Pandas DataFrame notnull() Method

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. 

Syntax: DataFrame.notnull()

The method returns a boolean mask of the same shape as the DataFrame, where each element is either True (indicating the value is not NA) or False (indicating the value is NA).

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).

Use Cases for notnull() Method in Pandas

Detecting Non-Missing Values in a DataFrame

To demonstrate the use of notnull(), let’s create a sample DataFrame and check for non-missing values:

# importing pandas as pd
import pandas as pd

# Creating the first dataframe 
df = 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 dataframe
df

Now, let’s use the dataframe.notnull() function to find all the non-missing values in the dataframe. 

# find non-na values
df.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.

Handling Missing Values

Let’s now work with a DataFrame that includes missing values:

# importing pandas as pd
import pandas as pd

# Creating the dataframe 
df = 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 values
df.notnull()

Output : 

The resulting mask indicates non-missing values as True.

Here, notice that:

• The empty string ("") is considered a non-NA value and is marked as True.
• np.nan, None, and other missing values are correctly identified and marked as False.

The notnull() method is a powerful function for identifying non-missing values in your pandas DataFrame.