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:
In most cases, it is possible to use numpy or Python objects, but pandas objects are preferable because the associated names will be used to annotate the axes. Additionally, you can use Categorical types for the grouping variables to control the order of plot elements.
This function always treats one of the variables as categorical and draws data at ordinal positions (0, 1, … n) on the relevant axis, even when the data has a numeric or date type.
参数：`x, y, hue`：names of variables in `data` or vector data, optional
参数：`x, y, hue`：`data`或向量数据中的变量名称，可选
> Inputs for plotting long-form data. See examples for interpretation.
> 用于绘制长格式数据的输入。查看样例以进一步理解。
`data`：DataFrame, array, or list of arrays, optional
`data`：DataFrame，数组，数组列表，可选
> Dataset for plotting. If `x` and `y` are absent, this is interpreted as wide-form. Otherwise it is expected to be long-form.
> 用于绘图的数据集。如果`x`和`y`都缺失，那么数据将被视为宽格式。否则数据被视为长格式。
`order, hue_order`：lists of strings, optional
`order, hue_order`：字符串列表，可选
> Order to plot the categorical levels in, otherwise the levels are inferred from the data objects.
> 控制分类变量（对应的条形图）的绘制顺序，若缺失则从数据中推断分类变量的顺序。
`orient`：“v” | “h”, optional
`orient`：“v” | “h”，可选
> Orientation of the plot (vertical or horizontal). This is usually inferred from the dtype of the input variables, but can be used to specify when the “categorical” variable is a numeric or when plotting wide-form data.
> Color for all of the elements, or seed for a gradient palette.
> 所有元素的颜色，或渐变调色板的种子颜色。
`palette`：palette name, list, or dict, optional
`palette`：调色板名称，列表或字典，可选
> Colors to use for the different levels of the `hue` variable. Should be something that can be interpreted by [`color_palette()`](seaborn.color_palette.html#seaborn.color_palette "seaborn.color_palette"), or a dictionary mapping hue levels to matplotlib colors.
> 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 spec.
> 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.
> 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.
> Proportion of data believed to be outliers. Used in conjunction with k_depth to determine the number of percentiles to draw. Defaults to 0.007 as a proportion of outliers. Should be in range [0, 1].
Use [`catplot()`](seaborn.catplot.html#seaborn.catplot"seaborn.catplot") to combine [`boxenplot()`](#seaborn.boxenplot"seaborn.boxenplot") and a [`FacetGrid`](seaborn.FacetGrid.html#seaborn.FacetGrid"seaborn.FacetGrid"). This allows grouping within additional categorical variables. Using [`catplot()`](seaborn.catplot.html#seaborn.catplot"seaborn.catplot") is safer than using [`FacetGrid`](seaborn.FacetGrid.html#seaborn.FacetGrid"seaborn.FacetGrid") directly, as it ensures synchronization of variable order across facets:
Show point estimates and confidence intervals using scatter plot glyphs.
通过绘制散点连线显示数据点的估计值和置信区间。
A point plot represents an estimate of central tendency for a numeric variable by the position of scatter plot points and provides some indication of the uncertainty around that estimate using error bars.
点图代表散点图位置的数值变量的中心趋势估计，并使用误差线提供关于该估计的不确定性的一些指示。
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.
In most cases, it is possible to use numpy or Python objects, but pandas objects are preferable because the associated names will be used to annotate the axes. Additionally, you can use Categorical types for the grouping variables to control the order of plot elements.
This function always treats one of the variables as categorical and draws data at ordinal positions (0, 1, … n) on the relevant axis, even when the data has a numeric or date type.
`estimator`：callable that maps vector -> scalar, optional
`estimator`：调用函数实现向量 -> 标量的映射，可选
> Statistical function to estimate within each categorical bin.
> 在每个分箱内进行估计的统计函数。
`ci`：float or “sd” or None, optional
`ci`：float 或 “sd” 或 None，可选
> Size of confidence intervals to draw around estimated values. If “sd”, skip bootstrapping and draw the standard deviation of the observations. If `None`, no bootstrapping will be performed, and error bars will not be drawn.
> Number of bootstrap iterations to use when computing confidence intervals.
> 计算置信区间时使用的引导迭代次数。
`units`：name of variable in `data` or vector data, optional
`units`：`data` 或vector data中变量的名称，可选
> Identifier of sampling units, which will be used to perform a multilevel bootstrap and account for repeated measures design.
> 采样单元的标识符，用于执行多级引导过程（计算置信区间等）并能够处理重复测量的设定。
`markers`：string or list of strings, optional
`markers`：字符串或字符串列表，可选
> Markers to use for each of the `hue` levels.
> 用于每个`hue`色调的级别的标记。
`linestyles`：string or list of strings, optional
`linestyles`：字符串或字符串列表，可选
> Line styles to use for each of the `hue` levels.
> 用于每个`hue`色调的级别的线条风格。
`dodge`：bool or float, optional
`dodge`：bool或float，可选
> Amount to separate the points for each level of the `hue` variable along the categorical axis.
> 用于沿着分类轴分离`hue`变量的每个级别数据点的数量。
`join`：bool, optional
`join`：bool，可选
> If `True`, lines will be drawn between point estimates at the same `hue` level.
> 如果为`True`，则在`hue`级别相同的点估计值之间绘制线条。
`scale`：float, optional
`scale`：float，可选
> Scale factor for the plot elements.
> 绘图元素的比例因子。
`orient`：“v” | “h”, optional
`orient`：“v” | “h”，可选
> Orientation of the plot (vertical or horizontal). This is usually inferred from the dtype of the input variables, but can be used to specify when the “categorical” variable is a numeric or when plotting wide-form data.
> Color for all of the elements, or seed for a gradient palette.
> 所有元素的颜色，或渐变调色板的种子颜色。
`palette`：palette name, list, or dict, optional
`palette`：调色板名称，列表或字典，可选
> Colors to use for the different levels of the `hue` variable. Should be something that can be interpreted by [`color_palette()`](seaborn.color_palette.html#seaborn.color_palette "seaborn.color_palette"), or a dictionary mapping hue levels to matplotlib colors.
Use [`catplot()`](seaborn.catplot.html#seaborn.catplot"seaborn.catplot") to combine a [`barplot()`](seaborn.barplot.html#seaborn.barplot"seaborn.barplot") and a [`FacetGrid`](seaborn.FacetGrid.html#seaborn.FacetGrid"seaborn.FacetGrid"). This allows grouping within additional categorical variables. Using [`catplot()`](seaborn.catplot.html#seaborn.catplot"seaborn.catplot") is safer than using [`FacetGrid`](seaborn.FacetGrid.html#seaborn.FacetGrid"seaborn.FacetGrid") directly, as it ensures synchronization of variable order across facets:
