Suppose I have two series:
s = pd.Series([20, 21, 12]
t = pd.Series([17,19 , 11]
I want to apply a two argument function to the two series to get a series of results (as a series). Now, one way to do it is as follows:
df = pd.concat([s, t], axis=1)
result = df.apply(lambda x: foo(x[s], x[t]), axis=1)
But this seems clunky. Is there any more elegant way?
There are many ways to do what you want.
Depending on the function in question, you may be able to apply it directly to the series. For example, calling s + t returns
0 37
1 40
2 23
dtype: int64
However, if your function is more complicated than simple arithmetic, you may need to get creative. One option is to use the built-in Python map function. For example, calling
list(map(np.add, s, t))
returns
[37, 40, 23]
If the two series have the same index, you can create a series with list comprehension:
result = pd.Series([foo(xs, xt) for xs,xt in zip(s,t)], index=s.index)
If you can't guarantee that the two series have the same index, concat is the way to go as it helps align the index.
If I understand you can use this to apply a function using 2 colums and copy the results in another column:
df['result'] = df.loc[:, ['s', 't']].apply(foo, axis=1)
It might be possible to use numpy.vectorize:
from numpy import vectorize
vect_foo = vectorize(foo)
result = vect_foo(s, t)