python seaborn 绘图入门: 回归模型 lmplot

lmplot

lmplot 是一种集合基础绘图与基于数据建立回归模型的绘图方法。旨在创建一个方便拟合数据集回归模型的绘图方法，利用'hue'、'col'、'row'参数来控制绘图变量。

同时可以使用模型参数来调节需要拟合的模型：order、logistic、lowess、robust、logx。

seaborn.lmplot(x, y, data, hue=None, col=None, row=None, palette=None, col_wrap=None, size=5, aspect=1, markers='o', sharex=True, sharey=True, hue_order=None, col_order=None, row_order=None, legend=True, legend_out=True, x_estimator=None, x_bins=None, x_ci='ci', scatter=True, fit_reg=True, ci=95, n_boot=1000, units=None, order=1, logistic=False, lowess=False, robust=False, logx=False, x_partial=None, y_partial=None, truncate=False, x_jitter=None, y_jitter=None, scatter_kws=None, line_kws=None)

Common Parameters:

hue, col, row: strings #定义数据子集的变量，并在不同的图像子集中绘制

Variables that define subsets of the data, which will be drawn on separate facets in the grid. See the *_order parameters to control the order of levels of this variable.

size: scalar, optional #定义子图的高度

Height (in inches) of each facet. See also: aspect.

markers: matplotlib marker code or list of marker codes, optional #定义散点的图标

Markers for the scatterplot. If a list, each marker in the list will be used for each level of the hue variable.

col_wrap: int, optional #设置每行子图数量

“Wrap” the column variable at this width, so that the column facets span multiple rows. Incompatible with a row facet.

order: int, optional #多项式回归，设定指数

If order is greater than 1, use numpy.polyfit to estimate a polynomial regression.

logistic: bool, optional #逻辑回归

If True, assume that y is a binary variable and use statsmodels to estimate a logistic regression model. Note that this is substantially more computationally intensive than linear regression, so you may wish to decrease the number of bootstrap resamples (n_boot) or set ci to None.

logx: bool, optional #转化为 log(x)

If True, estimate a linear regression of the form y ~ log(x), but plot the scatterplot and regression model in the input space. Note that x must be positive for this to work.

Senior Example Ⅰ for Practice

importseabornassns
sns.set_style("whitegrid")

tips=sns.load_dataset("tips")

#载入自带数据集#研究小费tips与总消费金额total_bill在吸烟与不吸烟人之间的关系

g=sns.lmplot(x="total_bill",y="tip",hue="smoker",data=tips,palette="Set1")

通过回归模型发现 total_bill=20 为分界点，不吸烟者的小费高于吸烟者

#研究在不同星期下，消费总额与消费的回归关系，col|hue控制子图不同的变量day，col_wrap控制每行子图数量，size控制子图高度
g=sns.lmplot(x="total_bill",y="tip",col="day",hue="day",data=tips,col_wrap=2,size=3)