tests.unit.test_time: many tests failed

[flaky-bot]

Many tests failed at the same time in this package.

• I will close this issue when there are no more failures in this package and
  there is at least one pass.
• No new issues will be filed for this package until this issue is closed.
• If there are already issues for individual test cases, I will close them when
  the corresponding test passes. You can close them earlier, if you prefer, and
  I won't reopen them while this issue is still open.

Here are the tests that failed:

• test_time_parsing[0:0:59.876543-expected16]
• test_time_parsing[01:01:01.010101-expected18]
• test_time_parsing[09:09:09.090909-expected20]
• test_time_parsing[11:11:11.111111-expected22]
• test_time_parsing[19:16:23.987654-expected23]
• test_time_parsing[23:59:59.999999-expected25]
• test_time_parsing[23:59:59.999999000-expected27]
• test_time_parsing[23:59:59.999999999-expected28]
• test_date_median[values0-expected0]
• test_date_median[values2-expected2]

---

commit: 508564f
buildURL: Build Status, Sponge
status: failed

[flaky-bot]

Looks like this issue is flaky. 😟

I'm going to leave this open and stop commenting.

A human should fix and close this.

---

When run at the same commit (508564f), this test passed in one build (Build Status, Sponge) and failed in another build (Build Status, Sponge).

[flaky-bot]

Looks like this issue is flaky. 😟

I'm going to leave this open and stop commenting.

A human should fix and close this.

---

When run at the same commit (508564f), this test passed in one build (Build Status, Sponge) and failed in another build (Build Status, Sponge).

[flaky-bot]

Looks like this issue is flaky. 😟

I'm going to leave this open and stop commenting.

A human should fix and close this.

---

When run at the same commit (508564f), this test passed in one build (Build Status, Sponge) and failed in another build (Build Status, Sponge).

[chalmerlowe]

Based on conversation in the pandas github issue tracker, the root cause of these failing tests appears to be that the pandas system is trying to store too large a number in an int32 memory location. The bug has been added to the pandas 1.5 milestone (pandas is currently at 1.5.0 release candidate stage)

See:
pandas-dev/pandas#48255

[tswast]

@chalmerlowe Per latest update on pandas-dev/pandas#48538, we may need to populate "microseconds" when constructing a pandas.Timestamp and limit the nanoseconds field to the leftovers (0-999).