TST: Parameterize & make tests more performant #55830
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WillAyd
reviewed
Nov 6, 2023
jbrockmendel
reviewed
Nov 6, 2023
| result = pd.read_pickle(handle) | ||
| handle.seek(0) # shouldn't close file handle | ||
| compare_element(result, expected, typ) | ||
| @pytest.mark.parametrize("typ, expected", flatten(create_pickle_data())) |
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how big is the memory footprint of create_pickle_data? i think doing this outside the test means it sticks around during the whole collection/runtime
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Doing a recursive getsizeof of the dict yields 426965 bytes so it doesn't seem too much. Additionally IIUC before this data was being yielded from the current_pickle_data fixture which caches & keeps the data around after first use
Locally in total for this PR probably a second or so. Another benefit for the tests that reduce the data size is to also to reduce the peak memory pressure of a test. |
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