applying a function to a pair of pandas series-CodePudding

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?

CodePudding user response：

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]

CodePudding user response：

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.

CodePudding user response：

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)

CodePudding user response：

It might be possible to use numpy.vectorize:

from numpy import vectorize

vect_foo = vectorize(foo)
result = vect_foo(s, t)