What is np.nan in Python

The np.nan is a constant representing a missing or undefined numerical value in a NumPy array. It stands for “not a number” and has a float type. The np.nan is equivalent to NaN and NAN.

Syntax and Examples

numpy.nan

Example 1: Basic use of the np.nan

import numpy as np

myarr = np.array([1, 0, np.nan, 3])

print(myarr)

Output

[ 1. 0. nan 3.]

The important thing I would like you to take away from this is that all of our integers have been converted to floats, and that’s because NumPy has defined the NaN data type as a float.

Due to implicit upcasting, all our elements have been converted to float data types. 

NaN items also precede every other item when mathematical operations are used.

Example 2: nan value in the console

import numpy as np

print(np.nan)

Output

nan

Example 3: Comparing np.nan values in Python

You can use the double equal(==) operator to compare two nan values in Python.

import numpy as np

print(np.nan == np.nan)

Output

False

What is the np.isnan() function?

The np.isnan() is a NumPy library function that tests element-wise for NaN and returns the result as a boolean array. It takes an array-like input and an optional output parameter. For example, if you have an array x = [1, 2, np.nan], you can use the np.isnan(x) function to get [False, False, True].

import numpy as np

print(np.isnan(np.nan))

Output

True

FAQ

How is np.nan different from None?

The main difference between None and np.nan is that None is used in Python to represent the absence of a value, and np.nan is specifically used in NumPy to represent missing or null numerical values.

Unlike None, np.nan is a floating-point value, and its type is float.

How to check for np.nan values in a NumPy array?

You can use the np.isnan() function to check for np.nan values in a NumPy array. The np.isnan() function returns a Boolean array, with True values where the elements in the input array are np.nan and False values where the elements are not np.nan.

How to replace np.nan values in a NumPy array?

You can use the np.where() function to replace np.nan values with a specified value in a Numpy array.

import numpy as np

arr = np.array([1, 2, np.nan, 4, 5])
result = np.where(np.isnan(arr), 0, arr)
print(result)

Output

[1. 2. 0. 4. 5.]

How np.nan can be handled in computations?

When you use np.nan in numerical operations, it propagates through the computation and results in another np.nan value.

Conclusion

The np.nan stands for “Not a Number “, a special floating-point value used in Python’s NumPy library to represent missing or undefined values. The syntax is np.nan, which returns the nan value.

Related posts

Python isnan()

Numpy isfinite()