What's the best way to sum all values in a Pandas dataframe?

Question

I figured out these two methods. Is there a better one?

>>> import pandas as pd
>>> df = pd.DataFrame({\'A\': [5, 6, 7], \'B\': [7, 8, 9]}         


        

Answer 1

Updated for Pandas 0.24+

df.to_numpy().sum()

---

Prior to Pandas 0.24+

df.values

Is the underlying numpy array

df.values.sum()

Is the numpy sum method and is faster

Answer 2

Adding some numbers to support this:

import numpy as np, pandas as pd
import timeit
df = pd.DataFrame(np.arange(int(1e6)).reshape(500000, 2), columns=list("ab"))

def pandas_test():
    return df['a'].sum()

def numpy_test():
    return df['a'].to_numpy().sum()

timeit.timeit(numpy_test, number=1000)  # 0.5032469799989485
timeit.timeit(pandas_test, number=1000)  # 0.6035906639990571

So we get a 20% performance on my machine just for Series summations!