# Check for NaN in Pandas DataFrame

• Last Updated : 26 Aug, 2022

NaN stands for Not A Number and is one of the common ways to represent the missing value in the data. It is a special floating-point value and cannot be converted to any other type than float.

NaN value is one of the major problems in Data Analysis. It is very essential to deal with NaN in order to get the desired results.

## Check for NaN Value in Pandas DataFrame

The ways to check for NaN in Pandas DataFrame are as follows:

• Check for NaN with isnull().values.any() method
• Count the NaN Using isnull().sum() Method
• Check for NaN Using isnull().values.any() Method
• Count the NaN Using isnull().sum().sum() Method

Example:

## Python3

 `# importing libraries``import` `pandas as pd``import` `numpy as np`  `num ``=` `{``'Integers'``: [``10``, ``15``, ``30``, ``40``, ``55``, np.nan,``                    ``75``, np.nan, ``90``, ``150``, np.nan]}` `# Create the dataframe``df ``=` `pd.DataFrame(num, columns``=``[``'Integers'``])` `# Applying the method``check_nan ``=` `df[``'Integers'``].isnull().values.``any``()` `# printing the result``print``(check_nan)`

Output:

`True`

It is also possible to to get the exact positions where NaN values are present. We can do so by removing .values.any() from isnull().values.any() .

## Python3

 `df[``'Integers'``].isnull()`

Output:

```0     False
1     False
2     False
3     False
4     False
5      True
6     False
7      True
8     False
9     False
10     True
Name: Integers, dtype: bool```

Example:

## Python3

 `# importing libraries``import` `pandas as pd``import` `numpy as np`  `num ``=` `{``'Integers'``: [``10``, ``15``, ``30``, ``40``, ``55``, np.nan,``                    ``75``, np.nan, ``90``, ``150``, np.nan]}` `# Create the dataframe``df ``=` `pd.DataFrame(num, columns``=``[``'Integers'``])` `# applying the method``count_nan ``=` `df[``'Integers'``].isnull().``sum``()` `# printing the number of values present``# in the column``print``(``'Number of NaN values present: '` `+` `str``(count_nan))`

Output:

`Number of NaN values present: 3`

Example:

## Python3

 `# importing libraries``import` `pandas as pd``import` `numpy as np` `nums ``=` `{``'Integers_1'``: [``10``, ``15``, ``30``, ``40``, ``55``, np.nan, ``75``,``                       ``np.nan, ``90``, ``150``, np.nan],``        ``'Integers_2'``: [np.nan, ``21``, ``22``, ``23``, np.nan, ``24``, ``25``,``                       ``np.nan, ``26``, np.nan, np.nan]}` `# Create the dataframe``df ``=` `pd.DataFrame(nums, columns``=``[``'Integers_1'``, ``'Integers_2'``])` `# applying the method``nan_in_df ``=` `df.isnull().values.``any``()` `# Print the dataframe``print``(nan_in_df)`

Output:

`True`

To get the exact positions where NaN values are present, we can do so by removing .values.any() from isnull().values.any() .

Example:

## Python3

 `# importing libraries``import` `pandas as pd``import` `numpy as np` `nums ``=` `{``'Integers_1'``: [``10``, ``15``, ``30``, ``40``, ``55``, np.nan, ``75``,``                       ``np.nan, ``90``, ``150``, np.nan],``        ``'Integers_2'``: [np.nan, ``21``, ``22``, ``23``, np.nan, ``24``, ``25``,``                       ``np.nan, ``26``, np.nan, np.nan]}` `# Create the dataframe``df ``=` `pd.DataFrame(nums, columns``=``[``'Integers_1'``, ``'Integers_2'``])` `# applying the method``nan_in_df ``=` `df.isnull().``sum``().``sum``()` `# printing the number of values present in``# the whole dataframe``print``(``'Number of NaN values present: '` `+` `str``(nan_in_df))`

Output:

`Number of NaN values present: 3`

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