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curl -X POST \
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{"found_url":"https:\/\/www.w3resource.com\/python-exercises\/numpy\/python-numpy-exercise-110.php","crawl_time":1773430005,"first_indexed_time":1561520985,"http_code":200,"src_unparsed":"com,w3resource!www,\/python-exercises\/numpy\/python-numpy-exercise-110.php s443","src_root_hash":"10695350316657548561","history_drop_reason":null,"meta_title":"NumPy: Remove nan values from a given array - w3resource","meta_descriptions":["NumPy Array Object Exercises, Practice and Solution: Write a NumPy program to remove nan values from a given array."],"attrs_boilerpipe_text":"Last update on August 29 2025 12:45:45 (UTC\/GMT +8 hours)\nRemove nan Values from Array\nWrite a NumPy program to remove nan values from a given array.\nSample Solution\n:\nPython Code:\n# Importing the NumPy library and aliasing it as 'np'\nimport numpy as np\n\n# Creating a NumPy array 'x' containing integers and 'np.nan' (representing missing values)\nx = np.array([200, 300, np.nan, np.nan, np.nan, 700])\n\n# Creating a 2D NumPy array 'y' containing integers and 'np.nan' (representing missing values)\ny = np.array([[1, 2, 3], [np.nan, 0, np.nan], [6, 7, np.nan]])\n\n# Printing the original array 'x'\nprint(\"Original array:\")\nprint(x)\n\n# Removing 'np.nan' values from array 'x' and storing the result in 'result'\nresult = x[np.logical_not(np.isnan(x))]\n\n# Printing the array 'x' after removing 'np.nan' values\nprint(\"After removing nan values:\")\nprint(result)\n\n# Printing a new line\nprint(\"\\nOriginal array:\")\n\n# Printing the original array 'y'\nprint(y)\n\n# Removing 'np.nan' values from array 'y' and storing the result in 'result'\nresult = y[np.logical_not(np.isnan(y))]\n\n# Printing the array 'y' after removing 'np.nan' values\nprint(\"After removing nan values:\")\nprint(result)\nSample Output:\nOriginal array:\n[200. 300.  nan  nan  nan 700.]\nAfter removing nan values:\n[200. 300. 700.]\n\nOriginal array:\n[[ 1.  2.  3.]\n [nan  0. nan]\n [ 6.  7. nan]]\nAfter removing nan values:\n[1. 2. 3. 0. 6. 7.]\nExplanation:\nThe above code shows how to remove NaN (Not-a-Number) elements from two NumPy arrays: a 1D array 'x' and a 2D array 'y'.\nx = np.array([200, 300, np.nan, np.nan, np.nan ,700]): Create a 1D NumPy array 'x' containing some numbers and NaN values.\ny = np.array([[1, 2, 3], [np.nan, 0, np.nan] ,[6,7,np.nan]]): Create a 2D NumPy array 'y' containing some numbers and NaN values.\nresult = x[np.logical_not(np.isnan(x))]: Use np.isnan(x) to create a boolean array where 'True' represents a NaN value in 'x'. Then, use np.logical_not to invert this boolean array. Finally, use boolean indexing to extract elements from 'x' where the corresponding value in the inverted boolean array is 'True' (i.e., not NaN). Store the result in the variable 'result'.\nresult = y[np.logical_not(np.isnan(y))]: Repeat the same process for the 2D array 'y'. Note that 'y' is flattened before removing NaN values, so the resulting 'result' variable will be a 1D array.\nPictorial Presentation:\nFor more Practice: Solve these Related Problems:\nWrite a NumPy program to remove nan values from a 1D array using boolean indexing and np.isnan.\nCreate a function that flattens a 2D array, removes all nan entries, and returns a clean 1D array.\nTest the removal process on an array with mixed nan placements and verify that only valid numbers remain.\nImplement a solution that replaces nan values with the mean of the non-nan elements instead of removing them.\nGo to:\nNumPy Array Exercises Home ↩\nNumPy Exercises Home ↩\nPREV :\nConvert NumPy Array to Image\nNEXT :\nCreate Cartesian Product of Two Arrays\nPython-Numpy Code Editor:\nHave another way to solve this solution? 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np.nan ,700\\]): Create a 1D NumPy array 'x' containing some numbers and NaN values.\n\ny = np.array(\\[\\[1, 2, 3\\], \\[np.nan, 0, np.nan\\] ,\\[6,7,np.nan\\]\\]): Create a 2D NumPy array 'y' containing some numbers and NaN values.\n\nresult = x\\[np.logical\\_not(np.isnan(x))\\]: Use np.isnan(x) to create a boolean array where 'True' represents a NaN value in 'x'. Then, use np.logical\\_not to invert this boolean array. Finally, use boolean indexing to extract elements from 'x' where the corresponding value in the inverted boolean array is 'True' (i.e., not NaN). Store the result in the variable 'result'.\n\nresult = y\\[np.logical\\_not(np.isnan(y))\\]: Repeat the same process for the 2D array 'y'. Note that 'y' is flattened before removing NaN values, so the resulting 'result' variable will be a 1D array.\n\n**Pictorial Presentation:**\n\n![Python NumPy: Remove nan values from a given array](https:\/\/www.w3resource.com\/w3r_images\/python-numpy-image-exercise-110.svg)\n\nFor more Practice: Solve these Related Problems:\n\n- Write a NumPy program to remove nan values from a 1D array using boolean indexing and np.isnan.\n- Create a function that flattens a 2D array, removes all nan entries, and returns a clean 1D array.\n- Test the removal process on an array with mixed nan placements and verify that only valid numbers remain.\n- Implement a solution that replaces nan values with the mean of the non-nan elements instead of removing them.\n\nGo to:\n\n- [NumPy Array Exercises Home ↩](https:\/\/www.w3resource.com\/python-exercises\/numpy\/index-array.php)\n- [NumPy Exercises Home ↩](https:\/\/www.w3resource.com\/python-exercises\/numpy\/index.php)\n\n**PREV :** [Convert NumPy Array to Image](https:\/\/www.w3resource.com\/python-exercises\/numpy\/python-numpy-exercise-109.php)  \n **NEXT :**  [Create Cartesian Product of Two Arrays](https:\/\/www.w3resource.com\/python-exercises\/numpy\/python-numpy-exercise-111.php)\n\n**Python-Numpy Code Editor:**\n\n**Have another way to solve this solution? Contribute your code (and comments) through Disqus.**\n\n**What is the difficulty level of this exercise?**\n\nTest your Programming skills with w3resource's [quiz.](https:\/\/www.w3resource.com\/quizzes\/index.php)\n\n\n\nFollow us on [Facebook](https:\/\/www.facebook.com\/W3resource-103553425799800) and [Twitter](https:\/\/twitter.com\/w3resource) for latest update.\n***\n- [Daily Coding Challenges & Projects](https:\/\/www.w3resource.com\/daily-learning\/daily-learning.php)\n- [Weekly Trends and Language Statistics](https:\/\/www.w3resource.com\/weekly-trends-and-language-statistics.php)\n***\n***\nLoad Disqus Comments\n***\n\n[This work is licensed under a Creative Commons Attribution 4.0 International License.](http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/)\n\n©w3resource.com 2011-2026\n\n- [Read our Privacy Policy](https:\/\/www.w3resource.com\/privacy.php)\n- [About](https:\/\/www.w3resource.com\/about.php)\n- [Contact](https:\/\/www.w3resource.com\/contact.php)\n- [Feedback](https:\/\/www.w3resource.com\/feedback.php)\n- [Advertise](https:\/\/www.w3resource.com\/advertise.php)\n\n[**](https:\/\/www.w3resource.com\/python-exercises\/numpy\/python-numpy-exercise-110.php#h_one)","attrs_readable_markdown":"Last update on August 29 2025 12:45:45 (UTC\/GMT +8 hours)\n***\nRemove nan Values from Array\n\nWrite a NumPy program to remove nan values from a given array.\n\n**Sample Solution**:\n\n**Python Code:**\n```\n\n```\nSample Output:\n```\nOriginal array:\n[200. 300.  nan  nan  nan 700.]\nAfter removing nan values:\n[200. 300. 700.]\n\nOriginal array:\n[[ 1.  2.  3.]\n [nan  0. nan]\n [ 6.  7. nan]]\nAfter removing nan values:\n[1. 2. 3. 0. 6. 7.]\n```\n**Explanation:**\n\nThe above code shows how to remove NaN (Not-a-Number) elements from two NumPy arrays: a 1D array 'x' and a 2D array 'y'.\n\nx = np.array(\\[200, 300, np.nan, np.nan, np.nan ,700\\]): Create a 1D NumPy array 'x' containing some numbers and NaN values.\n\ny = np.array(\\[\\[1, 2, 3\\], \\[np.nan, 0, np.nan\\] ,\\[6,7,np.nan\\]\\]): Create a 2D NumPy array 'y' containing some numbers and NaN values.\n\nresult = x\\[np.logical\\_not(np.isnan(x))\\]: Use np.isnan(x) to create a boolean array where 'True' represents a NaN value in 'x'. Then, use np.logical\\_not to invert this boolean array. Finally, use boolean indexing to extract elements from 'x' where the corresponding value in the inverted boolean array is 'True' (i.e., not NaN). Store the result in the variable 'result'.\n\nresult = y\\[np.logical\\_not(np.isnan(y))\\]: Repeat the same process for the 2D array 'y'. Note that 'y' is flattened before removing NaN values, so the resulting 'result' variable will be a 1D array.\n\n**Pictorial Presentation:**\n\n![Python NumPy: Remove nan values from a given array](https:\/\/www.w3resource.com\/w3r_images\/python-numpy-image-exercise-110.svg)\n\nFor more Practice: Solve these Related Problems:\n\n- Write a NumPy program to remove nan values from a 1D array using boolean indexing and np.isnan.\n- Create a function that flattens a 2D array, removes all nan entries, and returns a clean 1D array.\n- Test the removal process on an array with mixed nan placements and verify that only valid numbers remain.\n- Implement a solution that replaces nan values with the mean of the non-nan elements instead of removing them.\n\nGo to:\n\n- [NumPy Array Exercises Home ↩](https:\/\/www.w3resource.com\/python-exercises\/numpy\/index-array.php)\n- [NumPy Exercises Home ↩](https:\/\/www.w3resource.com\/python-exercises\/numpy\/index.php)\n\n**PREV :** [Convert NumPy Array to Image](https:\/\/www.w3resource.com\/python-exercises\/numpy\/python-numpy-exercise-109.php)  \n **NEXT :**  [Create Cartesian Product of Two Arrays](https:\/\/www.w3resource.com\/python-exercises\/numpy\/python-numpy-exercise-111.php)\n\n**Python-Numpy Code Editor:**\n\n**Have another way to solve this solution? Contribute your code (and comments) through Disqus.**","meta_canonical":null}

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Boilerpipe Text	Last update on August 29 2025 12:45:45 (UTC/GMT +8 hours) Remove nan Values from Array Write a NumPy program to remove nan values from a given array. Sample Solution : Python Code: # Importing the NumPy library and aliasing it as 'np' import numpy as np # Creating a NumPy array 'x' containing integers and 'np.nan' (representing missing values) x = np.array([200, 300, np.nan, np.nan, np.nan, 700]) # Creating a 2D NumPy array 'y' containing integers and 'np.nan' (representing missing values) y = np.array([[1, 2, 3], [np.nan, 0, np.nan], [6, 7, np.nan]]) # Printing the original array 'x' print("Original array:") print(x) # Removing 'np.nan' values from array 'x' and storing the result in 'result' result = x[np.logical_not(np.isnan(x))] # Printing the array 'x' after removing 'np.nan' values print("After removing nan values:") print(result) # Printing a new line print("\nOriginal array:") # Printing the original array 'y' print(y) # Removing 'np.nan' values from array 'y' and storing the result in 'result' result = y[np.logical_not(np.isnan(y))] # Printing the array 'y' after removing 'np.nan' values print("After removing nan values:") print(result) Sample Output: Original array: [200. 300. nan nan nan 700.] After removing nan values: [200. 300. 700.] Original array: [[ 1. 2. 3.] [nan 0. nan] [ 6. 7. nan]] After removing nan values: [1. 2. 3. 0. 6. 7.] Explanation: The above code shows how to remove NaN (Not-a-Number) elements from two NumPy arrays: a 1D array 'x' and a 2D array 'y'. x = np.array([200, 300, np.nan, np.nan, np.nan ,700]): Create a 1D NumPy array 'x' containing some numbers and NaN values. y = np.array([[1, 2, 3], [np.nan, 0, np.nan] ,[6,7,np.nan]]): Create a 2D NumPy array 'y' containing some numbers and NaN values. result = x[np.logical_not(np.isnan(x))]: Use np.isnan(x) to create a boolean array where 'True' represents a NaN value in 'x'. Then, use np.logical_not to invert this boolean array. Finally, use boolean indexing to extract elements from 'x' where the corresponding value in the inverted boolean array is 'True' (i.e., not NaN). Store the result in the variable 'result'. result = y[np.logical_not(np.isnan(y))]: Repeat the same process for the 2D array 'y'. Note that 'y' is flattened before removing NaN values, so the resulting 'result' variable will be a 1D array. Pictorial Presentation: For more Practice: Solve these Related Problems: Write a NumPy program to remove nan values from a 1D array using boolean indexing and np.isnan. Create a function that flattens a 2D array, removes all nan entries, and returns a clean 1D array. Test the removal process on an array with mixed nan placements and verify that only valid numbers remain. Implement a solution that replaces nan values with the mean of the non-nan elements instead of removing them. Go to: NumPy Array Exercises Home ↩ NumPy Exercises Home ↩ PREV : Convert NumPy Array to Image NEXT : Create Cartesian Product of Two Arrays Python-Numpy Code Editor: Have another way to solve this solution? Contribute your code (and comments) through Disqus.
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Sample Solution: Python Code: ``` ``` Sample Output: ``` Original array: [200. 300. nan nan nan 700.] After removing nan values: [200. 300. 700.] Original array: [[ 1. 2. 3.] [nan 0. nan] [ 6. 7. nan]] After removing nan values: [1. 2. 3. 0. 6. 7.] ``` Explanation: The above code shows how to remove NaN (Not-a-Number) elements from two NumPy arrays: a 1D array 'x' and a 2D array 'y'. x = np.array(\[200, 300, np.nan, np.nan, np.nan ,700\]): Create a 1D NumPy array 'x' containing some numbers and NaN values. y = np.array(\[\[1, 2, 3\], \[np.nan, 0, np.nan\] ,\[6,7,np.nan\]\]): Create a 2D NumPy array 'y' containing some numbers and NaN values. result = x\[np.logical\_not(np.isnan(x))\]: Use np.isnan(x) to create a boolean array where 'True' represents a NaN value in 'x'. Then, use np.logical\_not to invert this boolean array. Finally, use boolean indexing to extract elements from 'x' where the corresponding value in the inverted boolean array is 'True' (i.e., not NaN). Store the result in the variable 'result'. result = y\[np.logical\_not(np.isnan(y))\]: Repeat the same process for the 2D array 'y'. Note that 'y' is flattened before removing NaN values, so the resulting 'result' variable will be a 1D array. Pictorial Presentation: ![Python NumPy: Remove nan values from a given array](https://www.w3resource.com/w3r_images/python-numpy-image-exercise-110.svg) For more Practice: Solve these Related Problems: - Write a NumPy program to remove nan values from a 1D array using boolean indexing and np.isnan. - Create a function that flattens a 2D array, removes all nan entries, and returns a clean 1D array. - Test the removal process on an array with mixed nan placements and verify that only valid numbers remain. - Implement a solution that replaces nan values with the mean of the non-nan elements instead of removing them. 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Readable Markdown	Last update on August 29 2025 12:45:45 (UTC/GMT +8 hours) * Remove nan Values from Array Write a NumPy program to remove nan values from a given array. Sample Solution: Python Code: ``` ``` Sample Output: ``` Original array: [200. 300. nan nan nan 700.] After removing nan values: [200. 300. 700.] Original array: [[ 1. 2. 3.] [nan 0. nan] [ 6. 7. nan]] After removing nan values: [1. 2. 3. 0. 6. 7.] ``` Explanation: The above code shows how to remove NaN (Not-a-Number) elements from two NumPy arrays: a 1D array 'x' and a 2D array 'y'. x = np.array(\[200, 300, np.nan, np.nan, np.nan ,700\]): Create a 1D NumPy array 'x' containing some numbers and NaN values. y = np.array(\[\[1, 2, 3\], \[np.nan, 0, np.nan\] ,\[6,7,np.nan\]\]): Create a 2D NumPy array 'y' containing some numbers and NaN values. result = x\[np.logical\_not(np.isnan(x))\]: Use np.isnan(x) to create a boolean array where 'True' represents a NaN value in 'x'. Then, use np.logical\_not to invert this boolean array. Finally, use boolean indexing to extract elements from 'x' where the corresponding value in the inverted boolean array is 'True' (i.e., not NaN). Store the result in the variable 'result'. result = y\[np.logical\_not(np.isnan(y))\]: Repeat the same process for the 2D array 'y'. Note that 'y' is flattened before removing NaN values, so the resulting 'result' variable will be a 1D array. Pictorial Presentation: ![Python NumPy: Remove nan values from a given array](https://www.w3resource.com/w3r_images/python-numpy-image-exercise-110.svg) For more Practice: Solve these Related Problems: - Write a NumPy program to remove nan values from a 1D array using boolean indexing and np.isnan. - Create a function that flattens a 2D array, removes all nan entries, and returns a clean 1D array. - Test the removal process on an array with mixed nan placements and verify that only valid numbers remain. - Implement a solution that replaces nan values with the mean of the non-nan elements instead of removing them. Go to: - [NumPy Array Exercises Home ↩](https://www.w3resource.com/python-exercises/numpy/index-array.php) - [NumPy Exercises Home ↩](https://www.w3resource.com/python-exercises/numpy/index.php) PREV : [Convert NumPy Array to Image](https://www.w3resource.com/python-exercises/numpy/python-numpy-exercise-109.php) NEXT : [Create Cartesian Product of Two Arrays](https://www.w3resource.com/python-exercises/numpy/python-numpy-exercise-111.php) Python-Numpy Code Editor: Have another way to solve this solution? Contribute your code (and comments) through Disqus.**
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