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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 |
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117039129 | MDU6SXNzdWUxMTcwMzkxMjk= | 659 | groupby very slow compared to pandas | anntzer 1322974 | closed | 0 | 9 | 2015-11-16T02:43:57Z | 2022-05-15T02:38:30Z | 2022-05-15T02:38:30Z | CONTRIBUTOR | ``` import timeit import numpy as np from pandas import DataFrame from xray import Dataset, DataArray df = DataFrame({"a": np.r_[np.arange(500.), np.arange(500.)], "b": np.arange(1000.)}) print(timeit.repeat('df.groupby("a").agg("mean")', globals={"df": df}, number=10)) print(timeit.repeat('df.groupby("a").agg(np.mean)', globals={"df": df, "np": np}, number=10)) ds = Dataset({"a": DataArray(np.r_[np.arange(500.), np.arange(500.)]), "b": DataArray(np.arange(1000.))}) print(timeit.repeat('ds.groupby("a").mean()', globals={"ds": ds}, number=10)) ``` This outputs
i.e. xray's groupby is ~100 times slower than pandas' one (and 200 times slower than passing (This is the actual order or magnitude of the data size and redundancy I want to handle, i.e. thousands of points with very limited duplication.) |
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111795064 | MDU6SXNzdWUxMTE3OTUwNjQ= | 627 | string coordinate gets converted to object coordinate upon addition of variable to dataset | anntzer 1322974 | closed | 0 | 10 | 2015-10-16T09:29:58Z | 2021-03-27T21:19:33Z | 2021-03-27T21:19:33Z | CONTRIBUTOR | With the current HEAD, consider ``` import numpy as np from xray import * ds = Dataset({"1": DataArray(np.zeros(3), dims=["a"], coords={"a": list("xyz")})}) print(ds) ds["2"] = DataArray(np.zeros(2), dims=["a"], coords={"a": list("xy")}) print(ds) ``` This outputs
Note that the dtype of the Python3.5, numpy 1.10.1, xray master (6ea7eb2b388075cc838c5ddf0ddaa47020cfcb89) With 0.6.0 the coordinate is of object dtype both before and after. I forgot why I tried master but I must have had a good reason... |
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114732169 | MDU6SXNzdWUxMTQ3MzIxNjk= | 643 | "naive" iteration is very slow | anntzer 1322974 | closed | 0 | 2 | 2015-11-03T02:53:04Z | 2019-01-15T21:09:07Z | 2019-01-15T21:09:07Z | CONTRIBUTOR | ``` $ ipython Python 3.5.0 (default, Sep 20 2015, 11:28:25) Type "copyright", "credits" or "license" for more information. IPython 4.0.0 -- An enhanced Interactive Python. ? -> Introduction and overview of IPython's features. %quickref -> Quick reference. help -> Python's own help system. object? -> Details about 'object', use 'object??' for extra details. Using matplotlib backend: Qt4Agg In [1]: from xray import DataArray Iteration over a Python listIn [2]: %%timeit t = list(range(10000)) for _ in t: pass ...: 10000 loops, best of 3: 87.3 µs per loop Iteration over a ndarrayIn [3]: %%timeit t = np.arange(10000) for _ in t: pass ...: 1000 loops, best of 3: 472 µs per loop Iteration over a DataArrayIn [4]: %%timeit t = DataArray(np.arange(10000)) for _ in t: pass ...: 1 loops, best of 3: 818 ms per loop ``` I'm not sure how much can be done about this as iterating over a DataArray needs to create a bunch of temporary objects (and I understand the emphasis is as usual on vectorized operations, etc.) but a >1500 fold difference certainly doesn't look good. |
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completed | xarray 13221727 | issue | ||||||
170458908 | MDU6SXNzdWUxNzA0NTg5MDg= | 958 | Test failure with matplotlib 2.0b3 | anntzer 1322974 | closed | 0 | 1 | 2016-08-10T16:21:16Z | 2018-10-26T23:12:28Z | 2018-10-26T23:12:28Z | CONTRIBUTOR | mpl 2.0b3 / xarray HEAD Arch Linux, Python 3.5.2 ``` ============================================================================================= FAILURES ============================================================================================= ____________ TestPlot.test_subplot_kws _____________ self = <xarray.test.test_plot.TestPlot testMethod=test_subplot_kws>
xarray/test/test_plot.py:148: self = <xarray.test.test_plot.TestPlot testMethod=test_subplot_kws>, a1 = (1.0, 0.0, 0.0, 1), a2 = 'r'
xarray/test/init.py:164: AssertionError --------------------------------------------------------------------------------------- Captured stderr call --------------------------------------------------------------------------------------- /usr/lib/python3.5/site-packages/matplotlib/cbook.py:137: MatplotlibDeprecationWarning: The axisbg attribute was deprecated in version 2.0. Use facecolor instead. warnings.warn(message, mplDeprecation, stacklevel=1) /home/antony/src/extern/xarray/xarray/test/test_plot.py:148: MatplotlibDeprecationWarning: The get_axis_bgcolor function was deprecated in version 2.0. Use get_facecolor instead. self.assertEqual(ax.get_axis_bgcolor(), 'r') ``` |
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completed | xarray 13221727 | issue | ||||||
117297089 | MDExOlB1bGxSZXF1ZXN0NTA5MTEzMzQ= | 661 | Document pandas' better groupby performance. | anntzer 1322974 | closed | 0 | 1 | 2015-11-17T07:04:50Z | 2015-11-17T09:10:04Z | 2015-11-17T08:54:31Z | CONTRIBUTOR | 0 | pydata/xarray/pulls/661 | cf. #659. |
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xarray 13221727 | pull |
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