将列从一个数据框映射到另一个数据框以创建新列[重复]

Question

我有一个数据框

id  store    address
1    100        xyz
2    200        qwe
3    300        asd
4    400        zxc
5    500        bnm

我有另一个数据框 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创建新列

Answer 1

df.merge

out = (df1.merge(df2, left_on='store', right_on='store_code')
          .reindex(columns=['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

u = df1.sort_values('store')
v = df2.sort_values('store_code')[['warehouse']].reset_index(drop=1)
out = pd.concat([u, v], 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
    

假设您的数据框已经在store 上排序，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/df.map

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，这很有效。

mapping = dict(df2[['store_code', 'warehouse']].values)
df1['warehouse'] = 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

Answer 2

使用map 或join：

df1['warehouse'] = df1['store'].map(df2.set_index('store_code')['warehouse'])
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

---

df1 = df1.join(df2.set_index('store_code'), on=['store']).drop('serialNo', 1)
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