Pandas Apply Function That returns two new columns

Please be aware of the huge memory consumption and low speed of the accepted answer: https://ys-l.github.io/posts/2015/08/28/how-not-to-use-pandas-apply/ !

Using the suggestion presented there, the correct answer would be like this:

def run_loopy(df):
    Cs, Ds = [], []
    for _, row in df.iterrows():
        c, d, = myfunc1(row['A'])
        Cs.append(c)
        Ds.append(d)
    return pd.Series({'C': Cs,
                      'D': Ds})

def myfunc1(a):
    c = a + 10
    d = a + 50
    return c, d

df[['C', 'D']] = run_loopy(df)

It works for me:

def myfunc1(row):
    C = row['A'] + 10
    D = row['A'] + 50
    return C, D

df = pd.DataFrame(np.random.randint(0,10,size=(2, 2)), columns=list('AB'))

df[['C', 'D']] = df.apply(myfunc1, axis=1, result_type='expand')
df

add: ==>> result_type='expand',

regards!

Based on your latest error, you can avoid the error by returning the new columns as a Series

def myfunc1(row):
    C = row['A'] + 10
    D = row['A'] + 50
    return pd.Series([C, D])

df[['C', 'D']] = df.apply(myfunc1 ,axis=1)