CODEWITHSUNDEEP
pythonpandasnumpydataframe
piR
piR

Reputation: 11

operation in dataframe python

Sorry for my english.

I have a question. I have this data frame and I would like to calculate the result of the profit that is in my dataframe : if Sellings - (Couts_fixe + Couts_Variables) >=0 : corporation tax = (Sellings - (Couts_fixe + Couts_Variables))*Taxes else: corporation tax = 0

I think it is something like that but that doesn't work.

I have Write this :

if (df['Sellings']-df['Couts_Tot']) >=0:
 df['Taxes_Soc'] = (df['Sellings'] - df['Couts_Fixes'] - df['Couts_Variables'])*df['Taxes'] 
else : df['Taxes_Soc'] = 0

and they answers : "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."

as you can see we have an monthly selling and cost and taxes

Thanks for your help.

Upvotes: 0

Views: 45

Answers (2)

piR
piR

Reputation: 11

I have try this and it's work someone can say if its correct to write this like this in python or it's an unusual way to write it

df['Taxes_Soc'] = np.repeat(0, 12)
for i in range(12):
    if (df['Sellings'][i]-df['Couts_Tot'][i]) >= 0:
        df['Taxes_Soc'][i] = (df['Sellings'][i] - df['Couts_Fixes'][i] - df['Couts_Variables'][i])*df['Taxes'][i]
        i += 1
    else : 
        df['Taxes_Soc'][i] = 0
        i += 1

Upvotes: 0

jkr
jkr

Reputation: 19250

You are getting the error The truth value of a Series is ambiguous because you are using a pd.Series of booleans in an if statement. The if statement expects a single boolean, so it doesn't know what to do with a Series of them.

What you are looking for is indexing with a boolean mask (a series of booleans). You can create your mask, and then modify rows depending on the value of the mask. The mask has a True or False value for each row.

The syntax ~mask gets the boolean not of the mask, so it switches the Trues to falses, and the falses to Trues.

mask = (df['Sellings'] - df['Couts_Tot']) >= 0

df.loc[mask, 'Taxes_Soc'] = (
        (df.loc[mask, 'Sellings'] 
        - df.loc[mask, 'Couts_Fixes'] 
        - df.loc[mask, 'Couts_Variables'])
    * df.loc[mask, 'Taxes'])
df.loc[~mask, 'Taxes_Soc'] = 0

Upvotes: 1

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