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  • Date in matplotlib conversion does not handle "YYYY-MM-DD" format for xarray=0.21.1 · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1049066201 https://github.com/pydata/xarray/issues/6263#issuecomment-1049066201 https://api.github.com/repos/pydata/xarray/issues/6263 IC_kwDOAMm_X84-h3rZ jklymak 1562854 2022-02-23T18:09:32Z 2022-02-23T18:09:32Z CONTRIBUTOR

It could, after the units are set to dates, but all it would do is pass to datetime64, so the recommendation would be that users do that explicitly.

If the units are not set to dates (ie. this is the first call on the axis) then strings are interpreted as categories in Matplotlib, and all sorts of hilarity ensues if the strings are all dates....

 
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   Date in matplotlib conversion does not handle "YYYY-MM-DD" format for xarray=0.21.1 1130073503
1049060894 https://github.com/pydata/xarray/issues/6263#issuecomment-1049060894 https://api.github.com/repos/pydata/xarray/issues/6263 IC_kwDOAMm_X84-h2Ye dcherian 2448579 2022-02-23T18:03:52Z 2022-02-23T18:03:52Z MEMBER

@jklymak it seems a benefit of the pandas converter is that it converts strings to dates. Can the matplotlib converter do the same?

 
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   Date in matplotlib conversion does not handle "YYYY-MM-DD" format for xarray=0.21.1 1130073503
1035102667 https://github.com/pydata/xarray/issues/6263#issuecomment-1035102667 https://api.github.com/repos/pydata/xarray/issues/6263 IC_kwDOAMm_X849smnL Illviljan 14371165 2022-02-10T16:07:53Z 2022-02-10T16:07:53Z MEMBER

xarray now relies on matplotlibs converters instead of automatically registering pandas converters, see #6109.

A pure matplotlib version doesn't work either so importing xarray shouldn't all of a sudden change that: ```python import numpy as np import matplotlib.pyplot as plt

times = np.arange(np.datetime64('2001-01-02'), np.datetime64('2002-02-03'), np.timedelta64(75, 'm')) y = np.random.randn(len(times))

fig, ax = plt.subplots() ax.plot(times, y) ax.set_xlim(["2002-01-03","2002-01-20"]) One way is to use datetime64 in set_xlim, which makes sense to me since `times` is `datetime64` as well:python import numpy as np import matplotlib.pyplot as plt

times = np.arange(np.datetime64('2001-01-02'), np.datetime64('2002-02-03'), np.timedelta64(75, 'm')) y = np.random.randn(len(times))

fig, ax = plt.subplots() ax.plot(times, y) ax.set_xlim(np.array(["2002-01-03","2002-01-20"], dtype="datetime64")) ```

Or use pandas converters like xarray did before: ```python import numpy as np import matplotlib.pyplot as plt import pandas as pd

pd.plotting.register_matplotlib_converters()

times = np.arange(np.datetime64('2001-01-02'), np.datetime64('2002-02-03'), np.timedelta64(75, 'm')) y = np.random.randn(len(times))

fig, ax = plt.subplots() ax.plot(times, y) ax.set_xlim(["2002-01-03","2002-01-20"]) ```

 
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   Date in matplotlib conversion does not handle "YYYY-MM-DD" format for xarray=0.21.1 1130073503

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