How to use pandas df column value in if-else expression to calculate additional columns

Question

I am trying to calculate additional metrics from existing pandas dataframe by using an if/else condition on existing column values.

if(df['Sell_Ind']=='N').any():
    df['MarketValue'] = df.apply(lambda row: row.SharesUnits * row.CurrentPrice, axis=1).astype(float).round(2)
elif(df['Sell_Ind']=='Y').any():
    df['MarketValue'] = df.apply(lambda row: row.SharesUnits * row.Sold_price, axis=1).astype(float).round(2)
else:
    df['MarketValue'] = df.apply(lambda row: 0)

For the if condition the MarketValue is calculated correctly but for the elif condition, its not giving the correct value. Can anyone point me as what wrong I am doing in this code.

jezrael · Accepted Answer · 2018-01-11 11:37:18Z

I think you need numpy.select, apply can be removed and multiple columns by mul:

m1 = df['Sell_Ind']=='N'
m2 = df['Sell_Ind']=='Y'
a = df.SharesUnits.mul(df.CurrentPrice).astype(float).round(2)
b = df.SharesUnits.mul(df.Sold_price).astype(float).round(2)
df['MarketValue'] =  np.select([m1, m2], [a,b], default=0)

Sample:

df = pd.DataFrame({'Sold_price':[7,8,9,4,2,3],
                   'SharesUnits':[1,3,5,7,1,0],
                   'CurrentPrice':[5,3,6,9,2,4],
                   'Sell_Ind':list('NNYYTT')})

#print (df)

m1 = df['Sell_Ind']=='N'
m2 = df['Sell_Ind']=='Y'
a = df.SharesUnits.mul(df.CurrentPrice).astype(float).round(2)
b = df.SharesUnits.mul(df.Sold_price).astype(float).round(2)
df['MarketValue'] =  np.select([m1, m2], [a,b], default=0)
print (df)
   CurrentPrice Sell_Ind  SharesUnits  Sold_price  MarketValue
0             5        N            1           7          5.0
1             3        N            3           8          9.0
2             6        Y            5           9         45.0
3             9        Y            7           4         28.0
4             2        T            1           2          0.0
5             4        T            0           3          0.0

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