Check for NaN in Pandas DataFrame

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