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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.] ``` 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 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: [Pandas: How to Replace Empty Strings with NaN](https://www.statology.org/pandas-replace-empty-string-with-nan/) [Pandas: How to Replace NaN Values with String](https://www.statology.org/pandas-replace-nan-with-string/) Posted in [Programming](https://www.statology.org/category/programming/)  [Zach Bobbitt](https://www.statology.org/author/admin/) Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike. My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations. ## Post navigation [Prev How to Perform Bootstrapping in Python (With Example)](https://www.statology.org/bootstrapping-in-python/) [Next How to Use groupby() and transform() Functions in Pandas](https://www.statology.org/pandas-groupby-transform/) ## One Reply to “How to Remove NaN Values from NumPy Array (3 Methods)” 1.  **Sudheer** says: [September 7, 2022 at 4:42 am](https://www.statology.org/numpy-remove-nan/#comment-101872) Is there any way of removing NaN values without changing array size? [Reply](https://www.statology.org/numpy-remove-nan/#comment-101872) ### Leave a Reply [Cancel reply](https://www.statology.org/numpy-remove-nan/#respond) ## Search ## ABOUT STATOLOGY [](https://www.statology.org/about/)Statology makes learning statistics easy by explaining topics in simple and straightforward ways. 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*** 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.] ``` 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 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: [Pandas: How to Replace Empty Strings with NaN](https://www.statology.org/pandas-replace-empty-string-with-nan/) [Pandas: How to Replace NaN Values with String](https://www.statology.org/pandas-replace-nan-with-string/)  Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike. My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.