Check for NaN in Pandas DataFrame (original) (raw)

Last Updated : 30 Jan, 2023

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().sum().any() Method
• Count the NaN Using isnull().sum().sum() Method

Method 1: Using isnull().values.any() 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 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

Method 2: Using isnull().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

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

Method 3: Using isnull().sum().any() Method

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().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 .sum().any() from isnull().sum().any() .

Method 4: Using isnull().sum().sum() Method

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: 8