menu
home
About
 store
Store
 paid
Pricing
login
Log in
team_dashboard
Dashboard
near_me
Linear Algebra
casino
Prob and Stats
function
Other math topics
smart_toy
Machine Learning
Pandas
NumPy
Matplotlib
PySpark
MySQL
Dagster (NEW)
SkyTowner
BETA
search
Search
Login
Unlock 100+ guides
menu
menu
web
SkyTowner
search toc
close
history_edu
Blog
 help
Help
Outline
Example
Comments
Log in or sign up
Cancel
Post
account_circle
Profile
exit_to_app
Sign out
What does this mean?
Why is this true?
Give me some examples!
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.
fullscreen_exit
Shrink
north_east
Navigate to
Pandas
655 guides
keyboard_arrow_down
near_me
Linear Algebra
54 guides
 casino
Prob and Stats
38 guides
 smart_toy
Machine Learning
36 guides
 function
Other math topics
10 guides
Dagster
11 guides
Pandas
655 guides
NumPy
319 guides
Matplotlib
83 guides
PySpark
147 guides
MySQL
295 guides
chevron_leftHandling Missing Values
Adding missing dates in Datetime IndexChecking if a certain value in a DataFrame is NaNChecking if a DataFrame contains any missing valuesConverting a column with missing values to integer typeCounting non-missing valuesCounting number of rows with missing valuesCounting the number of NaN in each row of a DataFrameCounting number of NaN values in each column of a DataFrameCounting the total number of NaN values of a DataFrameFilling missing values using another columnFilling missing values with the mean of the columnFinding columns with missing valuesGetting integer indexes of rows with NaNGetting rows with missing valuesGetting rows with missing values in certain columnsGetting index of rows with missing values (NaNs)Getting index of rows without missing valuesMapping NaN values to 0 and non-NaN values to 1Mapping NaN values to False and non-NaN values to TrueRemoving columns where some rows contain missing valuesRemoving rows from a DataFrame with missing valuesReplacing all NaN values of a DataFrameReplacing all NaN values with zeros in a DataFrameReplacing missing valuesReplacing missing values with constantsReplacing NaN with blank stringReplacing NaNs for certain columnsReplacing NaNs with preceding valuesReplacing values with NaNsUsing interpolation to fill missing values
check_circle
Mark as learned
thumb_up
0
thumb_down
0
chat_bubble_outline
0
Comment
auto_stories Bi-column layout
settings

Checking if a certain value in a DataFrame is missing (NaN) in Pandas

schedule Aug 10, 2023
Last updated
local_offer
Python●Pandas
Tags
tocTable of Contents
expand_more
Example
mode_heat
Master the mathematics behind data science with 100+ top-tier guides
Start your free 7-days trial now!

To check if a certain value in a DataFrame is NaN, use the methods isna() and at.

Example

Consider the following DataFrame:

df = pd.DataFrame({"A":[2,3],"B":[4,None]}, index=["a","b"])
df
A B
a 2 4.0
b 3 NaN

To check if the entry at [bB] is missing:

pd.isna(df.at["b","B"])
True
robocat
Published by Isshin Inada
Edited by 0 others
Did you find this page useful?
thumb_up
thumb_down
Comment
Citation
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!
Navigation
Home Blog
Contact us
Resources
Python Pandas MySQL Beautiful Soup Matplotlib NumPy PySpark
Community
Join our Discord
 mail
Join our newsletter for updates on new comprehensive DS/ML guides
Terms
|
Privacy