""" The config module holds package-wide configurables and provides a uniform API for working with them. Overview ======== This module supports the following requirements: - options are referenced using keys in dot.notation, e.g. "x.y.option - z". - keys are case-insensitive. - functions should accept partial/regex keys, when unambiguous. - options can be registered by modules at import time. - options can be registered at init-time (via core.config_init) - options have a default value, and (optionally) a description and validation function associated with them. - options can be deprecated, in which case referencing them should produce a warning. - deprecated options can optionally be rerouted to a replacement so that accessing a deprecated option reroutes to a differently named option. - options can be reset to their default value. - all option can be reset to their default value at once. - all options in a certain sub - namespace can be reset at once. - the user can set / get / reset or ask for the description of an option. - a developer can register and mark an option as deprecated. - you can register a callback to be invoked when the option value is set or reset. Changing the stored value is considered misuse, but is not verboten. Implementation ============== - Data is stored using nested dictionaries, and should be accessed through the provided API. - "Registered options" and "Deprecated options" have metadata associated with them, which are stored in auxiliary dictionaries keyed on the fully-qualified key, e.g. "x.y.z.option". - the config_init module is imported by the package's __init__.py file. placing any register_option() calls there will ensure those options are available as soon as pandas is loaded. If you use register_option in a module, it will only be available after that module is imported, which you should be aware of. - `config_prefix` is a context_manager (for use with the `with` keyword) which can save developers some typing, see the docstring. """ from __future__ import annotations from contextlib import contextmanager import re from typing import ( TYPE_CHECKING, Any, NamedTuple, cast, ) import warnings from pandas._typing import F from pandas.util._exceptions import find_stack_level if TYPE_CHECKING: from collections.abc import ( Callable, Generator, Sequence, ) class DeprecatedOption(NamedTuple): key: str category: type[Warning] msg: str | None rkey: str | None removal_ver: str | None class RegisteredOption(NamedTuple): key: str defval: Any doc: str validator: Callable[[object], Any] | None cb: Callable[[str], Any] | None # holds deprecated option metadata _deprecated_options: dict[str, DeprecatedOption] = {} # holds registered option metadata _registered_options: dict[str, RegisteredOption] = {} # holds the current values for registered options _global_config: dict[str, Any] = {} # keys which have a special meaning _reserved_keys: list[str] = ["all"] class OptionError(AttributeError, KeyError): """ Exception raised for pandas.options. Backwards compatible with KeyError checks. See Also -------- options : Access and modify global pandas settings. Examples -------- >>> pd.options.context Traceback (most recent call last): OptionError: No such option """ __module__ = "pandas.errors" # # User API def _get_single_key(pat: str) -> str: keys = _select_options(pat) if len(keys) == 0: _warn_if_deprecated(pat) raise OptionError(f"No such keys(s): {pat!r}") if len(keys) > 1: raise OptionError("Pattern matched multiple keys") key = keys[0] _warn_if_deprecated(key) key = _translate_key(key) return key def get_option(pat: str) -> Any: """ Retrieve the value of the specified option. This method allows users to query the current value of a given option in the pandas configuration system. Options control various display, performance, and behavior-related settings within pandas. Parameters ---------- pat : str Regexp which should match a single option. .. warning:: Partial matches are supported for convenience, but unless you use the full option name (e.g. x.y.z.option_name), your code may break in future versions if new options with similar names are introduced. Returns ------- Any The value of the option. Raises ------ OptionError : if no such option exists See Also -------- set_option : Set the value of the specified option or options. reset_option : Reset one or more options to their default value. describe_option : Print the description for one or more registered options. Notes ----- For all available options, please view the :ref:`User Guide ` or use ``pandas.describe_option()``. Examples -------- >>> pd.get_option("display.max_columns") # doctest: +SKIP 4 """ key = _get_single_key(pat) # walk the nested dict root, k = _get_root(key) return root[k] def set_option(*args) -> None: """ Set the value of the specified option or options. This method allows fine-grained control over the behavior and display settings of pandas. Options affect various functionalities such as output formatting, display limits, and operational behavior. Settings can be modified at runtime without requiring changes to global configurations or environment variables. Parameters ---------- *args : str | object | dict Arguments provided in pairs, which will be interpreted as (pattern, value), or as a single dictionary containing multiple option-value pairs. pattern: str Regexp which should match a single option value: object New value of option .. warning:: Partial pattern matches are supported for convenience, but unless you use the full option name (e.g. x.y.z.option_name), your code may break in future versions if new options with similar names are introduced. Returns ------- None No return value. Raises ------ ValueError if odd numbers of non-keyword arguments are provided TypeError if keyword arguments are provided OptionError if no such option exists See Also -------- get_option : Retrieve the value of the specified option. reset_option : Reset one or more options to their default value. describe_option : Print the description for one or more registered options. option_context : Context manager to temporarily set options in a ``with`` statement. Notes ----- For all available options, please view the :ref:`User Guide ` or use ``pandas.describe_option()``. Examples -------- Option-Value Pair Input: >>> pd.set_option("display.max_columns", 4) >>> df = pd.DataFrame([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]) >>> df 0 1 ... 3 4 0 1 2 ... 4 5 1 6 7 ... 9 10 [2 rows x 5 columns] >>> pd.reset_option("display.max_columns") Dictionary Input: >>> pd.set_option({"display.max_columns": 4, "display.precision": 1}) >>> df = pd.DataFrame([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]) >>> df 0 1 ... 3 4 0 1 2 ... 4 5 1 6 7 ... 9 10 [2 rows x 5 columns] >>> pd.reset_option("display.max_columns") >>> pd.reset_option("display.precision") """ # Handle dictionary input if len(args) == 1 and isinstance(args[0], dict): args = tuple(kv for item in args[0].items() for kv in item) nargs = len(args) if not nargs or nargs % 2 != 0: raise ValueError("Must provide an even number of non-keyword arguments") for k, v in zip(args[::2], args[1::2], strict=True): key = _get_single_key(k) opt = _get_registered_option(key) if opt and opt.validator: opt.validator(v) # walk the nested dict root, k_root = _get_root(key) root[k_root] = v if opt.cb: opt.cb(key) def describe_option(pat: str = "", _print_desc: bool = True) -> str | None: """ Print the description for one or more registered options. Call with no arguments to get a listing for all registered options. Parameters ---------- pat : str, default "" String or string regexp pattern. Empty string will return all options. For regexp strings, all matching keys will have their description displayed. _print_desc : bool, default True If True (default) the description(s) will be printed to stdout. Otherwise, the description(s) will be returned as a string (for testing). Returns ------- None If ``_print_desc=True``. str If the description(s) as a string if ``_print_desc=False``. See Also -------- get_option : Retrieve the value of the specified option. set_option : Set the value of the specified option or options. reset_option : Reset one or more options to their default value. Notes ----- For all available options, please view the :ref:`User Guide `. Examples -------- >>> pd.describe_option("display.max_columns") # doctest: +SKIP display.max_columns : int If max_cols is exceeded, switch to truncate view... """ keys = _select_options(pat) if len(keys) == 0: raise OptionError(f"No such keys(s) for {pat=}") s = "\n".join([_build_option_description(k) for k in keys]) if _print_desc: print(s) return None return s def reset_option(pat: str) -> None: """ Reset one or more options to their default value. This method resets the specified pandas option(s) back to their default values. It allows partial string matching for convenience, but users should exercise caution to avoid unintended resets due to changes in option names in future versions. Parameters ---------- pat : str/regex If specified only options matching ``pat*`` will be reset. Pass ``"all"`` as argument to reset all options. .. warning:: Partial matches are supported for convenience, but unless you use the full option name (e.g. x.y.z.option_name), your code may break in future versions if new options with similar names are introduced. Returns ------- None No return value. See Also -------- get_option : Retrieve the value of the specified option. set_option : Set the value of the specified option or options. describe_option : Print the description for one or more registered options. Notes ----- For all available options, please view the :ref:`User Guide `. Examples -------- >>> pd.reset_option("display.max_columns") # doctest: +SKIP """ keys = _select_options(pat) if len(keys) == 0: raise OptionError(f"No such keys(s) for {pat=}") if len(keys) > 1 and len(pat) < 4 and pat != "all": raise ValueError( "You must specify at least 4 characters when " "resetting multiple keys, use the special keyword " '"all" to reset all the options to their default value' ) for k in keys: set_option(k, _registered_options[k].defval) def get_default_val(pat: str): key = _get_single_key(pat) return _get_registered_option(key).defval class DictWrapper: """provide attribute-style access to a nested dict""" d: dict[str, Any] def __init__(self, d: dict[str, Any], prefix: str = "") -> None: object.__setattr__(self, "d", d) object.__setattr__(self, "prefix", prefix) def __setattr__(self, key: str, val: Any) -> None: prefix = object.__getattribute__(self, "prefix") if prefix: prefix += "." prefix += key # you can't set new keys # can you can't overwrite subtrees if key in self.d and not isinstance(self.d[key], dict): set_option(prefix, val) else: raise OptionError("You can only set the value of existing options") def __getattr__(self, key: str): prefix = object.__getattribute__(self, "prefix") if prefix: prefix += "." prefix += key try: v = object.__getattribute__(self, "d")[key] except KeyError as err: raise OptionError("No such option") from err if isinstance(v, dict): return DictWrapper(v, prefix) else: return get_option(prefix) def __dir__(self) -> list[str]: return list(self.d.keys()) options = DictWrapper(_global_config) # DictWrapper defines a custom setattr object.__setattr__(options, "__module__", "pandas") # # Functions for use by pandas developers, in addition to User - api @contextmanager def option_context(*args) -> Generator[None]: """ Context manager to temporarily set options in a ``with`` statement. This method allows users to set one or more pandas options temporarily within a controlled block. The previous options' values are restored once the block is exited. This is useful when making temporary adjustments to pandas' behavior without affecting the global state. Parameters ---------- *args : str | object | dict An even amount of arguments provided in pairs which will be interpreted as (pattern, value) pairs. Alternatively, a single dictionary of {pattern: value} may be provided. Returns ------- None No return value. Yields ------ None No yield value. See Also -------- get_option : Retrieve the value of the specified option. set_option : Set the value of the specified option. reset_option : Reset one or more options to their default value. describe_option : Print the description for one or more registered options. Notes ----- For all available options, please view the :ref:`User Guide ` or use ``pandas.describe_option()``. Examples -------- >>> from pandas import option_context >>> with option_context("display.max_rows", 10, "display.max_columns", 5): ... pass >>> with option_context({"display.max_rows": 10, "display.max_columns": 5}): ... pass """ if len(args) == 1 and isinstance(args[0], dict): args = tuple(kv for item in args[0].items() for kv in item) if len(args) % 2 != 0 or len(args) < 2: raise ValueError( "Provide an even amount of arguments as " "option_context(pat, val, pat, val...)." ) ops = tuple(zip(args[::2], args[1::2], strict=True)) undo: tuple[tuple[Any, Any], ...] = () try: undo = tuple((pat, get_option(pat)) for pat, val in ops) for pat, val in ops: set_option(pat, val) yield finally: for pat, val in undo: set_option(pat, val) def register_option( key: str, defval: object, doc: str = "", validator: Callable[[object], Any] | None = None, cb: Callable[[str], Any] | None = None, ) -> None: """ Register an option in the package-wide pandas config object Parameters ---------- key : str Fully-qualified key, e.g. "x.y.option - z". defval : object Default value of the option. doc : str Description of the option. validator : Callable, optional Function of a single argument, should raise `ValueError` if called with a value which is not a legal value for the option. cb a function of a single argument "key", which is called immediately after an option value is set/reset. key is the full name of the option. Raises ------ ValueError if `validator` is specified and `defval` is not a valid value. """ import keyword import tokenize key = key.lower() if key in _registered_options: raise OptionError(f"Option '{key}' has already been registered") if key in _reserved_keys: raise OptionError(f"Option '{key}' is a reserved key") # the default value should be legal if validator: validator(defval) # walk the nested dict, creating dicts as needed along the path path = key.split(".") for k in path: if not re.match("^" + tokenize.Name + "$", k): raise ValueError(f"{k} is not a valid identifier") if keyword.iskeyword(k): raise ValueError(f"{k} is a python keyword") cursor = _global_config msg = "Path prefix to option '{option}' is already an option" for i, p in enumerate(path[:-1]): if not isinstance(cursor, dict): raise OptionError(msg.format(option=".".join(path[:i]))) if p not in cursor: cursor[p] = {} cursor = cursor[p] if not isinstance(cursor, dict): raise OptionError(msg.format(option=".".join(path[:-1]))) cursor[path[-1]] = defval # initialize # save the option metadata _registered_options[key] = RegisteredOption( key=key, defval=defval, doc=doc, validator=validator, cb=cb ) def deprecate_option( key: str, category: type[Warning], msg: str | None = None, rkey: str | None = None, removal_ver: str | None = None, ) -> None: """ Mark option `key` as deprecated, if code attempts to access this option, a warning will be produced, using `msg` if given, or a default message if not. if `rkey` is given, any access to the key will be re-routed to `rkey`. Neither the existence of `key` nor that if `rkey` is checked. If they do not exist, any subsequence access will fail as usual, after the deprecation warning is given. Parameters ---------- key : str Name of the option to be deprecated. must be a fully-qualified option name (e.g "x.y.z.rkey"). category : Warning Warning class for the deprecation. msg : str, optional Warning message to output when the key is referenced. if no message is given a default message will be emitted. rkey : str, optional Name of an option to reroute access to. If specified, any referenced `key` will be re-routed to `rkey` including set/get/reset. rkey must be a fully-qualified option name (e.g "x.y.z.rkey"). used by the default message if no `msg` is specified. removal_ver : str, optional Specifies the version in which this option will be removed. used by the default message if no `msg` is specified. Raises ------ OptionError If the specified key has already been deprecated. """ key = key.lower() if key in _deprecated_options: raise OptionError(f"Option '{key}' has already been defined as deprecated.") _deprecated_options[key] = DeprecatedOption(key, category, msg, rkey, removal_ver) # # functions internal to the module def _select_options(pat: str) -> list[str]: """ returns a list of keys matching `pat` if pat=="all", returns all registered options """ # short-circuit for exact key if pat in _registered_options: return [pat] # else look through all of them keys = sorted(_registered_options.keys()) if pat == "all": # reserved key return keys return [k for k in keys if re.search(pat, k, re.I)] def _get_root(key: str) -> tuple[dict[str, Any], str]: path = key.split(".") cursor = _global_config for p in path[:-1]: cursor = cursor[p] return cursor, path[-1] def _get_deprecated_option(key: str): """ Retrieves the metadata for a deprecated option, if `key` is deprecated. Returns ------- DeprecatedOption (namedtuple) if key is deprecated, None otherwise """ try: d = _deprecated_options[key] except KeyError: return None else: return d def _get_registered_option(key: str): """ Retrieves the option metadata if `key` is a registered option. Returns ------- RegisteredOption (namedtuple) if key is deprecated, None otherwise """ return _registered_options.get(key) def _translate_key(key: str) -> str: """ if `key` is deprecated and a replacement key defined, will return the replacement key, otherwise returns `key` as-is """ d = _get_deprecated_option(key) if d: return d.rkey or key else: return key def _warn_if_deprecated(key: str) -> bool: """ Checks if `key` is a deprecated option and if so, prints a warning. Returns ------- bool - True if `key` is deprecated, False otherwise. """ d = _get_deprecated_option(key) if d: if d.msg: warnings.warn( d.msg, d.category, stacklevel=find_stack_level(), ) else: msg = f"'{key}' is deprecated" if d.removal_ver: msg += f" and will be removed in {d.removal_ver}" if d.rkey: msg += f", please use '{d.rkey}' instead." else: msg += ", please refrain from using it." warnings.warn( msg, d.category, stacklevel=find_stack_level(), ) return True return False def _build_option_description(k: str) -> str: """Builds a formatted description of a registered option and prints it""" o = _get_registered_option(k) d = _get_deprecated_option(k) s = f"{k} " if o.doc: s += "\n".join(o.doc.strip().split("\n")) else: s += "No description available." if o: with warnings.catch_warnings(): warnings.simplefilter("ignore", FutureWarning) warnings.simplefilter("ignore", DeprecationWarning) s += f"\n [default: {o.defval}] [currently: {get_option(k)}]" if d: rkey = d.rkey or "" s += "\n (Deprecated" s += f", use `{rkey}` instead." s += ")" return s # helpers @contextmanager def config_prefix(prefix: str) -> Generator[None]: """ contextmanager for multiple invocations of API with a common prefix supported API functions: (register / get / set )__option Warning: This is not thread - safe, and won't work properly if you import the API functions into your module using the "from x import y" construct. Example ------- import pandas._config.config as cf with cf.config_prefix("display.font"): cf.register_option("color", "red") cf.register_option("size", " 5 pt") cf.set_option(size, " 6 pt") cf.get_option(size) ... etc' will register options "display.font.color", "display.font.size", set the value of "display.font.size"... and so on. """ # Note: reset_option relies on set_option, and on key directly # it does not fit in to this monkey-patching scheme global register_option, get_option, set_option def wrap(func: F) -> F: def inner(key: str, *args, **kwds): pkey = f"{prefix}.{key}" return func(pkey, *args, **kwds) return cast(F, inner) _register_option = register_option _get_option = get_option _set_option = set_option set_option = wrap(set_option) get_option = wrap(get_option) register_option = wrap(register_option) try: yield finally: set_option = _set_option get_option = _get_option register_option = _register_option # These factories and methods are handy for use as the validator # arg in register_option def is_type_factory(_type: type[Any]) -> Callable[[Any], None]: """ Parameters ---------- `_type` - a type to be compared against (e.g. type(x) == `_type`) Returns ------- validator - a function of a single argument x , which raises ValueError if type(x) is not equal to `_type` """ def inner(x) -> None: if type(x) != _type: raise ValueError(f"Value must have type '{_type}'") return inner def is_instance_factory(_type: type | tuple[type, ...]) -> Callable[[Any], None]: """ Parameters ---------- `_type` - the type to be checked against Returns ------- validator - a function of a single argument x , which raises ValueError if x is not an instance of `_type` """ if isinstance(_type, tuple): type_repr = "|".join(map(str, _type)) else: type_repr = f"'{_type}'" def inner(x) -> None: if not isinstance(x, _type): raise ValueError(f"Value must be an instance of {type_repr}") return inner def is_one_of_factory(legal_values: Sequence) -> Callable[[Any], None]: callables = [c for c in legal_values if callable(c)] legal_values = [c for c in legal_values if not callable(c)] def inner(x) -> None: if x not in legal_values: if not any(c(x) for c in callables): uvals = [str(lval) for lval in legal_values] pp_values = "|".join(uvals) msg = f"Value must be one of {pp_values}" if len(callables): msg += " or a callable" raise ValueError(msg) return inner def is_nonnegative_int(value: object) -> None: """ Verify that value is None or a positive int. Parameters ---------- value : None or int The `value` to be checked. Raises ------ ValueError When the value is not None or is a negative integer """ if value is None: return elif isinstance(value, int): if value >= 0: return msg = "Value must be a nonnegative integer or None" raise ValueError(msg) # common type validators, for convenience # usage: register_option(... , validator = is_int) is_int = is_type_factory(int) is_bool = is_type_factory(bool) is_float = is_type_factory(float) is_str = is_type_factory(str) is_text = is_instance_factory((str, bytes)) def is_callable(obj: object) -> bool: """ Parameters ---------- `obj` - the object to be checked Returns ------- validator - returns True if object is callable raises ValueError otherwise. """ if not callable(obj): raise ValueError("Value must be a callable") return True # import set_module here would cause circular import get_option.__module__ = "pandas" set_option.__module__ = "pandas" describe_option.__module__ = "pandas" reset_option.__module__ = "pandas" option_context.__module__ = "pandas"