How to check if any value is NaN in a Pandas DataFrame


In Python Pandas, what's the best way to check whether a DataFrame has one (or more) NaN values?

I know about the function pd.isnan, but this returns a DataFrame of booleans for each element. This post right here doesn't exactly answer my question either.


jwilner's response is spot on. I was exploring to see if there's a faster option, since in my experience, summing flat arrays is (strangely) faster than counting. This code seems faster:


enter image description here

import numpy as np
import pandas as pd
import perfplot

def setup(n): df = pd.DataFrame(np.random.randn(n)) df[df > 0.9] = np.nan return df

def isnull_any(df): return df.isnull().any()

def isnull_values_sum(df): return df.isnull().values.sum() > 0

def isnull_sum(df): return df.isnull().sum() > 0

def isnull_values_any(df): return df.isnull().values.any() “out.png”, setup=setup, kernels=[isnull_any, isnull_values_sum, isnull_sum, isnull_values_any], n_range=[2 ** k for k in range(25)], )

df.isnull().sum().sum() is a bit slower, but of course, has additional information -- the number of NaNs.

Deleting DataFrame row in Pandas based on column value

Convert pandas dataframe to NumPy array