python数据清洗补齐_我的世界fill填充上半砖

fillna(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)

import numpy as np
from numpy import nan
import pandas as pd
data=pd.DataFrame(np.arange(3,19,1).reshape(4,4),index=list('abcd'))
print(data)
data.iloc[0:2,0:3]=nan
print(data)

      0     1     2   3
a   NaN   NaN   NaN   6
b   NaN   NaN   NaN  10
c  11.0  12.0  13.0  14
d  15.0  16.0  17.0  18

print(data.fillna(0))   ### 用0填充缺失数据

      0     1     2   3
a   0.0   0.0   0.0   6
b   0.0   0.0   0.0  10
c  11.0  12.0  13.0  14
d  15.0  16.0  17.0  18

print(data.fillna(data.mean()))     ### 用每列特征的均值填充缺失数据

      0     1     2   3
a  13.0  14.0  15.0   6
b  13.0  14.0  15.0  10
c  11.0  12.0  13.0  14
d  15.0  16.0  17.0  18

print(data.fillna(data.median()))   ### 用每列特征的中位数填充缺失数据

     0     1     2   3
a  13.0  14.0  15.0   6
b  13.0  14.0  15.0  10
c  11.0  12.0  13.0  14
d  15.0  16.0  17.0  18

print(data.fillna(method='bfill'))   ### 用相邻后面（back）特征填充前面空值

      0     1     2   3
a  11.0  12.0  13.0   6
b  11.0  12.0  13.0  10
c  11.0  12.0  13.0  14
d  15.0  16.0  17.0  18

data=pd.DataFrame(np.arange(3,19,1).reshape(4,4),index=list('abcd'))
data.iloc[1:2,:]=nan
print(data)

     0     1     2     3
a   3.0   4.0   5.0   6.0
b   NaN   NaN   NaN   NaN
c  11.0  12.0  13.0  14.0
d  15.0  16.0  17.0  18.0

print(data.fillna(method='bfill'))   ### 用相邻前面（before）特征填充后面空值 

      0     1     2     3
a   3.0   4.0   5.0   6.0
b   3.0   4.0   5.0   6.0
c  11.0  12.0  13.0  14.0
d  15.0  16.0  17.0  18.0

values={0:10,1:20,2:30}
print(data.fillna(value=values))   ### 用字典对不同的列填充不同的缺失数据

      0     1     2   3
a  10.0  20.0  30.0   6
b  10.0  20.0  30.0  10
c  11.0  12.0  13.0  14
d  15.0  16.0  17.0  18