## Question

Asked By – hlin117

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.

**Now we will see solution for issue: How to check if any value is NaN in a Pandas DataFrame **

## Answer

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`

.

This question is answered By – S Anand

**This answer is collected from stackoverflow and reviewed by FixPython community admins, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0 **