github: issue_comments: 1 row where author_association = "MEMBER", issue = 1006588071 and user = 14808389 sorted by updated

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id	html_url	issue_url	node_id	user	created_at	updated_at ▲	author_association	body	reactions	performed_via_github_app	issue
939513064	https://github.com/pydata/xarray/issues/5816#issuecomment-939513064	https://api.github.com/repos/pydata/xarray/issues/5816	IC_kwDOAMm_X843_9To	keewis 14808389	2021-10-10T16:38:18Z	2021-10-10T16:39:18Z	MEMBER	I agree, this is would be really helpful. There's lots to do, though: a small script reports about 170 functions / methods without example sections (and 6 numpy wrappers), and all others could use reviews: analysis script ```python import itertools import numpy as np import xarray as xr def public_api(obj, base_name): api = dir(obj) public_api = tuple(name for name in api if not name.startswith("_")) public_api_objects = {name: getattr(obj, name) for name in public_api} # uppercase indicates classes public_functions = { f"{base_name}.{name}": obj for name, obj in public_api_objects.items() if not name[0].isupper() and callable(obj) } return public_functions def is_dict_method(fqn): _, name = fqn.split(".") return hasattr(dict, name) def has_section(docstring, name): lines = docstring.split("\n") marker = "-" len(name) for current, next in itertools.zip_longest(lines, lines[1:]): if next is None or next.strip() != marker: continue if current.strip() == name: return True return False def is_numpy_wrapper(fqn, docstring): *_, name = fqn.split(".") segment = f"Refer to `numpy.{name}` for full documentation." shortened_segment = f"Refer to `numpy.{name[:4]}` for full documentation." if segment in docstring or shortened_segment in docstring: return True numpy_func = getattr(np, name, None) if numpy_func is None: return False numpy_docstring = numpy_func.__doc__ return numpy_docstring in docstring def format_names(names): return "\n".join(f" - {name}" for name in names) namespaces = { "xarray": xr, "xarray.DataArray": xr.DataArray, "xarray.Dataset": xr.Dataset, } funcs = dict( itertools.chain.from_iterable( public_api(namespace, base_name=name).items() for name, namespace in namespaces.items() ) ) docstrings = {name: func.__doc__ for name, func in funcs.items()} without_docstring = tuple( name for name, docstring in docstrings.items() if docstring is None or not docstring.strip() ) filtered_docstrings = tuple( (name, docstring) for name, docstring in docstrings.items() if ( docstring is not None and not has_section(docstring, name="Examples") and not is_dict_method(name) ) ) without_examples_xarray = tuple( name for name, docstring in filtered_docstrings if not is_numpy_wrapper(name, docstring) ) without_examples_numpy = tuple( name for name, docstring in filtered_docstrings if is_numpy_wrapper(name, docstring) ) print( f"functions without examples ({len(without_examples_xarray)}):", format_names(without_examples_xarray), sep="\n", ) print() print( f"numpy wrappers without examples ({len(without_examples_numpy)}):", format_names(without_examples_numpy), sep="\n", ) print() print( f"functions without docstring ({len(without_docstring)}):", format_names(without_docstring), sep="\n", ) ``` analysis report in current <tt>main</tt> (with coarse filtering of advanced / deprecated API) ``` functions without examples (170): - xarray.decode_cf - xarray.get_options - xarray.infer_freq - xarray.load_dataarray - xarray.load_dataset - xarray.open_dataarray - xarray.open_dataset - xarray.open_mfdataset - xarray.open_zarr - xarray.polyval - xarray.unify_chunks - xarray.DataArray.all - xarray.DataArray.any - xarray.DataArray.as_numpy - xarray.DataArray.assign_attrs - xarray.DataArray.astype - xarray.DataArray.bfill - xarray.DataArray.broadcast_equals - xarray.DataArray.chunk - xarray.DataArray.close - xarray.DataArray.combine_first - xarray.DataArray.compute - xarray.DataArray.conj - xarray.DataArray.count - xarray.DataArray.cumprod - xarray.DataArray.cumsum - xarray.DataArray.curvefit - xarray.DataArray.drop - xarray.DataArray.drop_duplicates - xarray.DataArray.drop_isel - xarray.DataArray.drop_sel - xarray.DataArray.drop_vars - xarray.DataArray.dropna - xarray.DataArray.equals - xarray.DataArray.expand_dims - xarray.DataArray.ffill - xarray.DataArray.fillna - xarray.DataArray.from_cdms2 - xarray.DataArray.from_dict - xarray.DataArray.from_iris - xarray.DataArray.from_series - xarray.DataArray.get_axis_num - xarray.DataArray.get_index - xarray.DataArray.groupby_bins - xarray.DataArray.head - xarray.DataArray.identical - xarray.DataArray.interp_like - xarray.DataArray.load - xarray.DataArray.max - xarray.DataArray.mean - xarray.DataArray.median - xarray.DataArray.min - xarray.DataArray.persist - xarray.DataArray.plot - xarray.DataArray.polyfit - xarray.DataArray.prod - xarray.DataArray.reduce - xarray.DataArray.reindex_like - xarray.DataArray.rename - xarray.DataArray.reorder_levels - xarray.DataArray.reset_coords - xarray.DataArray.reset_index - xarray.DataArray.rolling_exp - xarray.DataArray.searchsorted - xarray.DataArray.set_close - xarray.DataArray.squeeze - xarray.DataArray.std - xarray.DataArray.str - xarray.DataArray.sum - xarray.DataArray.tail - xarray.DataArray.thin - xarray.DataArray.to_cdms2 - xarray.DataArray.to_dataframe - xarray.DataArray.to_dataset - xarray.DataArray.to_dict - xarray.DataArray.to_index - xarray.DataArray.to_iris - xarray.DataArray.to_masked_array - xarray.DataArray.to_netcdf - xarray.DataArray.to_numpy - xarray.DataArray.to_pandas - xarray.DataArray.to_series - xarray.DataArray.transpose - xarray.DataArray.unify_chunks - xarray.DataArray.var - xarray.DataArray.weighted - xarray.Dataset.all - xarray.Dataset.any - xarray.Dataset.apply - xarray.Dataset.argmax - xarray.Dataset.argmin - xarray.Dataset.as_numpy - xarray.Dataset.assign_attrs - xarray.Dataset.astype - xarray.Dataset.bfill - xarray.Dataset.broadcast_equals - xarray.Dataset.broadcast_like - xarray.Dataset.chunk - xarray.Dataset.close - xarray.Dataset.combine_first - xarray.Dataset.compute - xarray.Dataset.conj - xarray.Dataset.count - xarray.Dataset.cumprod - xarray.Dataset.cumsum - xarray.Dataset.curvefit - xarray.Dataset.differentiate - xarray.Dataset.drop - xarray.Dataset.drop_dims - xarray.Dataset.drop_vars - xarray.Dataset.dropna - xarray.Dataset.dump_to_store - xarray.Dataset.equals - xarray.Dataset.expand_dims - xarray.Dataset.ffill - xarray.Dataset.from_dataframe - xarray.Dataset.from_dict - xarray.Dataset.get_index - xarray.Dataset.groupby_bins - xarray.Dataset.head - xarray.Dataset.identical - xarray.Dataset.info - xarray.Dataset.interp_like - xarray.Dataset.isel - xarray.Dataset.load - xarray.Dataset.load_store - xarray.Dataset.max - xarray.Dataset.mean - xarray.Dataset.median - xarray.Dataset.merge - xarray.Dataset.min - xarray.Dataset.persist - xarray.Dataset.plot - xarray.Dataset.polyfit - xarray.Dataset.prod - xarray.Dataset.rank - xarray.Dataset.reduce - xarray.Dataset.reindex_like - xarray.Dataset.rename - xarray.Dataset.rename_dims - xarray.Dataset.rename_vars - xarray.Dataset.reorder_levels - xarray.Dataset.reset_coords - xarray.Dataset.reset_index - xarray.Dataset.rolling_exp - xarray.Dataset.sel - xarray.Dataset.set_close - xarray.Dataset.set_coords - xarray.Dataset.squeeze - xarray.Dataset.stack - xarray.Dataset.std - xarray.Dataset.sum - xarray.Dataset.tail - xarray.Dataset.thin - xarray.Dataset.to_array - xarray.Dataset.to_dask_dataframe - xarray.Dataset.to_dataframe - xarray.Dataset.to_dict - xarray.Dataset.to_netcdf - xarray.Dataset.to_pandas - xarray.Dataset.to_zarr - xarray.Dataset.transpose - xarray.Dataset.unify_chunks - xarray.Dataset.unstack - xarray.Dataset.var - xarray.Dataset.weighted numpy wrappers without examples (6): - xarray.DataArray.argsort - xarray.DataArray.clip - xarray.DataArray.conjugate - xarray.Dataset.argsort - xarray.Dataset.clip - xarray.Dataset.conjugate functions without docstring (1): - xarray.DataArray.dt ``` For some of those it might be difficult to write examples for (i.e. the I/O methods / functions), and modifying the docstring of the numpy wrappers would require some refactoring. Most of the remaining ones are pretty easy to figure out and write examples for, however. If we remove the entry barrier as much as possible maybe these can be good first PRs with high impact? I'm not really sure how to do that, though... I'd try to group them by difficulty and create sub-issues with a check list for each group (so they're easy to find), and maybe also explain what a good example looks like or link to a appropriate explanation. The latter might be a good thing to add to the contributor's guide, too.	{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }		Link API docs to user guide and other examples 1006588071

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