在列中输入缺失值和最近邻居的平均值 [英] Input missed values with mean of nearest neighbors in column

问题描述

我有一个DataFrame:

I have a DataFrame:

df = pd.DataFrame(data=[676, 0, 670, 0, 668], index=['2012-01-31 00:00:00','2012-02-29 00:00:00',
                                                     '2012-03-31 00:00:00','2012-04-30 00:00:00',
                                                     '2012-05-31 00:00:00'])  
df.index.name = "Date"
df.columns = ["Number"]

外观如下:

              Number
Date    
2012-01-31 00:00:00 676
2012-02-29 00:00:00 0
2012-03-31 00:00:00 670
2012-04-30 00:00:00 0
2012-05-31 00:00:00 668

如何分别用(676 + 670)/2和(670 + 668)/2输入第二和第四值?

How can i input 2nd and 4th values with (676+670)/2 and (670+668)/2 correspondinly?

我可以将值另存为np.array并将其放入数组中，但这很繁琐！

I can save values as np.array and imput them in array, but that's rediculous!

推荐答案

我使用where方法，并指定将任何0替换为np.nan.一旦将0指定为NaN，就可以使用fillna方法.通过使用ffill和bfill，我们用相应的先前值和后续值填充所有NaN，将它们相加并除以2.

I use where method and specify to replace any 0 with np.nan. Once we have specified 0 to be NaN we can use fillna method. By using ffill and bfill we fill all NaN with the corresponding previous and proceeding values, add them, and divide by 2.

df.where(df.replace(to_replace=0, value=np.nan),
 other=(df.fillna(method='ffill') + df.fillna(method='bfill'))/2)

                     Number
Date                       
2012-01-31 00:00:00   676.0
2012-02-29 00:00:00   673.0
2012-03-31 00:00:00   670.0
2012-04-30 00:00:00   669.0
2012-05-31 00:00:00   668.0

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