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.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).
Syntax: DataFrame.notnull()
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 dataframe.
Python3
# importing pandas as pdimport pandas as pd# Creating the first dataframedf = pd.DataFrame({"A":[14, 4, 5, 4, 1], "B":["Sam", "olivia", "terica", "megan", "amanda"], "C":[20 + 5j, 20 + 3j, 7, 3, 8], "D":[14, 3, 6, 2, 6]})# Print the dataframedf |
Let’s use the dataframe.notnull() function to find all the non-missing values in the dataframe.
Python3
# find non-na valuesdf.notnull() |
Output :
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.
Python3
# importing pandas as pdimport pandas as pd# Creating the dataframedf = pd.DataFrame({"A":["Sandy", "alex", "brook", "kelly", np.nan], "B":[np.nan, "olivia", "terica", "", "amanda"], "C":[20 + 5j, 20 + 3j, 7, None, 8], "D":[14.8, 3, None, 2.3, 6]})# find non-missing valuesdf.notnull() |
Output :
Notice, the empty string also got mapped to true indicating that it is not a NaN value.
