PANDAS vlookup against series with common index using map

Question

import pandas as pd
import numpy as np

pb = {"mark_up_id":{"0":"123","1":"456","2":"789","3":"111","4":"222"},"mark_up":{"0":1.2987,"1":1.5625,"2":1.3698,"3":1.3333,"4":1.4589}}

data = {"id":{"0":"K69","1":"K70","2":"K71","3":"K72","4":"K73","5":"K74","6":"K75","7":"K79","8":"K86","9":"K100"},"cost":{"0":29.74,"1":9.42,"2":9.42,"3":9.42,"4":9.48,"5":9.48,"6":24.36,"7":5.16,"8":9.8,"9":3.28},"mark_up_id":{"0":"123","1":"456","2":"789","3":"111","4":"222","5":"333","6":"444","7":"555","8":"666","9":"777"}}

pb = pd.DataFrame(data=pb).set_index('mark_up_id')
df = pd.DataFrame(data=data)

I know that I can use something like:

df['mark_up_id'].map(pb['mark_up'])

to perform a v-look-up. I'd like to take the mark-up this returns and multiply it by each cost with a common index to yield a new column called price.

I know I can merge the two and then run the calculation. That's how I produced the desired output. I'd like to be able to do this similar to how you'd loop through a dictionary and use the keys to find values in another dictionary and perform some kind of computation inside of a loop. Considering PANDAS dataframes sit on top of dictionaries, there must be a way of using a combination of join/map/apply to do this without actually joining the two data-sets in memory.

Desired output:

desired_output = {"cost":{"0":29.74,"1":9.42,"2":9.42,"3":9.42,"4":9.48},"id":{"0":"K69","1":"K70","2":"K71","3":"K72","4":"K73"},"mark_up_id":{"0":"123","1":"456","2":"111","3":"123","4":"789"},"price":{"0":38.623338,"1":14.71875,"2":12.559686,"3":12.233754,"4":12.985704}}
do = pd.DataFrame(data=desired_output)

Bonus Points:

Explain the difference between the accepted answer and...

pb.loc[df['mark_up_id']]['mark_up'] * df.set_index('mark_up_id')['cost']

and why the following lambda function that i derived the above from hits an error...

df.apply(lambda x : x['cost']*pb.loc[x['mark_up_id']],axis=1 )

returns an error saying:

KeyError: ('the label [333] is not in the [index]', u'occurred at index 5')

Answer 1

Try

df['price'] = df['mark_up_id'].map(pb['mark_up']) * df['cost']

You get

    cost    id  mark_up_id  price
0   29.74   K69 123         38.623338
1   9.42    K70 456         14.718750
2   9.42    K71 111         12.559686
3   9.42    K72 123         12.233754
4   9.48    K73 789         12.985704

Answer 2

UPDATE: for updated question:

In [79]: df = df.assign(price=df['mark_up_id'].map(pb['mark_up']) * df['cost']).dropna()

In [80]: df
Out[80]:
    cost   id mark_up_id      price
0  29.74  K69        123  38.623338
1   9.42  K70        456  14.718750
2   9.42  K71        789  12.903516
3   9.42  K72        111  12.559686
4   9.48  K73        222  13.830372

---

Old answer:

In [67]: df = df.assign(price=df['mark_up_id'].map(pb['mark_up']) * df['cost'])

In [68]: df
Out[68]:
    cost   id mark_up_id      price
0  29.74  K69        123  38.623338
1   9.42  K70        456  14.718750
2   9.42  K71        111  12.559686
3   9.42  K72        123  12.233754
4   9.48  K73        789  12.985704