blocks|key|304100|text|我不确定您的输入是什么，但可能是您的y轴比例太大(20000)，请尝试减少此数字。以下代码适用于我：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|304101|import+matplotlib.pyplot+as+plt
import+numpy+as+np

#created+my+variable
v+=+np.random.normal(0,1,1000)


fig,+ax+=+plt.subplots()


plt.hist(v,+bins=500,+normed=1,+color='#7F38EC',+histtype='step')

#plot
plt.title("Gaussian")
plt.axis([-1,+2,+0,+1])+#changed+20000+to+1

plt.show()|code-block|syntax|javascript|304102|编辑：|304103|如果您想要y轴上的值的实际计数，可以设置normed=0。然后就可以去掉plt.axis([-1,+2,+0,+1])了。|offset|length|style|CODE|304104|import+matplotlib.pyplot+as+plt
import+numpy+as+np

#function
v+=+np.random.normal(0,1,500000)


fig,+ax+=+plt.subplots()

#+changed+normed=1+to+normed=0
plt.hist(v,+bins=500,+normed=0,+color='#7F38EC',+histtype='step')

#plot
plt.title("Gaussian")
#plt.axis([-1,+2,+0,+20000])+

plt.show()|304105|entityMap^0|0|0|0|K|8|10|N|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|V|8|@$K|W|L|X|M|N]|$K|Y|L|Z|M|N]]|9|@]|A|$]]|$1|O|3|P|5|D|7|10|8|@]|9|@]|A|$E|F]]|$1|Q|3|-4|5|6|7|11|8|@]|9|@]|A|$]]]|R|$]]

I am not sure what your input is, but possibly your y-axis scale is too large (20000), try reducing this number. The following code works for me:

<pre><code>import matplotlib.pyplot as plt
import numpy as np

#created my variable
v = np.random.normal(0,1,1000)


fig, ax = plt.subplots()


plt.hist(v, bins=500, normed=1, color='#7F38EC', histtype='step')

#plot
plt.title("Gaussian")
plt.axis([-1, 2, 0, 1]) #changed 20000 to 1

plt.show()
</code></pre>

Edit:

If you want the actual count of values on y-axis, you can set <code>normed=0</code>. And would just get rid of the <code>plt.axis([-1, 2, 0, 1])</code>.

<pre><code>import matplotlib.pyplot as plt
import numpy as np

#function
v = np.random.normal(0,1,500000)


fig, ax = plt.subplots()

# changed normed=1 to normed=0
plt.hist(v, bins=500, normed=0, color='#7F38EC', histtype='step')

#plot
plt.title("Gaussian")
#plt.axis([-1, 2, 0, 20000]) 

plt.show()
</code></pre>

blocks|key|304232|text|您可以尝试sklearn高斯混合模型估计，如下所示：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|304233|import+numpy+as+np
import+sklearn.mixture

gmm+=+sklearn.mixture.GMM()

#+sample+data
a+=+np.random.randn(1000)

#+result
r+=+gmm.fit(a[:,+np.newaxis])+#+GMM+requires+2D+data+as+of+sklearn+version+0.16
print("mean+:+%25f,+var+:+%25f"+%25+(r.means_[0,+0],+r.covars_[0,+0]))|code-block|syntax|javascript|304234|参考：http://scikit-learn.org/stable/modules/mixture.html#mixture|offset|length|304235|请注意，通过这种方式，您不需要使用直方图来估计样本分布。|304236|entityMap|0|LINK|mutability|MUTABLE|url|http://scikit-learn.org/stable/modules/mixture.html#mixture^0|0|0|3|1N|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|W|8|@]|9|@$I|X|J|Y|1|Z]]|A|$]]|$1|K|3|L|5|6|7|10|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|11|8|@]|9|@]|A|$]]]|N|$O|$5|P|Q|R|A|$S|T]]]]

You can try sklearn gaussian mixture model estimation as below :

<pre><code>import numpy as np
import sklearn.mixture

gmm = sklearn.mixture.GMM()

# sample data
a = np.random.randn(1000)

# result
r = gmm.fit(a[:, np.newaxis]) # GMM requires 2D data as of sklearn version 0.16
print("mean : %f, var : %f" % (r.means_[0, 0], r.covars_[0, 0]))
</code></pre>

Reference : <a href="http://scikit-learn.org/stable/modules/mixture.html#mixture" rel="noreferrer">http://scikit-learn.org/stable/modules/mixture.html#mixture</a>

Note that in this way, you don't need to estimate your sample distribution with an histogram.

blocks|key|304282|text|看看将任意曲线拟合到数据的this+answer。基本上，您可以使用scipy.optimize.curve_fit来适应您的数据所需的任何函数。下面的代码展示了如何将高斯函数拟合到一些随机数据(归功于this+SciPy-用户邮件列表帖子)。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|304283|import+numpy
from+scipy.optimize+import+curve_fit
import+matplotlib.pyplot+as+plt

#+Define+some+test+data+which+is+close+to+Gaussian
data+=+numpy.random.normal(size=10000)

hist,+bin_edges+=+numpy.histogram(data,+density=True)
bin_centres+=+(bin_edges[:-1]+%2B+bin_edges[1:])/2

#+Define+model+function+to+be+used+to+fit+to+the+data+above:
def+gauss(x,+*p):
++++A,+mu,+sigma+=+p
++++return+A*numpy.exp(-(x-mu)**2/(2.*sigma**2))

#+p0+is+the+initial+guess+for+the+fitting+coefficients+(A,+mu+and+sigma+above)
p0+=+[1.,+0.,+1.]

coeff,+var_matrix+=+curve_fit(gauss,+bin_centres,+hist,+p0=p0)

#+Get+the+fitted+curve
hist_fit+=+gauss(bin_centres,+*coeff)

plt.plot(bin_centres,+hist,+label='Test+data')
plt.plot(bin_centres,+hist_fit,+label='Fitted+data')

#+Finally,+lets+get+the+fitting+parameters,+i.e.+the+mean+and+standard+deviation:
print+'Fitted+mean+=+',+coeff[1]
print+'Fitted+standard+deviation+=+',+coeff[2]

plt.show()|code-block|syntax|javascript|304284|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/10143572/623518|1|http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html|2|http://mail.scipy.org/pipermail/scipy-user/2011-January/028177.html^0|Y|O|D|B|0|Y|O|1|2T|4|2|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@$9|X|A|Y|B|C]]|D|@$9|Z|A|10|1|11]|$9|12|A|13|1|14]|$9|15|A|16|1|17]]|E|$]]|$1|F|3|G|5|H|7|18|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|19|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]|S|$5|N|O|P|E|$Q|T]]|U|$5|N|O|P|E|$Q|V]]]]

Take a look at <a href="https://stackoverflow.com/a/10143572/623518">this answer</a> for fitting arbitrary curves to data. Basically you can use <a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html" rel="noreferrer"><code>scipy.optimize.curve_fit</code></a> to fit any function you want to your data. The code below shows how you can fit a Gaussian to some random data (credit to <a href="http://mail.scipy.org/pipermail/scipy-user/2011-January/028177.html" rel="noreferrer">this</a> SciPy-User mailing list post).

<pre><code>import numpy
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt

# Define some test data which is close to Gaussian
data = numpy.random.normal(size=10000)

hist, bin_edges = numpy.histogram(data, density=True)
bin_centres = (bin_edges[:-1] + bin_edges[1:])/2

# Define model function to be used to fit to the data above:
def gauss(x, *p):
 A, mu, sigma = p
 return A*numpy.exp(-(x-mu)**2/(2.*sigma**2))

# p0 is the initial guess for the fitting coefficients (A, mu and sigma above)
p0 = [1., 0., 1.]

coeff, var_matrix = curve_fit(gauss, bin_centres, hist, p0=p0)

# Get the fitted curve
hist_fit = gauss(bin_centres, *coeff)

plt.plot(bin_centres, hist, label='Test data')
plt.plot(bin_centres, hist_fit, label='Fitted data')

# Finally, lets get the fitting parameters, i.e. the mean and standard deviation:
print 'Fitted mean = ', coeff[1]
print 'Fitted standard deviation = ', coeff[2]

plt.show()
</code></pre>

blocks|key|521025|text|这是一个古老的问题，但是对于任何想要绘制适合于一系列的密度的人来说，你可以尝试matplotlib的.plot(kind='kde')。Docs+here。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|521026|以熊猫为例：|521027|mydf.x.plot(kind='kde')|code-block|syntax|javascript|521028|entityMap|0|LINK|mutability|MUTABLE|url|http://pandas.pydata.org/pandas-docs/stable/visualization.html#other-plots^0|1E|H|21|4|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@$9|X|A|Y|1|Z]]|E|$]]|$1|F|3|G|5|6|7|10|8|@]|D|@]|E|$]]|$1|H|3|I|5|J|7|11|8|@]|D|@]|E|$K|L]]|$1|M|3|-4|5|6|7|12|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]]]

Kind of an old question, but for anybody looking just to plot a density fit for a series, you could try matplotlib's <code>.plot(kind='kde')</code>. Docs <a href="http://pandas.pydata.org/pandas-docs/stable/visualization.html#other-plots" rel="nofollow">here</a>.

Example with pandas:

<pre><code>mydf.x.plot(kind='kde')
</code></pre>

I have a histogram (see below) and I am trying to find the mean and standard deviation along with code which fits a curve to my histogram. I think there is something in SciPy or matplotlib that can help, but every example I've tried doesn't work. 

<pre><code>import matplotlib.pyplot as plt
import numpy as np

with open('gau_b_g_s.csv') as f:
 v = np.loadtxt(f, delimiter= ',', dtype="float", skiprows=1, usecols=None)

fig, ax = plt.subplots()

plt.hist(v, bins=500, color='#7F38EC', histtype='step')

plt.title("Gaussian")
plt.axis([-1, 2, 0, 20000])

plt.show()
</code></pre>

Fit a gaussian function

我有一个直方图(见下文)，我正在尝试找出均值和标准差以及将曲线拟合到直方图的代码。我认为在SciPy或matplotlib中有一些东西可以提供帮助，但我尝试过的每个示例都不起作用。import matplotlib.pyplot as pltimport numpy as npwith open('gau_b_g_s....

问拟合高斯函数
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