Run function exactly once for each row in a Pandas dataframe

The way I do it (because I also don't like the idea of looping with df.itertuples) is:

df.apply(do_irreversible_thing, axis=1)

and then your function should be like:

def do_irreversible_thing(x):    print x.a, x.b

this way you should be able to run your function over each row.

OR

if you can't modify your function you could apply it like this

df.apply(lambda x: do_irreversible_thing(x[0],x[1]), axis=1)

It's unclear what your function is doing but to apply a function to each row you can do so by passing axis=1 to apply your function row-wise and pass the column elements of interest:

In [155]:def foo(a,b):    return a*b​df = pd.DataFrame([(0, 1), (2, 3), (4, 5)], columns=['a', 'b'])df.apply(lambda x: foo(x['a'], x['b']), axis=1)Out[155]:0     01     62    20dtype: int64

However, so long as your function does not depend on the df mutating on each row, then you can just use a vectorised method to operate on the entire column:

In [156]:df['a'] * df['b']Out[156]:0     01     62    20dtype: int64

The reason is that because the functions are vectorised then it will scale better whilst the apply is just syntactic sugar for iterating on your df so it's a for loop essentially