Examples · Try it in your browser! >>> import numpy as np >>> np.isnan(np.nan) True >>> np.isnan(np.inf) False >>> np.isnan([np.log(-1.),1.,np.log(0)]) array([ True, False, False]) Go BackOpen In Tab ·

May 2, 2026 - NumPy.isnan() method Example-3: >>> import numpy as np >>> np.isnan([np.log(-2.),2.,np.log(0)]) Output: array([ True, False, False]) Python - NumPy Code Editor: PREV : isinf() function NEXT : isnat() function  · Follow us on Facebook and ...

Examples · Try it in your browser! >>> import numpy as np >>> np.isnan(np.nan) True >>> np.isnan(np.inf) False >>> np.isnan([np.log(-1.),1.,np.log(0)]) array([ True, False, False]) Go BackOpen In Tab ·

March 8, 2024 - # Python Program illustrating # numpy.isnan() method import numpy as geek # Returns True/False value for each element b = geek.arange(20).reshape(5, 4) print("\n",b) print("\nIs NaN(Not a Number): \n", geek.isnan(b)) b = [[1j], [geek.nan]] ...

numpy.isnan(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'isnan'>

Examples · >>> import numpy as np >>> np.isnan(np.nan) True >>> np.isnan(np.inf) False >>> np.isnan([np.log(-1.),1.,np.log(0)]) array([ True, False, False]) On this page

February 9, 2025 - Here’s how: import numpy as np # Example array with NaN values arr = np.array([1, 2, np.nan, 4, np.nan, 6]) # Replace NaN values with a specific number, e.g., 0 arr_cleaned = np.where(np.isnan(arr), 0, arr) print("Array after replacing NaN ...

Examples · >>> import numpy as np >>> np.isnan(np.nan) True >>> np.isnan(np.inf) False >>> np.isnan([np.log(-1.),1.,np.log(0)]) array([ True, False, False]) On this page

March 8, 2024 - # Python Program illustrating # numpy.isnan() method import numpy as geek # Returns True/False value for each element b = geek.arange(20).reshape(5, 4) print("\n",b) print("\nIs NaN(Not a Number): \n", geek.isnan(b)) b = [[1j], [geek.nan]] ...

January 23, 2024 - What is nan in Python (float('nan'), math.nan, np.nan) a_nan = np.array([0, 1, np.nan, float('nan')]) print(a_nan) # [ 0. 1. nan nan] source: numpy_nan_replace.py · Since comparing NaN with == returns False, use np.isnan() to check if the value is NaN. numpy.isnan — NumPy v1.26 Manual ·

Examples · Try it in your browser! >>> import numpy as np >>> np.isnan(np.nan) True >>> np.isnan(np.inf) False >>> np.isnan([np.log(-1.),1.,np.log(0)]) array([ True, False, False]) Go BackOpen In Tab ·

The numpy.isnan() function is used to check for NaN in an array. It returns a Boolean array of the same shape as the input, indicating whether each element is NaN or not. This is particularly useful in data cleaning, preprocessing, and analysis where NaN values might indicate missing or erroneous ...

>>> np.isnan(np.nan) True >>> np.isnan(np.inf) False >>> np.isnan([np.log(-1.),1.,np.log(0)]) array([ True, False, False])

numpy.isnan is a function provided by the NumPy library that is used to test element-wise whether a given array contains NaN values. It returns a boolean array of the same shape as the input array, where each element is True if the corresponding ...

October 27, 2025 - Python’s numpy.isnan() takes an array as input and does an element-wise check for NaN values and returns the results as a boolean array highlighting the locations where an NaN was found.

Examples · >>> np.isnan(np.nan) True >>> np.isnan(np.inf) False >>> np.isnan([np.log(-1.),1.,np.log(0)]) array([ True, False, False]) On this page

September 30, 2026 - These answers cover the type and missing-value questions that come up when NumPy arrays move into pandas. np.nan is a floating-point NaN value used to mark an unavailable numeric entry. Use np.isnan to detect it in a NumPy array.

In the example below, we use the np.isnan() function to create a mask that identifies NaN values specifically in the 'age' field of a structured array − · import numpy as np # Creating a structured array with NaN values dtype = [('name', ...

Examples · >>> np.isnan(np.nan) True >>> np.isnan(np.inf) False >>> np.isnan([np.log(-1.),1.,np.log(0)]) array([ True, False, False]) On this page

This example demonstrates how numpy.isnan() can be used in conjunction with pandas and matplotlib to analyze and visualize missing data patterns in a real-world dataset. It showcases the function's utility in data preprocessing, visualization, and statistical analysis.