How to count nan values in a pandas DataFrame?

Question

What is the best way to account for (not a number) nan values in a pandas DataFrame?

The following code:

import numpy as np
import pandas as pd
dfd =         


        

Answer 1

Yet another way to count all the nans in a df:

num_nans = df.size - df.count().sum()

Timings:

import timeit

import numpy as np
import pandas as pd

df_scale = 100000
df = pd.DataFrame(
    [[1, np.nan, 100, 63], [2, np.nan, 101, 63], [2, 12, 102, 63],
     [2, 14, 102, 63], [2, 14, 102, 64], [1, np.nan, 200, 63]] * df_scale,
    columns=['group', 'value', 'value2', 'dummy'])

repeat = 3
numbers = 100

setup = """import pandas as pd
from __main__ import df
"""

def timer(statement, _setup=None):
    print (min(
        timeit.Timer(statement, setup=_setup or setup).repeat(
            repeat, numbers)))

timer('df.size - df.count().sum()')
timer('df.isna().sum().sum()')
timer('df.isnull().sum().sum()')

prints:

3.998805362999999
3.7503365439999996
3.689461442999999

so pretty much equivalent