Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about **How to check if any value is NaN in a Pandas DataFrame** **in Python**. So Here I am Explain to you all the possible Methods here.

Without wasting your time, Let’s start This Article.

Table of Contents

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

**How to check if any value is NaN in a Pandas DataFrame?**To find out which rows have NaNs in a specific column:

`nan_rows = df[df['name column'].isnull()]`

**check if any value is NaN in a Pandas DataFrame**To find out which rows have NaNs in a specific column:

`nan_rows = df[df['name column'].isnull()]`

## Method 1

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`

.

## Method 2

To find out which rows have NaNs in a specific column:

nan_rows = df[df['name column'].isnull()]

**Conclusion**

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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