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如何在同一张图上绘制 4 个直方图

发布于 2024-11-04 02:53:11 字数 141 浏览 0 评论 0原文

我有以下问题：
我在 matplotlib.pyplot 中使用 hist()
我正在尝试在同一个图表上创建 4 个直方图。以及它们中每一个的近似高斯。
如何在同一个图表上绘制 4 个直方图，而不使它们互相阻挡（并排）？有什么想法吗？

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如若梦似彩虹 2024-11-11 02:53:11

matplotlib 文档中有几个示例。这看起来像是回答了你的问题：

import numpy as np
import pylab as P
#
# first create a single histogram
#
mu, sigma = 200, 25
x = mu + sigma*P.randn(10000)
#
# finally: make a multiple-histogram of data-sets with different length
#
x0 = mu + sigma*P.randn(10000)
x1 = mu + sigma*P.randn(7000)
x2 = mu + sigma*P.randn(3000)

# and exercise the weights option by arbitrarily giving the first half
# of each series only half the weight of the others:

w0 = np.ones_like(x0)
w0[:len(x0)/2] = 0.5
w1 = np.ones_like(x1)
w1[:len(x1)/2] = 0.5
w2 = np.ones_like(x2)
w0[:len(x2)/2] = 0.5



P.figure()

n, bins, patches = P.hist( [x0,x1,x2], 10, weights=[w0, w1, w2], histtype='bar')

P.show()

There are several examples in the matplotlib documentation. This one looks like it answers your question:

import numpy as np
import pylab as P
#
# first create a single histogram
#
mu, sigma = 200, 25
x = mu + sigma*P.randn(10000)
#
# finally: make a multiple-histogram of data-sets with different length
#
x0 = mu + sigma*P.randn(10000)
x1 = mu + sigma*P.randn(7000)
x2 = mu + sigma*P.randn(3000)

# and exercise the weights option by arbitrarily giving the first half
# of each series only half the weight of the others:

w0 = np.ones_like(x0)
w0[:len(x0)/2] = 0.5
w1 = np.ones_like(x1)
w1[:len(x1)/2] = 0.5
w2 = np.ones_like(x2)
w0[:len(x2)/2] = 0.5



P.figure()

n, bins, patches = P.hist( [x0,x1,x2], 10, weights=[w0, w1, w2], histtype='bar')

P.show()

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