在多索引 dataframe 上計算基於時間的滾動平均值

Question

我有一個多索引 dataframe，我將它與掩碼組合在一起。 之后，我想計算基於時間的滾動平均值。

time = pd.date_range('2000-05-01', freq='24H', periods=10)
mult_index = pd.MultiIndex.from_product([time, [0,1]], names=["time", "number"])
data = pd.DataFrame(range(20), index=mult_index)
mask = list(range(5)) * 4
data.groupby(mask).rolling("2d", on=mult_index.levels[0]).mean()

但是，這引發了異常：

Traceback (most recent call last):
File "C:\Users\bi4372\.conda\envs\EnergyTimeSeriesFramework\lib\site-packages\IPython\core\interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-77-e484a6b352eb>", line 1, in <module>
rolling.count()
File "C:\Users\bi4372\.conda\envs\EnergyTimeSeriesFramework\lib\site-packages\pandas\core\window\common.py", line 40, in outer
return self._groupby.apply(f)
File "C:\Users\bi4372\.conda\envs\EnergyTimeSeriesFramework\lib\site-packages\pandas\core\groupby\groupby.py", line 735, in apply
result = self._python_apply_general(f)
File "C:\Users\bi4372\.conda\envs\EnergyTimeSeriesFramework\lib\site-packages\pandas\core\groupby\groupby.py", line 751, in _python_apply_general
keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis)
File "C:\Users\bi4372\.conda\envs\EnergyTimeSeriesFramework\lib\site-packages\pandas\core\groupby\ops.py", line 206, in apply
res = f(group)
File "C:\Users\bi4372\.conda\envs\EnergyTimeSeriesFramework\lib\site-packages\pandas\core\window\common.py", line 38, in f
return getattr(x, name)(*args, **kwargs)
File "C:\Users\bi4372\.conda\envs\EnergyTimeSeriesFramework\lib\site-packages\pandas\core\window\rolling.py", line 1969, in count
return self._apply(window_func, center=self.center, name="count")
File "C:\Users\bi4372\.conda\envs\EnergyTimeSeriesFramework\lib\site-packages\pandas\core\window\rolling.py", line 518, in _apply
return self._wrap_results(results, block_list, obj, exclude)
File "C:\Users\bi4372\.conda\envs\EnergyTimeSeriesFramework\lib\site-packages\pandas\core\window\rolling.py", line 331, in _wrap_results
final.append(Series(self._on, index=obj.index, name=name))
File "C:\Users\bi4372\.conda\envs\EnergyTimeSeriesFramework\lib\site-packages\pandas\core\series.py", line 292, in __init__
f"Length of passed values is {len(data)}, "
ValueError: Length of passed values is 10, index implies 4

有誰知道如何解決這個問題？ 如果我在沒有多索引 dataframe 的情況下嘗試它，一切正常：

time = pd.date_range('2000-05-01', freq='24H', periods=10)
data = pd.DataFrame(range(10), index=time)
mask = list(range(5)) * 2
data.groupby(mask).rolling("2d").mean()

在此先感謝您的幫助。

編輯 1 - 澄清問題：

在下面的答案中，DavideBrex 提出了一種重置索引以解決此問題的方法。 但是，此解決方案的結果是編號為 0 的行會干擾編號為 1 的行。我想避免這種行為。 請參閱以下附加示例：

time = pd.date_range('2000-05-01', freq='24H', periods=3)
mult_index = pd.MultiIndex.from_product([time, [0,1]], names=["time", "number"])
data = pd.DataFrame(range(6), index=mult_index)
data.columns=["col"]
mask = [0,0,1,1,0,1]
res = data.reset_index(level='number').groupby(mask).rolling('3d').mean()

期望的結果是

              number  col
  time                   
0 2000-05-01     0.0  0.0
  2000-05-01     1.0  1.0
  2000-05-03     0.0  4.0
1 2000-05-02     0.0  2.0
  2000-05-02     1.0  3.0
  2000-05-03     1.0  4.0

然而，真實的結果是：

                number       col
  time                          
0 2000-05-01  0.000000  0.000000
  2000-05-01  0.500000  0.500000
  2000-05-03  0.000000  4.000000
1 2000-05-02  0.000000  2.000000
  2000-05-02  0.500000  2.500000
  2000-05-03  0.666667  3.333333

Answer 1

問題是 groupby 給出了 4 行的組：

for i, item in data.groupby(mask):
    print(item)

給出：

                    0
time       number    
2000-05-01 0        0
2000-05-03 1        5
2000-05-06 0       10
2000-05-08 1       15
                    0
time       number    
2000-05-01 1        1
2000-05-04 0        6
2000-05-06 1       11
2000-05-09 0       16
.....      ..      ...

但是您隨后在滾動 function 中給出 10 個值：

print(mult_index.levels[0])
DatetimeIndex(['2000-05-01', '2000-05-02', '2000-05-03', '2000-05-04',
               '2000-05-05', '2000-05-06', '2000-05-07', '2000-05-08',
               '2000-05-09', '2000-05-10'],
              dtype='datetime64[ns]', name='time', freq='24H')

嘗試這個：

time = pd.date_range('2000-05-01', freq='24H', periods=10)
mult_index = pd.MultiIndex.from_product([time, [0,1]], names=["time", "number"])
data = pd.DataFrame(range(20), index=mult_index)
data.columns=["col"]
mask = list(range(5)) * 4
res = data.reset_index(level='number').groupby(mask).rolling('2d').mean()
res

Output：

    time    number  col
0   2000-05-01  0.0 0.0
0   2000-05-03  1.0 5.0
0   2000-05-06  0.0 10.0
0   2000-05-08  1.0 15.0
1   2000-05-01  1.0 1.0
1   2000-05-04  0.0 6.0
1   2000-05-06  1.0 11.0
1   2000-05-09  0.0 16.0
2   2000-05-02  0.0 2.0
2   2000-05-04  1.0 7.0
2   2000-05-07  0.0 12.0
2   2000-05-09  1.0 17.0
3   2000-05-02  1.0 3.0
3   2000-05-05  0.0 8.0
3   2000-05-07  1.0 13.0
3   2000-05-10  0.0 18.0
4   2000-05-03  0.0 4.0
4   2000-05-05  1.0 9.0
4   2000-05-08  0.0 14.0
4   2000-05-10  1.0 19.0

從答案