Py之seaborn：数据可视化seaborn库(一)的柱状图、箱线图(置信区间图)、散点图/折线图、核密度图/等高线图、盒形图/小提琴图/LV多框图的简介、使用方法之最强攻略(建议收藏)（一）

一、如何选择图表类型？

二、seaborn库中单独绘图的11种函数讲解：数据可视化Seaborn库的柱状图、箱线图(置信区间图)、散点图/折线图、核密度图/等高线图、盒形图/小提琴图/LV多框图的简介、使用方法之最强攻略(建议收藏)

1、countplot函数：柱状图(类别特征计算重复个数)

seaborn.countplot(*, x=None, y=None, hue=None, data=None, order=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, dodge=True, ax=None, **kwargs)

官方文档解释：

http://seaborn.pydata.org/generated/seaborn.countplot.html?highlight=countplot#seaborn.countplot

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 barplot(), so you can compare counts across nested variables.

Input data can be passed in a variety of formats, including:

Vectors of data represented as lists, numpy arrays, or pandas Series objects passed directly to the x, y, and/or hue parameters.

A “long-form” DataFrame, in which case the x, y, and hue variables will determine how the data are plotted.

A “wide-form” DataFrame, such that each numeric column will be plotted.

An array or list of vectors.

使用条形显示每个分类箱中的观察值的个数。

计数图可以看作是一个分类变量的直方图，而不是定量变量。基本API和选项与barplot()相同，因此可以跨嵌套变量比较计数。

输入数据可以以多种格式传递，包括:

表示为列表、numpy数组或pandas系列对象的数据向量，直接传递给x、y和/或hue参数。

一个“long-form”数据帧，在这种情况下，x, y和hue变量将决定数据如何绘制。

一种“wide-form”数据帧，这样每个数字列都将被绘制出来。

向量的数组或列表。

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.

在大多数情况下，可以使用numpy或Python对象，但pandas对象更合适，因为关联的名称将用于注释坐标轴。此外，您可以为分组变量使用类别类型来控制绘图元素的顺序。

该函数总是将其中一个变量视为类别变量，并在相关轴上的顺序位置(0,1，…n)绘制数据，即使数据具有数字或日期类型。

2、catplot函数：柱状图、箱型图(置信区间)、散点图、小提琴图等

seaborn.catplot(*, x=None, y=None, hue=None, data=None, row=None, col=None, col_wrap=None, estimator=<function mean at 0x7fecadf1cee0>, ci=95, n_boot=1000, units=None, seed=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)

http://seaborn.pydata.org/generated/seaborn.catplot.html?highlight=catplot#seaborn.catplot

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:

stripplot() (with kind="strip"; the default)

swarmplot() (with kind="swarm")

Categorical distribution plots:

boxplot() (with kind="box")

violinplot() (with kind="violin")

boxenplot() (with kind="boxen")

Categorical estimate plots:

pointplot() (with kind="point")

barplot() (with kind="bar")

countplot() (with kind="count")

用于在FacetGrid上绘制分类图的图形级界面。

这个函数提供了对几个轴级函数的访问，这些函数使用一种可视表示表示数值和一个或多个分类变量之间的关系。kind参数选择要使用的axis级函数:

分类散点图:

swarmplot() (with kind="swarm"

分类分布绘图:

分类预测绘图:

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.

As in the case with the underlying plot functions, if variables have a categorical data type, the levels of the categorical variables, and their order will be inferred from the objects. Otherwise you may have to use alter the dataframe sorting or use the function parameters (orient, order, hue_order, etc.) to set up the plot correctly.

额外的关键字参数被传递给底层函数，因此您应该参考每个函数的文档来查看特定种类的选项。

请注意，与直接使用ax级函数不同的是，数据必须在一个长格式的数据框架中传递，通过向x、y、hue等传递字符串来指定变量。

与基础绘图函数的情况一样，如果变量具有类别数据类型，则类别变量的级别及其顺序将从对象中推断出来。否则，你可能不得不使用alter the dataframe sorted或使用函数参数(orient, order, hue_order等)来正确设置绘图。

(1)、CatPlotByG

(2)、CatPlotByHG