在熊猫 value_counts() 中使用条件

Question

When applying conditions based on value_counts() to an dataframe, a "boolean dataframe" is obtained, as in the example below:

• The csv I am using for the example is this one .

import pandas as pd
sal = pd.read_csv("Salaries.csv")
sal[sal["Year"] == 2013]["JobTitle"].value_counts() == 1

Instead of obtaining these booleans, is it possible to filter the dataframe in order to display the actual data of the rows that returned True to the condition?

In the example, the filtered dataframe would have the information (EmployeeName, BasePay, Id...) about each employee that have an unique JobTitle.

Answer 1

IIUC transform与nunique

targetdf=sal[sal[sal["Year"] == 2013].groupby(["JobTitle"]).transform('nunique')==1].copy()