Python Pandas: Apply function using column names as named arguments

Question

Is there a way in pandas to apply a function to a dataframe using the column names as argument names? For example, I have a function and a dataframe.

df = pd.DataFrame({'A':[1,2,3],
               'B':[1,2,3],
               'C':[1,2,3],
               'D':[1,2,3]})    
def f(A,B,C):
   #Pretend code is more complicated
   return A + B + C

Is there a way I can do something like

df.apply(f)

and have pandas match the columns to named arguments?

I know I can rewrite the function to take a row instead of named arguments, but keep in mind that f is just a toy example and my real function is more complicated

EDIT:

Figured it out based @juanpa.arrivillaga answer:

df[list(f.__code__.co_varnames)].apply((lambda row: f(**row)), axis=1)

Answer 1

The function to apply f needs to accept either rows/columns, depending on axis=0,1, of df as an argument, not the column name. You can write a wrapper for this purpose.

def wrapper(x, A, B, C):
    return f(x[A], x[B], x[C])

df.apply(wrapper, axis=1, args=('A','B','C'))

Output:

0    3
1    6
2    9
dtype: int64

Answer 2

if you are interesting for "apply" function, here is the case

df = pd.DataFrame({'A':[1,2,3],
                  'B':[1,2,3],
                  'C':[1,2,3],
                  'D':[1,2,3]})     


def func(row):
    row['result'] = row['A'] + row['B'] + row['C']
    return row

df.apply(func, axis = 1)


    Out[67]: 
       A  B  C  D  result
    0  1  1  1  1       3
    1  2  2  2  2       6
    2  3  3  3  3       9

UPD

If you have to use function "f" and don't want to change it, may be this:

df['res'] = f(df['A'], df['B'], df['C'])
df

    Out[70]: 
       A  B  C  D  res
    0  1  1  1  1    3
    1  2  2  2  2    6
    2  3  3  3  3    9

Answer 3

There is no good way in general. However, if your column names alight exactly you can wrap the function in another function that splats the row argument into your function, because Series objects are mappings!

So given:

>>> import pandas as pd
>>> df = pd.DataFrame({'A':[1,2,3],
...                'B':[1,2,3],
...                'C':[1,2,3],
...                'D':[1,2,3]})
>>> df
   A  B  C  D
0  1  1  1  1
1  2  2  2  2
2  3  3  3  3
>>> def f(A, B, C): return A + B + C
...

We could almost do:

>>> df.apply(lambda row: f(**row), axis=1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/juan/anaconda3/envs/ecqm-catalog/lib/python3.7/site-packages/pandas/core/frame.py", line 6014, in apply
    return op.get_result()
  File "/Users/juan/anaconda3/envs/ecqm-catalog/lib/python3.7/site-packages/pandas/core/apply.py", line 142, in get_result
    return self.apply_standard()
  File "/Users/juan/anaconda3/envs/ecqm-catalog/lib/python3.7/site-packages/pandas/core/apply.py", line 248, in apply_standard
    self.apply_series_generator()
  File "/Users/juan/anaconda3/envs/ecqm-catalog/lib/python3.7/site-packages/pandas/core/apply.py", line 277, in apply_series_generator
    results[i] = self.f(v)
  File "<stdin>", line 1, in <lambda>
TypeError: ("f() got an unexpected keyword argument 'D'", 'occurred at index 0')

If you know what the columns you need, you can select/drop to get the correct series:

>>> df.drop('D',axis=1).apply(lambda row: f(**row), axis=1)
0    3
1    6
2    9