创建散点图对象:scatter = Scatter().add_xaxis([x for x, y in data]).add_yaxis("Data Points", [y for x, y in data]) 设置系列选项:set_series_opts(label_opts=opts.LabelOpts(is_show=False)) 设置全局选项:set_global_opts(title_opts=opts.TitleOpts(title="Scatter Plot"), xaxis_opts...
hist_kws={'color':'g','label':'直方图'}, kde_kws={'color':'b','label':'密度曲线'}, bins=20, ax=axes[1]) 散点图常规散点图:scatterplot #语法 ''' seaborn.scatterplot(x=None, y=None, hue=None, style=None, size=None, data=None, palette=None, hue_order=None, hue_norm=None...
# g = g.map(plt.plot,"count" , "spread", marker=".") # #map里是横轴纵轴 # g.add_legend() 方法一: plt.figure(figsize=[20,20]) scatter=plt.scatter(result[:,0],result[:,1],c=subset_label,label=subset_label) plt.legend(*scatter.legend_elements(),title="classes") plt.show()...
f, ax = plt.subplots() sns.violinplot(data=data) sns.despine(offset=10, trim=True); # offset 两坐标轴离开距离; 你也可以通过往despine()中添加参数去控制边框 sns.set_style("whitegrid") sns.boxplot(data=data, palette="deep") sns.despine(left=True) # 删除左边边框 st = sns.axes_sty...
scatter = ( Scatter() .add_xaxis([x for x, y in data]) .add_yaxis("Data Points", [y for x, y in data]) .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) .set_global_opts( title_opts=opts.TitleOpts(title="Scatter Plot"), ...
'legend.scatterpoints': 1, 'lines.solid_capstyle': u'round', 'text.color': '.15', 'xtick.color': '.15', 'xtick.direction': u'out', 'xtick.major.size': 0.0, 'xtick.minor.size': 0.0, 'ytick.color': '.15', 'ytick.direction': u'out', ...
ax:Axes with plot 3.regplot() 绘制数据的散点分布并且可以进行线性回归模型拟合 seaborn.regplot(x, y, data=None, 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,...
sns.violinplot(x="day", y="total_bill", hue="smoker", # split表示当两种类别嵌套时分别用不同颜色表示 # inner表示小提琴内部的数据点表示形式 split=True, inner="quart", # 设定hue对应类别的颜色 palette={"Yes": "y", "No": "b"}, ...
importmatplotlib.pyplotaspltfrommplcursorsimportcursor# 创建数据x = [1,2,3,4,5] y = [2,4,6,8,10]# 绘制散点图plt.scatter(x, y, label='Data Points')# 添加标题和标签plt.title('Interactive Scatter Plot') plt.xlabel('X-axis') plt.ylabel('Y-axis')# 使用mplcursors添加悬停信息cursor(...
plt.xlabel("Intent", fontdict=LABEL_FONT) ax = sns.scatterplot(x="Intent", y="Number of User Examples", data=df, s=100) 开发者ID:watson-developer-cloud,项目名称:assistant-dialog-skill-analysis,代码行数:26,代码来源:summary_generator.py ...