How to Check Whether a Pandas Dataframe Is Empty?

I use the len function. It's much faster than empty. len(df.index) is even faster.

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(10000, 4), columns=list('ABCD'))

def empty(df):
    return df.empty

def lenz(df):
    return len(df) == 0

def lenzi(df):
    return len(df.index) == 0

'''
%timeit empty(df)
%timeit lenz(df)
%timeit lenzi(df)

10000 loops, best of 3: 13.9 µs per loop
100000 loops, best of 3: 2.34 µs per loop
1000000 loops, best of 3: 695 ns per loop

len on index seems to be faster
'''

To see if a dataframe is empty, I argue that one should test for the length of a dataframe's columns index:

if len(df.columns) == 0: 1

Reason:

According to the Pandas Reference API, there is a distinction between:

• an empty dataframe with 0 rows and 0 columns
• an empty dataframe with rows containing NaN hence at least 1 column

Arguably, they are not the same. The other answers are imprecise in that df.empty, len(df), or len(df.index) make no distinction and return index is 0 and empty is True in both cases.