Python 将正态分布拟合到一维数据
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Fitting a Normal distribution to 1D data
提问by Adel
I have a 1 dimensional array. I can compute the "mean" and "standard deviation" of this sample and plot the "Normal distribution" but I have a problem:
我有一个一维数组。我可以计算这个样本的“平均值”和“标准偏差”并绘制“正态分布”，但我有一个问题：
I want to plot the data and Normal distribution in the same figure.
我想在同一个图中绘制数据和正态分布。
I dont know how to plot both the data and the normal distribution.
我不知道如何绘制数据和正态分布。
Any Idea about "Gaussian probability density function in scipy.stats"?
关于“scipy.stats 中的高斯概率密度函数”的任何想法？
s = np.std(array)
m = np.mean(array)
plt.plot(norm.pdf(array,m,s))
采纳答案by Warren Weckesser
You can use matplotlibto plot the histogram and the PDF (as in the link in @MrE's answer). For fitting and for computing the PDF, you can use scipy.stats.norm, as follows.
您可以使用matplotlib绘制直方图和 PDF（如@MrE 回答中的链接）。为了拟合和计算 PDF，您可以使用scipy.stats.norm，如下所示。
import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
# Generate some data for this demonstration.
data = norm.rvs(10.0, 2.5, size=500)
# Fit a normal distribution to the data:
mu, std = norm.fit(data)
# Plot the histogram.
plt.hist(data, bins=25, density=True, alpha=0.6, color='g')
# Plot the PDF.
xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 100)
p = norm.pdf(x, mu, std)
plt.plot(x, p, 'k', linewidth=2)
title = "Fit results: mu = %.2f, std = %.2f" % (mu, std)
plt.title(title)
plt.show()
Here's the plot generated by the script:
这是脚本生成的图：
回答by YXD
To see both the normal distribution and your actual data you should plot your data as a histogram, then draw the probability density function over this. See the example on http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.normal.htmlfor exactly how to do this.
要查看正态分布和实际数据，您应该将数据绘制为直方图，然后在其上绘制概率密度函数。请参阅http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.normal.html上的示例，了解如何执行此操作。
回答by LonelyDaoist
There is a much simpler way to do it using seaborn:
使用seaborn有一种更简单的方法：
import seaborn as sns
from scipy.stats import norm
data = norm.rvs(5,0.4,size=1000) # you can use a pandas series or a list if you want
sns.distplot(data)
plt.show()
for more information:seaborn.distplot
更多信息：seaborn.distplot
回答by Jérome Chevalier
Here you are not fitting a normal distribution. Replacing sns.distplot(data)by sns.distplot(data, fit=norm, kde=False)should do the trick.
在这里，您没有拟合正态分布。更换sns.distplot(data)的sns.distplot(data, fit=norm, kde=False)应该做的伎俩。
