Adding additional calculations to a numpy.where statement

Question

So, I currently have the below code- which calculates a new column based on two others, depending on which one has data present. I would like to add an additional step to the code. Where if the data in a column being used in the calculations (Total-S_%S, Sulphate-S_%S and Sulphate-S(HCL Leachable)_%S) is negative, the program will times the negatives value by -0.5, and then continue with the calculations as described in the code already. I'm not really sure where to start with this. Thanks,

df['Sulphide-S(calc)-C_%S'] = np.where(
    df["Sulphate-S(HCL Leachable)_%S"].isna(),
    df["Total-S_%S"]- df["Sulphate-S_%S"],
    df["Total-S_%S"]- df["Sulphate-S(HCL Leachable)_%S"])

Stanislav Nosulenko · Accepted Answer · 2020-12-04 17:23:02Z

4

The first option below doesn't really work after the additional input from this answer's comments, but I will keep it here for reference. Please look at the second option for the actually working solution You could create a nested np.where() statement like this:

df['Sulphide-S(calc)-C_%S'] = np.where((df["Total-S_%S"] < 0) & (df["Sulphate-S(HCL Leachable)_%S"] >= 0), 
    np.where(
        df["Sulphate-S(HCL Leachable)_%S"].isna(),
        df["Total-S_%S"]- df["Sulphate-S_%S"],
        df["Total-S_%S"]- df["Sulphate-S(HCL Leachable)_%S"]),
    np.where(
        df["Sulphate-S(HCL Leachable)_%S"].isna(),
        df["Total-S_%S"] * (-0.5) - df["Sulphate-S_%S"] * (-0.5),
        df["Total-S_%S"] * (-0.5) - df["Sulphate-S(HCL Leachable)_%S"] * (-0.5))

Working solution below

Otherwise, you could create 2 columns, each of which would have processed values from the initital ones, like this:

df["Sulphate-S_%S_PROCESSED"] = np.where(df["Sulphate-S_%S"] < 0,
    df["Sulphate-S_%S"] * -0.5,
    df["Sulphate-S_%S"])

and then conduct you computations on these new columns

edited Dec 4, 2020 at 17:23

answered Dec 4, 2020 at 14:40

Stanislav Nosulenko

1949 bronze badges

Sign up to request clarification or add additional context in comments.

3 Comments

Stanislav Nosulenko Over a year ago

This solution is for the case where you want the negatives to be processed in case BOTH columns are negative, which I believe is what you are looking for?

Amy Over a year ago

Thanks for showing how to add a nested np.where! Much appreciated. And not quite, I have adjusted my question a bit above. I would want the calculation of (*-0.5) to occur to the each individual column (ex. Total-S_%S, etc.). So, there may only be a negative in one of the three columns, or both which are used in the following calculation outlined in the code I started with. Does this make sense? The creating processed columns in this case may be easier by the looks of it

Stanislav Nosulenko Over a year ago

You're welcome. I modified my answer to reflect your added comment. The first option could still work, but I guess it would require more nested conditions, which becomes cumbersome. I would use the added columns if I were you.

Collectives™ on Stack Overflow

Adding additional calculations to a numpy.where statement

1 Answer 1

3 Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

3 Comments

Your Answer

Sign up or log in

Post as a guest

Related