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114 rows where labels_id = 536144505

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Link labels_id issues_id
117039129,536144505 topic-performance 536144505 groupby very slow compared to pandas 117039129
164948082,536144505 topic-performance 536144505 Tweaks for opening datasets 164948082
206632333,536144505 topic-performance 536144505 PERF: Add benchmarking? 206632333
218260909,536144505 topic-performance 536144505 round-trip performance with save_mfdataset / open_mfdataset 218260909
223231729,536144505 topic-performance 536144505 xr.concat consuming too much resources 223231729
224553135,536144505 topic-performance 536144505 slow performance with open_mfdataset 224553135
225774140,536144505 topic-performance 536144505 selecting a point from an mfdataset 225774140
226549366,536144505 topic-performance 536144505 `decode_cf_datetime()` slow because `pd.to_timedelta()` is slow if floats are passed 226549366
229474101,536144505 topic-performance 536144505 concat prealigned objects 229474101
229807027,536144505 topic-performance 536144505 Speed up `decode_cf_datetime` 229807027
231061878,536144505 topic-performance 536144505 Huge memory use when using FacetGrid 231061878
233350060,536144505 topic-performance 536144505 If a NetCDF file is chunked on disk, open it with compatible dask chunks 233350060
236347050,536144505 topic-performance 536144505 Feature/benchmark 236347050
243927150,536144505 topic-performance 536144505 Excessive memory usage when printing multi-file Dataset 243927150
252358450,536144505 topic-performance 536144505 Automatic parallelization for dask arrays in apply_ufunc 252358450
252541496,536144505 topic-performance 536144505 open_mfdataset reads coords from disk multiple times 252541496
255989233,536144505 topic-performance 536144505 DataArray.unstack taking unreasonable amounts of memory 255989233
276688437,536144505 topic-performance 536144505 Performance regression when selecting 276688437
280673215,536144505 topic-performance 536144505 Needs performance check / improvements in value assignment of DataArray 280673215
290084668,536144505 topic-performance 536144505 speed up opening multiple files with changing data variables 290084668
305702311,536144505 topic-performance 536144505 DataArray.rolling().mean() is way slower than it should be 305702311
307318224,536144505 topic-performance 536144505 Slicing DataArray can take longer than not slicing 307318224
330918967,536144505 topic-performance 536144505 DataArray.interp() : poor performance 330918967
331668890,536144505 topic-performance 536144505 Slow performance of isel 331668890
334366223,536144505 topic-performance 536144505 Slow performance with isel on stacked coordinates 334366223
365973662,536144505 topic-performance 536144505 Stack + to_array before to_xarray is much faster that a simple to_xarray 365973662
397063221,536144505 topic-performance 536144505 open_mfdataset in v.0.11.1 is very slow 397063221
416962458,536144505 topic-performance 536144505 Performance: numpy indexes small amounts of data 1000 faster than xarray 416962458
485508509,536144505 topic-performance 536144505 Resample excecution time is significantly longer in version 0.12 than 0.11 485508509
494095795,536144505 topic-performance 536144505 optimize compatibility checks in merge.unique_variable 494095795
503163130,536144505 topic-performance 536144505 Speed up isel and __getitem__ 503163130
503983776,536144505 topic-performance 536144505 Improve indexing performance benchmarks 503983776
517799069,536144505 topic-performance 536144505 Should performance be equivalent when opening with chunks or re-chunking a dataset?  517799069
521754870,536144505 topic-performance 536144505 Should we cache some small properties? 521754870
522780826,536144505 topic-performance 536144505 Leave empty slot when not using accessors 522780826
522935511,536144505 topic-performance 536144505 2x~5x speed up for isel() in most cases 522935511
535686852,536144505 topic-performance 536144505 Strided rolling 535686852
560860376,536144505 topic-performance 536144505 Performance problem when doing computation between two arrays with discontinuous indexes  560860376
587048587,536144505 topic-performance 536144505 weighted operations: performance optimisations 587048587
675482176,536144505 topic-performance 536144505 Optimize ndrolling nanreduce 675482176
711626733,536144505 topic-performance 536144505 Wrap numpy-groupies to speed up Xarray's groupby aggregations 711626733
713834297,536144505 topic-performance 536144505 Allow skipna in .dot() 713834297
718436141,536144505 topic-performance 536144505 Resample is ~100x slower than Pandas resample; Speed is related to resample period (unlike Pandas) 718436141
756425955,536144505 topic-performance 536144505 Comprehensive benchmarking suite 756425955
775875024,536144505 topic-performance 536144505 Slow initilization of dataset.interp 775875024
776042664,536144505 topic-performance 536144505 Faster interp 776042664
776520994,536144505 topic-performance 536144505 Attribute style access is slow 776520994
776542812,536144505 topic-performance 536144505 speedup attribute style access and tab completion 776542812
776595030,536144505 topic-performance 536144505 Speed up Dataset._construct_dataarray 776595030
782943813,536144505 topic-performance 536144505 Poor performance of repr of large arrays, particularly jupyter repr 782943813
785329941,536144505 topic-performance 536144505 Improve performance of xarray.corr() on big datasets 785329941
809366777,536144505 topic-performance 536144505 Better rolling reductions 809366777
929818771,536144505 topic-performance 536144505 Very poor html repr performance on large multi-indexes 929818771
931016490,536144505 topic-performance 536144505 Do not transpose 1d arrays during interpolation 931016490
938141608,536144505 topic-performance 536144505 Faster unstacking of dask arrays 938141608
950882492,536144505 topic-performance 536144505 Polyfit performance on large datasets - Suboptimal dask task graph 950882492
954574705,536144505 topic-performance 536144505 Fix performance bug from cftime import 954574705
969079775,536144505 topic-performance 536144505 Performance issues using map_blocks with cftime indexes. 969079775
1001197796,536144505 topic-performance 536144505 vectorized groupby binary ops 1001197796
1037894157,536144505 topic-performance 536144505 Slow performance of `DataArray.unstack()` from checking `variable.data` 1037894157
1043746973,536144505 topic-performance 536144505 Reimplement `.polyfit()` with `apply_ufunc` 1043746973
1120583442,536144505 topic-performance 536144505 [PERFORMANCE]: `isin` on `CFTimeIndex`-backed `Coordinate` slow  1120583442
1152047670,536144505 topic-performance 536144505 Read/Write performance optimizations for netcdf files 1152047670
1307112340,536144505 topic-performance 536144505 `interp` performance with chunked dimensions 1307112340
1411110855,536144505 topic-performance 536144505 Add import ASV benchmark 1411110855
1412019155,536144505 topic-performance 536144505 Lazy Imports 1412019155
1412895383,536144505 topic-performance 536144505 xarray 2022.10.0 much slower then 2022.6.0 1412895383
1421441672,536144505 topic-performance 536144505 Optimize some copying 1421441672
1423916687,536144505 topic-performance 536144505 Dataset insertion benchmark 1423916687
1423948375,536144505 topic-performance 536144505 Insertion speed of new dataset elements 1423948375
1428264468,536144505 topic-performance 536144505 Fix type in benchmarks/merge.py 1428264468
1428274982,536144505 topic-performance 536144505 Expand benchmarks for dataset insertion and creation 1428274982
1471685307,536144505 topic-performance 536144505 Disable bottleneck by default? 1471685307
1485474624,536144505 topic-performance 536144505 absolufy-imports - Only in xarray folder 1485474624
1490160140,536144505 topic-performance 536144505 Improve performance for backend datetime handling 1490160140
1495605827,536144505 topic-performance 536144505 groupby+map performance regression on MultiIndex dataset 1495605827
1497031605,536144505 topic-performance 536144505 Aggregating a dimension using the Quantiles method with `skipna=True` is very slow 1497031605
1498386428,536144505 topic-performance 536144505 Some alignment optimizations 1498386428
1523232313,536144505 topic-performance 536144505 Add lazy backend ASV test 1523232313
1555497796,536144505 topic-performance 536144505 Add benchmarks for to_dataframe and to_dask_dataframe 1555497796
1561508426,536144505 topic-performance 536144505 Opening datasets with large object dtype arrays is very slow 1561508426
1575938277,536144505 topic-performance 536144505 Dataset.where performances regression. 1575938277
1581046647,536144505 topic-performance 536144505 Differences in `to_netcdf` for dask and numpy backed arrays 1581046647
1607502760,536144505 topic-performance 536144505 Removed `.isel` for `DatasetRolling.construct` consistent rolling behavior 1607502760
1618336774,536144505 topic-performance 536144505 [skip-ci] Fix groupby binary ops benchmarks 1618336774
1643132089,536144505 topic-performance 536144505 [skip-ci] Add compute to groupby benchmarks 1643132089
1646267547,536144505 topic-performance 536144505 open_mfdataset very slow 1646267547
1646350377,536144505 topic-performance 536144505 Use read1 instead of read to get magic number 1646350377
1657036222,536144505 topic-performance 536144505 flox performance regression for cftime resampling 1657036222
1658287592,536144505 topic-performance 536144505 Avoid recasting a CFTimeIndex 1658287592
1658287743,536144505 topic-performance 536144505 align: Avoid reindexing when join="exact" 1658287743
1658291950,536144505 topic-performance 536144505 align ignores `copy` 1658291950
1658319105,536144505 topic-performance 536144505 [skip-ci] Add alignment benchmarks 1658319105
1688781350,536144505 topic-performance 536144505 [skip-ci] Add cftime groupby, resample benchmarks 1688781350
1689364566,536144505 topic-performance 536144505 Speed up .dt accessor by preserving Index objects. 1689364566
1699099029,536144505 topic-performance 536144505 Improve concat performance 1699099029
1704950804,536144505 topic-performance 536144505 Slow performance of concat() 1704950804
1710742907,536144505 topic-performance 536144505 Improve interp performance 1710742907
1710752209,536144505 topic-performance 536144505 Improve to_dask_dataframe performance 1710752209
1718427036,536144505 topic-performance 536144505 Avoid explicit loop when updating OrderedSet 1718427036

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