问题描述
我有一组X，Y数据点(大约10k)，很容易将其绘制为散点图，但我想将其表示为热图.
I have a set of X,Y data points (about 10k) that are easy to plot as a scatter plot but that I would like to represent as a heatmap.
我浏览了MatPlotLib中的示例，它们似乎都已经从热图单元格值开始以生成图像.
I looked through the examples in MatPlotLib and they all seem to already start with heatmap cell values to generate the image.
有没有一种方法可以将所有不同的一堆x，y转换为热图(其中x，y频率较高的区域将是暖和的")?
Is there a method that converts a bunch of x,y, all different, to a heatmap (where zones with higher frequency of x,y would be "warmer")?
推荐答案
如果您不想要六角形，可以使用numpy的histogram2d函数:
If you don't want hexagons, you can use numpy's histogram2d function:
import numpy as np
import numpy.random
import matplotlib.pyplot as plt
# Generate some test data
x = np.random.randn(8873)
y = np.random.randn(8873)
heatmap, xedges, yedges = np.histogram2d(x, y, bins=50)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
plt.clf()
plt.imshow(heatmap.T, extent=extent, origin='lower')
plt.show()
这将产生50x50的热图.如果您想使用512x384，则可以将bins=(512, 384)放入对histogram2d的调用中.
This makes a 50x50 heatmap. If you want, say, 512x384, you can put bins=(512, 384) in the call to histogram2d.
示例:
这篇关于使用散点数据集在MatPlotLib中生成热图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！