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我有一个包含以下列的数据框：{'day','measurement'}

一天内可能会有几次测量（或根本没有测量）

例如：

day     |    measurement
1       |     20.1
1       |     20.9
3       |     19.2
4       |     20.0
4       |     20.2

和一系列系数： coef={-1:0.2, 0:0.6, 1:0.2}

我的目标是对数据进行重新采样并使用系数对其进行平均（应该忽略缺失的数据）。

这是我为计算而编写的代码

window=[-1,0,-1]
df['resampled_measurement'][df['day']==d]=[coef[i]*df['measurement'][df['day']==d-i].mean() for i in window if df['measurement'][df['day']==d-i].shape[0]>0].sum()
df['resampled_measurement'][df['day']==d]/=[coef[i] for i in window if df['measurement'][df['day']==d-i].shape[0]>0].sum()

对于上面的示例，输出应为：

Day  measurement
1    20.500
2    19.850
3    19.425
4    19.875

问题是代码永远运行，我很确定有更好的方法来重新采样系数。

任何建议将不胜感激！

4

1 回答 1

2

这是您正在寻找的可能的解决方案：

        # This is your data
In [2]: data = pd.DataFrame({
   ...:     'day': [1, 1, 3, 4, 4],
   ...:     'measurement': [20.1, 20.9, 19.2, 20.0, 20.2]
   ...: })

        # Pre-compute every day's average, filling the gaps
In [3]: measurement = data.groupby('day')['measurement'].mean()

In [4]: measurement = measurement.reindex(pd.np.arange(data.day.min(), data.day.max() + 1))

In [5]: coef = pd.Series({-1: 0.2, 0: 0.6, 1: 0.2})

        # Create a matrix with the time-shifted measurements
In [6]: matrix = pd.DataFrame({key: measurement.shift(key) for key, val in coef.iteritems()})

In [7]: matrix
Out[7]:
       -1     0     1
day
1     NaN  20.5   NaN
2    19.2   NaN  20.5
3    20.1  19.2   NaN
4     NaN  20.1  19.2

        # Take a weighted average of the matrix
In [8]: (matrix * coef).sum(axis=1) / (matrix.notnull() * coef).sum(axis=1)
Out[8]:
day
1    20.500
2    19.850
3    19.425
4    19.875
dtype: float64
于 2015-04-20T14:39:53.363 回答