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"""
Analyze docstrings to detect errors.
If no argument is provided, it does a quick check of docstrings and returns
a csv with all API functions and results of basic checks.
If a function or method is provided in the form "pandas.function",
"pandas.module.class.method", etc. a list of all errors in the docstring for
the specified function or method.
Usage::
$ ./validate_docstrings.py
$ ./validate_docstrings.py pandas.DataFrame.head
"""
from __future__ import annotations
import argparse
import collections
import doctest
import importlib
import json
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import os
import tempfile
import matplotlib
import matplotlib.pyplot as plt
from numpydoc.docscrape import get_doc_object
ERROR_MSGS as NUMPYDOC_ERROR_MSGS,
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# With template backend, matplotlib plots nothing
matplotlib.use("template")
# Styler methods are Jinja2 objects who's docstrings we don't own.
IGNORE_VALIDATION = {
"Styler.env",
"Styler.template_html",
"Styler.template_html_style",
"Styler.template_html_table",
"Styler.template_latex",
"Styler.template_string",
"Styler.loader",
"errors.InvalidComparison",
"errors.LossySetitemError",
"errors.IncompatibilityWarning",
"errors.PyperclipWindowsException",
PRIVATE_CLASSES = ["NDFrame", "IndexOpsMixin"]
"GL04": "Private classes ({mentioned_private_classes}) should not be "
"mentioned in public docstrings",
"PD01": "Use 'array-like' rather than 'array_like' in docstrings.",
"SA05": "{reference_name} in `See Also` section does not need `pandas` "
"prefix, use {right_reference} instead.",
"EX03": "flake8 error: line {line_number}, col {col_number}: {error_code} "
"{error_message}",
"EX04": "Do not import {imported_library}, as it is imported "
"automatically for the examples (numpy as np, pandas as pd)",
ALL_ERRORS = set(NUMPYDOC_ERROR_MSGS).union(set(ERROR_MSGS))
duplicated_errors = set(NUMPYDOC_ERROR_MSGS).intersection(set(ERROR_MSGS))
assert not duplicated_errors, (f"Errors {duplicated_errors} exist in both pandas "
"and numpydoc, should they be removed from pandas?")
def pandas_error(code, **kwargs):
Copy of the numpydoc error function, since ERROR_MSGS can't be updated
with our custom errors yet.
return code, ERROR_MSGS[code].format(**kwargs)
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def get_api_items(api_doc_fd):
"""
Yield information about all public API items.
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Parse api.rst file from the documentation, and extract all the functions,
methods, classes, attributes... This should include all pandas public API.
Parameters
----------
api_doc_fd : file descriptor
A file descriptor of the API documentation page, containing the table
of contents with all the public API.
Yields
------
name : str
The name of the object (e.g. 'pandas.Series.str.upper').
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func : function
The object itself. In most cases this will be a function or method,
but it can also be classes, properties, cython objects...
section : str
The name of the section in the API page where the object item is
located.
subsection : str
The name of the subsection in the API page where the object item is
located.
"""
current_module = "pandas"
previous_line = current_section = current_subsection = ""
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position = None
for line in api_doc_fd:
line_stripped = line.strip()
if len(line_stripped) == len(previous_line):
if set(line_stripped) == set("-"):
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current_section = previous_line
continue
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current_subsection = previous_line
continue
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if line_stripped.startswith(".. currentmodule::"):
current_module = line_stripped.replace(".. currentmodule::", "").strip()
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continue
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continue
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continue
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position = None
continue
if line_stripped in IGNORE_VALIDATION:
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func = importlib.import_module(current_module)
for part in line_stripped.split("."):
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func = getattr(func, part)
f"{current_module}.{line_stripped}",
func,
current_section,
current_subsection,
)
class PandasDocstring(Validator):
def __init__(self, func_name: str, doc_obj=None) -> None:
self.func_name = func_name
if doc_obj is None:
doc_obj = get_doc_object(Validator._load_obj(func_name))
super().__init__(doc_obj)
@property
def name(self):
return self.func_name
@property
def mentioned_private_classes(self):
return [klass for klass in PRIVATE_CLASSES if klass in self.raw_doc]
@property
def examples_source_code(self):
lines = doctest.DocTestParser().get_examples(self.raw_doc)
return [line.source for line in lines]
def validate_pep8(self):
if not self.examples:
return
# F401 is needed to not generate flake8 errors in examples
# that do not user numpy or pandas
content = "".join(
(
"import numpy as np # noqa: F401\n",
"import pandas as pd # noqa: F401\n",
*self.examples_source_code,
)
)
file = tempfile.NamedTemporaryFile(mode="w", encoding="utf-8", delete=False)
try:
file.write(content)
file.flush()
sys.executable,
"-m",
"flake8",
"--format=%(row)d\t%(col)d\t%(code)s\t%(text)s",
"--ignore=E203,E3,W503,W504,E402,E731,E128,E124,E704",
file.name,
]
response = subprocess.run(cmd, capture_output=True, check=False, text=True)
for output in ("stdout", "stderr"):
out = getattr(response, output)
out = out.replace(file.name, "")
messages = out.strip("\n").splitlines()
if messages:
error_messages.extend(messages)
finally:
file.close()
os.unlink(file.name)
for error_message in error_messages:
line_number, col_number, error_code, message = error_message.split(
"\t", maxsplit=3
)
# Note: we subtract 2 from the line number because
# 'import numpy as np\nimport pandas as pd\n'
# is prepended to the docstrings.
yield error_code, message, int(line_number) - 2, int(col_number)
def non_hyphenated_array_like(self):
return "array_like" in self.raw_doc
def pandas_validate(func_name: str):
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"""
Call the numpydoc validation, and add the errors specific to pandas.
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Parameters
----------
func_name : str
Name of the object of the docstring to validate.
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Returns
-------
dict
Information about the docstring and the errors found.
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"""
func_obj = Validator._load_obj(func_name)
# Some objects are instances, e.g. IndexSlice, which numpydoc can't validate
doc_obj = get_doc_object(func_obj, doc=func_obj.__doc__)
doc = PandasDocstring(func_name, doc_obj)
result = validate(doc_obj)
mentioned_errs = doc.mentioned_private_classes
if mentioned_errs:
result["errors"].append(
pandas_error("GL04", mentioned_private_classes=", ".join(mentioned_errs))
result["errors"].extend(
pandas_error(
"SA05",
reference_name=rel_name,
right_reference=rel_name[len("pandas."):],
)
for rel_name in doc.see_also
if rel_name.startswith("pandas.")
)
result["examples_errs"] = ""
if doc.examples:
for error_code, error_message, line_number, col_number in doc.validate_pep8():
result["errors"].append(
pandas_error(
error_code=error_code,
error_message=error_message,
line_number=line_number,
col_number=col_number,
)
)
examples_source_code = "".join(doc.examples_source_code)
result["errors"].extend(
pandas_error("EX04", imported_library=wrong_import)
for wrong_import in ("numpy", "pandas")
if f"import {wrong_import}" in examples_source_code
)
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if doc.non_hyphenated_array_like():
result["errors"].append(pandas_error("PD01"))
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def validate_all(prefix, ignore_deprecated=False):
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"""
Execute the validation of all docstrings, and return a dict with the
results.
Parameters
----------
prefix : str or None
If provided, only the docstrings that start with this pattern will be
validated. If None, all docstrings will be validated.
ignore_deprecated: bool, default False
If True, deprecated objects are ignored when validating docstrings.
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Returns
-------
dict
A dictionary with an item for every function/method... containing
all the validation information.
"""
result = {}
seen = {}
for func_name, _, section, subsection in get_all_api_items():
if prefix and not func_name.startswith(prefix):
continue
doc_info = pandas_validate(func_name)
if ignore_deprecated and doc_info["deprecated"]:
continue
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result[func_name] = doc_info
shared_code_key = doc_info["file"], doc_info["file_line"]
shared_code = seen.get(shared_code_key, "")
result[func_name].update(
{
"in_api": True,
"section": section,
"subsection": subsection,
"shared_code_with": shared_code,
}
)
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seen[shared_code_key] = func_name
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return result
def get_all_api_items():
base_path = pathlib.Path(__file__).parent.parent
api_doc_fnames = pathlib.Path(base_path, "doc", "source", "reference")
for api_doc_fname in api_doc_fnames.glob("*.rst"):
with open(api_doc_fname, encoding="utf-8") as f:
yield from get_api_items(f)
def print_validate_all_results(
output_format: str,
prefix: str | None,
ignore_deprecated: bool,
ignore_errors: dict[str, set[str]],
):
if output_format not in ("default", "json", "actions"):
raise ValueError(f'Unknown output_format "{output_format}"')
if ignore_errors is None:
ignore_errors = {}
result = validate_all(prefix, ignore_deprecated)
if output_format == "json":
sys.stdout.write(json.dumps(result))
return 0
prefix = "##[error]" if output_format == "actions" else ""
exit_status = 0
for func_name, res in result.items():
error_messages = dict(res["errors"])
actual_failures = set(error_messages)
expected_failures = (ignore_errors.get(func_name, set())
| ignore_errors.get(None, set()))
for err_code in actual_failures - expected_failures:
sys.stdout.write(
f'{prefix}{res["file"]}:{res["file_line"]}:'
f'{err_code}:{func_name}:{error_messages[err_code]}\n'
)
exit_status += 1
for err_code in ignore_errors.get(func_name, set()) - actual_failures:
sys.stdout.write(
f'{prefix}{res["file"]}:{res["file_line"]}:'
f"{err_code}:{func_name}:"
"EXPECTED TO FAIL, BUT NOT FAILING\n"
)
exit_status += 1
return exit_status
def print_validate_one_results(func_name: str,
ignore_errors: dict[str, set[str]]) -> int:
def header(title, width=80, char="#") -> str:
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full_line = char * width
side_len = (width - len(title) - 2) // 2
adj = "" if len(title) % 2 == 0 else " "
title_line = f"{char * side_len} {title}{adj} {char * side_len}"
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return f"\n{full_line}\n{title_line}\n{full_line}\n\n"
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result = pandas_validate(func_name)
result["errors"] = [(code, message) for code, message in result["errors"]
if code not in ignore_errors.get(None, set())]
sys.stderr.write(header(f"Docstring ({func_name})"))
sys.stderr.write(f"{result['docstring']}\n")
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sys.stderr.write(header("Validation"))
if result["errors"]:
sys.stderr.write(f'{len(result["errors"])} Errors found for `{func_name}`:\n')
for err_code, err_desc in result["errors"]:
sys.stderr.write(f"\t{err_code}\t{err_desc}\n")
else:
sys.stderr.write(f'Docstring for "{func_name}" correct. :)\n')
if result["examples_errs"]:
sys.stderr.write(header("Doctests"))
sys.stderr.write(result["examples_errs"])
return len(result["errors"]) + len(result["examples_errs"])
def _format_ignore_errors(raw_ignore_errors):
ignore_errors = collections.defaultdict(set)
if raw_ignore_errors:
for error_codes in raw_ignore_errors:
obj_name = None
if " " in error_codes:
obj_name, error_codes = error_codes.split(" ")
# function errors "pandas.Series PR01,SA01"
if obj_name:
if obj_name in ignore_errors:
raise ValueError(
f"Object `{obj_name}` is present in more than one "
"--ignore_errors argument. Please use it once and specify "
"the errors separated by commas.")
ignore_errors[obj_name] = set(error_codes.split(","))
unknown_errors = ignore_errors[obj_name] - ALL_ERRORS
if unknown_errors:
raise ValueError(
f"Object `{obj_name}` is ignoring errors {unknown_errors} "
f"which are not known. Known errors are: {ALL_ERRORS}")
# global errors "PR02,ES01"
else:
ignore_errors[None].update(set(error_codes.split(",")))
unknown_errors = ignore_errors["*"] - ALL_ERRORS
if unknown_errors:
raise ValueError(
f"Unknown errors {unknown_errors} specified using --ignore_errors "
"Known errors are: {ALL_ERRORS}")
return ignore_errors
def main(
func_name,
output_format,
prefix,
ignore_deprecated,
ignore_errors
):
"""
Main entry point. Call the validation for one or for all docstrings.
"""
if func_name is None:
return print_validate_all_results(
prefix,
ignore_deprecated,
return print_validate_one_results(func_name, ignore_errors)
format_opts = "default", "json", "actions"
func_help = (
"function or method to validate (e.g. pandas.DataFrame.head) "
"if not provided, all docstrings are validated and returned "
"as JSON"
)
argparser = argparse.ArgumentParser(description="validate pandas docstrings")
argparser.add_argument("function", nargs="?", default=None, help=func_help)
argparser.add_argument(
"--format",
default="default",
choices=format_opts,
help="format of the output when validating "
"multiple docstrings (ignored when validating one). "
"It can be {str(format_opts)[1:-1]}",
)
argparser.add_argument(
"--prefix",
default=None,
help="pattern for the "
"docstring names, in order to decide which ones "
'will be validated. A prefix "pandas.Series.str."'
"will make the script validate all the docstrings "
"of methods starting by this pattern. It is "
"ignored if parameter function is provided",
)
argparser.add_argument(
"--ignore_deprecated",
default=False,
action="store_true",
help="if this flag is set, "
"deprecated objects are ignored when validating "
"all docstrings",
)
argparser.add_argument(
"--ignore_errors",
default=None,
action="append",
help="comma-separated list of error codes "
"(e.g. 'PR02,SA01'), with optional object path "
"to ignore errors for a single object "
"(e.g. pandas.DataFrame.head PR02,SA01). "
"Partial validation for more than one function"
"can be achieved by repeating this parameter.",
args = argparser.parse_args(sys.argv[1:])
main(args.function,
args.format,
args.prefix,
args.ignore_deprecated,
_format_ignore_errors(args.ignore_errors),