Analyzing employee data
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, maximum, and standard deviations for salary across 5 year experience buckets at Company XYZ? (e.g. get the corresponding average, median, minimum, maximum, and standard deviations for experience buckets 0-5, 5-10, 10-15, etc.) You can assume the data is imported into a dataframe named, df.
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