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.
# importing pandas as pdimport pandas as pd # Creating the dataframe df = pd.read_csv("nba.csv") # Print the dataframedf |
Lets use the isna() function to detect the missing values.
# detect the missing valuesdf.isna() |
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
# importing pandas as pdimport pandas as pd # Creating the series sr = pd.Series([12, 5, None, 5, None, 11]) # Print the seriessr |
Let’s detect all the missing values in the series.
# to detect the missing valuessr.isna() |
Output :
