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By [stats writer](https://scales.arabpsychology.com/author/stats/ "View all posts by stats writer") / June 28, 2024 Table of Contents - [How can NaN values be removed from a NumPy array?](https://scales.arabpsychology.com/stats/how-can-nan-values-be-removed-from-a-numpy-array/#How_can_NaN_values_be_removed_from_a_NumPy_array) - - [Example 1: Remove NaN Values Using isnan()](https://scales.arabpsychology.com/stats/how-can-nan-values-be-removed-from-a-numpy-array/#Example_1_Remove_NaN_Values_Using_isnan) - [Example 2: Remove NaN Values Using isfinite()](https://scales.arabpsychology.com/stats/how-can-nan-values-be-removed-from-a-numpy-array/#Example_2_Remove_NaN_Values_Using_isfinite) - [Example 3: Remove NaN Values Using logical\_not()](https://scales.arabpsychology.com/stats/how-can-nan-values-be-removed-from-a-numpy-array/#Example_3_Remove_NaN_Values_Using_logical_not) - [Additional Resources](https://scales.arabpsychology.com/stats/how-can-nan-values-be-removed-from-a-numpy-array/#Additional_Resources) NaN (Not a Number) values can occur in a NumPy array when there is missing or invalid data. These values can affect the accuracy of calculations and analysis. To remove these NaN values, the NumPy library offers several methods. One approach is to use the np.isnan() function to identify the indices of the array that contain the NaN values. These indices can then be used to filter out the NaN values using the np.delete() function. Another method is to replace the NaN values with a specified value using the np.nan\_to\_num() function. Additionally, the np.where() function can be used to replace the NaN values with a specific value or interpolate them based on neighboring values. These methods allow for the removal of NaN values from a NumPy array, ensuring the accuracy of data analysis. # Remove NaN Values from NumPy Array (3 Methods) *** You can use the following methods to remove NaN values from a NumPy array: **Method 1: Use isnan()** ``` new_data = data[~np.isnan(data)] ``` **Method 2: Use isfinite()** ``` new_data = data[np.isfinite(data)] ``` **Method 3: Use logical\_not()** ``` new_data = data[np.logical_not(np.isnan(data))] ``` Each of these methods produce the same result, but the first method is the shortest to type out so it tends to be used most often. The following examples show how to use each method in practice. ### **Example 1: Remove NaN Values Using isnan()** The following code shows how to remove NaN values from a NumPy array by using the **isnan()** function: ``` import numpy as np #create array of data data = np.array([4, np.nan, 6, np.nan, 10, 11, 14, 19, 22]) #define new array of data with nan values removed new_data = data[~np.isnan(data)] #view new array print(new_data) [ 4. 6. 10. 11. 14. 19. 22.] ``` Notice that the two NaN values have been successfully removed from the NumPy array. This method simply keeps all of the elements in the array that are not (~) NaN values. ### **Example 2: Remove NaN Values Using isfinite()** The following code shows how to remove NaN values from a NumPy array by using the **isfinite()** function: ``` import numpy as np #create array of data data = np.array([4, np.nan, 6, np.nan, 10, 11, 14, 19, 22]) #define new array of data with nan values removed new_data = data[np.isfinite(data)] #view new array print(new_data) [ 4. 6. 10. 11. 14. 19. 22.] ``` This method simply keeps all of the elements in the array that are finite values. Since NaN values are not finite, they’re removed from the array. ### **Example 3: Remove NaN Values Using logical\_not()** The following code shows how to remove NaN values from a NumPy array by using the **logical\_not()** function: ``` import numpy as np #create array of data data = np.array([4, np.nan, 6, np.nan, 10, 11, 14, 19, 22]) #define new array of data with nan values removed new_data = data[np.logical_not(np.isnan(data))] #view new array print(new_data) [ 4. 6. 10. 11. 14. 19. 22.] ``` Notice that the two NaN values have been successfully removed from the NumPy array. While this method is equivalent to the previous two, it requires more typing so it’s not used as often. ### **Additional Resources** The following tutorials explain how to perform other common operations in Python: ### Cite this article APA MLA CHICAGO HARVARD IEEE AMA stats writer (2024). *How can NaN values be removed from a NumPy array?*. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/?p=155983 stats writer. "How can NaN values be removed from a NumPy array?." *PSYCHOLOGICAL SCALES*, 28 Jun. 2024, https://scales.arabpsychology.com/?p=155983. stats writer. "How can NaN values be removed from a NumPy array?." PSYCHOLOGICAL SCALES, 2024. https://scales.arabpsychology.com/?p=155983. stats writer (2024) 'How can NaN values be removed from a NumPy array?', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/?p=155983. \[1\] stats writer, "How can NaN values be removed from a NumPy array?," *PSYCHOLOGICAL SCALES*, vol. X, no. Y, ص Z-Z, June, 2024. stats writer. How can NaN values be removed from a NumPy array?. *PSYCHOLOGICAL SCALES*. 2024;vol(issue):pages. Download Citation (.RIS) Download BibTeX (.BIB) [Download Post (.PDF)](https://scales.arabpsychology.com/stats/how-can-nan-values-be-removed-from-a-numpy-array/?wpa_download_pdf=1) ### Share ## Related terms: 1. 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NaN (Not a Number) values can occur in a NumPy array when there is missing or invalid data. These values can affect the accuracy of calculations and analysis. To remove these NaN values, the NumPy library offers several methods. One approach is to use the np.isnan() function to identify the indices of the array that contain the NaN values. These indices can then be used to filter out the NaN values using the np.delete() function. Another method is to replace the NaN values with a specified value using the np.nan\_to\_num() function. Additionally, the np.where() function can be used to replace the NaN values with a specific value or interpolate them based on neighboring values. These methods allow for the removal of NaN values from a NumPy array, ensuring the accuracy of data analysis. *** You can use the following methods to remove NaN values from a NumPy array: **Method 1: Use isnan()** ``` new_data = data[~np.isnan(data)] ``` **Method 2: Use isfinite()** ``` new_data = data[np.isfinite(data)] ``` **Method 3: Use logical\_not()** ``` new_data = data[np.logical_not(np.isnan(data))] ``` Each of these methods produce the same result, but the first method is the shortest to type out so it tends to be used most often. The following examples show how to use each method in practice. ### **Example 1: Remove NaN Values Using isnan()** The following code shows how to remove NaN values from a NumPy array by using the **isnan()** function: ``` import numpy as np #create array of data data = np.array([4, np.nan, 6, np.nan, 10, 11, 14, 19, 22]) #define new array of data with nan values removed new_data = data[~np.isnan(data)] #view new array print(new_data) [ 4. 6. 10. 11. 14. 19. 22.] ``` Notice that the two NaN values have been successfully removed from the NumPy array. This method simply keeps all of the elements in the array that are not (~) NaN values. ### **Example 2: Remove NaN Values Using isfinite()** The following code shows how to remove NaN values from a NumPy array by using the **isfinite()** function: ``` import numpy as np #create array of data data = np.array([4, np.nan, 6, np.nan, 10, 11, 14, 19, 22]) #define new array of data with nan values removed new_data = data[np.isfinite(data)] #view new array print(new_data) [ 4. 6. 10. 11. 14. 19. 22.] ``` This method simply keeps all of the elements in the array that are finite values. Since NaN values are not finite, they’re removed from the array. ### **Example 3: Remove NaN Values Using logical\_not()** The following code shows how to remove NaN values from a NumPy array by using the **logical\_not()** function: ``` import numpy as np #create array of data data = np.array([4, np.nan, 6, np.nan, 10, 11, 14, 19, 22]) #define new array of data with nan values removed new_data = data[np.logical_not(np.isnan(data))] #view new array print(new_data) [ 4. 6. 10. 11. 14. 19. 22.] ``` Notice that the two NaN values have been successfully removed from the NumPy array. While this method is equivalent to the previous two, it requires more typing so it’s not used as often. ### **Additional Resources** The following tutorials explain how to perform other common operations in Python: ### Cite this article APAMLACHICAGOHARVARDIEEEAMA stats writer (2024). *How can NaN values be removed from a NumPy array?*. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/?p=155983 stats writer. "How can NaN values be removed from a NumPy array?." *PSYCHOLOGICAL SCALES*, 28 Jun. 2024, https://scales.arabpsychology.com/?p=155983. stats writer. "How can NaN values be removed from a NumPy array?." PSYCHOLOGICAL SCALES, 2024. https://scales.arabpsychology.com/?p=155983. stats writer (2024) 'How can NaN values be removed from a NumPy array?', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/?p=155983. \[1\] stats writer, "How can NaN values be removed from a NumPy array?," *PSYCHOLOGICAL SCALES*, vol. X, no. Y, ص Z-Z, June, 2024. stats writer. How can NaN values be removed from a NumPy array?. *PSYCHOLOGICAL SCALES*. 2024;vol(issue):pages.