Python | Pandas dataframe.notna()

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.