Python | Pandas dataframe.isna()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Pandas dataframe.isna() function is used to detect missing values. It return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings ” or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True).

Syntax: DataFrame.isna()

Returns : Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.

For link to the CSV file used in the example, click here



Example #1: Use isna() function to detect the missing values in a dataframe.

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing pandas as pd
import pandas as pd
  
# Creating the dataframe 
df = pd.read_csv("nba.csv")
  
# Print the dataframe
df

chevron_right


Lets use the isna() function to detect the missing values.

filter_none

edit
close

play_arrow

link
brightness_4
code

# detect the missing values
df.isna()

chevron_right


Output :

In the output, cells corresponding to the missing values contains true value else false.
 
Example #2: Use isna() function to detect missing values in a pandas series object

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing pandas as pd
import pandas as pd
  
# Creating the series 
sr = pd.Series([12, 5, None, 5, None, 11])
  
# Print the series
sr

chevron_right


Let’s detect all the missing values in the series.

filter_none

edit
close

play_arrow

link
brightness_4
code

# to detect the missing values
sr.isna()

chevron_right


Output :

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.




My Personal Notes arrow_drop_up

Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.