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.notna() 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.notna()
Returns : Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value
Example #1: Use notna() function to find all the non-missing value in the dataframe.
Python3
# importing pandas as pd import pandas as pd # Creating the first dataframe df = pd.DataFrame({"A":[14, 4, 5, 4, 1], "B":[5, 2, 54, 3, 2], "C":[20, 20, 7, 3, 8], "D":[14, 3, 6, 2, 6]}) # Print the dataframe df |
Let’s use the dataframe.notna() function to find all the non-missing values in the dataframe.
Python3
# find non-na values df.notna() |
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 notna() function to find the non-missing values, when there are missing values in the dataframe.
Python3
# importing pandas as pd import pandas as pd # Creating the dataframe df = pd.DataFrame({"A":[12, 4, 5, None, 1], "B":[7, 2, 54, 3, None], "C":[20, 16, 11, 3, 8], "D":[14, 3, None, 2, 6]}) # find non-missing values df.notna() |
Output :
As we can see in the output, cells which were having na values were mapped as false and all the cells which were having non-missing values were mapped as true.
