Add values to new column Pandas with condition

I've a Dataframe (i write only the interested column)

what im trying to do is create a new column an add a value.
This value is the Profit And Loss of the trade.
The trouble is that CAD and EUR needs the same calculation, JPY a different one and XAU another one different. So i need to add the value to new column but based on condition.

i tried this simple way but i get an ValueError:

      if self.dataframe['Symbol'] in ['CAD','EUR']:
       self.dataframe['PnL_Profit'] = round((self.dataframe['Entry'] - self.dataframe['TP']) * (self.dataframe['Lots_Size'] * 100000) / self.dataframe['TP'],2)
    elif self.dataframe['Symbol'] == 'XAU':
        self.dataframe['PnL_Profit']  = round((self.dataframe['Entry'] - self.dataframe['TP']) * (self.dataframe['Lots_Size'] * 100),2)
    else:
        self.dataframe['PnL_Profit']  = round((self.dataframe['Entry'] - self.dataframe['TP']) * (self.dataframe['Lots_Size'] * 100000),2)

But i get this kind of error:

        raise ValueError(
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

im stuck, apparently everything is write good, but i dont know where's the error. i tried also Match...Case but i get the same error

It seems to me in Pandas, you can't use the standard Python if-else conditional block in the way you're using it. I believe the error you're getting is because Python does not know how to interpret self.dataframe['Symbol'] in ['CAD','EUR'], as this is ambiguous when 'Symbol' is a Pandas Series, not a single value.

Maybe you can use something like the below:

# create masks for the conditions
mask_cad_eur = self.dataframe['Symbol'].isin(['CAD','EUR'])
mask_xau = self.dataframe['Symbol'] == 'XAU'
mask_others = ~(mask_cad_eur | mask_xau)  # this mask is for symbols that are neither 'CAD', 'EUR', nor 'XAU'

# calculate 'PnL_Profit' for each condition
self.dataframe.loc[mask_cad_eur, 'PnL_Profit'] = ((self.dataframe.loc[mask_cad_eur, 'Entry'] - self.dataframe.loc[mask_cad_eur, 'TP']) * self.dataframe.loc[mask_cad_eur, 'LOTS'] * 100000) / self.dataframe.loc[mask_cad_eur, 'TP']
self.dataframe.loc[mask_xau, 'PnL_Profit'] = (self.dataframe.loc[mask_xau, 'Entry'] - self.dataframe.loc[mask_xau, 'TP']) * self.dataframe.loc[mask_xau, 'LOTS'] * 100
self.dataframe.loc[mask_others, 'PnL_Profit'] = (self.dataframe.loc[mask_others, 'Entry'] - self.dataframe.loc[mask_others, 'TP']) * self.dataframe.loc[mask_others, 'LOTS'] * 100000

# round the results to 2 decimal places
self.dataframe['PnL_Profit'] = self.dataframe['PnL_Profit'].round(2)

This will create a new column 'PnL_Profit' in your dataframe where each row's value is calculated based on your conditions.

I note that in your code you've used Lots_Size but in the dataframe provided it's named LOTS. So, I used LOTS in the code provided. Make sure to correct it based on your dataframe.

You could use a dictionary of formulas and combine it with eval:

formulas = {'CAD': '(Entry-TP)*LOTS*100_000/TP',
            'EUR': '(Entry-TP)*LOTS*100_000/TP',
            'JPY': '(Entry-TP)*LOTS*100',
            'XAU': '(Entry-TP)*LOTS*100_000',
            }

df['PnL_Profit'] = (df.groupby('Symbol', group_keys=False)
                      .apply(lambda g: g.eval(formulas[g.name]))
                      .round(2)
                    )

Output:

  Symbol     Entry       TP  LOTS  PnL_Profit
0    CAD     1.023     1.33     1   -23082.71
1    EUR     1.530     1.43     3    20979.02
2    JPY   165.120   166.00    10     -880.00
3    XAU  1956.000  1960.00     7 -2800000.00

To avoid duplicating the formula definitions it's also possible to name them, in particular if you find a meaningful name:

formula_X = '(Entry-TP)*LOTS*100_000/TP'
formula_Y = '(Entry-TP)*LOTS*100'
formula_Z = '(Entry-TP)*LOTS*100_000'

formulas = {'CAD': formula_X,
            'EUR': formula_X,
            'JPY': formula_Y,
            'XAU': formula_Z,
            }