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  • xarray · 3 ✖
id node_id number title user state locked assignee milestone comments created_at updated_at ▲ closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
324105458 MDExOlB1bGxSZXF1ZXN0MTg4ODAwMTI4 2152 ENH: Plotting for groupby_bins maahn 222557 closed 0     12 2018-05-17T16:53:33Z 2018-10-23T15:20:40Z 2018-10-23T08:19:23Z NONE   0 pydata/xarray/pulls/2152

DataArrays created with e.g. groupy_bins have coords arrays consisting of pd._libs.interval.Interval. Therefore, they cannot be plotted. The small patch replaces the the pd._libs.interval.Interval values with the interval's center point and adds _center to the label name. It looks like this: https://gist.github.com/maahn/91da0a8d299ef6567827749cbe2f1913 I don't think there is any need for additional documentation except the whats-new.rst or tests(?), but I'm also happy to add them if you think it is required.

  • [x] Tests added (for all bug fixes or enhancements)
  • [x] Tests passed (for all non-documentation changes)
  • [x] Fully documented, including whats-new.rst for all changes and api.rst for new API 
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     xarray 13221727 pull
350112372 MDExOlB1bGxSZXF1ZXN0MjA4MDQxMTcy 2364 Faster unstack maahn 222557 closed 0     2 2018-08-13T16:58:30Z 2018-08-15T00:19:13Z 2018-08-15T00:18:28Z NONE   0 pydata/xarray/pulls/2364
  • [x] Closes #1560 as proposed in issue discussion
  • [X] Tests added (for all bug fixes or enhancements)
  • [x] Tests passed (for all non-documentation changes)
  • [x] ASV benchmark added
  • [x] What's new added.
 
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     xarray 13221727 pull
166449498 MDU6SXNzdWUxNjY0NDk0OTg= 908 Histogram plot of DataArray can be extremely slow maahn 222557 closed 0     3 2016-07-19T22:11:38Z 2016-10-22T00:58:11Z 2016-10-22T00:58:11Z NONE      

The speed of plotting a histogram of a large DataArray depends a lot how you do it:

``` import xarray as xr import numpy as np import matplotlib.pyplot as plt

nPoints = 100000 data = xr.DataArray(np.random.random(nPoints),dims=['time'],coords=[np.arange(nPoints)]) ```

It take sonly some ms if you use

``` plt.figure() %time data.plot.hist()

plt.figure() %time plt.hist(data.values) ```

However, if you omit .values it takes extremely long:

In [12]: %time plt.hist(data) CPU times: user 2min, sys: 9.73 s, total: 2min 9s Wall time: 2min 3s

Do if one forgets to add .values or to use xarray's plot routine, one can be stuck for a long time. I have to kill python 1-2 per day due to that issue.

 
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   completed xarray 13221727 issue

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