Create multiple columns from multiple return value of lambda function in python DataFrame

Question

Att, I want to create multiple columns from lambda function's multiple return values in python DataFrame.

Similar with the last line of my demo code.

Is there any way to achieve this?

y = np.random.rand(2,5)
df = pd.DataFrame(y, columns = ["y1", "y2", "y3", "y4", "y5"])
print(df)
def f_polyfit(y1, y2, y3, y4, y5, degree):
    y = [y1, y2, y3, y4, y5]
    x = [1, 2, 3, 4, 5]
    coeffs = np.polyfit(x, y, degree)   
    coeffs = coeffs.tolist()
    # constructe the polynomial formula
    p = np.poly1d(coeffs)
    # fit values, and mean
    y_fit = p(x)                        
    y_avg = np.sum(y)/len(y)          
    ssreg = np.sum((y_fit-y_avg)**2)   
    sstot = np.sum((y - y_avg)**2)  
    R2 = ssreg / sstot
    return coeffs[0], R2
# df["slope"], df["R2"] = zip(df.apply(lambda x:f_polyfit(x["y1"], x["y2"], x["y3"], x["y4"], x["y5"], degree = 1),  axis = 1))

Answer 1

One way would be to wrap the return value in pd.Series in order to assign to new dataframe columns.

g = lambda x: pd.Series(f_polyfit(x.y1, x.y2, x.y3, x.y5, x.y5, degree=1))
df[['slope', 'R2']] = df.apply(g, axis=1)