Numerical Computing with Python

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For many operations, pandas has multiple ways to do the same thing. In the preceding recipe, the criteria for salary uses two separate boolean expressions. Similarly to SQL, Series have a between method, with the salary criteria equivalently written as follows:

>>> criteria_sal = employee.BASE_SALARY.between(80000, 120000)

Another useful application of isin is to provide a sequence of values automatically generated by some other pandas statements. This would avoid any manual investigating to find the exact string names to store in a list. Conversely, let's try to exclude the rows from the top five most frequently occurring departments:

>>> top_5_depts = employee.DEPARTMENT.value_counts().index[:5]>>> criteria = ~employee.DEPARTMENT.isin(top_5_depts) ...