how to efficiently multiply certain value for each row in python dataframe

Question

I have one dataframe and one series, the dataframe is the base data about the cost for each respondents, and series is discount rate that should be multiplied to the base data.

For example, base data is (dataframe):

      cost1  cost2
John    100     50
Tom      50    100
Andy     50    200
Mark     80    300

and discount data is (series):

John    0.7
Tom     0.6
Andy    0.9
Mark    0.5

The desired output is therefore:

      cost1  cost2
John     70     35
Tom      30     60
Andy     45    180
Mark     40    150

The method I have come up with is:

customer_list = base.index.tolist()

for k in customer_list:
    base.loc[k] = base.loc[k]*discount.loc[k]

This works (gives me the desired output), but I feel it's quite inefficient. In my actual application, the dataframe is quite large, so it takes quite time to run such iteration.

Any suggestions for the efficient calculation? I think there might be some efficient way to do such calculation as a whole, using the index. (base * discount certainly did not work though...)

thank you in advance!

Answer 1

Use mul with an axis param:

df.mul(df2, axis=0)

      cost1  cost2
John   70.0   35.0
Tom    30.0   60.0
Andy   45.0  180.0
Mark   40.0  150.0