您需要concat
:
print (pd.concat([df1[['Type','Breed','Behaviour']],
df2[['Type','Breed','Behaviour']]], ignore_index=True))
Type Breed Behaviour
0 Golden Big Fun
1 Corgi Small Crazy
2 Bulldog Medium Strong
3 Pug Small Sleepy
4 German Shepard Big Cool
5 Puddle Small Aggressive
更普遍的是使用intersection
兩個DataFrames
列:
cols = df1.columns.intersection(df2.columns)
print (cols)
Index(['Type', 'Breed', 'Behaviour'], dtype='object')
print (pd.concat([df1[cols], df2[cols]], ignore_index=True))
Type Breed Behaviour
0 Golden Big Fun
1 Corgi Small Crazy
2 Bulldog Medium Strong
3 Pug Small Sleepy
4 German Shepard Big Cool
5 Puddle Small Aggressive
比較一般如果df1
和df2
沒有NaN
值使用dropna
去除柱與NaN
:
print (pd.concat([df1 ,df2], ignore_index=True))
Bark Sound Behaviour Breed Common Color Other Color Type
0 NaN Fun Big Gold White Golden
1 NaN Crazy Small Brown White Corgi
2 NaN Strong Medium Black Grey Bulldog
3 Ak Sleepy Small NaN NaN Pug
4 Woof Cool Big NaN NaN German Shepard
5 Ek Aggressive Small NaN NaN Puddle
print (pd.concat([df1 ,df2], ignore_index=True).dropna(1))
Behaviour Breed Type
0 Fun Big Golden
1 Crazy Small Corgi
2 Strong Medium Bulldog
3 Sleepy Small Pug
4 Cool Big German Shepard
5 Aggressive Small Puddle
非常感謝你。有用! –