Matching column values with elements in a list of lists of lists and adding corresponding values from another list

assets = [[['Ferrari', 'BMW', 'Suzuki'], ['Ducati', 'Honda']], [['Apple', 'Samsung', 'Oppo']]]
price = [[[853600, 462300, 118900], [96500, 16700]], [[1260, 750, 340]]]

I have a dataframe as follows :

Looking for code to get the Total_Cost , i.e 462300 + 96500 + 1260 = 560060

I tried the for loop and succeeded, I want the advanced code if any.

Here is a possible solution:

df = pd.DataFrame({'Car': ['BMW', 'Ferrari'], 'Bike': ['Ducati', 'Honda'], 'Phone': ['Apple', 'Oppo']})

asset_price = {asset: price[a][b][c] 
                for a, asset_list in enumerate(assets) 
                for b, asset_sub_list in enumerate(asset_list) 
                for c, asset in enumerate(asset_sub_list)
}

df['Total_Cost'] = df.apply(lambda row: sum([asset_price[asset] for asset in row]), axis=1)
print(df)

       Car    Bike  Phone  Total_Cost
0      BMW  Ducati  Apple      560060
1  Ferrari   Honda   Oppo      870640

You can also use numpy approach import numpy as np depending on your use-case. But I will suggest the first approach which is more simple and easy to understand.

df = pd.DataFrame({'Car': ['BMW', 'Ferrari'], 'Bike': ['Ducati', 'Honda'], 'Phone': ['Apple', 'Oppo']})

flat_assets = np.concatenate([np.concatenate(row) for row in assets])
flat_price = np.concatenate([np.concatenate(row) for row in price])

asset_dict = dict(zip(flat_assets, flat_price))
asset_prices = np.array([asset_dict[row] for row in df.values.flatten() 
                            if row in asset_dict])

df['Total Cost'] = np.sum(asset_prices.reshape(-1, 3), axis=1)
print(df)

       Car    Bike  Phone  Total Cost
0      BMW  Ducati  Apple      560060
1  Ferrari   Honda   Oppo      870640

An alternative approach:

First build a dataframe df_price which maps prices onto the assets and the classification (Car, Bike, and Phone):

df_price = (
    pd.DataFrame({"assets": assets, "price": price}).explode(["assets", "price"])
    .assign(cols=["Car", "Bike", "Phone"]).explode(["assets", "price"])
)

Result:

    assets   price   cols
0  Ferrari  853600    Car
0      BMW  462300    Car
0   Suzuki  118900    Car
0   Ducati   96500   Bike
0    Honda   16700   Bike
1    Apple    1260  Phone
1  Samsung     750  Phone
1     Oppo     340  Phone

(I have inserted the classification here due to the comment on the other answer: "... But if the nested lists of asset is having common name (say : Honda in place if Suzuki ) then Honda car and Honda Bike will take one price".

Then join the prices onto the .melted main dataframe df, .pivot (using the auxilliary column idx), sum up the prices in the rows, and bring the result in shape.

res = (
    df.melt(var_name="cols", value_name="assets", ignore_index=False)
    .merge(df_price, on=["cols", "assets"])
    .assign(idx=lambda df: df.groupby("cols").cumcount())
    .pivot(index="idx", columns="cols")
    .assign(total=lambda df: df.loc[:, "price"].sum(axis=1))
    .loc[:, ["assets", "total"]]
    .droplevel(0, axis=1).rename(columns={"": "Total_Costs"})
)

Result:

cols    Bike      Car  Phone  Total_Costs
idx                                      
0     Ducati      BMW  Apple     560060.0
1      Honda  Ferrari   Oppo     870640.0