Python 有100个左右的概率（float），如何按每0.1差值制作频率分布直方图（0-0.1，0.1-0.2等等）？_百度知道
Python 有100个左右的概率（float），如何按每0.1差值制作频率分布直方图（0-0.1，0.1-0.2等等）？
计算某个基因型经过100次模拟之后的概率分布，0-0.1，0.1-0.2，0.2-0.3.。。。。。这样的分栏显示在直方图里。怎样在Python中实现呀？？谢谢啦！！！...
计算某个基因型经过100次模拟之后的概率分布，0-0.1，0.1-0.2，0.2-0.3.。。。。。这样的分栏显示在直方图里。怎样在Python中实现呀？？
谢谢啦！！！
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画图需要引入额外的包，不过只是计算出十个数值还是很简单。假设原始的概率值存在数组p中 例如 p = [0.05, 0.31, ...]x&=&[0.0]&*&10&#&初始化10个区间LL&=&0&#&有效数据个数for&y&in&p:&&&&if&p&&&0&||&p&&=&1:&#&跳过非法值&&&&&&&&continue&&&&LL&+=&1;&&&&x[int(10&*&y)]&+=&1.0x&=&[y/LL&for&y&in&x]&#&频数-&频率
我明白你的逻辑了，谢谢！不过感觉这是C语言不是Python吧。顺便再问下哈，我已经引入了matplotlib包，有什么简单的画图命令来解决这种情况么？
不知道题主是怎么以为这是C语言的。。无语。。。画图命令 示例import&matplotlib.pyplot&as&pltx&=&[1.0*y/10&for&y&in&range(9)]z&=&xplt.plot(x,z)plt.show()
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Statistical functions ()
This module contains a large number of probability distributions as
well as a growing library of statistical functions.
Each univariate distribution is an instance of a subclass of
( for discrete distributions):
([momtype,&a,&b,&xtol,&…])
A generic continuous random variable class meant for subclassing.
([a,&b,&name,&badvalue,&…])
A generic discrete random variable class meant for subclassing.
(histogram,&*args,&**kwargs)
Generates a distribution given by a histogram.
Continuous distributions
An alpha continuous random variable.
An anglit continuous random variable.
An arcsine continuous random variable.
Argus distribution
A beta continuous random variable.
A beta prime continuous random variable.
A Bradford continuous random variable.
A Burr (Type III) continuous random variable.
A Burr (Type XII) continuous random variable.
A Cauchy continuous random variable.
A chi continuous random variable.
A chi-squared continuous random variable.
A cosine continuous random variable.
Crystalball distribution
A double gamma continuous random variable.
A double Weibull continuous random variable.
An Erlang continuous random variable.
An exponential continuous random variable.
An exponentially modified Normal continuous random variable.
An exponentiated Weibull continuous random variable.
An exponential power continuous random variable.
An F continuous random variable.
A fatigue-life (Birnbaum-Saunders) continuous random variable.
A Fisk continuous random variable.
A folded Cauchy continuous random variable.
A folded normal continuous random variable.
A frechet_r continuous random variable.
A frechet_l continuous random variable.
A generalized logistic continuous random variable.
A generalized normal continuous random variable.
A generalized Pareto continuous random variable.
A generalized exponential continuous random variable.
A generalized extreme value continuous random variable.
A Gauss hypergeometric continuous random variable.
A gamma continuous random variable.
A generalized gamma continuous random variable.
A generalized half-logistic continuous random variable.
A Gilbrat continuous random variable.
A Gompertz (or truncated Gumbel) continuous random variable.
A right-skewed Gumbel continuous random variable.
A left-skewed Gumbel continuous random variable.
A Half-Cauchy continuous random variable.
A half-logistic continuous random variable.
A half-normal continuous random variable.
The upper half of a generalized normal continuous random variable.
A hyperbolic secant continuous random variable.
An inverted gamma continuous random variable.
An inverse Gaussian continuous random variable.
An inverted Weibull continuous random variable.
A Johnson SB continuous random variable.
A Johnson SU continuous random variable.
Kappa 4 parameter distribution.
Kappa 3 parameter distribution.
General Kolmogorov-Smirnov one-sided test.
Kolmogorov-Smirnov two-sided test for large N.
A Laplace continuous random variable.
A Levy continuous random variable.
A left-skewed Levy continuous random variable.
A Levy-stable continuous random variable.
A logistic (or Sech-squared) continuous random variable.
A log gamma continuous random variable.
A log-Laplace continuous random variable.
A lognormal continuous random variable.
A Lomax (Pareto of the second kind) continuous random variable.
A Maxwell continuous random variable.
A Mielke’s Beta-Kappa continuous random variable.
A Moyal continuous random variable.
A Nakagami continuous random variable.
A non-central chi-squared continuous random variable.
A non-central F distribution continuous random variable.
A non-central Student’s T continuous random variable.
A normal continuous random variable.
A Normal Inverse Gaussian continuous random variable.
A Pareto continuous random variable.
A pearson type III continuous random variable.
A power-function continuous random variable.
A power log-normal continuous random variable.
A power normal continuous random variable.
An R-distributed continuous random variable.
A reciprocal continuous random variable.
A Rayleigh continuous random variable.
A Rice continuous random variable.
A reciprocal inverse Gaussian continuous random variable.
A semicircular continuous random variable.
A skew-normal random variable.
A Student’s T continuous random variable.
A trapezoidal continuous random variable.
A triangular continuous random variable.
A truncated exponential continuous random variable.
A truncated normal continuous random variable.
A Tukey-Lamdba continuous random variable.
A uniform continuous random variable.
A Von Mises continuous random variable.
A Von Mises continuous random variable.
A Wald continuous random variable.
Weibull minimum continuous random variable.
Weibull maximum continuous random variable.
A wrapped Cauchy continuous random variable.
Multivariate distributions
A multivariate normal random variable.
A matrix normal random variable.
A Dirichlet random variable.
A Wishart random variable.
An inverse Wishart random variable.
A multinomial random variable.
A matrix-valued SO(N) random variable.
A matrix-valued O(N) random variable.
A matrix-valued U(N) random variable.
A random correlation matrix.
Discrete distributions
A Bernoulli discrete random variable.
A binomial discrete random variable.
A Boltzmann (Truncated Discrete Exponential) random variable.
Laplacian discrete random variable.
A geometric discrete random variable.
A hypergeometric discrete random variable.
A Logarithmic (Log-Series, Series) discrete random variable.
A negative binomial discrete random variable.
A Planck discrete exponential random variable.
A Poisson discrete random variable.
A uniform discrete random variable.
Skellam discrete random variable.
A Zipf discrete random variable.
Statistical functions
Several of these functions have a similar version in scipy.stats.mstats
which work for masked arrays.
(a[,&axis,&ddof,&bias,&nan_policy])
Compute several descriptive statistics of the passed array.
(a[,&axis,&dtype])
Compute the geometric mean along the specified axis.
(a[,&axis,&dtype])
Calculate the harmonic mean along the specified axis.
(a[,&axis,&fisher,&bias,&nan_policy])
Compute the kurtosis (Fisher or Pearson) of a dataset.
(a[,&axis,&nan_policy])
Test whether a dataset has normal kurtosis.
(a[,&axis,&nan_policy])
Return an array of the modal (most common) value in the passed array.
(a[,&moment,&axis,&nan_policy])
Calculate the nth moment about the mean for a sample.
(a[,&axis,&nan_policy])
Test whether a sample differs from a normal distribution.
(a[,&axis,&bias,&nan_policy])
Compute the skewness of a data set.
(a[,&axis,&nan_policy])
Test whether the skew is different from the normal distribution.
(data[,&n])
Return the nth k-statistic (1&=n&=4 so far).
(data[,&n])
Returns an unbiased estimator of the variance of the k-statistic.
(a[,&limits,&inclusive,&axis])
Compute the trimmed mean.
(a[,&limits,&inclusive,&axis,&ddof])
Compute the trimmed variance.
(a[,&lowerlimit,&axis,&inclusive,&…])
Compute the trimmed minimum.
(a[,&upperlimit,&axis,&inclusive,&…])
Compute the trimmed maximum.
(a[,&limits,&inclusive,&axis,&ddof])
Compute the trimmed sample standard deviation.
(a[,&limits,&inclusive,&axis,&ddof])
Compute the trimmed standard error of the mean.
(a[,&axis,&nan_policy])
Compute the coefficient of variation, the ratio of the biased standard deviation to the mean.
Find repeats and repeat counts.
(a,&proportiontocut[,&axis])
Return mean of array after trimming distribution from both tails.
(a[,&numbins,&defaultreallimits,&weights])
Return a cumulative frequency histogram, using the histogram function.
(*args,&**kwds)
is deprecated!
is deprecated and will be removed in a future version.
(a,&score[,&kind])
The percentile rank of a score relative to a list of scores.
(a,&per[,&limit,&…])
Calculate the score at a given percentile of the input sequence.
(a[,&numbins,&defaultreallimits,&weights])
Return a relative frequency histogram, using the histogram function.
(x,&values[,&statistic,&…])
Compute a binned statistic for one or more sets of data.
(x,&y,&values[,&…])
Compute a bidimensional binned statistic for one or more sets of data.
(sample,&values[,&…])
Compute a multidimensional binned statistic for a set of data.
Compute the O’Brien transform on input data (any number of arrays).
(data[,&alpha])
Bayesian confidence intervals for the mean, var, and std.
‘Frozen’ distributions for mean, variance, and standard deviation of data.
(a[,&axis,&ddof,&nan_policy])
Calculate the standard error of the mean (or standard error of measurement) of the values in the input array.
(scores,&compare[,&axis,&ddof])
Calculate the relative z-scores.
(a[,&axis,&ddof])
Calculate the z score of each value in the sample, relative to the sample mean and standard deviation.
(x[,&axis,&rng,&scale,&nan_policy,&…])
Compute the interquartile range of the data along the specified axis.
(a[,&low,&high])
Iterative sigma-clipping of array elements.
(a,&proportiontocut[,&axis])
Slices off a proportion of items from both ends of an array.
(a,&proportiontocut[,&tail,&axis])
Slices off a proportion from ONE end of the passed array distribution.
Performs a 1-way ANOVA.
Calculate a Pearson correlation coefficient and the p-value for testing non-correlation.
(a[,&b,&axis,&nan_policy])
Calculate a Spearman rank-order correlation coefficient and the p-value to test for non-correlation.
Calculate a point biserial correlation coefficient and its p-value.
(x,&y[,&initial_lexsort,&nan_policy])
Calculate Kendall’s tau, a correlation measure for ordinal data.
(x,&y[,&rank,&weigher,&additive])
Compute a weighted version of Kendall’s \(\tau\).
Calculate a linear least-squares regression for two sets of measurements.
(y[,&x,&alpha])
Computes the Theil-Sen estimator for a set of points (x, y).
(a,&popmean[,&axis,&nan_policy])
Calculate the T-test for the mean of ONE group of scores.
(a,&b[,&axis,&equal_var,&nan_policy])
Calculate the T-test for the means of two independent samples of scores.
(mean1,&std1,&nobs1,&…)
T-test for means of two independent samples from descriptive statistics.
(a,&b[,&axis,&nan_policy])
Calculate the T-test on TWO RELATED samples of scores, a and b.
(rvs,&cdf[,&args,&N,&alternative,&mode])
Perform the Kolmogorov-Smirnov test for goodness of fit.
(f_obs[,&f_exp,&ddof,&axis])
Calculate a one-way chi square test.
(f_obs[,&f_exp,&ddof,&axis,&…])
Cressie-Read power divergence statistic and goodness of fit test.
(data1,&data2)
Compute the Kolmogorov-Smirnov statistic on 2 samples.
(x,&y[,&use_continuity,&alternative])
Compute the Mann-Whitney rank test on samples x and y.
(rankvals)
Tie correction factor for ties in the Mann-Whitney U and Kruskal-Wallis H tests.
(a[,&method])
Assign ranks to data, dealing with ties appropriately.
Compute the Wilcoxon rank-sum statistic for two samples.
(x[,&y,&zero_method,&correction])
Calculate the Wilcoxon signed-rank test.
(*args,&**kwargs)
Compute the Kruskal-Wallis H-test for independent samples
Compute the Friedman test for repeated measurements
(pvalues[,&method,&weights])
Methods for combining the p-values of independent tests bearing upon the same hypothesis.
Perform the Jarque-Bera goodness of fit test on sample data.
Perform the Ansari-Bradley test for equal scale parameters
Perform Bartlett’s test for equal variances
(*args,&**kwds)
Perform Levene test for equal variances.
Perform the Shapiro-Wilk test for normality.
(x[,&dist])
Anderson-Darling test for data coming from a particular distribution
(samples[,&midrank])
The Anderson-Darling test for k-samples.
(x[,&n,&p,&alternative])
Perform a test that the probability of success is p.
(*args,&**kwds)
Perform Fligner-Killeen test for equality of variance.
(*args,&**kwds)
Mood’s median test.
(x,&y[,&axis])
Perform Mood’s test for equal scale parameters.
(x[,&lmbda,&alpha])
Return a positive dataset transformed by a Box-Cox power transformation.
(x[,&brack,&method])
Compute optimal Box-Cox transform parameter for input data.
(lmb,&data)
The boxcox log-likelihood function.
(pk[,&qk,&base])
Calculate the entropy of a distribution for given probability values.
(u_values,&v_values[,&…])
Compute the first Wasserstein distance between two 1D distributions.
(u_values,&v_values[,&…])
Compute the energy distance between two 1D distributions.
Circular statistical functions
(samples[,&high,&low,&axis])
Compute the circular mean for samples in a range.
(samples[,&high,&low,&axis])
Compute the circular variance for samples assumed to be in a range
(samples[,&high,&low,&axis])
Compute the circular standard deviation for samples assumed to be in the range [low to high].
Contingency table functions
(observed[,&correction,&lambda_])
Chi-square test of independence of variables in a contingency table.
(observed)
Compute the expected frequencies from a contingency table.
Return a list of the marginal sums of the array a.
(table[,&alternative])
Performs a Fisher exact test on a 2x2 contingency table.
Plot-tests
(x[,&brack,&dist])
Calculate the shape parameter that maximizes the PPCC
(x,&a,&b[,&dist,&plot,&N])
Calculate and optionally plot probability plot correlation coefficient.
(x[,&sparams,&dist,&fit,&plot,&rvalue])
Calculate quantiles for a probability plot, and optionally show the plot.
(x,&la,&lb[,&plot,&N])
Compute parameters for a Box-Cox normality plot, optionally show it.
Masked statistics functions
Univariate and multivariate kernel density estimation (scipy.stats.kde)
(dataset[,&bw_method])
Representation of a kernel-density estimate using Gaussian kernels.
For many more stat related functions install the software R and the
interface package rpy.发布于 07/15 16:56
概率分布有两种类型：离散（discrete）概率分布和连续（continuous）概率分布。
离散概率分布也称为概率质量函数（probability mass function）。离散概率分布的例子有伯努利分布（Bernoulli distribution）、二项分布（binomial distribution）、泊松分布（Poisson distribution）和几何分布（geometric distribution）等。
连续概率分布也称为概率密度函数（probability density function），它们是具有连续取值（例如一条实线上的值）的函数。正态分布（normal distribution）、指数分布（exponential distribution）和β分布（beta distribution）等都属于连续概率分布。
1、两点分布（伯努利分布）
伯努利试验：
伯努利试验是在同样的条件下重复地、各次之间相互独立地进行的一种试验。
即只先进行一次伯努利试验，该事件发生的概率为p，不发生的概率为1-p。这是一个最简单的分布，任何一个只有两种结果的随机现象都服从0-1分布。
最常见的例子为抛硬币
方差D = p*(1-p)^2+(1-p)*(0-p)^2 = p*(1-p)
&2、二项分布（n重伯努利分布）（X~B(n,p））
即做n个两点分布的实验
D = np(1-p)
对于二项分布，可以参考
二项分布的应用场景主要是，对于已知次数n，关心发生k次成功。
，即为二项分布公式可求。
对于抛硬币的问题，做100次实验，观察其概率分布函数：
# -*- coding: utf-8 -*-
from scipy.stats import binom
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
## 设置属性防止中文乱码
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False
fig,ax = plt.subplots(1,1)
#平均值, 方差, 偏度, 峰度
mean,var,skew,kurt = binom.stats(n,p,moments='mvsk')
print (mean,var,skew,kurt)
#ppf:累积分布函数的反函数。q=0.01时，ppf就是p(X&x)=0.01时的x值。
x = np.arange(binom.ppf(0.01, n, p),binom.ppf(0.99, n, p))
ax.plot(x, binom.pmf(x, n, p),'o')
plt.title(u'二项分布概率质量函数')
plt.show()
　首先导入库函数以及设置对中文的支持
观察概率分布图，可以看到，对于n = 100次实验中，有50次成功的概率（正面向上）的概率最大。
3、几何分布（X ～ GE(p)）
在n次伯努利实验中，第k次实验才得到第一次成功的概率分布。其中：P(k) = (1-p)^(k-1)*p
E = 1/p& 推到方法就是利用利用错位相减法然后求lim - k -&无穷&
D = (1-p)/p^2& 推到方法利用了D(x) = E(x)^2-E(x^2)，其中E(x^2)求解同上
几何分布可以参考：
# -*- coding: utf-8 -*-
from scipy.stats import geom
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
## 设置属性防止中文乱码
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False
fig,ax = plt.subplots(1,1)
#平均值, 方差, 偏度, 峰度
mean,var,skew,kurt = geom.stats(p,moments='mvsk')
print (mean,var,skew,kurt)
#ppf:累积分布函数的反函数。q=0.01时，ppf就是p(X&x)=0.01时的x值。
x = np.arange(geom.ppf(0.01, p),geom.ppf(0.99, p))
ax.plot(x, geom.pmf(x, p),'o')
plt.title(u'几何分布概率质量函数')
plt.show()
因此，可以看到，对于抛硬币问题，抛个两三次就能成功。
4、泊松分布（X~P(λ)）
描述单位时间/面积内，随机事件发生的次数。P(x = k) =&λ^k/k!*e^(-λ)& &k = 0,1,2, ...& & λ &0
泊松分布可作为二项分布的极限而得到。一般的说，若&&，其中n很大，p很小，因而&&不太大时，X的分布接近于泊松分布&&。
λ：单位时间/面积下，随机事件的平均发生率
譬如：某一服务设施一定时间内到达的人数、一个月内机器损坏的次数等。
&假设某地区，一年中发生枪击案的平均次数为2。
# -*- coding: utf-8 -*-
from scipy.stats import poisson
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
## 设置属性防止中文乱码
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False
fig,ax = plt.subplots(1,1)
#平均值, 方差, 偏度, 峰度
mean,var,skew,kurt = poisson.stats(mu,moments='mvsk')
print (mean,var,skew,kurt)
#ppf:累积分布函数的反函数。q=0.01时，ppf就是p(X&x)=0.01时的x值。
x = np.arange(poisson.ppf(0.01, mu),poisson.ppf(0.99, mu))
ax.plot(x, poisson.pmf(x, mu),'o')
plt.title(u'poisson分布概率质量函数')
plt.show()
因此，一年内的枪击案发生次数的分布如上所示。
与二项分布对比：
# -*- coding: utf-8 -*-
from scipy.stats import binom
from scipy.stats import poisson
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
## 设置属性防止中文乱码
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False
fig,ax = plt.subplots(1,1)
#平均值, 方差, 偏度, 峰度
mean,var,skew,kurt = binom.stats(n,p,moments='mvsk')
print (mean,var,skew,kurt)
#ppf:累积分布函数的反函数。q=0.01时，ppf就是p(X&x)=0.01时的x值。
x = np.arange(binom.ppf(0.01, n, p),binom.ppf(0.99, n, p))
p1, = ax.plot(x, binom.pmf(x, n, p),'b*',label = 'binom')
mu = n * p
# 平均值, 方差, 偏度, 峰度
mean, var, skew, kurt = poisson.stats(mu, moments='mvsk')
print (mean,var,skew,kurt)
# ppf:累积分布函数的反函数。q=0.01时，ppf就是p(X&x)=0.01时的x值。
x = np.arange(poisson.ppf(0.01, mu), poisson.ppf(0.99, mu))
p2, = ax.plot(x, poisson.pmf(x, mu), 'ro', label='poisson')
plt.legend(handles=[p1, p2])
plt.title(u'对比')
plt.show()
&5、均匀分布（X~U(a,b)）
对于随机变量x的概率密度函数：
则称随机变量X服从区间[a,b]上的均匀分布。
E = 0.5（a+b）
D = （b-a）^2 / 12
均匀分布在自然情况下极为罕见，而人工栽培的有一定株行距的植物群落即是均匀分布。这表明X落在[a,b]的子区间内的概率只与子区间长度有关，而与子区间位置无关，因此X落在[a,b]的长度相等的子区间内的可能性是相等的，所谓的均匀指的就是这种等可能性。
落在某一点的概率都是相同的
若[x1,x2]是[a,b]的任一子区间，则
P{x1≤x≤x2}=(x2-x1)/(b-a)
这表明X落在[a,b]的子区间内的概率只与子区间长度有关，而与子区间位置无关。
# -*- coding: utf-8 -*-
from scipy.stats import uniform
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
## 设置属性防止中文乱码
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False
fig, ax = plt.subplots(1, 1)
# 平均值, 方差, 偏度, 峰度
mean, var, skew, kurt = uniform.stats(loc, scale, moments='mvsk')
print (mean,var,skew,kurt)
# ppf:累积分布函数的反函数。q=0.01时，ppf就是p(X&x)=0.01时的x值。
x = np.linspace(uniform.ppf(0.01, loc, scale), uniform.ppf(0.99, loc, scale), 100)
ax.plot(x, uniform.pdf(x, loc, scale), 'b-', label='uniform')
plt.title(u'均匀分布概率密度函数')
plt.show()
&6、指数分布X~ E（λ）
&D = 1/λ^2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
fig,ax&=&plt.subplots(1,1) & lambdaUse&=&2 loc&=&0 scale&=&1.0/lambdaUse & #平均值, 方差, 偏度, 峰度 mean,var,skew,kurt&=&expon.stats(loc,scale,moments='mvsk') print&mean,var,skew,kurt #ppf:累积分布函数的反函数。q=0.01时，ppf就是p(X&x)=0.01时的x值。 x&=&np.linspace(expon.ppf(0.01,loc,scale),expon.ppf(0.99,loc,scale),100) ax.plot(x, expon.pdf(x,loc,scale),'b-',label&=&'expon') & plt.title(u'指数分布概率密度函数') plt.show()
&指数分布通常用来表示随机事件发生的时间间隔，其中lambda和poisson分布的是一个概念（我认为），不知道为什么知乎上：，他们为啥说这俩不一样呢？我觉得这两种分布的期望肯定不一样啊，一个描述发生次数，一个描述两次的时间间隔，互为倒数也是应该的啊。
指数分布常用来表示旅客进机场的时间间隔、电子产品的寿命分布（需要高稳定的产品，现实中要考虑老化的问题）
指数分布的特性：无记忆性
比如灯泡的使用寿命服从指数分布，无论他已经使用多长一段时间，假设为s，只要还没有损坏，它能再使用一段时间t 的概率与一件新产品使用时间t 的概率一样。
这个证明过程简单表示：
P(s+t| s) = P(s+t , s)/P(s) = F（s+t）/F（s）=P(t)
7、正态分布（X~N(μ，σ^2)）
&正态分布是比较常见的，譬如学生考试成绩的人数分布等
fig,ax&=&plt.subplots(1,1)
scale&=&2.0
#平均值, 方差, 偏度, 峰度
mean,var,skew,kurt&=&norm.stats(loc,scale,moments='mvsk')
print&mean,var,skew,kurt
#ppf:累积分布函数的反函数。q=0.01时，ppf就是p(X&x)=0.01时的x值。
x&=&np.linspace(norm.ppf(0.01,loc,scale),norm.ppf(0.99,loc,scale),100)
ax.plot(x, norm.pdf(x,loc,scale),'b-',label&=&'norm')
plt.title(u'正太分布概率密度函数')
plt.show()
大数定理：
随着样本的增加，样本的平均数将接近于总体的平均数，故推断中，一般会使用样本平均数估计总体平均数。
&大数定律讲的是样本均值收敛到总体均值
中心极限定理：
独立同分布的事件，具有相同的期望和方差，则事件服从中心极限定理。他表示了对于抽取样本，n足够大的时候，样本分布符合x~N(μ,σ^2)
中心极限定理告诉我们，当样本量足够大时，样本均值的分布慢慢变成正态分布
本文转载自：https://www.cnblogs.com/pinking/p/7898313.html
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