.51
2014-12-30 21:00:00  40177.833333  41.79
2014-12-30 21:30:00  38238.000000  31.50
2014-12-30 22:00:00  36395.333333  37.54
2014-12-30 22:30:00  34543.333333  39.55
2014-12-30 23:00:00  32652.000000  40.88
2014-12-30 23:30:00  30941.333333  38.16

我想使用demand

将1D列重新取样为price（1天），使用np.average()列作为权重

我已经看了几个例子，但有些事情并没有完全点击。我最接近的是：

dftwei = dft.price.resample('1D').apply(lambda x: np.average(x, weights=dft.demand, axis=0))

但问题在于：

ValueError: Length of weights not compatible with specified axis.

未指定axis=0时，错误为：

TypeError: Axis must be specified when shapes of a and weights differ.

问题可能在于如何指定weights。权重必须是48的长度，但我怀疑lambda函数使用price的全长。谢谢！

Answer 1

您可以创建自己的加权平均值：

wp = (df['demand'] * df['price']).resample('H').sum()
wp / df.resample('H')['price'].sum()

2012-01-01 00:00:00    31066.720251
2012-01-01 01:00:00    30509.935034
2012-01-01 02:00:00    29351.065288
2012-01-01 03:00:00    27558.233718
...

Answer 2

似乎你只是通过smidgen关闭了子集。要对一天中的平均值进行重新整理，请对整个数据框进行重新采样，然后仅对当天进行平均分析：

import pandas as pd
import numpy as np

df = pd.DataFrame([('2012-01-01 00:00', 30940.500000, 42.18),
    ('2012-01-01 00:30', 31189.166667, 43.48),
    ('2012-01-01 01:00', 30873.166667, 42.28),
    ('2012-01-01 01:30', 30110.833333, 38.48),
    ('2012-01-01 02:00', 29721.500000, 37.28),
    ('2012-01-01 02:30', 28970.000000, 36.24),
    ('2012-01-01 03:00', 27955.000000, 32.16),
    ('2012-01-02 20:30', 41685.500000, 40.51),
    ('2012-01-02 21:00', 40177.833333, 41.79),
    ('2012-01-02 21:30', 38238.000000, 31.50),
    ('2012-01-02 22:00', 36395.333333, 37.54),
    ('2012-01-02 22:30', 34543.333333, 39.55),
    ('2012-01-02 23:00', 32652.000000, 40.88),
    ('2012-01-02 23:30', 30941.333333, 38.16)])

df[0] = pd.to_datetime(df[0])
df.set_axis(['date', 'demand', 'price'], axis=1, inplace=True)
df.set_index('date', inplace=True)

#
# Above is just setup, here's the rub:
#
df.resample('1D').apply(lambda x: np.average(x.demand, weights=x.price))

pandas resample应用np.average

2 个答案: