Pandas Cookbook

How to do it...

1. Read in the employee dataset, and create a DatetimeIndex with the HIRE_DATE column:

>>> employee = pd.read_csv('data/employee.csv',                            parse_dates=['JOB_DATE', 'HIRE_DATE'],                            index_col='HIRE_DATE')>>> employee.head()

2. Let's first do a simple grouping by just gender, and find the average salary for each:

>>> employee.groupby('GENDER')['BASE_SALARY'].mean().round(-2)GENDER
Female    52200.0
Male      57400.0
Name: BASE_SALARY, dtype: float64

3. Let's find the average salary based on hire date, and group everyone into 10-year buckets:

>>> employee.resample('10AS')['BASE_SALARY'].mean().round(-2)HIRE_DATE 1958-01-01 81200.0 1968-01-01 ...