如何创建“点图"在 Matplotlib 中?(不是散点图)

本文介绍了如何创建“点图"在 Matplotlib 中?(不是散点图)的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我想创建我的统计书所称的点状图"，其中图中的点数等于观测值的数目.这是来自

在该示例中，X轴上 0 值上方有六个点，表示六个观测值零.

似乎点状图"可以有几种变体.在查找如何使用Matplotlib进行创建时，我只遇到了一个散点图，即一个数据点，该数据点表示X和Y值之间的关系.

我要使用Matplotlib创建的情节类型可能吗?

解决方案

假设您有一些数据会产生如下所示的直方图，

import numpy as np;np.random.seed(13)导入matplotlib.pyplot作为plt数据= np.random.randint(0,12，size = 72)plt.hist(data, bins=np.arange(13)-0.5, ec="k")plt.show()

您可以通过计算直方图并绘制所有可能点的散点图来创建点图，如果点的颜色超过直方图给出的数字，则点的颜色为白色.

import numpy as np;np.random.seed(13)导入matplotlib.pyplot作为plt数据= np.random.randint(0,12，size = 72)bins = np.arange(13)-0.5hist,edges = np.histogram(data, bins=bins)y = np.arange(1，hist.max()+ 1)x = np.arange(12)X，Y = np.网格(x，y)plt.scatter(X,Y, c=Y<=hist, cmap="Greys")plt.show()

或者，您可以将不需要的点设置为 nan,

Y = Y.astype(np.float)Y[Y>hist] = np.nanplt.scatter(X,Y)

I'd like to create what my statistics book calls a "dot plot" where the number of dots in the plot equals the number of observations. Here's an example from mathisfun:

In the example, there are six dots above the 0 value on the X-axis representing the six observations of the value zero.

It seems that a "dot plot" can have several variations. In looking up how to create this with Matplotlib, I only came across what I know of as a scatter plot with a data point representing the relationship between the X and Y value.

Is the type of plot I'm trying to create possible with Matplotlib?

解决方案

Supoose you have some data that would produce a histogram like the following,

import numpy as np; np.random.seed(13) import matplotlib.pyplot as plt data = np.random.randint(0,12,size=72) plt.hist(data, bins=np.arange(13)-0.5, ec="k") plt.show()

You may create your dot plot by calculating the histogram and plotting a scatter plot of all possible points, the color of the points being white if they exceed the number given by the histogram.

import numpy as np; np.random.seed(13) import matplotlib.pyplot as plt data = np.random.randint(0,12,size=72) bins = np.arange(13)-0.5 hist, edges = np.histogram(data, bins=bins) y = np.arange(1,hist.max()+1) x = np.arange(12) X,Y = np.meshgrid(x,y) plt.scatter(X,Y, c=Y<=hist, cmap="Greys") plt.show()

Alternatively you may set the unwanted points to nan,

Y = Y.astype(np.float) Y[Y>hist] = np.nan plt.scatter(X,Y)