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perc_endsedgesZ lower_outZ upper_outrGrGrH _lv_outliers"s   z_LVPlotter._lv_outlierscCs ddddddd}||S)NcSs |d|S)NrfrGr/rrKrGrGrH-rNz-_LVPlotter._width_functions..cSsd| |dS)NrrrGrrGrGrHr.rNcSsdd| |d|S)NrrrGrrGrGrHr/rNrrG)rCZ width_funcZwidth_functionsrGrGrH_width_functions+s z_LVPlotter._width_functions)g?g ?grNc ! s\|dur in|}|dur in|}|dur4in|}t|j|jd} | D]\} || qTtddd|jd} | D]\} || qtd|jd} | D]\} || q|jdk} |d t|}t |d krx| |d | d d t |d|dd|d |d g}ddg}| rV||}}n ||}}|j ||fi|n||\}||jddfdd}fdd}tfddt|D}|t |}t|}g}|jr||}tj|}| rR|ddg}||g}tt |}|}n8|||g}ddg}|}tt |}|j ||fi|t |d kr|j||fi||dg}tjjd|}||dg}tjjd|}|d|fddtt||D}t|fi|} | tt d d t ||!| dS)Nr~z.15g?butt)r*alphasolid_capstyler'r)rPr*rLrrrrJrIr')r*rJr'rcSs|d|dS)NrrrG)brGrGrHheightlsz"_LVPlotter._lvplot..heightcs2tj||d|df||dd}|S)NrrTfillPatchesr*r$rrrKwr,rr:rGrH vert_perc_boxps z)_LVPlotter._lvplot..vert_perc_boxcs2tj|d||df||dd}|S)NrrTrrrrrGrH horz_perc_boxvs  z)_LVPlotter._lvplot..horz_perc_boxcs g|]\}}||qSrGrGrJrr)rrKrrGrHre}sz&_LVPlotter._lvplot..)rrrZnew_mapg333333?cmapc s(g|] \}}|d||dqS)rrrGr)box_funcrKr$rGrHres)"copyrQrr'rRrr2rhrrgrSr>rlrXrrrdrr;r4rrrSrTrrr)LinearSegmentedColormap from_listr|r set_arrayradd_collection)!rCr@r9r*r:rzbox_kws flier_kwsline_kwsZbox_default_kwsrLZline_default_kwsZflier_default_kwsr7ysxsxxyyrrrZw_arear%Zoutliers hex_colorZ xs_medianZ ys_medianZ xs_outliersZ ys_outliersrdrrF collectionrG)rrrKrr:r$rH_lvplot2s              z_LVPlotter._lvplotc Cst|jD]\}}|jdurf|jdkr(q t|}|jdkr` for more information. a< .. note:: By default, this function treats one of the variables as categorical and draws data at ordinal positions (0, 1, ... n) on the relevant axis. This can be disabled with the `native_scale` parameter. See the :ref:`tutorial ` for more information. z x, y, hue : names of variables in ``data`` or vector data, optional Inputs for plotting long-form data. See examples for interpretation. z x, y, hue : names of variables in ``data`` Inputs for plotting long-form data. See examples for interpretation. z data : DataFrame, array, or list of arrays, optional Dataset for plotting. If ``x`` and ``y`` are absent, this is interpreted as wide-form. Otherwise it is expected to be long-form. z data : DataFrame Long-form (tidy) dataset for plotting. Each column should correspond to a variable, and each row should correspond to an observation. z order, hue_order : lists of strings, optional Order to plot the categorical levels in; otherwise the levels are inferred from the data objects. a~ estimator : string or callable that maps vector -> scalar, optional Statistical function to estimate within each categorical bin. errorbar : string, (string, number) tuple, callable or None Name of errorbar method (either "ci", "pi", "se", or "sd"), or a tuple with a method name and a level parameter, or a function that maps from a vector to a (min, max) interval, or None to hide errorbar. n_boot : int, optional Number of bootstrap samples used to compute confidence intervals. units : name of variable in ``data`` or vector data, optional Identifier of sampling units, which will be used to perform a multilevel bootstrap and account for repeated measures design. seed : int, numpy.random.Generator, or numpy.random.RandomState, optional Seed or random number generator for reproducible bootstrapping. a$ orient : "v" | "h", optional Orientation of the plot (vertical or horizontal). This is usually inferred based on the type of the input variables, but it can be used to resolve ambiguity when both `x` and `y` are numeric or when plotting wide-form data. z] color : matplotlib color, optional Single color for the elements in the plot. z palette : palette name, list, or dict, optional Color palette that maps the hue variable. If the palette is a dictionary, keys should be names of levels and values should be matplotlib colors. z hue_norm : tuple or :class:`matplotlib.colors.Normalize` object Normalization in data units for colormap applied to the `hue` variable when it is numeric. Not relevant if `hue` is categorical. a  saturation : float, optional Proportion of the original saturation to draw colors at. Large patches often look better with slightly desaturated colors, but set this to `1` if you want the plot colors to perfectly match the input color. zN capsize : float, optional Width of the "caps" on error bars./ zS errwidth : float, optional Thickness of error bar lines (and caps). z width : float, optional Width of a full element when not using hue nesting, or width of all the elements for one level of the major grouping variable. z dodge : bool, optional When hue nesting is used, whether elements should be shifted along the categorical axis. za linewidth : float, optional Width of the gray lines that frame the plot elements. z native_scale : bool, optional When True, numeric or datetime values on the categorical axis will maintain their original scaling rather than being converted to fixed indices. z formatter : callable, optional Function for converting categorical data into strings. Affects both grouping and tick labels. alegend : "auto", "brief", "full", or False How to draw the legend. If "brief", numeric `hue` and `size` variables will be represented with a sample of evenly spaced values. If "full", every group will get an entry in the legend. If "auto", choose between brief or full representation based on number of levels. If `False`, no legend data is added and no legend is drawn. zr ax : matplotlib Axes, optional Axes object to draw the plot onto, otherwise uses the current Axes. zY ax : matplotlib Axes Returns the Axes object with the plot drawn onto it. zH boxplot : A traditional box-and-whisker plot with a similar API. zL violinplot : A combination of boxplot and kernel density estimation. z stripplot : A scatterplot where one variable is categorical. Can be used in conjunction with other plots to show each observation. z swarmplot : A categorical scatterplot where the points do not overlap. Can be used with other plots to show each observation. zK barplot : Show point estimates and confidence intervals using bars. zL countplot : Show the counts of observations in each categorical bin. zk pointplot : Show point estimates and confidence intervals using scatterplot glyphs. zG catplot : Combine a categorical plot with a :class:`FacetGrid`. z< boxenplot : An enhanced boxplot for larger datasets. ) Zcategorical_narrativeZnew_categorical_narrativeZ input_paramsZstring_input_paramsZcategorical_dataZlong_form_dataZ order_varsZstat_api_paramsr2r*rYhue_normrrrrrr' native_scale formatterr?ax_inax_outrrrrr!r"r rrrrr)r$r%r&rDrZr2r*rYrrrr/r'whisrzcKsRt|||||||||| | | | | }|dur2t}|t|d||||S)N)r)r-r)gcarSrQrX)r,r$r%r&rDrZr2r*rYrrrr/r'rrzkwargsplotterrGrGrHrs rav Draw a box plot to show distributions with respect to categories. A box plot (or box-and-whisker plot) shows the distribution of quantitative data in a way that facilitates comparisons between variables or across levels of a categorical variable. The box shows the quartiles of the dataset while the whiskers extend to show the rest of the distribution, except for points that are determined to be "outliers" using a method that is a function of the inter-quartile range. {categorical_narrative} Parameters ---------- {categorical_data} {input_params} {order_vars} {orient} {color} {palette} {saturation} {width} {dodge} fliersize : float, optional Size of the markers used to indicate outlier observations. {linewidth} whis : float, optional Maximum length of the plot whiskers as proportion of the interquartile range. Whiskers extend to the furthest datapoint within that range. More extreme points are marked as outliers. {ax_in} kwargs : key, value mappings Other keyword arguments are passed through to :meth:`matplotlib.axes.Axes.boxplot`. Returns ------- {ax_out} See Also -------- {violinplot} {stripplot} {swarmplot} {catplot} Examples -------- .. include:: ../docstrings/boxplot.rst scottrrgrr1)r$r%r&rDrZrbrcrdrer_rr`rarr2r'r*rYrrzcKsLt|||||||||| | | | | ||||||}|dur>t}|||Sr)rYr)rrX)r,r$r%r&rDrZrbrcrdrer_rr`rarr2r'r*rYrrzrrrGrGrHrs   ra9 Draw a combination of boxplot and kernel density estimate. A violin plot plays a similar role as a box and whisker plot. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Unlike a box plot, in which all of the plot components correspond to actual datapoints, the violin plot features a kernel density estimation of the underlying distribution. This can be an effective and attractive way to show multiple distributions of data at once, but keep in mind that the estimation procedure is influenced by the sample size, and violins for relatively small samples might look misleadingly smooth. {categorical_narrative} Parameters ---------- {categorical_data} {input_params} {order_vars} bw : {{'scott', 'silverman', float}}, optional Either the name of a reference rule or the scale factor to use when computing the kernel bandwidth. The actual kernel size will be determined by multiplying the scale factor by the standard deviation of the data within each bin. cut : float, optional Distance, in units of bandwidth size, to extend the density past the extreme datapoints. Set to 0 to limit the violin range within the range of the observed data (i.e., to have the same effect as ``trim=True`` in ``ggplot``. scale : {{"area", "count", "width"}}, optional The method used to scale the width of each violin. If ``area``, each violin will have the same area. If ``count``, the width of the violins will be scaled by the number of observations in that bin. If ``width``, each violin will have the same width. scale_hue : bool, optional When nesting violins using a ``hue`` variable, this parameter determines whether the scaling is computed within each level of the major grouping variable (``scale_hue=True``) or across all the violins on the plot (``scale_hue=False``). gridsize : int, optional Number of points in the discrete grid used to compute the kernel density estimate. {width} inner : {{"box", "quartile", "point", "stick", None}}, optional Representation of the datapoints in the violin interior. If ``box``, draw a miniature boxplot. If ``quartiles``, draw the quartiles of the distribution. If ``point`` or ``stick``, show each underlying datapoint. Using ``None`` will draw unadorned violins. split : bool, optional When using hue nesting with a variable that takes two levels, setting ``split`` to True will draw half of a violin for each level. This can make it easier to directly compare the distributions. {dodge} {orient} {linewidth} {color} {palette} {saturation} {ax_in} Returns ------- {ax_out} See Also -------- {boxplot} {stripplot} {swarmplot} {catplot} Examples -------- .. include:: ../docstrings/violinplot.rst rrgy&1|?皙?)r$r%r&rDrZr2r*rYrrrrr'rdrrrrzrrrcCsNt|||||||||| | | | | ||||}|dur:t}||||||Sr)rr)rrX)r,r$r%r&rDrZr2r*rYrrrrr'rdrrrrzrrrrrGrGrHr_ s ra Draw an enhanced box plot for larger datasets. This style of plot was originally named a "letter value" plot because it shows a large number of quantiles that are defined as "letter values". It is similar to a box plot in plotting a nonparametric representation of a distribution in which all features correspond to actual observations. By plotting more quantiles, it provides more information about the shape of the distribution, particularly in the tails. For a more extensive explanation, you can read the paper that introduced the plot: https://vita.had.co.nz/papers/letter-value-plot.html {categorical_narrative} Parameters ---------- {categorical_data} {input_params} {order_vars} {orient} {color} {palette} {saturation} {width} {dodge} k_depth : {{"tukey", "proportion", "trustworthy", "full"}} or scalar The number of boxes, and by extension number of percentiles, to draw. All methods are detailed in Wickham's paper. Each makes different assumptions about the number of outliers and leverages different statistical properties. If "proportion", draw no more than `outlier_prop` extreme observations. If "full", draw `log(n)+1` boxes. {linewidth} scale : {{"exponential", "linear", "area"}}, optional Method to use for the width of the letter value boxes. All give similar results visually. "linear" reduces the width by a constant linear factor, "exponential" uses the proportion of data not covered, "area" is proportional to the percentage of data covered. outlier_prop : float, optional Proportion of data believed to be outliers. Must be in the range (0, 1]. Used to determine the number of boxes to plot when `k_depth="proportion"`. trust_alpha : float, optional Confidence level for a box to be plotted. Used to determine the number of boxes to plot when `k_depth="trustworthy"`. Must be in the range (0, 1). showfliers : bool, optional If False, suppress the plotting of outliers. {ax_in} box_kws: dict, optional Keyword arguments for the box artists; passed to :class:`matplotlib.patches.Rectangle`. line_kws: dict, optional Keyword arguments for the line denoting the median; passed to :meth:`matplotlib.axes.Axes.plot`. flier_kws: dict, optional Keyword arguments for the scatter denoting the outlier observations; passed to :meth:`matplotlib.axes.Axes.scatter`. Returns ------- {ax_out} See Also -------- {violinplot} {boxplot} {catplot} Examples -------- .. include:: ../docstrings/boxenplot.rst rr+)r$r%r&rDrZrrr2r*rYrrr'r r r r?rzcKst|tt||d|d}|dur,t}|j|jdksB|sT|j|j||d| || | |}| | | |\} }t |j || |} |j| ||d|dd|d| } |t| d | | d |j||| | |d |||j||jd |S) NFr,r-rDr2r3r?r4rDr rYrDnormrGr]rr)rrr'rrr*rrrb)r# get_semanticslocalsr)rr=r>rAscale_categorical_attachr_r\rr)map_huerrSrQr_add_axis_labelsr|)r,r$r%r&rDrZrrr2r*rYrrr'r r r r?rzrrrGrGrHr sD       ra5 Draw a categorical scatterplot using jitter to reduce overplotting. A strip plot can be drawn on its own, but it is also a good complement to a box or violin plot in cases where you want to show all observations along with some representation of the underlying distribution. {new_categorical_narrative} Parameters ---------- {input_params} {categorical_data} {order_vars} jitter : float, ``True``/``1`` is special-cased, optional Amount of jitter (only along the categorical axis) to apply. This can be useful when you have many points and they overlap, so that it is easier to see the distribution. You can specify the amount of jitter (half the width of the uniform random variable support), or just use ``True`` for a good default. dodge : bool, optional When using ``hue`` nesting, setting this to ``True`` will separate the strips for different hue levels along the categorical axis. Otherwise, the points for each level will be plotted on top of each other. {orient} {color} {palette} size : float, optional Radius of the markers, in points. edgecolor : matplotlib color, "gray" is special-cased, optional Color of the lines around each point. If you pass ``"gray"``, the brightness is determined by the color palette used for the body of the points. {linewidth} {native_scale} {formatter} {legend} {ax_in} kwargs : key, value mappings Other keyword arguments are passed through to :meth:`matplotlib.axes.Axes.scatter`. Returns ------- {ax_out} See Also -------- {swarmplot} {boxplot} {violinplot} {catplot} Examples -------- .. include:: ../docstrings/stripplot.rst )r$r%r&rDrZrr2r*rYrrr'r r r r?rrzcKst|tt||d|d}|dur,t}|j|jdksB|sT|j|j||d| ||j sh|S| | |}| || |\} }t |j|||}|j| || d|dd|d| } | dur| d } |t| d | d |j||| ||d |||j||jd |S)NFrr4rrrGr]rrr)rr'rr*rrrrb)r#rrr)rr=r>rArrr@r_r\rr)r rrSrQrr!r|)r,r$r%r&rDrZrr2r*rYrrr'r r r r?rrzrrrGrGrHr8 sJ       ra Draw a categorical scatterplot with points adjusted to be non-overlapping. This function is similar to :func:`stripplot`, but the points are adjusted (only along the categorical axis) so that they don't overlap. This gives a better representation of the distribution of values, but it does not scale well to large numbers of observations. This style of plot is sometimes called a "beeswarm". A swarm plot can be drawn on its own, but it is also a good complement to a box or violin plot in cases where you want to show all observations along with some representation of the underlying distribution. {new_categorical_narrative} Parameters ---------- {categorical_data} {input_params} {order_vars} dodge : bool, optional When using ``hue`` nesting, setting this to ``True`` will separate the strips for different hue levels along the categorical axis. Otherwise, the points for each level will be plotted in one swarm. {orient} {color} {palette} size : float, optional Radius of the markers, in points. edgecolor : matplotlib color, "gray" is special-cased, optional Color of the lines around each point. If you pass ``"gray"``, the brightness is determined by the color palette used for the body of the points. {linewidth} {native_scale} {formatter} {legend} {ax_in} kwargs : key, value mappings Other keyword arguments are passed through to :meth:`matplotlib.axes.Axes.scatter`. Returns ------- {ax_out} See Also -------- {boxplot} {violinplot} {stripplot} {catplot} Examples -------- .. include:: ../docstrings/swarmplot.rst r)ci_iz.26 deprecated)r$r%r&rDrZrrrr'rr2r*rYrrrrrrr#rzcKsft||}|turd}t|||||||||| | | | | ||||||}|durVt}||||S)Nr)r _deprecate_cirgrr)rrX)r,r$r%r&rDrZrrrr'rr2r*rYrrrrrrr#rzrrrGrGrHr! s    r!aP Show point estimates and errors as rectangular bars. A bar plot represents an estimate of central tendency for a numeric variable with the height of each rectangle and provides some indication of the uncertainty around that estimate using error bars. Bar plots include 0 in the quantitative axis range, and they are a good choice when 0 is a meaningful value for the quantitative variable, and you want to make comparisons against it. For datasets where 0 is not a meaningful value, a point plot will allow you to focus on differences between levels of one or more categorical variables. It is also important to keep in mind that a bar plot shows only the mean (or other estimator) value, but in many cases it may be more informative to show the distribution of values at each level of the categorical variables. In that case, other approaches such as a box or violin plot may be more appropriate. {categorical_narrative} Parameters ---------- {categorical_data} {input_params} {order_vars} {stat_api_params} {orient} {color} {palette} {saturation} {width} errcolor : matplotlib color Color used for the error bar lines. {errwidth} {capsize} {dodge} {ax_in} kwargs : key, value mappings Other keyword arguments are passed through to :meth:`matplotlib.axes.Axes.bar`. Returns ------- {ax_out} See Also -------- {countplot} {pointplot} {catplot} Examples -------- .. include:: ../docstrings/barplot.rst orI)r$r%r&rDrZrrrr'rrrrrrdr2r*rYrr#rrrzcCs\t||}t|||||||||| | | | | ||||||||}|durNt}|||Sr)rr&rr)rrX)r,r$r%r&rDrZrrrr'rrrrrrdr2r*rYrr#rrrzrrGrGrHr s     r a Show point estimates and errors using dot marks. A point plot represents an estimate of central tendency for a numeric variable by the position of the dot and provides some indication of the uncertainty around that estimate using error bars. Point plots can be more useful than bar plots for focusing comparisons between different levels of one or more categorical variables. They are particularly adept at showing interactions: how the relationship between levels of one categorical variable changes across levels of a second categorical variable. The lines that join each point from the same `hue` level allow interactions to be judged by differences in slope, which is easier for the eyes than comparing the heights of several groups of points or bars. It is important to keep in mind that a point plot shows only the mean (or other estimator) value, but in many cases it may be more informative to show the distribution of values at each level of the categorical variables. In that case, other approaches such as a box or violin plot may be more appropriate. {categorical_narrative} Parameters ---------- {categorical_data} {input_params} {order_vars} {stat_api_params} markers : string or list of strings, optional Markers to use for each of the ``hue`` levels. linestyles : string or list of strings, optional Line styles to use for each of the ``hue`` levels. dodge : bool or float, optional Amount to separate the points for each level of the ``hue`` variable along the categorical axis. join : bool, optional If ``True``, lines will be drawn between point estimates at the same ``hue`` level. scale : float, optional Scale factor for the plot elements. {orient} {color} {palette} {errwidth} {capsize} label : string, optional Label to represent the plot in a legend, only relevant when not using `hue`. {ax_in} Returns ------- {ax_out} See Also -------- {barplot} {catplot} Examples -------- .. include:: ../docstrings/pointplot.rst ) r$r%r&rDrZr2r*rYrrrrzc Ksd}d}d}d}d}d}d}d}|dur:|dur:d}|}n2|durT|durTd}|}n|durl|durltdt||||||||||||||| | |||| }d|_| durt} || | | S)Nrrr/rLz'Cannot pass values for both `x` and `y`rh)rrrr)rrX)r,r$r%r&rDrZr2r*rYrrrrzrrrrr'rrrrrrGrGrHr"f s6    r"a Show the counts of observations in each categorical bin using bars. A count plot can be thought of as a histogram across a categorical, instead of quantitative, variable. The basic API and options are identical to those for :func:`barplot`, so you can compare counts across nested variables. Note that the newer :func:`histplot` function offers more functionality, although its default behavior is somewhat different. {categorical_narrative} Parameters ---------- {categorical_data} {input_params} {order_vars} {orient} {color} {palette} {saturation} {dodge} {ax_in} kwargs : key, value mappings Other keyword arguments are passed through to :meth:`matplotlib.axes.Axes.bar`. Returns ------- {ax_out} See Also -------- {barplot} {catplot} Examples -------- .. include:: ../docstrings/countplot.rst strip)r$r%r&rrcol_wraprrrr'rrDrZ row_order col_orderraspectkindr r r2r*rYr r? legend_outsharexsharey margin_titles facet_kwsr#c4Ksnzt|d}!Wn&ty8d|d}"t|"Yn0d| vrdd|d}#t|#t| dddg}$||$vr|t|tt | |d|d }%d D].}&|&|%j vr|%j |&durd |&d |%j |&<q|%j j |%j d }|j dd|jf}|%j d d}'|%j dd}(|duri}tf||(|'|||||||d |})|%j}*|r\|%j|%jdkrn|%j|%j| |d|%|)|*s|)S|%|| } |%||| \}} |%j|| |d|dur|durd}|dkrD| dd}+| dd},| dd}-| }.|.dd|.d|.ddd|.dd |%j|+|,||-|.d!n|dkr| dd},| dd}-| d"d#}/| }.|.dd|.d|.ddd|.dd durt|.dd$|.d<|%j|,||-|/|.d%|)j j!D]}0|%j"|0|%jd&q|)#|%j d'd|%j d(d|)$|)%|)j j!D]}0|)&|0d|0_'q2|rx|durx||||fvrx|)j(|| d)|)S|durd*|d+}"t|"|durd,|d+}"t|"|d-kr|dur|dur||d.}1}2}n.|dur|dur||d/}1}2}ntd0n ||}1}2t)t*t+t,t-t.d1|}3t/}%|3j0|%_0|%1|1|2|||| | | dus|rx|%j2d/ks|r|%j2d.kr|%j3} nd|dur|durd2}#|s|%j2d/krt|#4d3d't|s|%j2d.krt|#4d4d(t|%j5} |%6||d5|d6ks|dur$|%j7}|dur2in|}|j8||||||||||||dd7 t9| | |||d8}.|.8| |d9vrt:;||}|.j8||| | | d:tfi|})|)j<|!f|||d;|.|%j2d.kr|)#|%j=|%j>n|)#|%j>|%j=|d-kr,|dur|)j#d-d<|dur,|)j#d-d=|rj|durj||||fvrjt?t@t:jA| } |)j(|| d)|)S)>NrXz Plot kind 'z' is not recognizedrzzXcatplot is a figure-level function and does not accept target axes. You may wish to try r(swarmFr)rrrr0rr) r,rrr)r*r+rr/r0r,r4rrC0rTrrrrGr]rrrrr'rrrrrr"rbr$r%)r label_orderz+native_scale not yet implemented for `kind=`z(formatter not yet implemented for `kind=rhr/rLz/Either `x` or `y` must be None for kind='count')r1violinZboxenrr\rhzSetting `{}=False` with `color=None` may cause different levels of the `{}` variable to share colors. This will change in a future version.r/r0rr\) r,rrr*r+r)rr,r/r0r.r1dropna)rDrZr2r*rY)rr\)rrrr'rr()x_var)y_var)BglobalsrrrVrW UserWarningr<rrrr-r9r:rr1 duplicatedr>rr@r=rArrr_r\r rrrrhsqrtraxesflatr|set_axis_labels set_titles tight_layout_update_legend_datalegend_ add_legendr-rYrrrrrr3rr2rformatrrrTrSrQrr& map_dataframerrrrto_utf8)4r,r$r%r&rrr)rrrr'rrDrZr*r+rr,r-r r r2r*rYr r?r.r/r0r1r2r#rZ plot_funcrr[Zrefactored_kindsrrcol_namerow_namerr@rrrrrrzx_y_Z plotter_classrGrGrHr s`                                                ra( Figure-level interface for drawing categorical plots onto a FacetGrid. This function provides access to several axes-level functions that show the relationship between a numerical and one or more categorical variables using one of several visual representations. The `kind` parameter selects the underlying axes-level function to use: Categorical scatterplots: - :func:`stripplot` (with `kind="strip"`; the default) - :func:`swarmplot` (with `kind="swarm"`) Categorical distribution plots: - :func:`boxplot` (with `kind="box"`) - :func:`violinplot` (with `kind="violin"`) - :func:`boxenplot` (with `kind="boxen"`) Categorical estimate plots: - :func:`pointplot` (with `kind="point"`) - :func:`barplot` (with `kind="bar"`) - :func:`countplot` (with `kind="count"`) Extra keyword arguments are passed to the underlying function, so you should refer to the documentation for each to see kind-specific options. Note that unlike when using the axes-level functions directly, data must be passed in a long-form DataFrame with variables specified by passing strings to `x`, `y`, `hue`, etc. {categorical_narrative} After plotting, the :class:`FacetGrid` with the plot is returned and can be used directly to tweak supporting plot details or add other layers. Parameters ---------- {long_form_data} {string_input_params} row, col : names of variables in `data`, optional Categorical variables that will determine the faceting of the grid. {col_wrap} {stat_api_params} {order_vars} row_order, col_order : lists of strings, optional Order to organize the rows and/or columns of the grid in, otherwise the orders are inferred from the data objects. {height} {aspect} kind : str, optional The kind of plot to draw, corresponds to the name of a categorical axes-level plotting function. Options are: "strip", "swarm", "box", "violin", "boxen", "point", "bar", or "count". {native_scale} {formatter} {orient} {color} {palette} {hue_norm} legend : str or bool, optional Set to `False` to disable the legend. With `strip` or `swarm` plots, this also accepts a string, as described in the axes-level docstrings. {legend_out} {share_xy} {margin_titles} facet_kws : dict, optional Dictionary of other keyword arguments to pass to :class:`FacetGrid`. kwargs : key, value pairings Other keyword arguments are passed through to the underlying plotting function. Returns ------- g : :class:`FacetGrid` Returns the :class:`FacetGrid` object with the plot on it for further tweaking. Examples -------- .. include:: ../docstrings/catplot.rst c@sLeZdZdZdddZddZd d Zd d Zd dZddZ dddZ dS)rz6Modifies a scatterplot artist to show a beeswarm plot.rLrrcCs||_||_||_dSr)r2rr)rCr2rrrGrGrHr7 szBeeswarm.__init__cCs|j}|jj}|}|jdkr$dnd}||dd|f<|j\}}|j|} |jdkrl| ddddgf} |} | j dkrt | | j d} | } t | | d|d} t j| | f} t | dddf} | | }t |}|||| <|jdkr|ddddgf}n|ddddf}|j|j\}}ddd |j}t|d |d d k}|jdkr|j|||d n|j|||d |jdkr|t j||fn|t j||fdS)z?Swarm `points`, a PathCollection, around the `center` position.r/rrNrHr%r$)r/rLrnrdlog) log_scale)r?rdpirr2Tr transform get_sizesrrhrepeatr get_linewidthrr>c_argsort empty_likerinvertedrt add_gutters set_offsets)rCrrrzrQZ orig_xy_dataZcat_idxZ orig_x_dataZ orig_y_dataZorig_xysizesedgeradiisorterorig_xyrZnew_xyrZnew_xyZ new_x_dataZ new_y_dataZ swarm_axisrPrGrGrH__call__! s>         zBeeswarm.__call__c Cs|d}t|d}|ddD]^}|||}|||}t|dddf|}|t|}|||}t||g}q"|S)z.Adjust x position of points to avoid overlaps.)rrrrN)rh atleast_2d could_overlapposition_candidatesrrXfirst_non_overlapping_candidatevstack) rCraZmidliner3xyr_i neighbors candidatesrZ new_xyr_irGrGrHr_ s   zBeeswarm.beeswarmc CsZ|\}}}g}t|D].}|\}}} |||| kr@||qqFqt|dddS)zAReturn a list of all swarm points that could overlap with target.Nr)reversedrrhr) rCrhr3ry_ir_iriZxyr_jy_jr_jrGrGrHrd s    zBeeswarm.could_overlapcCs|g}|\}}}d}|D]z\}} } || } tt|| d| ddd} || ||f|| ||f} }|rz| |g}n|| g}||| }qt|S)z@Return a list of coordinates that might be valid by adjusting x.Trrg?)rhr>rlextendr)rCrhrirjx_irlrmZ left_firstx_jrnrodydxclcrZnew_candidatesrGrGrHre s $  zBeeswarm.position_candidatescCst|dkr|dS|dddf}|dddf}|dddf}|D]Z}|\}}} ||} ||} t| t| } t|| } t| | k}|rH|SqHtddS)z>Find the first candidate that does not overlap with the swarm.rNrrz !r)N)N)N)N)N)N)N)N)N)MtextwraprnumbersrrVcolorsysr functoolsrnumpyrhpandasr scipy.statsr _no_scipy ImportErrorZ external.kde matplotlibrSmatplotlib.collectionsr matplotlib.patchespatchesrmatplotlib.pyplotpyplotr)Zseaborn._oldcorer r r Zseaborn.relationalr seabornrZ seaborn.utilsrrrrZseaborn._statisticsrZseaborn.palettesrrrrseaborn.axisgridrr__all__r#rrr-rYrrrrrrQZ_categorical_docsrSrrGr{rrrrr!r r"rrrGrGrGrHsn               tjsbE4  +     37     OS   IM   ; ;?  = :>      ;?      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