Task

This challenge consists of writing three functions to report and manipulate on a dataset of player stats from the National Basketball Association 2015-16 season.

Your job is to complete a series of tasks in analysis.py . Each task is given by the docstring beneath the functions in this module. Consult the unit tests in tests/test_analysis.py and see the expected_results directory for a set of CSV files which the test suite will use to compare against your analysis functions.

Testing notes

Note that your code will be tested on submission with a different set of data (in an identical format) to ensure the result wasn't hardcoded.

You can mutate the parameter df in all functions. Feel free to edit the provided test suite as you need.

Note that Pandas' testing library offers excellent diffs upon test failure but uses the somewhat confusing left and right names rather than actual and expected Keep in mind that left == actual and right == expected. The call will be pd. testing.assert_frame_equal(actual, expected) throughout the challenge.

Rubric

Your solutions will be evaluated mainly on correctness but use of idiomatic, maintainable Pandas code is taken into consideration as well.

Outside resources

You may consult Pandas and Python documentation.

Data