Python | Pandas dataframe.notna()

27 Aug, 2021
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 pdimport pandas as pd # Creating the first dataframedf = 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 dataframedf
Let’s use the dataframe.notna() function to find all the non-missing values in the dataframe.  
Python3
# find non-na valuesdf.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 pdimport pandas as pd # Creating the dataframedf = 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 valuesdf.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.  
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anikakapoor
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