How to map a function using multiple columns in pandas?

Use pd.DataFrame.apply(), as below:

df['d'] = df.apply(lambda x: some_func(a = x['a'], b = x['b'], c = x['c']), axis=1)

NOTE: As @ashishsingal asked about columns, the axis argument should be provided with a value of 1, as the default is 0 (as in the documentation and copied below).

axis : {0 or ‘index’, 1 or ‘columns’}, default 0

  • 0 or ‘index’: apply function to each column
  • or ‘columns’: apply function to each row

For what it's worth on such an old question; I find that zipping function arguments into tuples and then applying the function as a list comprehension is much faster than using df.apply. For example:

import pandas as pd

# Setup:
df = pd.DataFrame(np.random.rand(10000, 3), columns=list("abc"))
def some_func(a, b, c):
    return a*b*c

# Using apply:
%timeit df['d'] = df.apply(lambda x: some_func(a = x['a'], b = x['b'], c = x['c']), axis=1)

222 ms ± 63.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

# Using tuples + list comprehension:
%timeit df["d"] = [some_func(*a) for a in tuple(zip(df["a"], df["b"], df["c"]))]

8.07 ms ± 640 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

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