Pandas: Summing all elements in a dataframe? [duplicate]

Question

Given a Pandas dataframe df, we can sum the columns like this

[x for x in df.sum()]

and produce the sum of sums like this.

sum([x for x in df.sum()])

Can this be done using only dataframe operations, without resorting to Python's sum()?

Answer 1

We can do stack

df.stack().sum()

Answer 2

Use np.sum:

np.sum(df.to_numpy())

or as @jakub points out:

df.to_numpy().sum()

Timings:

Using...

df = pd.DataFrame(np.arange(10000).reshape(100,-1))

%timeit df.to_numpy().sum()
# 12.1 µs ± 357 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

%timeit np.sum(df.to_numpy())
# 14 µs ± 263 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

%timeit df.stack().sum()
# 469 µs ± 30.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%timeit df.sum().sum()
# 381 µs ± 21.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)