search
Search
Publish
menu
menu search toc more_vert
Robocat
Guest 0reps
Thanks for the thanks!
close
Outline
Comments
Log in or sign up
Cancel
Post
account_circle
Profile
exit_to_app
Sign out
help Ask a question
Share on Twitter
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
icon_star
Doc Search
icon_star
Code Search Beta
SORRY NOTHING FOUND!
mic
Start speaking...
Voice search is only supported in Safari and Chrome.
Navigate to
A
A
share
thumb_up_alt
bookmark
arrow_backShare
Twitter
Facebook

Removing columns where some rows contain missing values (NaNs) in Pandas DataFrame

Programming
chevron_right
Python
chevron_right
Pandas
chevron_right
Cookbooks
chevron_right
DataFrame Cookbooks
chevron_right
Handling Missing Values
schedule Jul 1, 2022
Last updated
local_offer PandasPython
Tags
tocTable of Contents
expand_more

To remove columns where some rows contain missing values (NaN), use the DataFrame's dropna(~) method.

Example

Consider the following DataFrame:

df = pd.DataFrame({"A":[pd.np.NaN,2], "B":[3,4], "C":[5,pd.np.NaN]}, index=["a","b"])
df
A B C
a NaN 3 5
b 2.0 4 NaN

To remove columns where the value for row a is missing:

df.dropna(subset=["a"], axis=1)
B C
a 3 5.0
b 4 NaN

Here, axis=1 means that we are removing columns instead of rows.

To remove columns where the value for rows a or b is missing:

df.dropna(subset=["a","b"], axis=1)
B
a 3
b 4
NOTE

Click here for our full documentation on dropna(~).

robocat
Published by Isshin Inada
Edited by 0 others
Did you find this page useful?
thumb_up
thumb_down
Ask a question or leave a feedback...
thumb_up
0
thumb_down
0
chat_bubble_outline
0
settings
Enjoy our search
Hit / to insta-search docs and recipes!