a oEb'B@sdZddlmZddlmZmZddlZddlZddl m Z ddl m Z m Z ddZd d Zd d Zd dZddZddZd0ddZddZd1ddZdddddZd2d d!d"d#Zd$d%d&d'd(Zd$d%d)d*d+Zd,d,d,d-d.d/ZdS)3zV Module that contains many useful utilities for validating data or function arguments ) annotations)IterableSequenceN)find_stack_level)is_bool is_integerc Csl|dkrtdt|t|krht||}t||}|dkrDdnd}t|d|d|d|d d S) z Checks whether 'args' has length of at most 'compat_args'. Raises a TypeError if that is not the case, similar to in Python when a function is called with too many arguments. rz*'max_fname_arg_count' must be non-negativeargument argumentsz() takes at most  z (z given)N) ValueErrorlen TypeError)fnameargsmax_fname_arg_count compat_argsZ max_arg_countZactual_arg_countr rW/home/ja/django-apps/lartica_env/lib/python3.9/site-packages/pandas/util/_validators.py_check_arg_lengths  rc Cs|D]}zR||}||}|dur*|dus:|dur@|dur@d}n||k}t|sXtdWn"ty|||||u}Yn0|std|d|dqdS)z Check that the keys in `arg_val_dict` are mapped to their default values as specified in `compat_args`. Note that this function is to be called only when it has been checked that arg_val_dict.keys() is a subset of compat_args NFz'match' is not a booleanzthe 'z=' parameter is not supported in the pandas implementation of z())rr )rZ arg_val_dictrkeyv1v2matchrrr_check_for_default_values+s"   rcCs,t||||tt||}t|||dS)a Checks whether the length of the `*args` argument passed into a function has at most `len(compat_args)` arguments and whether or not all of these elements in `args` are set to their default values. Parameters ---------- fname : str The name of the function being passed the `*args` parameter args : tuple The `*args` parameter passed into a function max_fname_arg_count : int The maximum number of arguments that the function `fname` can accept, excluding those in `args`. Used for displaying appropriate error messages. Must be non-negative. compat_args : dict A dictionary of keys and their associated default values. In order to accommodate buggy behaviour in some versions of `numpy`, where a signature displayed keyword arguments but then passed those arguments **positionally** internally when calling downstream implementations, a dict ensures that the original order of the keyword arguments is enforced. Raises ------ TypeError If `args` contains more values than there are `compat_args` ValueError If `args` contains values that do not correspond to those of the default values specified in `compat_args` N)rdictzipr)rrrrkwargsrrr validate_argsQs rcCs8t|t|}|r4t|d}t|d|ddS)z} Checks whether 'kwargs' contains any keys that are not in 'compat_args' and raises a TypeError if there is one. rz'() got an unexpected keyword argument ''N)setlistr)rrrdiffZbad_argrrr_check_for_invalid_keyszs r#cCs$|}t|||t|||dS)a Checks whether parameters passed to the **kwargs argument in a function `fname` are valid parameters as specified in `*compat_args` and whether or not they are set to their default values. Parameters ---------- fname : str The name of the function being passed the `**kwargs` parameter kwargs : dict The `**kwargs` parameter passed into `fname` compat_args: dict A dictionary of keys that `kwargs` is allowed to have and their associated default values Raises ------ TypeError if `kwargs` contains keys not in `compat_args` ValueError if `kwargs` contains keys in `compat_args` that do not map to the default values specified in `compat_args` N)copyr#r)rrrkwdsrrrvalidate_kwargss r&cCsht||t|||tt||}|D] }||vr,t|d|dq,||t|||dS)a Checks whether parameters passed to the *args and **kwargs argument in a function `fname` are valid parameters as specified in `*compat_args` and whether or not they are set to their default values. Parameters ---------- fname: str The name of the function being passed the `**kwargs` parameter args: tuple The `*args` parameter passed into a function kwargs: dict The `**kwargs` parameter passed into `fname` max_fname_arg_count: int The minimum number of arguments that the function `fname` requires, excluding those in `args`. Used for displaying appropriate error messages. Must be non-negative. compat_args: dict A dictionary of keys that `kwargs` is allowed to have and their associated default values. Raises ------ TypeError if `args` contains more values than there are `compat_args` OR `kwargs` contains keys not in `compat_args` ValueError if `args` contains values not at the default value (`None`) `kwargs` contains keys in `compat_args` that do not map to the default value as specified in `compat_args` See Also -------- validate_args : Purely args validation. validate_kwargs : Purely kwargs validation. z-() got multiple values for keyword argument 'rN)rtuplevaluesrrrupdater&)rrrrrZ args_dictrrrrvalidate_args_and_kwargss& r*TFcCsNt|}|r|p|du}|r*|p(t|t}|sJtd|dt|jd|S)aR Ensure that argument passed in arg_name can be interpreted as boolean. Parameters ---------- value : bool Value to be validated. arg_name : str Name of the argument. To be reflected in the error message. none_allowed : bool, default True Whether to consider None to be a valid boolean. int_allowed : bool, default False Whether to consider integer value to be a valid boolean. Returns ------- value The same value as input. Raises ------ ValueError If the value is not a valid boolean. NzFor argument "z$" expected type bool, received type .)r isinstanceintr type__name__)valuearg_name none_allowed int_allowedZ good_valuerrrvalidate_bool_kwargs r4c sni}dvr0tfdd|jDr0d}t||vrr|rT|d|d}t||dd}|||<D]2\}} z||} WntyYqz0| || <qzt|dkrnt|dkr|dd}|d||<nt|d krVdvr d }t|d |d |d }tj |t t d|d||d<|d||d<nd|d}t||S)a Argument handler for mixed index, columns / axis functions In an attempt to handle both `.method(index, columns)`, and `.method(arg, axis=.)`, we have to do some bad things to argument parsing. This translates all arguments to `{index=., columns=.}` style. Parameters ---------- data : DataFrame args : tuple All positional arguments from the user kwargs : dict All keyword arguments from the user arg_name, method_name : str Used for better error messages Returns ------- kwargs : dict A dictionary of keyword arguments. Doesn't modify ``kwargs`` inplace, so update them with the return value here. Examples -------- >>> df = pd.DataFrame(range(2)) >>> validate_axis_style_args(df, (str.upper,), {'columns': id}, ... 'mapper', 'rename') {'columns': , 'index': } This emits a warning >>> validate_axis_style_args(df, (str.upper, id), {}, ... 'mapper', 'rename') {'index': , 'columns': } axisc3s|]}|vVqdS)Nr).0xrrr -z+validate_axis_style_args..z;Cannot specify both 'axis' and any of 'index' or 'columns'.z# got multiple values for argument 'rrrz:Cannot specify both 'axis' and any of 'index' or 'columns'zInterpreting call '.z(a, b)' as '.z(index=a, columns=b)'. Use named arguments to remove any ambiguity. In the future, using positional arguments for 'index' or 'columns' will raise a 'TypeError'.) stacklevelzCannot specify all of 'z', 'index', 'columns'.) anyZ_AXIS_TO_AXIS_NUMBERrZ_get_axis_namegetitemsr r warningswarn FutureWarningr) datarrr1 method_nameoutmsgr5kvaxrr8rvalidate_axis_style_argssF&        rJcCsddlm}|dur&|dur&tdnl|dur@|dur@||}nR|durz|durz|rt|ttfrtdt|jdn|dur|durtd||fS)a$ Validate the keyword arguments to 'fillna'. This checks that exactly one of 'value' and 'method' is specified. If 'method' is specified, this validates that it's a valid method. Parameters ---------- value, method : object The 'value' and 'method' keyword arguments for 'fillna'. validate_scalar_dict_value : bool, default True Whether to validate that 'value' is a scalar or dict. Specifically, validate that it is not a list or tuple. Returns ------- value, method : object r)clean_fill_methodNz(Must specify a fill 'value' or 'method'.z>"value" parameter must be a scalar or dict, but you passed a ""z)Cannot specify both 'value' and 'method'.) Zpandas.core.missingrKr r,r!r'rr.r/)r0methodZvalidate_scalar_dict_valuerKrrrvalidate_fillna_kwargsbs   rNzfloat | Iterable[float]z np.ndarray)qreturncCsjt|}d}|jdkrBd|kr,dksfnt||dn$tdd|Dsft||d|S)a Validate percentiles (used by describe and quantile). This function checks if the given float or iterable of floats is a valid percentile otherwise raises a ValueError. Parameters ---------- q: float or iterable of floats A single percentile or an iterable of percentiles. Returns ------- ndarray An ndarray of the percentiles if valid. Raises ------ ValueError if percentiles are not in given interval([0, 1]). zApercentiles should all be in the interval [0, 1]. Try {} instead.rrgY@css&|]}d|kodknVqdS)rrNr)r6qsrrrr9r:z&validate_percentile..)npZasarrayndimr formatall)rOZq_arrrFrrrvalidate_percentiles  rVz!bool | int | Sequence[bool | int] ascendingcs<dddt|ttfs*t|dfiSfdd|DS)z8Validate ``ascending`` kwargs for ``sort_index`` method.FT)r2r3rXcsg|]}t|dfiqSrW)r4)r6itemr8rr r:z&validate_ascending..)r,r!r'r4rWrr8rvalidate_ascendings r[z str | Noneztuple[bool, bool])closedrPcCsFd}d}|durd}d}n$|dkr(d}n|dkr6d}ntd||fS)a% Check that the `closed` argument is among [None, "left", "right"] Parameters ---------- closed : {None, "left", "right"} Returns ------- left_closed : bool right_closed : bool Raises ------ ValueError : if argument is not among valid values FNTleftrightz/Closed has to be either 'left', 'right' or None)r )r\Z left_closedZ right_closedrrrvalidate_endpointssr_) inclusiverPcCs6d}t|tr"ddddd|}|dur2td|S)aD Check that the `inclusive` argument is among {"both", "neither", "left", "right"}. Parameters ---------- inclusive : {"both", "neither", "left", "right"} Returns ------- left_right_inclusive : tuple[bool, bool] Raises ------ ValueError : if argument is not among valid values N)TT)TF)FT)FF)Zbothr]r^Zneitherz?Inclusive has to be either 'both', 'neither', 'left' or 'right')r,strr>r )r`Zleft_right_inclusiverrrvalidate_inclusives rbr-)loclengthrPcCsZt|std|d||dkr,||7}d|kr@|ksVntd|d||S)z Check that we have an integer between -length and length, inclusive. Standardize negative loc to within [0, length]. The exceptions we raise on failure match np.insert. z loc must be an integer between -z and r)rr IndexError)rcrdrrrvalidate_insert_locsrf)TF)T)T)__doc__ __future__rtypingrrr@numpyrRZpandas.util._exceptionsrZpandas.core.dtypes.commonrrrrrr#r&r*r4rJrNrVr[r_rbrfrrrrs*  &) 8 (` '# !"