PR Summary

Closes #18743

date2num was slowed down for long lists of datetime. This pr simply tries to convert them to datetime64 before we convert to a num which seems fast.

I think that if we had a list of date times with timezones it would still be quite slow, but this fixes the naive-datetime case. Open to better suggestions..

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.units
import numpy as np
import datetime
import time

# region # synthetic ...
def datetime_range(startDate, endDate, deltaDate):
    current = startDate
    while current < endDate:
        yield current
        current += deltaDate
    return

listOfDates = [date for date in datetime_range(datetime.datetime(2020, 1, 1, 0, 1),
                              datetime.datetime(2020, 12, 31, 23, 59),
                              datetime.timedelta(minutes=1))]

start = time.perf_counter()
mdates.date2num(listOfDates)
stop = time.perf_counter()
print(f'{stop-start:1.2f} s')

Master

11.79 s

This PR:

1.99 s

PR Checklist

• Has pytest style unit tests (and pytest passes).
• Is Flake 8 compliant (run flake8 on changed files to check).
• New features are documented, with examples if plot related.
• Documentation is sphinx and numpydoc compliant (the docs should build without error).
• Conforms to Matplotlib style conventions (install flake8-docstrings and pydocstyle<4 and run flake8 --docstring-convention=all).
• New features have an entry in doc/users/next_whats_new/ (follow instructions in README.rst there).
• API changes documented in doc/api/next_api_changes/ (follow instructions in README.rst there).