credit

docs/13.md

 # seaborn.catplot
 
+> 译者：[LIJIANcoder97](https://github.com/LIJIANcoder97)
+
 ```py
 seaborn.catplot(x=None, y=None, hue=None, data=None, row=None, col=None, col_wrap=None, estimator=<function mean>, ci=95, n_boot=1000, units=None, order=None, hue_order=None, row_order=None, col_order=None, kind='strip', height=5, aspect=1, orient=None, color=None, palette=None, legend=True, legend_out=True, sharex=True, sharey=True, margin_titles=False, facet_kws=None, **kwargs)
 ```

docs/14.md

 # seaborn.stripplot
 
+> 译者：[LIJIANcoder97](https://github.com/LIJIANcoder97)
+
 ```py
 seaborn.stripplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, jitter=True, dodge=False, orient=None, color=None, palette=None, size=5, edgecolor='gray', linewidth=0, ax=None, **kwargs)
 ```

docs/16.md

 # seaborn.boxplot
 
+> 译者：[FindNorthStar](https://github.com/FindNorthStar)
+
 ```py
 seaborn.boxplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, width=0.8, dodge=True, fliersize=5, linewidth=None, whis=1.5, notch=False, ax=None, **kwargs)
 ```

docs/17.md

 # seaborn.violinplot
 
+> 译者：[FindNorthStar](https://github.com/FindNorthStar)
+
 ```py
 seaborn.violinplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, bw='scott', cut=2, scale='area', scale_hue=True, gridsize=100, width=0.8, inner='box', split=False, dodge=True, orient=None, linewidth=None, color=None, palette=None, saturation=0.75, ax=None, **kwargs)
 ```

docs/18.md

 # seaborn.boxenplot
 
+> 译者：[FindNorthStar](https://github.com/FindNorthStar)
+
 ```py
 seaborn.boxenplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, width=0.8, dodge=True, k_depth='proportion', linewidth=None, scale='exponential', outlier_prop=None, ax=None, **kwargs)
 ```

docs/19.md

 # seaborn.pointplot
 
+> 译者：[FindNorthStar](https://github.com/FindNorthStar)
+
 ```py
 seaborn.pointplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, estimator=<function mean>, ci=95, n_boot=1000, units=None, markers='o', linestyles='-', dodge=False, join=True, scale=1, orient=None, color=None, palette=None, errwidth=None, capsize=None, ax=None, **kwargs)
 ```

docs/24.md

 # seaborn.distplot 
+
+> 译者：[hyuuo](https://github.com/hyuuo)
+
 ```py
 seaborn.distplot(a, bins=None, hist=True, kde=True, rug=False, fit=None, hist_kws=None, kde_kws=None, rug_kws=None, fit_kws=None, color=None, vertical=False, norm_hist=False, axlabel=None, label=None, ax=None)
 ```

docs/25.md

 # seaborn.kdeplot
 
+> 译者：[hyuuo](https://github.com/hyuuo)
+
 ```py
 seaborn.kdeplot(data, data2=None, shade=False, vertical=False, kernel='gau', bw='scott', gridsize=100, cut=3, clip=None, legend=True, cumulative=False, shade_lowest=True, cbar=False, cbar_ax=None, cbar_kws=None, ax=None, **kwargs)
 ```

docs/30.md

 # seaborn.heatmap
 
+> 译者：[hyuuo](https://github.com/hyuuo)
+
 ```py
 seaborn.heatmap(data, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt='.2g', annot_kws=None, linewidths=0, linecolor='white', cbar=True, cbar_kws=None, cbar_ax=None, square=False, xticklabels='auto', yticklabels='auto', mask=None, ax=None, **kwargs)
 ```

docs/32.md

 # seaborn.FacetGrid
 
+> 译者：[hyuuo](https://github.com/hyuuo)
+
 ```py
 class seaborn.FacetGrid(data, row=None, col=None, hue=None, col_wrap=None, sharex=True, sharey=True, height=3, aspect=1, palette=None, row_order=None, col_order=None, hue_order=None, hue_kws=None, dropna=True, legend_out=True, despine=True, margin_titles=False, xlim=None, ylim=None, subplot_kws=None, gridspec_kws=None, size=None)
 ```

docs/33.md

 # seaborn.FacetGrid.map
 
+> 译者：[sfw134](https://github.com/sfw134)
+
 ```py
 FacetGrid.map(func, *args, **kwargs)
 ```

docs/34.md

 # seaborn.FacetGrid.map_dataframe
 
+> 译者：[sfw134](https://github.com/sfw134)
+
 ```py
 FacetGrid.map_dataframe(func, *args, **kwargs)
 ```

docs/4.md

 # 可视化分类数据
 
+> 译者：[hold2010](https://github.com/hold2010)
+
 在[绘制关系图](relational.html#relational-tutorial)的教程中，我们学习了如何使用不同的可视化方法来展示数据集中多个变量之间的关系。在示例中，我们专注于两个数值变量之间的主要关系。如果其中一个主要变量是“可分类的”（能被分为不同的组），那么我们可以使用更专业的可视化方法。
 
 在seaborn中，有几种不同的方法可以对分类数据进行可视化。类似于[`relplot()`](../generated/seaborn.relplot.html#seaborn.relplot "seaborn.relplot")与[`scatterplot()`](../generated/seaborn.scatterplot.html#seaborn.scatterplot "seaborn.scatterplot")或者[`lineplot()`](../generated/seaborn.lineplot.html#seaborn.lineplot "seaborn.lineplot")之间的关系，有两种方法可以制作这些图。有许多axes-level函数可以用不同的方式绘制分类数据，还有一个figure-level接口[`catplot()`](../generated/seaborn.catplot.html#seaborn.catplot "seaborn.catplot")，可以对它们进行统一的高级访问。