In Python, you can detect missing values (also known as NaN or null values) in various data structures such as NumPy arrays, Pandas dataframes, and Python lists.

Here's an example of how to detect missing values in a Pandas dataframe:

import pandas as pd
 
# create a sample dataframe with missing values
data = {'name': ['John', 'Jane', 'Mike', 'Susan'],
        'age': [30, 25, None, 40],
        'gender': ['M', 'F', None, 'F']}
df = pd.DataFrame(data)
 
# check for missing values in the dataframe
print(df.isnull())

This will output a boolean dataframe with True values where the corresponding element is missing:

    name    age gender
0  False  False  False
1  False  False  False
2  False   True   True
3  False  False  False

You can also check the total number of missing values in each column of the dataframe using the sum() method:

print(df.isnull().sum())

This will output:

name      0
age       1
gender    1
dtype: int64

Here, we can see that the age and gender columns have one missing value each.