如何用滚动平均值在 pandas 中填充nan值 [英] How to fill nan values with rolling mean in pandas

问题描述

我有一个数据框，它在几个地方包含nan值。我正在尝试执行数据清理，在其中使用前五个实例的平均值填充nan值。为此，我提出了以下建议。

I have a dataframe which contains nan values at few places. I am trying to perform data cleaning in which I fill the nan values with mean of it's previous five instances. To do so, I have come up with the following.

input_data_frame[var_list].fillna(input_data_frame[var_list].rolling(5).mean(), inplace=True)

但是，这不起作用。它没有满足nan值。在上述操作之前和之后，数据框的空计数没有变化。假设我的数据框只有整数列，如何用前五个实例的平均值填充NaN值？

But, this is not working. It isn't filling the nan values. There is no change in the dataframe's null count before and after the above operation. Assuming I have a dataframe with just integer column, How can I fill NaN values with mean of the previous five instances? Thanks in advance.

推荐答案

这应该有效：

input_data_frame[var_list]= input_data_frame[var_list].fillna(pd.rolling_mean(input_data_frame[var_list], 6, min_periods=1))

请注意，窗口为 6 ，因为它包含 NaN 本身的值（不计入平均值）。同样，其他 NaN 值也不用于平均值，因此，如果在窗口中找到的值少于5个，则将根据实际值计算平均值。

Note that the window is 6 because it includes the value of NaN itself (which is not counted in the average). Also the other NaN values are not used for the averages, so if less that 5 values are found in the window, the average is calculated on the actual values.

例如：

df = {'a': [1, 1,2,3,4,5, np.nan, 1, 1, 2, 3, 4, 5, np.nan] }
df = pd.DataFrame(data=df)
print df

      a
0   1.0
1   1.0
2   2.0
3   3.0
4   4.0
5   5.0
6   NaN
7   1.0
8   1.0
9   2.0
10  3.0
11  4.0
12  5.0
13  NaN

输出：

      a
0   1.0
1   1.0
2   2.0
3   3.0
4   4.0
5   5.0
6   3.0
7   1.0
8   1.0
9   2.0
10  3.0
11  4.0
12  5.0
13  3.0

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