How to Check if Cell Value is NaN in Pandas?

Check if Cell Value is NaN in Pandas

In this tutorial, we will learn how to check if a cell value is NaN (np.nan) in Pandas.

NaN means Not a Number. Pandas uses numpy.nan as NaN value.

To check if value at a specific location in Pandas is NaN or not, call numpy.isnan() function with the value passed as argument.

numpy.isnan(value)

If value equals numpy.nan, the expression returns True, else it returns False.

Examples

1. Check if a cell value is NaN in DataFrame

In this example, we will take a DataFrame with NaN values at some locations. We will check if values at specific locations are NaN or not.

import pandas as pd
import numpy as np

df = pd.DataFrame(
	[[np.nan, 72, 67],
	[23, 78, 62],
	[32, 74, np.nan],
	[np.nan, 54, 76]],
	columns=['a', 'b', 'c'])

value = df.at[0, 'a']  #nan
isNaN = np.isnan(value)
print("Is value at df[0, 'a'] NaN :", isNaN)

value = df.at[0, 'b']  #72
isNaN = np.isnan(value)
print("Is value at df[0, 'b'] NaN :", isNaN)

Output

Is value at df[0, 'a'] NaN : True
Is value at df[0, 'b'] NaN : False

2. Check if a cell value is NaN in DataFrame iteratively

In this example, we will take a DataFrame with NaN values at some locations. We will iterate over each of the cell values in this DataFrame and check if the value at this location is NaN or not.

import pandas as pd
import numpy as np

df = pd.DataFrame(
	[[np.nan, 72, 67],
	[23, 78, 62],
	[32, 74, np.nan],
	[np.nan, 54, 76]])

for i in range(df.shape[0]): #iterate over rows
    for j in range(df.shape[1]): #iterate over columns
        value = df.at[i, j] #get cell value
        print(np.isnan(value), end="\t")
    print()

Output

True    False   False
False   False   False
False   False   True
True    False   False

Summary

In this tutorial of Python Examples, we learned how to check if a specific cell value in Pandas is NaN or not using numpy.isnan() function.