Pandas之两个结构相同的DataFrame 互相补充|合并重叠数据 combine 和 combine_first

# Combine,后一个对象补齐前一个对象
# Series
s1 = Series([2,np.nan,4,np.nan], index=['A','B','C','D'])
s1
Out[29]: 
A    2.0
B    NaN
C    4.0
D    NaN
dtype: float64

s2 = Series([1,2,3,4], index=['A','B','C','D'])
s2
Out[31]: 
A    1
B    2
C    3
D    4
dtype: int64

# s1中没有的值被s2补齐了
s1.combine_first(s2)
Out[32]: 
A    2.0
B    2.0
C    4.0
D    4.0
dtype: float64

# DataFrame,和Series类似
df1 = DataFrame({'X':[1,np.nan,3,np.nan], 'Y':[5,np.nan,7,np.nan], 'Z':[9,np.nan,11,np.nan]})
df1
Out[36]: 
     X    Y     Z
0  1.0  5.0   9.0
1  NaN  NaN   NaN
2  3.0  7.0  11.0
3  NaN  NaN   NaN

df2 = DataFrame({'Z':[np.nan,10,np.nan,12], 'A':[1,2,3,4]})
df2
Out[38]: 
   A     Z
0  1   NaN
1  2  10.0
2  3   NaN
3  4  12.0

df1.combine_first(df2)
Out[39]: 
     A    X    Y     Z
0  1.0  1.0  5.0   9.0
1  2.0  NaN  NaN  10.0
2  3.0  3.0  7.0  11.0
3  4.0  NaN  NaN  12.0

import pandas as pd
from numpy import NaN


data1 = [{'a': '1', 'b': NaN}, {'a': NaN, 'b': '2'}]

data2 = [{'a': '2', 'b': '3'}, {'a': '4', 'b': NaN}]

df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# 用df2的数据填补df1的缺失值
df3 = df1.combine_first(df2)

print(df1)
print("######")
print(df2)
print("######")
print(df3)


     a    b
0    1   NaN
1   NaN   2


######
   a    b
0  2    3
1  4   NaN

######
   a  b
0  1  3
1  4  2