The notnull() method in Pandas is used to detect existing (non-missing) values in the data.
Example
import pandas as pd
# sample DataFrame with missing values
data = {'A': [1, None, 3],
'B': [4, 5, None]}
df = pd.DataFrame(data)
# detect non-missing values
not_null_values = df.notnull()
print(not_null_values)
'''
Output
A B
0 True True
1 False True
2 True False
'''
notnull() Syntax
The syntax of the notnull() method in Pandas is:
df.notnull()
notnull() Arguments
The notnull() method does not take any arguments.
notnull() Return Value
The notnull() method returns a Boolean same-sized object indicating if the values are non-NA. True stands for non-missing values and False stands for missing values.
Example: Filtering Data using notnull()
import pandas as pd
# sample DataFrame with missing values
data = {'A': [1, None, 3],
'B': [4, 5, None]}
df = pd.DataFrame(data)
# detect non-missing values
filtered_df = df[df['A'].notnull()]
print(filtered_df)
Output
A B
0 1.0 4.0
2 3.0 NaN
In this example, we used notnull() in combination with indexing to filter rows based on non-missing values in column A.