html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/6263#issuecomment-1035102667,https://api.github.com/repos/pydata/xarray/issues/6263,1035102667,IC_kwDOAMm_X849smnL,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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