pandas 将列从一个数据帧映射到另一个以创建新列
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Mapping columns from one dataframe to another to create a new column
提问by Shubham R
i have a dataframe
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
i have another dataframe df2
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
I want my final dataframe to look like:
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
i.e map from one dataframe onto another creating new column
即从一个数据框映射到另一个创建新列
回答by cs95
df.merge
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
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
The first sort call is redundant assuming your dataframe is already sorted on store, in which case you may remove it.
第一个排序调用是多余的，假设您的数据框已经在 上排序store，在这种情况下您可以将其删除。
df.replace/df.map
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
Alternatively, create a mapping explicitly. This works if you want to use it later.
或者，显式创建映射。如果您想稍后使用它，这将起作用。
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
回答by jezrael
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
