# # Copyright (c) 2012-2025 Snowflake Computing Inc. All rights reserved. # # Licensed to Modin Development Team under one or more contributor license agreements. # See the NOTICE file distributed with this work for additional information regarding # copyright ownership. The Modin Development Team licenses this file to you under the # Apache License, Version 2.0 (the "License"); you may not use this file except in # compliance with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software distributed under # the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific language # governing permissions and limitations under the License. # Code in this file may constitute partial or total reimplementation, or modification of # existing code originally distributed by the Modin project, under the Apache License, # Version 2.0. """Collection of general utility functions, mostly for internal use.""" import functools import importlib import inspect import os import re import types from collections.abc import Hashable, Sequence from pathlib import Path from textwrap import dedent, indent from typing import ( Any, Callable, Literal, NoReturn, Optional, Protocol, TypeVar, Union, runtime_checkable, ) import numpy as np import pandas from pandas._libs.lib import NoDefault, no_default from pandas.core.dtypes.inference import is_float, is_integer from pandas.util._decorators import Appender from snowflake.snowpark.modin.config import DocModule from snowflake.snowpark.modin.plugin.utils.error_message import ErrorMessage from snowflake.snowpark.modin.plugin.utils.warning_message import WarningMessage T = TypeVar("T") """Generic type parameter""" Fn = TypeVar("Fn", bound=Callable) """Function type parameter (used in decorators that don't change a function's signature)""" @runtime_checkable class SupportsPrivateToPandas(Protocol): # noqa: PR01 """Structural type for objects with a ``_to_pandas`` method (note the leading underscore).""" def _to_pandas(self) -> Any: # noqa: GL08 # TODO add proper return type pass @runtime_checkable class SupportsPublicToPandas(Protocol): # noqa: PR01 """Structural type for objects with a ``to_pandas`` method (without a leading underscore).""" def to_pandas(self) -> Any: # noqa: GL08 pass @runtime_checkable class SupportsPublicToNumPy(Protocol): # noqa: PR01 """Structural type for objects with a ``to_numpy`` method (without a leading underscore).""" def to_numpy(self) -> Any: # pragma: no cover # noqa: GL08 pass @runtime_checkable class SupportsPrivateToNumPy(Protocol): # noqa: PR01 """Structural type for objects with a ``_to_numpy`` method (note the leading underscore).""" def _to_numpy(self) -> Any: # pragma: no cover # noqa: GL08 pass PANDAS_API_URL_TEMPLATE = f"https://pandas.pydata.org/pandas-docs/version/{pandas.__version__}/reference/api/{{}}.html" MODIN_UNNAMED_SERIES_LABEL = "__reduced__" """ The '__reduced__' name is used internally by the query compiler as a column name to represent pandas Series objects that are not explicitly assigned a name, so as to distinguish between an N-element series and 1xN dataframe. """ def _make_api_url(token: str) -> str: """ Generate the link to pandas documentation. Parameters ---------- token : str Part of URL to use for generation. Returns ------- str URL to pandas doc. Notes ----- This function is extracted for better testability. """ return PANDAS_API_URL_TEMPLATE.format(token) def _get_indent(doc: str) -> int: """ Compute indentation in docstring. Parameters ---------- doc : str The docstring to compute indentation for. Returns ------- int Minimal indent (excluding empty lines). """ indents = _get_indents(doc) return min(indents) if indents else 0 def _get_indents(source: Union[list, str]) -> list: """ Compute indentation for each line of the source string. Parameters ---------- source : str or list of str String to compute indents for. Passed list considered as a list of lines of the source string. Returns ------- list of ints List containing computed indents for each line. """ indents = [] if not isinstance(source, list): source = source.splitlines() for line in source: if not line.strip(): continue for pos, ch in enumerate(line): # noqa: B007 if ch != " ": break indents.append(pos) return indents def format_string(template: str, **kwargs: str) -> str: """ Insert passed values at the corresponding placeholders of the specified template. In contrast with the regular ``str.format()`` this function computes proper indents for the placeholder values. Parameters ---------- template : str Template to substitute values in. **kwargs : dict Dictionary that maps placeholder names with values. Returns ------- str Formated string. """ # We want to change indentation only for those values which placeholders are located # at the start of the line, in that case the placeholder sets an indentation # that the filling value has to obey. # RegExp determining placeholders located at the beginning of the line. regex = r"^( *)\{(\w+)\}" for line in template.splitlines(): if line.strip() == "": continue match = re.search(regex, line) if match is None: continue nspaces = len(match.group(1)) key = match.group(2) value = kwargs.get(key) if not value: continue value = dedent(value) # Since placeholder is located at the beginning of a new line, # it already has '\n' before it, so to avoid double new lines # we want to discard the first leading '\n' at the value line, # the others leading '\n' are considered as being put on purpose if value[0] == "\n": value = value[1:] # `.splitlines()` doesn't preserve last empty line, # so we have to restore it further value_lines = value.splitlines() # We're not indenting the first line of the value, since it's already indented # properly because of the placeholder indentation. indented_lines = [ indent(line, " " * nspaces) if line != "\n" else line for line in value_lines[1:] ] # If necessary, restoring the last line dropped by `.splitlines()` if value[-1] == "\n": indented_lines += [" " * nspaces] indented_value = "\n".join([value_lines[0], *indented_lines]) kwargs[key] = indented_value return template.format(**kwargs) def align_indents(source: str, target: str) -> str: """ Align indents of two strings. Parameters ---------- source : str Source string to align indents with. target : str Target string to align indents. Returns ------- str Target string with indents aligned with the source. """ source_indent = _get_indent(source) target = dedent(target) return indent(target, " " * source_indent) def append_to_docstring(message: str) -> Callable[[Fn], Fn]: """ Create a decorator which appends passed message to the function's docstring. Parameters ---------- message : str Message to append. Returns ------- callable """ def decorator(func: Fn) -> Fn: to_append = align_indents(func.__doc__ or "", message) return Appender(to_append)(func) return decorator def _replace_doc( source_obj: object, target_obj: object, overwrite: bool, apilink: Optional[Union[str, list[str]]], parent_cls: Optional[Fn] = None, attr_name: Optional[str] = None, modify_doc: Optional[Callable[[object, str], str]] = None, ) -> None: """ Replace docstring in `target_obj`, possibly taking from `source_obj` and augmenting. Can append the link to pandas API online documentation. Parameters ---------- source_obj : object Any object from which to take docstring from. target_obj : object The object which docstring to replace. overwrite : bool Forces replacing the docstring with the one from `source_obj` even if `target_obj` has its own non-empty docstring. apilink : str | List[str], optional If non-empty, insert the link(s) to pandas API documentation. Should be the prefix part in the URL template, e.g. "pandas.DataFrame". parent_cls : class, optional If `target_obj` is an attribute of a class, `parent_cls` should be that class. This is used for generating the API URL as well as for handling special cases like `target_obj` being a property. attr_name : str, optional Gives the name to `target_obj` if it's an attribute of `parent_cls`. Needed to handle some special cases and in most cases could be determined automatically. modify_doc: Optional[Callable[[object, str],str]], default: None If not None, then call this function to retrieve a new docstring to use. """ if isinstance(target_obj, (staticmethod, classmethod)): # we cannot replace docs on decorated objects, we must replace them # on original functions instead target_obj = target_obj.__func__ source_doc = source_obj.__doc__ or "" target_doc = target_obj.__doc__ or "" overwrite = overwrite or not target_doc doc = source_doc if overwrite else target_doc if parent_cls and not attr_name: if isinstance(target_obj, property): attr_name = target_obj.fget.__name__ # type: ignore[union-attr] elif isinstance(target_obj, (staticmethod, classmethod)): attr_name = target_obj.__func__.__name__ else: attr_name = target_obj.__name__ # type: ignore[attr-defined] if ( source_doc.strip() and apilink and "pandas API documentation for " not in target_doc and (not (attr_name or "").startswith("_")) ): apilink_l = [apilink] if not isinstance(apilink, list) and apilink else apilink links = [] for link in apilink_l: if attr_name: token = f"{link}.{attr_name}" else: token = link url = _make_api_url(token) links.append(f"`{token} <{url}>`_") indent_line = " " * _get_indent(doc) notes_section = f"\n{indent_line}Note\n{indent_line}-----\n" url_line = f"{indent_line}See pandas API documentation for {', '.join(links).strip()} for more.\n" notes_section_with_url = notes_section + url_line if notes_section in doc: doc = doc.replace(notes_section, notes_section_with_url) else: doc += notes_section_with_url # apply custom modify doc if modify_doc is not None: doc = modify_doc(target_obj, doc) if parent_cls and isinstance(target_obj, property): if overwrite: target_obj.fget.__doc_inherited__ = True # type: ignore[union-attr] assert attr_name is not None setattr( parent_cls, attr_name, property(target_obj.fget, target_obj.fset, target_obj.fdel, doc), ) else: if overwrite: target_obj.__doc_inherited__ = True # type: ignore[attr-defined] target_obj.__doc__ = doc # This is a map from objects whose docstrings we are overriding to functions that # take a DocModule string and override the docstring according to the # DocModule. When we update DocModule, we can use this map to update all # inherited docstrings. _docstring_inheritance_calls: list[Callable[[str], None]] = [] # This is a set of (class, attribute_name) pairs whose docstrings we have # already replaced since we last updated DocModule. Note that we don't store # the attributes themselves since we replace property attributes instead of # modifying them in place: # https://github.com/modin-project/modin/blob/e9dbcc127913db77473a83936e8b6bb94ef84f0d/modin/utils.py#L353 _attributes_with_docstrings_replaced: set[tuple[type, str]] = set() def _documentable_obj(obj: object) -> bool: """Check if `obj` docstring could be patched.""" return bool( callable(obj) or (isinstance(obj, property) and obj.fget) or (isinstance(obj, (staticmethod, classmethod)) and obj.__func__) ) def _update_inherited_docstrings(doc_module: DocModule) -> None: """ Update all inherited docstrings. Parameters ---------- doc_module: DocModule The current DocModule """ _attributes_with_docstrings_replaced.clear() for doc_inheritance_call in _docstring_inheritance_calls: doc_inheritance_call(doc_module=doc_module.get()) def _inherit_docstrings_in_place( cls_or_func, doc_module, parent, excluded, overwrite_existing, apilink, modify_doc, ) -> None: # Import the docs module and get the class (e.g. `DataFrame`). imported_doc_module = importlib.import_module(doc_module) # Set the default parent so we can use it in case some docs are missing from # parent module. default_parent = parent # Try to get the parent object from the doc module, and if it isn't there, # get it from parent instead. We only do this if we are overriding pandas # documentation. We don't touch other docs. if doc_module != DocModule.default and "pandas" in str( getattr(parent, "__module__", "") ): parent_name = ( # DocModule should use the class BasePandasDataset to override the # docstrings of BasePandasDataset, even if BasePandasDataset # normally inherits docstrings from a different `parent`. # TODO(https://github.com/modin-project/modin/issues/7134): upstream # this fix to Modin. "BasePandasDataset" if getattr(cls_or_func, "__name__", "") == "BasePandasDataset" # For other classes, override docstrings with the class that has the # same name as the `parent` class, e.g. DataFrame inherits # docstrings from doc_module.DataFrame. else getattr(parent, "__name__", "") ) parent = getattr(imported_doc_module, parent_name, parent) if parent != default_parent: # Reset API link in case the docs are overridden. apilink = None overwrite_existing = True if parent not in excluded: _replace_doc( parent, cls_or_func, overwrite_existing, apilink, modify_doc=modify_doc ) if not isinstance(cls_or_func, types.FunctionType): seen = set() for base in cls_or_func.__mro__: # type: ignore[attr-defined] if base is object: continue for attr, obj in base.__dict__.items(): if attr in seen or (base, attr) in _attributes_with_docstrings_replaced: continue seen.add(attr) # Try to get the attribute from the docs class first, then # from the default parent (pandas), and if it's not in either, # set `parent_obj` to `None`. parent_obj = getattr(parent, attr, getattr(default_parent, attr, None)) if ( parent_obj in excluded or not _documentable_obj(parent_obj) or not _documentable_obj(obj) ): continue _replace_doc( parent_obj, obj, overwrite_existing, apilink, parent_cls=cls_or_func, attr_name=attr, modify_doc=modify_doc, ) _attributes_with_docstrings_replaced.add((base, attr)) def _inherit_docstrings( parent: object, excluded: list[object] = [], # noqa: B006 overwrite_existing: bool = False, apilink: Optional[Union[str, list[str]]] = None, modify_doc: Optional[Callable[[object, str], str]] = None, ) -> Callable[[Fn], Fn]: """ Create a decorator which overwrites decorated object docstring(s). It takes `parent` __doc__ attribute. Also overwrites __doc__ of methods and properties defined in the target or its ancestors if it's a class with the __doc__ of matching methods and properties from the `parent`. Parameters ---------- parent : object Parent object from which the decorated object inherits __doc__. excluded : list, default: [] List of parent objects from which the class does not inherit docstrings. overwrite_existing : bool, default: False Allow overwriting docstrings that already exist in the decorated class. apilink : str | List[str], optional If non-empty, insert the link(s) to pandas API documentation. Should be the prefix part in the URL template, e.g. "pandas.DataFrame". modify_doc: Optional[Callable[[str],str]], default: None If not None, then call this function to retrieve a new docstring to use. Returns ------- callable Decorator which replaces the decorated object's documentation with `parent` documentation. Notes ----- Keep in mind that the function will override docstrings even for attributes which are not defined in target class (but are defined in the ancestor class), which means that ancestor class attribute docstrings could also change. """ # NOTE this implementation fixes https://github.com/modin-project/modin/issues/7138, # which we need to fix upstream. def decorator(cls_or_func: Fn) -> Fn: inherit_docstring_in_place = functools.partial( _inherit_docstrings_in_place, cls_or_func=cls_or_func, parent=parent, excluded=excluded, overwrite_existing=overwrite_existing, apilink=apilink, modify_doc=modify_doc, ) inherit_docstring_in_place(doc_module=DocModule.get()) _docstring_inheritance_calls.append(inherit_docstring_in_place) return cls_or_func return decorator DocModule.subscribe(_update_inherited_docstrings) def expanduser_path_arg(argname: str) -> Callable[[Fn], Fn]: """ Decorate a function replacing its path argument with "user-expanded" value. Parameters ---------- argname : str Name of the argument which is containing a path to be expanded. Returns ------- callable Decorator which performs the replacement. """ def decorator(func: Fn) -> Fn: signature = inspect.signature(func) assert ( getattr(signature.parameters.get(argname), "name", None) == argname ), f"Function {func} does not take '{argname}' as argument" @functools.wraps(func) def wrapped(*args: tuple, **kw: dict) -> Any: params = signature.bind(*args, **kw) if patharg := params.arguments.get(argname, None): if isinstance(patharg, str) and patharg.startswith( "~" ): # pragma: no cover params.arguments[argname] = os.path.expanduser(patharg) elif isinstance(patharg, Path): # pragma: no cover params.arguments[argname] = patharg.expanduser() return func(*params.args, **params.kwargs) return func(*args, **kw) return wrapped # type: ignore[return-value] return decorator def extract_sections(text: str) -> list[dict[str, str]]: """ split docstring into sections. Args: text: input text Returns: list of dictionaries where each dictionary contains a key heading, and a key content. """ lines = text.split("\n") # produces list of lines that contain only - chars # this means that the line before is a heading for a start of a section (if not whitespace) heading_linenos = list( map( lambda t: t[0], filter( lambda t: len(t[1]) > 0 and t[1].count("-") == len(t[1]), zip(range(len(lines)), lines), ), ) ) last_lineno = 0 last_heading = "" sections = [] for lineno in heading_linenos: # is line before heading heading = "" if lineno - 1 >= 0: heading = lines[lineno - 1] # if only whitespace, it's a section starting with ---- (line sep) if len(heading.strip()) == 0: heading = "" # fetch section as string section_lines = lines[last_lineno : lineno - 1] # remove ----- if section has one if section_lines[0].startswith("-"): section_lines = section_lines[1:] sections.append({"heading": last_heading, "content": "\n".join(section_lines)}) last_lineno = lineno last_heading = heading # fetch section as string section_lines = lines[last_lineno:] # remove ----- if section has one if section_lines[0].startswith("-"): section_lines = section_lines[1:] sections.append({"heading": last_heading, "content": "\n".join(section_lines)}) return sections def assemble_sections_to_string(sections: list[dict[str, str]]) -> str: """ create docstring from individual sections given as dictionaries (see extract sections) Args: sections: list of dictionaries where each dictionary contains a key heading, and a key content. Returns: combined string """ return "\n".join( map( lambda section: section["heading"] + "\n" + "-" * len(section["heading"]) + "\n" + section["content"], sections, ) ) def find_index(seq: Sequence[Any], predicate: Callable) -> Optional[int]: """ find index of element that satisfies first condition from left to right Args: seq: iterable to check with predicate predicate: predicate function to determine whether a match occurred or not Returns: index of first matching element, if no match None """ indices = list( map( lambda t: t[0], filter(lambda t: predicate(t[1]), zip(range(len(seq)), seq)), ) ) if not indices: return None return indices[0] # operators are similar and use the same example, generate docstring depending on current API support def _create_operator_docstring( parent: object, excluded: list[object] = [], # noqa: B006 overwrite_existing: bool = False, apilink: Optional[Union[str, list[str]]] = None, ) -> Callable[[Fn], Fn]: def modify_doc(parent: object, doc: str) -> str: sections = extract_sections(doc) # add a Caution section to warn that fill_value / level are not supported yet. # before examples index = find_index(sections, lambda s: s["heading"] == "Examples") assert index is not None sections.insert( index, { "heading": "Caution", "content": "Snowpark pandas API does not support `fill_value` and `level` except for default values.", }, ) # When invalid numeric values are present, Snowpark denotes them with `None` while native pandas uses `NaN`. sections.insert( index, { "heading": "Caution", "content": "Snowpark pandas API denotes invalid numeric results with `None` while pandas uses `NaN`.", }, ) op_name = parent.__name__ # type: ignore[attr-defined] sections.insert( index, { "heading": "Caution", "content": "In Snowpark pandas API, whenever a binary operation involves a NULL value, " f"the result will preserve NULL values, e.g. NULL.{op_name}() will yield NULL.", }, ) # for div operators, add another caution section pointing out the different div-by-zero behavior if "floordiv" in op_name or "truediv" in op_name: sections.insert( index, { "heading": "Caution", "content": "Snowpark pandas API will always produce a division by zero error if the right hand side contains one or more zeroes. This is different from pandas which only produces a ZeroDivisionError exception when ``dtype='object'``.", }, ) if "pow" in op_name: # Zero raised to negative powers is supported in Snowpark and results in inf values. None ** 0 = 1 and 1 ** None = 1 sections.insert( index, { "heading": "Caution", "content": "In Snowpark pandas API, zero raised to negative powers produces `inf` value as the result while pandas raises a ValueError. In Snowpark pandas API, `None` raised to `0` and `1` raised to `None` produce `1.0` as the result. pandas produces `np.nan` for both cases.", }, ) # Currently unimplemented, behavior unknown. Raises NotImplementedError. # TODO: SNOW-916731 bug with %d and 0. # TODO: SNOW-916733 support string and non-numeric data as operands. if "mod" in op_name or "rmod" in op_name: sections.insert( index, { "heading": "Caution", "content": """ Snowpark pandas API computes `mod` differently from pandas and Python. Snowpark pandas API only supports numeric data with the `mod` operator. Below is a table highlighting the differences in modulo computation for Python, pandas, and Snowpark pandas. +---------------------------+---------------------------+---------------------------+ | Python | pandas | Snowpark pandas | +---------------------------+---------------------------+---------------------------+ | 7 % 5 = 2 | 7 % 5 = 2 | 7 % 5 = 2 | +---------------------------+---------------------------+---------------------------+ | 7 % -5 = -3 | 7 % -5 = -3 | 7 % -5 = 2 | +---------------------------+---------------------------+---------------------------+ | -7 % 5 = 3 | -7 % 5 = 3 | -7 % 5 = -2 | +---------------------------+---------------------------+---------------------------+ | -7 % -5 = -2 | -7 % -5 = -2 | -7 % -5 = -2 | +---------------------------+---------------------------+---------------------------+ """, }, ) # in original pandas, e.g. for https://pandas.pydata.org/pandas-docs/version/1.5.3/reference/api/pandas.Series.sub.html # and all the other operators a standard example is used with the following data and always fill_value=0 # a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) # b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) # a b # we explicitly re-generate it here to get the correct behavior without fill_value. # there are also obvious doc gaps in pandas, so the best would be to generate the examples properly using Snowpark pandas API here. # because Snowpark pandas API requires a connection, for now use pandas. import pandas as pd # we differ here with the data from pandas docs, because pandas has different div by zero behavior # this here is the original pandas example data: # a_data = {'data':[1, 1, 1, np.nan], 'index':["a", "b", "c", "d"]} # b_data = {'data': [0, 1, 2, np.nan, 1], 'index' : ["a", "b", "c", "d", "f"]} a_data = {"data": [1, -2, 0, np.nan], "index": ["a", "b", "c", "d"]} b_data = {"data": [-2, 1, 3, np.nan, 1], "index": ["a", "b", "c", "d", "f"]} a_doc = "pd.Series([1, -2, 0, np.nan], index=['a', 'b', 'c', 'd'])" b_doc = "pd.Series([-2, 1, 3, np.nan, 1], index=['a', 'b', 'c', 'd', 'f'])" # to avoid div-by-zero, swap for rtruediv and rfloordiv a,b if op_name in ["rtruediv", "rfloordiv"]: a_data, b_data = b_data, a_data a_doc, b_doc = b_doc, a_doc # Snowpark and native pandas can have different behavior for negative mod operations. if op_name in ["mod", "rmod"]: a_data = {"data": [1, 2, 0, np.nan], "index": ["a", "b", "c", "d"]} b_data = {"data": [2, 1, 3, np.nan, 1], "index": ["a", "b", "c", "d", "f"]} a_doc = "pd.Series([1, 2, 0, np.nan], index=['a', 'b', 'c', 'd'])" b_doc = "pd.Series([2, 1, 3, np.nan, 1], index=['a', 'b', 'c', 'd', 'f'])" a = pd.Series(**a_data) b = pd.Series(**b_data) assert hasattr(a, op_name) ans = getattr(a, op_name)(b) # in Snowflake, comparing with NULL returns NULL if op_name in ["eq", "ne", "ge", "le", "gt", "lt"]: ans = ans.astype(object) ans[["d", "f"]] = None content = ">>> a = " + a_doc + "\n" content += ">>> a\n" content += str(a) + "\n" content += ">>> b = " + b_doc + "\n" content += ">>> b\n" content += str(b) + "\n" content += f">>> a.{op_name}(b)\n" content += str(ans) + "\n" # replace examples section index = find_index(sections, lambda s: s["heading"] == "Examples") assert index is not None sections[index] = {"heading": "Examples", "content": content} return assemble_sections_to_string(sections) return _inherit_docstrings( parent, excluded, overwrite_existing=overwrite_existing, apilink=apilink, modify_doc=modify_doc, ) # TODO add proper type annotation def to_pandas(modin_obj: SupportsPrivateToPandas) -> Any: """ Convert a Modin DataFrame/Series to a pandas DataFrame/Series. Parameters ---------- modin_obj : modin.DataFrame, modin.Series The Modin DataFrame/Series to convert. Returns ------- pandas.DataFrame or pandas.Series Converted object with type depending on input. """ return modin_obj._to_pandas() def hashable(obj: bool) -> bool: """ Return whether the `obj` is hashable. Parameters ---------- obj : object The object to check. Returns ------- bool """ # Happy path: if there's no __hash__ method, the object definitely isn't hashable if not hasattr(obj, "__hash__"): return False # Otherwise, we may still need to check for type errors, as in the case of `hash(([],))`. # (e.g. an unhashable object inside a tuple) try: hash(obj) except TypeError: return False return True def try_cast_to_pandas(obj: Any, squeeze: bool = False) -> Any: """ Convert `obj` and all nested objects from Modin to pandas if it is possible. If no convertion possible return `obj`. Parameters ---------- obj : object Object to convert from Modin to pandas. squeeze : bool, default: False Squeeze the converted object(s) before returning them. Returns ------- object Converted object. """ if isinstance(obj, SupportsPrivateToPandas): result = obj._to_pandas() if squeeze: result = result.squeeze(axis=1) return result if isinstance(obj, SupportsPublicToPandas): result = obj.to_pandas() if squeeze: result = result.squeeze(axis=1) # Query compiler case, it doesn't have logic about convertion to Series if ( isinstance(getattr(result, "name", None), str) and result.name == MODIN_UNNAMED_SERIES_LABEL ): result.name = None return result if isinstance(obj, (list, tuple)): return type(obj)([try_cast_to_pandas(o, squeeze=squeeze) for o in obj]) if isinstance(obj, dict): return {k: try_cast_to_pandas(v, squeeze=squeeze) for k, v in obj.items()} if callable(obj): module_hierarchy = getattr(obj, "__module__", None) module_hierarchy = ( [""] if module_hierarchy is None else module_hierarchy.split(".") ) fn_name = getattr(obj, "__name__", None) if fn_name and module_hierarchy[0] == "modin": return ( getattr(pandas.DataFrame, fn_name, obj) if module_hierarchy[-1] == "dataframe" else getattr(pandas.Series, fn_name, obj) ) return obj def wrap_into_list(*args: Any, skipna: bool = True) -> list[Any]: """ Wrap a sequence of passed values in a flattened list. If some value is a list by itself the function appends its values to the result one by one instead inserting the whole list object. Parameters ---------- *args : tuple Objects to wrap into a list. skipna : bool, default: True Whether or not to skip nan or None values. Returns ------- list Passed values wrapped in a list. """ def isnan(o: Any) -> bool: return o is None or (isinstance(o, float) and np.isnan(o)) res = [] for o in args: if skipna and isnan(o): continue if isinstance(o, list): res.extend(o) else: res.append(o) return res def wrap_udf_function(func: Callable) -> Callable: """ Create a decorator that makes `func` return pandas objects instead of Modin. Parameters ---------- func : callable Function to wrap. Returns ------- callable """ def wrapper(*args: Any, **kwargs: Any) -> Any: result = func(*args, **kwargs) # if user accidently returns modin DataFrame or Series # casting it back to pandas to properly process return try_cast_to_pandas(result) wrapper.__name__ = func.__name__ return wrapper def instancer(_class: Callable[[], T]) -> T: """ Create a dummy instance each time this is imported. This serves the purpose of allowing us to use all of pandas plotting methods without aliasing and writing each of them ourselves. Parameters ---------- _class : object Returns ------- object Instance of `_class`. """ return _class() def import_optional_dependency(name: str, message: str) -> types.ModuleType: """ Import an optional dependecy. Parameters ---------- name : str The module name. message : str Additional text to include in the ImportError message. Returns ------- module : ModuleType The imported module. """ try: return importlib.import_module(name) except ImportError: raise ImportError( f"Missing optional dependency '{name}'. {message} " + f"Use pip or conda to install {name}." ) from None def is_property(key: Any) -> bool: """Check whether `key` is a property.""" return type(key) == property def error_not_implemented_parameter(param: str, condition: bool) -> Optional[NoReturn]: """ Raises a ``NotImplementedError`` for a parameter ``param`` when its argument ``arg`` is not None. Parameters ---------- param : str Name of the parameter that is not implemented. arg : Any Value of the parameter that is not implemented. Raises ------ NotImplementedError if ``arg`` is None """ if condition: ErrorMessage.not_implemented(f"{param} is not implemented.") return None def warn_not_supported_parameter(param: str, condition: bool, fn: str) -> None: """ Invokes ``WarningMessage.ignored_argument`` for parameter ``param`` of function ``fn`` if its argument ``arg`` is not None. Parameters ---------- param : str Name of the parameter that has been ignored. arg : Any Value of the parameter that has been ignored. fn : str Name of the function with parameter ``param`` """ if condition: WarningMessage.ignored_argument(fn, param, "") def should_parse_header( header: Optional[Union[int, Sequence[int], Literal["infer"]]], names: Optional[Union[Sequence[Hashable], NoDefault]], ) -> bool: """ Returns whether the first row of a CSV file is used as the column names for a DataFrame. Parameters ---------- header : int, list of int, None Row number(s) to use as the column names, and the start of the data. names : array-like List of column names to use. Returns ------- Whether the first row of the CSV file will be used as column names. """ should_parse_first_row = header in ["infer", 0] and (names is None or header == 0) return should_parse_first_row def translate_pandas_default(arg: Any) -> Any: """ Returns None if `arg` is no_default, otherwise `arg`. Parameters ---------- arg : Any Argument `arg` to be translated. Returns ------- None if `arg` is no_default, otherwise the original value of `arg`. """ return None if arg is no_default else arg def validate_int_kwarg(value: int, arg_name: str, float_allowed: bool = False) -> int: """ check whether the argument passed is integer type Parameters ---------- value : int The integer value to be validated arg_name: str Name of the argument. To be reflected in the error message. float_allowed: bool Whether allow integer value to be represented in float, for example, 1.0. However, values like 2.3 is not valid. Returns ------- Integer representation of the value. ValueError is raised if the value is not a valid integer """ if is_integer(value) or (float_allowed and is_float(value) and value.is_integer()): # type: ignore[attr-defined] return int(value) raise ValueError(f"{arg_name} must be an integer") def doc_replace_dataframe_with_link(_obj: Any, doc: str) -> str: """ Helper function to be passed as the `modify_doc` parameter to `_inherit_docstrings`. This replaces all unqualified instances of "DataFrame" with ":class:`~modin.pandas.DataFrame`" to prevent it from linking automatically to snowflake.snowpark.DataFrame: see SNOW-1233342. To prevent it from overzealously replacing examples in doctests or already-qualified paths, it replaces matches of DataFrame that are preceded by whitespace and followed by whitespace or a comma (to cover cases like "param: Series or DataFrame, optional"). """ return re.sub( r"(?<=\s)DataFrame(?=[\s,])", ":class:`~modin.pandas.DataFrame`", doc, ) class classproperty: """ Decorator that allows creating read-only class properties. Parameters ---------- func : method Examples -------- >>> class A: ... field = 10 ... @classproperty ... def field_x2(cls): ... return cls.field * 2 ... >>> print(A.field_x2) 20 """ def __init__(self, func: Any) -> None: self.fget = func def __get__(self, instance: Any, owner: Any) -> Any: # noqa: GL08 return self.fget(owner)