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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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1284475176 | I_kwDOAMm_X85Mj4ko | 6726 | Long import time | leroyvn 34740232 | closed | 0 | 9 | 2022-06-25T07:01:18Z | 2022-10-28T16:25:41Z | 2022-10-28T16:25:41Z | NONE | What is your issue?Importing the xarray package takes a significant amount of time. For instance:
❯ time python -c "import scipy" python -c "import scipy" 0.29s user 0.23s system 297% cpu 0.175 total ❯ time python -c "import numpy" python -c "import numpy" 0.29s user 0.43s system 313% cpu 0.229 total ❯ time python -c "import datetime" python -c "import datetime" 0.05s user 0.00s system 99% cpu 0.051 total ``` I am obviously not surprised that importing xarray takes longer than importing Pandas, Numpy or the datetime module, but 1.5 s is something you clearly notice when it is done e.g. by a command-line application. I inquired about import performance and found out about a lazy module loader proposal by the Scientific Python community. AFAIK SciPy uses a similar system to populate its namespaces without import time penalty. Would it be possible for xarray to use delayed imports when relevant? |
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completed | xarray 13221727 | issue | ||||||
| 1047599975 | I_kwDOAMm_X84-cRtn | 5953 | Selecting with MultiIndex raises a TypeError | leroyvn 34740232 | closed | 0 | benbovy 4160723 | 1 | 2021-11-08T15:39:10Z | 2022-03-17T17:11:43Z | 2022-03-17T17:11:43Z | NONE | What happened: After updating xarray to v0.20.1, some of my multi-index-based selection code raises a What you expected to happen: Selection should work in the case I am considering. Minimal Complete Verifiable Example: ```python import xarray as xr import pandas as pd da = xr.DataArray(data=[0, 1, 2, 3], dims=("x",)).reindex( {"x": pd.MultiIndex.from_product((["foo", "bar"], [0, 1]), names=("str", "int"))} ) print(da.sel(x=("foo", 1))) ``` Expected output (what I get with xarray 0.19.0):
Actual output (with xarray 0.20.1):
Anything else we need to know?: Nothing I'm aware of Environment: Output of <tt>xr.show_versions()</tt> (with xarray 0.19.0)INSTALLED VERSIONS ------------------ commit: None python: 3.7.12 | packaged by conda-forge | (default, Oct 26 2021, 05:57:50) [Clang 11.1.0 ] python-bits: 64 OS: Darwin OS-release: 20.6.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: (None, 'UTF-8') libhdf5: 1.12.1 libnetcdf: 4.8.1 xarray: 0.19.0 pandas: 1.3.4 numpy: 1.21.4 scipy: 1.7.1 netCDF4: 1.5.8 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.5.1.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2021.11.0 distributed: 2021.11.0 matplotlib: 3.4.3 cartopy: None seaborn: 0.11.2 numbagg: None pint: 0.18 setuptools: 58.5.3 pip: 21.3.1 conda: None pytest: 6.2.5 IPython: 7.29.0 sphinx: 4.2.0Output of <tt>xr.show_versions()</tt> (with xarray 0.20.1)INSTALLED VERSIONS ------------------ commit: None python: 3.7.12 | packaged by conda-forge | (default, Oct 26 2021, 05:57:50) [Clang 11.1.0 ] python-bits: 64 OS: Darwin OS-release: 20.6.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: (None, 'UTF-8') libhdf5: 1.12.1 libnetcdf: 4.8.1 xarray: 0.20.1 pandas: 1.3.4 numpy: 1.21.4 scipy: 1.7.1 netCDF4: 1.5.8 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.5.1.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2021.11.0 distributed: 2021.11.0 matplotlib: 3.4.3 cartopy: None seaborn: 0.11.2 numbagg: None fsspec: 2021.11.0 cupy: None pint: 0.18 sparse: None setuptools: 58.5.3 pip: 21.3.1 conda: None pytest: 6.2.5 IPython: 7.29.0 sphinx: 4.2.0 |
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completed | xarray 13221727 | issue |
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