参考:Add a legend in a 3D scatterplot with scatter() in Matplotlib 在数据可视化的过程中,3D散点图是一种常用的方式来展示三维数据的分布。Matplotlib库提供了强大的工具来创建和定制这类图表。本文将详细介绍如何在Matplotlib中使用scatter()函数创建3D散点图,并特别强调如何添加图例来提高图表的可读性...
scatter(x, y, 点的大小, 颜色,标记),这是最主要的几个用法,如果括号中不写s= c=则按默认顺序,写了则按规定的来,不考虑顺序 import matplotlib.pyplot as plt #x,y,大小,颜色 plt.scatter([1,2,3,4],[2,4,6,8],[10,20,30,400],['r', 'b','y','k']) plt.scatter([1,2,3,4],[...
scatter(x, y, 点的大小, 颜色,标记),这是最主要的几个用法,如果括号中不写s= c=则按默认顺序,写了则按规定的来,不考虑顺序 importmatplotlib.pyplot as plt#x,y,大小,颜色plt.scatter([1,2,3,4],[2,4,6,8],[10,20,30,400],['r','b','y','k']) plt.scatter([1,2,3,4],[9,8,...
50)y1=np.sin(x)y2=np.cos(x)# 创建散点图plt.scatter(x,y1,label='sin(x) - how2matplotlib.com')plt.scatter(x,y2,label='cos(x) - how2matplotlib.com')# 添加图例plt.legend()# 设置标题和轴标签plt.title('Simple Scatter Plot with Legend')plt.xlabel('X axis')plt.ylabel('Y axis'...
legend for linescatterpointshe number of points in the legend for scatter plotscatteroffsetsa list of yoffsets for scatter symbols in legendframeonIf True, draw the legend on a patch (frame)shadowIf True, draw a shadow behind legendframealphaTransparency of the frameedgecolorFrame edgecolor...
scatterpoints # the number of points in the legend for scatter plot. # 为散点图图例条目创建的标记点数 scatteryoffsets # a list of yoffsets for scatter symbols in legend. # 为散点图图例条目创建的标记的垂直偏移量 frameon # If True, draw the legend on a patch (frame). ...
几个常用的 Matplotlib 绘图案例: line_plot():多条曲线 multiyy_plot():双 y 轴 time_series_with_peaks():时序曲线带有峰点标记 scatter_plot():散点图 marginal_histogram():散点图+直方图 correllogram(…
from matplotlib.legend_handlerimportHandlerLine2D # 设置legend图例 l1,=plt.plot(x,y1,marker='o',label='linear line')l2,=plt.plot(x,y2,color='red',linewidth=1.0,marker='o',label='square line')plt.legend(handler_map={l1:HandlerLine2D(numpoints=4)},handles=[l1,l2],labels=['up','dow...
matplotlib.pyplot的plt.legend函数的简介 1、参数解释 2、源代码 matplotlib.pyplot的plt.legend函数的使用方法 1、基础用法 matplotlib.pyplot的plt.legend函数的简介 legend模块定义了legend类,负责绘制与轴和/或图形相关的图例。Legend类...
def scatterplot(x_data, y_data, x_label="", y_label="", title="", color = "r", yscale_log=False): # Create the plot object _, ax = plt.subplots() # Plot the data, set the size (s), color and transparency (alpha)