CODEWITHSUNDEEP
pythonpandasdataframeconditional-statements
Fran Martinez
Fran Martinez

Reputation: 679

Apply lambda depending on column values on Pandas

I have a data frame that depending on the value of column Order I want to take the value of column Value and do some calculation.

DataFrame1

             Order  Shares   Value
2011-01-10   BUY    1300     340.99  
2011-01-10   SELL   1200     340.99
2011-01-11   SELL   1100     330.99

Line of code:

impacts['NewValue']=float(impacts.Order.apply(lambda x: (impacts.Value + (impacts.Value * 0.006)) if x == 'SELL' else (impacts.Value - (impacts.Value * 0.006))))

Error:

TypeError: ufunc 'multiply' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')

Is my understanding that error is caused by the contents of the numbers, therefore that's why I tried to cast it to a float.

Intended output

            Order  Shares   Value   NewValue
2011-01-10   BUY    1300   340.99  338.94
2011-01-10   SELL   1200   340.99  343.03
2011-01-11   SELL   1100   330.99  332.97

Any help is more than welcome. Thanks!

Upvotes: 2

Views: 6313

Answers (2)

cs95
cs95

Reputation: 402263

(Too long for a comment) Here's a slightly more condensed version of Wen's np.where:

i = np.where(df.Order == 'SELL', 1, -1) * 0.006
df.Value = df.Value.mul(i) + df.Value

print(df.Value)
2011-01-10    338.94406
2011-01-10    343.03594
2011-01-11    332.97594
dtype: float64

Use df.Order to determine the sign before the operation.

Upvotes: 2

BENY
BENY

Reputation: 323226

Hope it help:-) (Modified your own code only, Your sample code will return error)

df.apply(lambda x: (x.Value + (x.Value * 0.006)) if x.Order == 'SELL' else (x.Value - (x.Value * 0.006)),axis=1)
Out[790]: 
2011-01-10    338.94406
2011-01-10    343.03594
2011-01-11    332.97594
dtype: float64

To get df

df['NewValue']=df.apply(lambda x: (x.Value + (x.Value * 0.006)) if x.Order == 'SELL' else (x.Value - (x.Value * 0.006)),axis=1)
df
Out[792]: 
           Order  Shares   Value   NewValue
2011-01-10   BUY    1300  340.99  338.94406
2011-01-10  SELL    1200  340.99  343.03594
2011-01-11  SELL    1100  330.99  332.97594

I will use np.where

import numpy as np
np.where(df.Order=='SELL',(df.Value + (df.Value * 0.006)),(df.Value - (df.Value * 0.006)) )
Out[794]: array([ 338.94406,  343.03594,  332.97594])

After assign it back 

df['NewValue']=np.where(df.Order=='SELL',(df.Value + (df.Value * 0.006)),(df.Value - (df.Value * 0.006)) )
df
Out[796]: 
           Order  Shares   Value   NewValue
2011-01-10   BUY    1300  340.99  338.94406
2011-01-10  SELL    1200  340.99  343.03594
2011-01-11  SELL    1100  330.99  332.97594

Upvotes: 2

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