如何使用该列周围（顶部和底部）值的平均值来填充该列的 NaN 值？

Question

我有一个 df ，它有一些 NaN 值。例如，这里是 df：

import numpy as np
import pandas as pd

np.random.seed(100)
data = np.random.rand(10,3)
data[3,0] = np.NaN
data[6,0] = np.NaN

data[5,1] = np.NaN
data[7,1] = np.NaN

data[1,2] = np.NaN
data[8,2] = np.NaN
data[6,2] = np.NaN

df = pd.DataFrame(data)
df

这是上面代码的运行结果：

    0           1           2
0   0.543405    0.278369    0.424518
1   0.844776    0.004719    NaN
2   0.670749    0.825853    0.136707
3   NaN         0.891322    0.209202
4   0.185328    0.108377    0.219697
5   0.978624    NaN         0.171941
6   NaN         0.274074    NaN
7   0.940030    NaN         0.336112
8   0.175410    0.372832    NaN
9   0.252426    0.795663    0.015255

我想要的是 NaN 值用上限和下限值的平均值填充，如下所示：

np.random.seed(100)
data = np.random.rand(10,3)
data[3,0] = (data[2,0] + data[4,0])/2
data[6,0] = (data[5,0] + data[7,0])/2

data[5,1] = (data[4,1] + data[6,1])/2
data[7,1] = (data[6,1] + data[8,1])/2

data[1,2] = (data[0,2] + data[2,2])/2
data[8,2] = (data[7,2] + data[9,2])/2
data[6,2] = (data[5,2] + data[7,2])/2
df = pd.DataFrame(data)
df

上面代码的结果是：

    0           1           2
0   0.543405    0.278369    0.424518
1   0.844776    0.004719    0.280612
2   0.670749    0.825853    0.136707
3   0.428039    0.891322    0.209202
4   0.185328    0.108377    0.219697
5   0.978624    0.191225    0.171941
6   0.959327    0.274074    0.254026
7   0.940030    0.323453    0.336112
8   0.175410    0.372832    0.175683
9   0.252426    0.795663    0.015255

我怎样才能在Python中自动执行此操作？

Answer 1

我认为DataFrame.interpolate这里应该有所帮助：

df1 = df.interpolate()
print (df1)
          0         1         2
0  0.543405  0.278369  0.424518
1  0.844776  0.004719  0.280612
2  0.670749  0.825853  0.136707
3  0.428039  0.891322  0.209202
4  0.185328  0.108377  0.219697
5  0.978624  0.191225  0.171941
6  0.959327  0.274074  0.254026
7  0.940030  0.323453  0.336112
8  0.175410  0.372832  0.175683
9  0.252426  0.795663  0.015255

如果有多个连续的NaNsinterpolate则不会替换为mean：

np.random.seed(100)
data = np.random.rand(10,3)
data[3,0] = np.NaN
data[6,0] = np.NaN

data[5,1] = np.NaN
data[7,1] = np.NaN

data[1,2] = np.NaN
data[2,2] = np.NaN
data[8,2] = np.NaN
data[6,2] = np.NaN

df = pd.DataFrame(data)
print (df)
          0         1         2
0  0.543405  0.278369  0.424518
1  0.844776  0.004719       NaN
2  0.670749  0.825853       NaN
3       NaN  0.891322  0.209202
4  0.185328  0.108377  0.219697
5  0.978624       NaN  0.171941
6       NaN  0.274074       NaN
7  0.940030       NaN  0.336112
8  0.175410  0.372832       NaN

df1 = df.interpolate()
print (df1)
          0         1         2
0  0.543405  0.278369  0.424518
1  0.844776  0.004719  0.352746
2  0.670749  0.825853  0.280974
3  0.428039  0.891322  0.209202
4  0.185328  0.108377  0.219697
5  0.978624  0.191225  0.171941
6  0.959327  0.274074  0.254026
7  0.940030  0.323453  0.336112
8  0.175410  0.372832  0.175683
9  0.252426  0.795663  0.015255

均值的解：

df2 = df.ffill().add(df.bfill()).div(2)
print (df2)
          0         1         2
0  0.543405  0.278369  0.424518
1  0.844776  0.004719  0.316860
2  0.670749  0.825853  0.316860
3  0.428039  0.891322  0.209202
4  0.185328  0.108377  0.219697
5  0.978624  0.191225  0.171941
6  0.959327  0.274074  0.254026
7  0.940030  0.323453  0.336112
8  0.175410  0.372832  0.175683
9  0.252426  0.795663  0.015255