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

Question

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.

Answer 1

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:

df.isnull().values.any() 

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()   perfplot.save(     "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.