To query the memory usage of a Pandas DataFrame in Python, you can use the memory_usage() method. This method returns the memory usage of each column of the DataFrame.

Here's an example:

import pandas as pd
 
# create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': ['a', 'b', 'c']})
 
# get the memory usage of the DataFrame
memory_usage = df.memory_usage(deep=True)
 
print(memory_usage)

Output:

Index    128
A         24
B         24
C        156
dtype: int64

The memory_usage() method returns a Pandas Series object that shows the memory usage of each column of the DataFrame. The deep=True argument tells Pandas to include the memory usage of objects within each column (e.g. if a column contains strings, it will include the memory usage of each string).

Note that the index is also included in the memory usage calculation. In this example, the index takes up 128 bytes of memory.

You can sum the memory usage to get the total memory usage of the DataFrame:

total_memory_usage = memory_usage.sum()
print(total_memory_usage)

Output:

332
This shows that the total memory usage of the DataFrame is 332 bytes.