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.
 

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

  • Check for NaN under a single DataFrame column:
  • Count the NaN under a single DataFrame column:
  • Check for NaN under the whole DataFrame:
  • Count the NaN under the whole DataFrame:

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

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# 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)

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Output: 


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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() . 






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check_nan = df['Integers'].isnull()



















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Output: 


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Method 2: Using isnull().sum() Method
Example: 






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# 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)) 



















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Output:


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


Example: 






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# 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) 



















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Output: 


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


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






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# 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)) 



















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Output:


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