pandas的数据融合和拼接操作示例

import pandas as pd

se1=pd.Series({'ID':'x','a':1,'b':20})
se2=pd.Series({'ID':'x','c':1,'d':20})
df1=pd.Dataframe({'ID':['x','y','z'],'a':[1,2,3],'b':[1,2,3]})
df2=pd.Dataframe({'ID':['x','m','n'],'c':[1,2,3],'d':[1,2,3]})
print('print(pd.concat([se1,se2],axis=1).T)')
print(pd.concat([se1,se2],axis=1).T)
print("pd.merge(df1,df2,on='ID',how='inner')")#用双引号
print(pd.merge(df1,df2,on='ID',how='inner'))
print("pd.merge(df1,df2,on='ID',how='outer')")#用双引号
print(pd.merge(df1,df2,on='ID',how='outer'))
print("pd.merge(df1,df2,on='ID',how='right')")#用双引号
print(pd.merge(df1,df2,on='ID',how='right'))
print("pd.merge(df1,df2,on='ID',how='left')")#用双引号
print(pd.merge(df1,df2,on='ID',how='left'))
print("pd.concat([df1,df2],axis=0,join='outer',ignore_index=False)")
print(pd.concat([df1,df2],axis=0,join='outer',ignore_index=False))
print("pd.concat([df1,df2],axis=0,join='inner',ignore_index=False)")
pd.concat([df1,df2],axis=0,join='inner',ignore_index=False)

结果：

print(pd.concat([se1,se2],axis=1).T)
  ID    a    b    c    d
0  x    1   20  NaN  NaN
1  x  NaN  NaN    1   20
pd.merge(df1,df2,on='ID',how='inner')
  ID  a  b  c  d
0  x  1  1  1  1
pd.merge(df1,df2,on='ID',how='outer')
  ID    a    b    c    d
0  x  1.0  1.0  1.0  1.0
1  y  2.0  2.0  NaN  NaN
2  z  3.0  3.0  NaN  NaN
3  m  NaN  NaN  2.0  2.0
4  n  NaN  NaN  3.0  3.0
pd.merge(df1,df2,on='ID',how='right')
  ID    a    b  c  d
0  x  1.0  1.0  1  1
1  m  NaN  NaN  2  2
2  n  NaN  NaN  3  3
pd.merge(df1,df2,on='ID',how='left')
  ID  a  b    c    d
0  x  1  1  1.0  1.0
1  y  2  2  NaN  NaN
2  z  3  3  NaN  NaN
pd.concat([df1,df2],axis=0,join='outer',ignore_index=False)
  ID    a    b    c    d
0  x  1.0  1.0  NaN  NaN
1  y  2.0  2.0  NaN  NaN
2  z  3.0  3.0  NaN  NaN
0  x  NaN  NaN  1.0  1.0
1  m  NaN  NaN  2.0  2.0
2  n  NaN  NaN  3.0  3.0
pd.concat([df1,df2],axis=0,join='inner',ignore_index=False)
  ID
0  x
1  y
2  z
0  x
1  m
2  n