Below is a snippet from a table that contains information about employees that work at Company XYZ:
| Column name | Data type | Example value | Description |
|---|---|---|---|
| employee_name | string | Cindy | Name of employee |
| employee_id | integer | 1837204 | Unique id for each employee |
| yrs_of_experience | integer | 14 | total working years of experience |
| yrs_at_company | integer | 10 | total working years at Company XYZ |
| compensation | integer | 100000 | dollar value of employee compensation |
| career_track | string | technical | Potential values: technical, non-technical, executive |
Company XYZ Human Resource department is trying to understand compensation across the company and asked you to pull data to help them make a decision regarding employee compensation.
Can you pull the average, median, minimum, maximium, and standard deviations for salary across 5 year experience buckets at Company XYZ? (e.g. get the corresponding average, median, minimum, maximium, and standard deviations for expereience buckets 0-5, 5-10, 10-15, ect.) You can assume the data is imported into a dataframe named df.