search
Search
Map of Data Science
Guest 0reps
exit_to_appLog out
Map of data science
Thanks for the thanks!
close
account_circle
Profile
exit_to_app
Sign out
search
keyboard_voice
close
Searching Tips
Search for a recipe:
"Creating a table in MySQL"
Search for an API documentation: "@append"
Search for code: "!dataframe"
Apply a tag filter: "#python"
Useful Shortcuts
/ to open search panel
Esc to close search panel
to navigate between search results
d to clear all current filters
Enter to expand content preview Doc Search Code Search Beta SORRY NOTHING FOUND!
mic
Start speaking... Voice search is only supported in Safari and Chrome.
Shrink
Navigate to
A
A
brightness_medium
share
arrow_backShare Twitter Facebook

# Removing rows containing NaN in a NumPy array

NumPy
chevron_right
Cookbooks
schedule Jul 1, 2022
Last updated
local_offer PythonNumPy
Tags
expand_more
map
Check out the interactive map of data science

To remove rows containing `NaN` in a NumPy array, we can use a combination of the `isnan(~)` and `any(~)` methods.

# Example

Consider the following array:

``` np.array([[1,2,np.nan], [4,5,6]]) array([[ 1., 2., nan], [ 4., 5., 6.]]) ```

To remove rows containing `NaN`:

``` a = np.array([[1,2,np.nan], [4,5,6]])a[~np.isnan(a).any(axis=1)] array([[4., 5., 6.]]) ```

## Explanation

In the above code snippet, first we are checking each element in array `a` for `np.nan` using `isnan(~)`:

``` np.isnan(a) array([[False, False, True], [False, False, False]]) ```

Next `any(axis=1)` returns `True` if at least one element in each row evaluates to `True`:

``` np.isnan(a).any(axis=1) array([ True, False]) ```

Finally, the bitwise NOT (`~`) operator inverts the `True` and `False` values:

``` ~np.isnan(a).any(axis=1) array([False, True]) ```

Now we apply this boolean mask to the original array `a` to return only the rows not containing `NaN`:

``` a[~np.isnan(a).any(axis=1)] array([[4., 5., 6.]]) ```