Pandas map values from Dictionary efficiently

Question

I have a pandas dataframe df :-

ID	COST	1F	2F	3F	4G
1	362	0	1	1	1
2	269	0	1	0	0
3	346	1	1	1	1
4	342	0	0	0	0

I have a total_cost dictionary :

total_cost ={‘1F’ : 0.047,’2F’ : 0.03,’3F’: 0.023,’4G’: 0.025}

I want to add a TOTAL_COST column such that wherever 1 is present, COST*(value from total_cost dictionary) for that col is to be multiplied and added together.

The dataframe has around a milion records, what would be the most efficient way to do this?
Expected df :-

ID	COST	1F	2F	3F	4G	TOTAL_COST
1	362	0	1	1	1	28.236
2	269	0	1	0	0	8.07
3	346	1	1	1	1	43.25
4	342	0	0	0	0	0

Asked By: Scope

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Source

Answer 1

Use slicing to only get the columns present as keys in your dictionary, then multiply by it, aggregate as sum per row and multiply by the COST:

df['TOTAL_COST'] = df[list(total_cost)].mul(total_cost).sum(axis=1).mul(df['COST'])

Or, cast the dictionary as Series (the non matching columns will become NaN):

df['TOTAL_COST'] = df.mul(pd.Series(total_cost)).sum(axis=1).mul(df['COST'])

Output:

   ID  COST  1F  2F  3F  4G  TOTAL_COST
0   1   362   0   1   1   1      28.236
1   2   269   0   1   0   0       8.070
2   3   346   1   1   1   1      43.250
3   4   342   0   0   0   0       0.000

Answered By: mozway

Pandas map values from Dictionary efficiently

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