CODEWITHSUNDEEP
pythonpandasdataframe
PythonCub
PythonCub

Reputation: 19

Adding function per row in data frame into a list

Basically im trying to use a user defined function to calculate values in a row in each dataframe and presenting them to a new column ABCD.

dfx = pd.DataFrame({'A': [1,2,3,4,5], 'B': [10,20,30,40,50], 'C': 
[5,5,5,5,5], 'D' : [2,2,2,2,2]})
print(df)


   A   B  C  D  E(Desired)
0  1  10  5  2
1  2  20  5  2
2  3  30  5  2
3  4  40  5  2
4  5  50  5  2

def functionx(A,B,C,D):
    print( A * B + C / D)



dfx['ABCD'] = functionX

i tried using functionX but it does not work. How do i pass the function correctly through each row and produce a column E which is the result?

Upvotes: 0

Views: 116

Answers (2)

Chris
Chris

Reputation: 29742

Use unpacking with pandas.DataFrame.apply on axis=1:

dfx['E'] = dfx.apply(lambda x: functionx(*x), 1)
print(dfx)

Output:

   A   B  C  D      E
0  1  10  5  2   12.5
1  2  20  5  2   42.5
2  3  30  5  2   92.5
3  4  40  5  2  162.5
4  5  50  5  2  252.5

Upvotes: 0

ncica
ncica

Reputation: 7206

Add a new column in DataFrame with values based on other columns

You can achieve that by directly performing the required operation on the desired column element-wise:

import pandas as pd

dfx = pd.DataFrame({'A': [1,2,3,4,5], 'B': [10,20,30,40,50], 'C':
[5,5,5,5,5], 'D' : [2,2,2,2,2]})
print(dfx)

dfx['E'] =  dfx['A'] * dfx['B'] + dfx['C'] / dfx['D']
print(dfx)

output:

   A   B  C  D      E
0  1  10  5  2   12.5
1  2  20  5  2   42.5
2  3  30  5  2   92.5
3  4  40  5  2  162.5
4  5  50  5  2  252.5

or you can use DataFrame.apply() function to achieve this task:

dfx['E'] = dfx.apply(lambda row: row.A * row.B + row.C / row.D, axis = 1)

NOTE:

Apply a function along an axis(columns or rows) of the DataFrame:

Objects passed to the function are Series objects whose index is either the DataFrame’s index (axis=0) or the DataFrame’s columns (axis=1). By default (result_type=None), the final return type is inferred from the return type of the applied function. Otherwise, it depends on the result_type argument.

Upvotes: 2

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