What is the difference between pandas dtype vs dtypes

Question

For the below pandas code in jupyter I am trying to get the data type information .tab in jupyter provides me information that there is two attributes It has both dtype and dtypes

import pandas as pd
new_list = [True,False]
new_pd = pd.Series(new_list)
new_pd

As per the documentation both returns data type information

return from both are good and useful

Question is why there is same duplicate attributes . Which one to be used in which scenario or its a don't care anyone can be used ?

Answer 1

In a pd.Series object there is no difference. However, in pd.DataFrame objects you only have dtypes, which is a series with the data type of each column.

The good thing about this is that when you have a series you can treat it mostly uniformly as a NumPy array and use .dtype (which is a property present in every NumPy array) or as a data frame and use .dtypes (which is a property present in all Pandas objects). So in principle many functions for NumPy arrays or data frames already work with series out of the box.

Answer 2

You would use dtypes with a DataFrame to get the dtypefor each column/Series.