a Sic"@sddlmZddlmZddlmZddlmZddlZ ddl Z ddl m Z ddl mZddlmZdd lmZdd lmZerdd lmZeGd d d eZeGdddeZdS)) annotations) dataclass)warn)ClassVarN) DataFrame)GroupBy)Scale)Stat) TYPE_CHECKING) ArrayLikec@s4eZdZUdZdZded<dddddd d d Zd S) Countz Count distinct observations within groups. See Also -------- Hist : A more fully-featured transform including binning and/or normalization. Examples -------- .. include:: ../docstrings/objects.Count.rst TzClassVar[bool]group_by_orientrrstrdict[str, Scale]datagroupbyorientscalesreturncCsDddd|}||||<|||tijddgdjdd}|S)Nyxrr)subsetT)drop)getagglendropna reset_index)selfrrrrvarresr#S/var/www/html/django/DPS/env/lib/python3.9/site-packages/seaborn/_stats/counting.py__call__#s zCount.__call__N)__name__ __module__ __qualname____doc__r __annotations__r%r#r#r#r$r s  r c@seZdZUdZdZded<dZded<dZd ed <dZd ed <d Z ded<d Z ded<dZ ded<dZ ded<ddZ ddZddZddZddZdd Zd!d"dd#d!d$d%d&ZdS)'Hista: Bin observations, count them, and optionally normalize or cumulate. Parameters ---------- stat : str Aggregate statistic to compute in each bin: - `count`: the number of observations - `density`: normalize so that the total area of the histogram equals 1 - `percent`: normalize so that bar heights sum to 100 - `probability` or `proportion`: normalize so that bar heights sum to 1 - `frequency`: divide the number of observations by the bin width bins : str, int, or ArrayLike Generic parameter that can be the name of a reference rule, the number of bins, or the bin breaks. Passed to :func:`numpy.histogram_bin_edges`. binwidth : float Width of each bin; overrides `bins` but can be used with `binrange`. binrange : (min, max) Lowest and highest value for bin edges; can be used with either `bins` (when a number) or `binwidth`. Defaults to data extremes. common_norm : bool or list of variables When not `False`, the normalization is applied across groups. Use `True` to normalize across all groups, or pass variable name(s) that define normalization groups. common_bins : bool or list of variables When not `False`, the same bins are used for all groups. Use `True` to share bins across all groups, or pass variable name(s) to share within. cumulative : bool If True, cumulate the bin values. discrete : bool If True, set `binwidth` and `binrange` so that bins have unit width and are centered on integer values Notes ----- The choice of bins for computing and plotting a histogram can exert substantial influence on the insights that one is able to draw from the visualization. If the bins are too large, they may erase important features. On the other hand, bins that are too small may be dominated by random variability, obscuring the shape of the true underlying distribution. The default bin size is determined using a reference rule that depends on the sample size and variance. This works well in many cases, (i.e., with "well-behaved" data) but it fails in others. It is always a good to try different bin sizes to be sure that you are not missing something important. This function allows you to specify bins in several different ways, such as by setting the total number of bins to use, the width of each bin, or the specific locations where the bins should break. Examples -------- .. include:: ../docstrings/objects.Hist.rst countrstatautozstr | int | ArrayLikebinsNz float | Nonebinwidthztuple[float, float] | NonebinrangeTzbool | list[str] common_norm common_binsFbool cumulativediscretecCsgd}|d|dS)N)r,densitypercent probability proportion frequencyr-)Z_check_param_one_of)r Z stat_optionsr#r#r$ __post_init__vszHist.__post_init__c Csz|}|dur$||}}n|\}}|rFt|d|d} n0|durf|} t||| | } nt||||} | S)z6Inner function that takes bin parameters as arguments.Ng?g?)rminmaxnparangehistogram_bin_edges) r valsweightr/r0r1r6startstop bin_edgesstepr#r#r$_define_bin_edges}szHist._define_bin_edgesc Cs||}|dd}|jp |dk}||||j|j|j|}t|jttfrvt |d}| | f} t || d} n t |d} | S)z=Given data, return numpy.histogram parameters to define bins.rCNZnominal)r/range)r/) rr6rHr/r0r1 isinstancerintrr=r>dict) r rr scale_typerBweightsr6rFn_bins bin_rangebin_kwsr#r#r$_define_bin_paramss   zHist._define_bin_paramscCs ||||}|||j||SN)rSapply_eval)r rrrrNrRr#r#r$_get_bins_and_evalszHist._get_bins_and_evalc Csr||}|dd}|jdk}tj|fi|||d\}}t|} |dd| d} t|| d|d| iS)NrCr7)rOr7r,space)rr-r? histogramdiffpdr) r rrrRrBrOr7histedgeswidthcenterr#r#r$rVs    z Hist._evalcCs|d}|jdks|jdkr0|t|}n>|jdkrR|t|d}n|jdkrn|t|d}|jr|jdvr||d}n|}|jfi|j|iS) Nr,r9r:r8dr;rZ)r7r;)r-astypefloatsumr5cumsumassign)r rr^r#r#r$ _normalizes   zHist._normalizerrrrcs||jj}fdd|D}|r0|jdurR||||}||j||}n|jdurft|}n\t|j}t|jt|} | r|jjd} d dd| D} d | d | d } t | |||j ||}|r|j dur| |}n~|j dur|} nZ|j } t|j t|} | r\|jjd } d d d| D} d | d | d } t | t| ||j }ddd|}|jfi|||jiS)Ncsg|]}|jvrt|qSr#)orderr).0vrr#r$ z!Hist.__call__..TFz .common_binsz, css|] }|VqdSrTr#rjrr#r#r$ rnz Hist.__call__..z"Undefined variables(s) passed to `z`: .z .common_normcss|] }|VqdSrTr#ror#r#r$rprnrrr) __class__r&lowerr3rSrUrVrsetjoinrrWr2rhrgr-)r rrrrrN grouping_varsrRZ bin_groupby undefinedparamnamesmsgZ norm_grouperotherr#rlr$r%s>      z Hist.__call__)r&r'r(r)r-r*r/r0r1r2r3r5r6r<rHrSrWrVrhr%r#r#r#r$r+2s 9         r+) __future__r dataclassesrwarningsrtypingrnumpyr?pandasr]rZseaborn._core.groupbyrZseaborn._core.scalesrZseaborn._stats.baser r Z numpy.typingr r r+r#r#r#r$s