In Python, pandas provides a simple way to detect missing values in a dataset. The isna() function returns a Boolean mask indicating which values are missing (True) and which are not missing (False).

Here's an example:

import pandas as pd
 
# create a dataframe with missing values
df = pd.DataFrame({'A': [1, 2, None, 4, 5],
                   'B': [6, None, 8, 9, None]})
 
# detect missing values
missing = df.isna()
print(missing)

This code will create a dataframe with missing values in columns A and B, and then use the isna() function to detect the missing values. The output will be a Boolean mask indicating which values are missing:

       A      B
0  False  False
1  False   True
2   True  False
3  False  False
4  False   True

In this example, the isna() function returns a dataframe with the same shape as the original dataframe, where True values indicate missing values and False values indicate non-missing values.

Once you have detected missing values, you can use the methods mentioned in my previous answer to handle them, such as dropping rows or columns with missing values, filling missing values with a specified value, or interpolating missing values.