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.
dataframe.notnull() function detects existing/ non-missing values in the dataframe. The function returns a boolean object having the same size as that of the object on which it is applied, indicating whether each individual value is a
na value or not. All of the non-missing values gets mapped to true and missing values get mapped to false.
Note : Characters such as empty strings ” or numpy.inf are not considered NA values. (unless you set pandas.options.mode.use_inf_as_na = True).
Returns : Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.
Example #1: Use
notnull() function to find all the non-missing value in the datafram.
Let’s use the
dataframe.notnull() function to find all the non-missing values in the dataframe.
As we can see in the output, all the non-missing values in the dataframe has been mapped to true. There is no false value as there is no missing value in the dataframe
Example #2: Use
notnull() function to find the non-missing values, when there are missing values in the dataframe.
Notice, the empty string also got mapped to true indicating that it is not a
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