我有一个数据帧
id store address
1 100 xyz
2 200 qwe
3 300 asd
4 400 zxc
5 500 bnm
我有另一个数据帧df2
serialNo store_code warehouse
1 300 Land
2 500 Sea
3 100 Land
4 200 Sea
5 400 Land
我希望我的最终数据框看起来像：
id store address warehouse
1 100 xyz Land
2 200 qwe Sea
3 300 asd Land
4 400 zxc Land
5 500 bnm Sea
即从一个数据帧映射到另一个数据帧创建新列
df.merge
out = df1.merge(df2,left_on='store',right_on='store_code')
[['id','store','address','warehouse']]
print(out)
id store address warehouse
0 1 100 xyz Land
1 2 200 qwe Sea
2 3 300 asd Land
3 4 400 zxc Land
4 5 500 bnm Sea
pd.concat df.sort_values
out = pd.concat([df1.sort_values('store'),
df2.sort_values('store_code')[['warehouse']].reset_index(drop=1)],1)
print(out)
id store address warehouse
0 1 100 xyz Land
1 2 200 qwe Sea
2 3 300 asd Land
3 4 400 zxc Land
4 5 500 bnm Sea
假设您的数据帧已经在商店中排序,第一个排序调用是多余的,在这种情况下您可以将其删除.
df.replace
s = df1.store.replace(df2.set_index('store_code')['warehouse'])
print(s)
0 Land
1 Sea
2 Land
3 Land
4 Sea
df1['warehouse'] = s
print(df1)
id store address warehouse
0 1 100 xyz Land
1 2 200 qwe Sea
2 3 300 asd Land
3 4 400 zxc Land
4 5 500 bnm Sea
或者,显式创建映射.如果您想稍后使用它,则此方法有效.
mapping = dict(df2[['store_code','warehouse']].values) # separate step
df1['warehouse'] = df1.store.replace(mapping) # df1.store.map(mapping)
print(df1)
id store address warehouse
0 1 100 xyz Land
1 2 200 qwe Sea
2 3 300 asd Land
3 4 400 zxc Land
4 5 500 bnm Sea
