A[A==NDV]=numpy.nan
A==NDV will produce a boolean array that can be used as an index for A
Answer from Paul on Stack Overflow Top answer 1 of 2
82
A[A==NDV]=numpy.nan
A==NDV will produce a boolean array that can be used as an index for A
2 of 2
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You can also use np.where to replace a number with NaN.
arr = np.where(arr==NDV, np.nan, arr)
For example, the following result can be obtained via
arr = np.array([[1, 1, 2], [2, 0, 1]])
arr = np.where(arr==1, np.nan, arr)
This creates a new copy (unlike A[A==NDV]=np.nan) but in some cases that could be useful. For example, if the array was initially an int dtype, it will have to converted into a float array anyway (because replacing values with NaN won't work otherwise) and np.where can handle that.
Note.nkmk.me
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What is nan in Python (float('nan'), math.nan, np.nan) | note.nkmk.me
May 9, 2023 - In addition, nan is defined in math (standard library) and NumPy; both NaN and NAN are defined as aliases in NumPy. math.nan — Mathematical functions — Python 3.11.3 documentation
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Is np.nan a float?
Yes. np.nan is a floating-point value, so including it in a NumPy array of integers causes the array to use a floating-point dtype.
askpython.com
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Understanding np.nan in NumPy and Pandas - AskPython
Should I use np.isnan or pandas isna?
Use np.isnan for numeric NumPy values. Use pandas isna or a Series or DataFrame isna method when the data may contain pandas missing markers, None or NaT.
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Understanding np.nan in NumPy and Pandas - AskPython
What does np.nan do?
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.
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Understanding np.nan in NumPy and Pandas - AskPython
NumPy
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Miscellaneous — NumPy v2.5 Manual
>>> myarr = np.array([1., 0., np.nan, 3.]) >>> np.nonzero(myarr == np.nan) (array([], dtype=int64),)
DataCamp
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Python NaN: 4 Ways to Check for Missing Values in Python | DataCamp
February 15, 2024 - This article aims to equip you ... the approach largely depends on the data structure you're working with. NumPy's isnan() function is ideal for identifying NaNs in numeric arrays or single values, offering a straightforward and efficient solution....
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NumPy NaN | Working of NumPy NaN in Python with Examples
April 19, 2023 - In Python, NumPy NAN stands for not a number and is defined as a substitute for declaring value which are numerical values that are missing values in an array as NumPy is used to deal with arrays in Python and this can be initialized using numpy.nan ...
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Understanding np.nan in NumPy and Pandas - AskPython
September 30, 2026 - Keep the environment active while running the Python examples. The commands use the package releases installed when you create it. NumPy promotes this array to float64 because np.nan is a floating-point value and the other entries are integers.
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NumPy: Replace NaN (np.nan) using np.nan_to_num() and np.isnan() | note.nkmk.me
January 23, 2024 - You can use np.nan_to_num() to replace NaN.
NumPy
numpy.org › doc › stable › reference › constants.html
Constants — NumPy v2.5 Manual
>>> import numpy as np >>> np.nan nan >>> np.log(-1) np.float64(nan) >>> np.log([-1, 1, 2]) array([ nan, 0.
Interactive Chaos
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numpy.nan | Interactive Chaos
January 16, 2021 - Python scenarios · Full name · numpy.nan · Library · NumPy · Syntax · numpy.nan · Description · numpy.nan is a special value from the NumPy library that represents the null value. Parameters · numpy.nan does not accept arguments. Result · numpy.nan returns a value of type float.
Appdividend
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Numpy.nan (np.nan) in Python
July 11, 2025 - The np.nan is a special floating-point constant defined in the NumPy library that represents "Not a Number" (NaN).
NumPy
numpy.org › doc › 2.1 › reference › generated › numpy.isnan.html
numpy.isnan — NumPy v2.1 Manual
>>> 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])
Index.dev
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4 Easy Ways to Check for NaN Values in Python (+Examples)
In Python, NaN can be represented using different methods: Using the built-in float function: nan_value = float('nan') Using NumPy: import numpy as np nan_value = np.nan
Python Guides
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How NumPy Create NaN Array in Python?
May 16, 2025 - Read ValueError: setting an array element with a sequence error in Python · One of the simplest ways to create a NaN array is by using the np.full() function combined with NumPy’s built-in np.nan constant in Python.
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33. Dealing with NaN | Numerical Programming | python-course.eu
February 3, 2025 - Warning: Do not perform comparison between "NaN" values or "Nan" values and regular numbers. A simple or simplified reasoning is this: Two things are "not a number", so they can be anything but most probably not the same.
Saturn Cloud
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Efficiently Checking if Arbitrary Object is NaN in Python Numpy and Pandas | Saturn Cloud Blog
May 1, 2026 - In NumPy, you can use the isnan() function to check for NaN values in an array.
Turing
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How to Check for NaN Values in Python?[With Examples]
PythonCopy · This method returns True if the value is NaN, and False otherwise. If you're working with arrays or large datasets, the NumPy library provides a convenient function called isnan() to check for NaN in Python values.
NumPy
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numpy.nan_to_num — NumPy v2.5 Manual
Replace NaN with zero and infinity with large finite numbers (default behaviour) or with the numbers defined by the user using the nan, posinf and/or neginf keywords.
NumPy
numpy.org › doc › stable › reference › generated › numpy.isnan.html
numpy.isnan — NumPy v2.5 Manual
>>> 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])
Reintech
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Handling Missing Data and NaN Values in NumPy - Reintech
June 13, 2026 - NumPy uses IEEE 754 floating-point special value np.nan to represent missing or undefined numerical data. Unlike None in Python, NaN is a float type that propagates through calculations—any operation involving NaN typically returns NaN.