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.
 

img

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: 

Python3



filter_none

edit
close

play_arrow

link
brightness_4
code

# 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)

chevron_right


Output: 

img

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

filter_none

edit
close

play_arrow

link
brightness_4
code

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

chevron_right


Output: 

img

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

Python3



filter_none

edit
close

play_arrow

link
brightness_4
code

# 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))

chevron_right


Output:

img

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

Example: 

Python3

filter_none

edit
close

play_arrow

link
brightness_4
code

# 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)

chevron_right


Output: 

img

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: 

Python3

filter_none

edit
close

play_arrow

link
brightness_4
code

# 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))

chevron_right


Output:

img




My Personal Notes arrow_drop_up


If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.