python 实现检验33品种数据是否是正态分布

yipeiwu_com6年前Python基础

我就废话不多说了,直接上代码吧!

# -*- coding: utf-8 -*-
"""
Created on Thu Jun 22 17:03:16 2017
@author: yunjinqi 
 
E-mail:yunjinqi@qq.com 
 
Differentiate yourself in the world from anyone else.
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.tsa.stattools as ts
import statsmodels.api as sm
from statsmodels.graphics.api import qqplot
from statsmodels.sandbox.stats.runs import runstest_1samp
import scipy.stats as sts 
 
namelist=['cu','al','zn','pb','sn','au','ag','rb','hc','bu','ru','m9','y9','a9',
    'p9','c9','cs','jd','l9','v9','pp','j9','jm','i9','sr','cf',
    'zc','fg','ta','ma','oi','rm','sm']
j=0
for i in namelist:
 
 filename='C:/Users/HXWD/Desktop/数据/'+i+'.csv'
 data=pd.read_csv(filename,encoding='gbk')
 data.columns=['date','open','high','low','close','amt','opi']
 data.head()
 data=np.log(data['close'])
 r=data-data.shift(1)
 r=r.dropna()
 #print(r)
 rate = np.array(list(r))
 print('品种{}数据长度{}均值{}标准差{}方差{}偏度{}峰度{}'.format(i,len(rate),
   rate.mean(),rate.std(),rate.var(),sts.skew(rate),
   sts.kurtosis(rate)))
#结果

品种cu数据长度4976均值0.00012152573153376814标准差0.014276535327917023方差0.0002038194609692628偏度-0.16028824462338614峰度2.642455989417427
品种al数据长度5406均值-2.3195089066551237e-05标准差0.009053990835143359方差8.197475004285994e-05偏度-0.34748915595295604峰度5.083890815632417
品种zn数据长度2455均值-0.00011823058103745542标准差0.016294570963077237方差0.00026551304287075983偏度-0.316153612624431峰度1.7208737518119293
品种pb数据长度1482均值-9.866770650275384e-05标准差0.011417348325010642方差0.0001303558427746233偏度-0.21599833469407717峰度5.878332673854807
品种sn数据长度510均值0.00034131697514080907标准差0.013690993291257949方差0.00018744329730127014偏度0.024808842588775293峰1.072347367872859
品种au数据长度2231均值0.0001074021979121701标准差0.012100456199756058方差0.00014642104024221482偏度-0.361814930575112峰度4.110915875328322
品种ag数据长度1209均值-0.0003262089978362889标准差0.014853094655086982方差0.00022061442083297348偏度-0.2248883178719188峰度4.296247290616826
品种rb数据长度1966均值-6.984154093694264e-05标准差0.013462363746262961方差0.00018123523763669528偏度0.07827546016742666峰度5.198115698123077
品种hc数据长度758均值-7.256339078572361e-05标准差0.01710980071993581方差0.000292745280675916偏度-0.08403481899486816峰度3.6250669416786323
品种bu数据长度864均值-0.0006258998207218544标准差0.01716581014361468方差0.0002946650378866246偏度-0.41242405508236435峰度2.437556911829674
品种ru数据长度4827均值5.17426767764321e-05标准差0.016747187916000945方差0.00028046830309384806偏度-0.1986573449586119峰度1.736876616149547
品种m9数据长度4058均值8.873778774208505e-05标准差0.012812626470272115方差0.0001641633970667177偏度-0.12119836197638824峰度2.159984922606264
品种y9数据长度2748均值4.985975458693667e-05标准差0.012855191360434762方差0.00016525594491339655偏度-0.33456507243405786峰度2.566586342814616
品种a9数据长度5392均值9.732600802295795e-05标准差0.010601259945310599方差0.00011238671242804687偏度-0.08768586026629852峰度3.898562231789457
品种p9数据长度2311均值-0.00021108840931287863标准差0.014588073181583774方差0.00021281187915124373偏度-0.2881364812318466峰度1.693401619226936
品种c9数据长度3075均值0.00010060972262212708标准差0.007206853641314312方差5.1938739407325355e-05偏度-5.204419912904765e-05峰6.074899127691497
品种cs数据长度573均值-0.0006465907683602394标准差0.011237570390237955方差0.00012628298827555283偏度0.10170996173895988峰度1.176384982024672
品种jd数据长度847均值-9.035290965408637e-05标准差0.01167344224455134方差0.00013626925383687581偏度-0.0682866825422671峰度2.0899893901516133
品种l9数据长度2370均值-0.00014710186232216803标准差0.014902467199956509方差0.00022208352864577958偏度-0.2105262196327885峰度1.8796065573836
品种v9数据长度1927均值-5.190379527562386e-05标准差0.010437020362123387方差0.00010893139403937818偏度-0.050531345744352064峰度3.47595007264211
品种pp数据长度773均值-0.0003789841804842144标准差0.01439578332841083方差0.00020723857763855122偏度0.05479337073436029峰度1.3397870170464232
品种j9数据长度1468均值-0.00021854062264841954标准差0.01639429047795793方差0.000268772760275662偏度-0.10048542944058193峰度5.156597958913997
品种jm数据长度997均值-0.00011645794468155402标准差0.01792430947223131方差0.000321280870056321偏度0.0010592028961588294峰度3.743159578760195
品种i9数据长度862均值-0.0007372124442033161标准差0.021187573227350754方差0.0004489132592643504偏度0.00014411506989559858峰度1.585951370650
品种sr数据长度2749均值0.00012213466321006727标准差0.012183745931527473方差0.00014844366492401223偏度-0.038613285961243735峰度2.520231613626
品种cf数据长度3142均值2.2008517526768612e-05标准差0.010657271857464626方差0.00011357744344390753偏度-0.034412876065561426峰度5.6421501855702
品种zc数据长度475均值0.00041282070613302206标准差0.015170141171075784方差0.00023013318315036853偏度-0.1393361750238265峰度1.2533894316392926
品种fg数据长度1068均值-1.57490340832121e-05标准差0.013148411070446203方差0.00017288071367743227偏度0.008980132282547534峰度1.9028507879273144
品种ta数据长度2518均值-0.00023122774877981512标准差0.013637519813532077方差0.00018598194666447998偏度-0.9126347458178135峰度10.954670464918
品种ma数据长度700均值-0.00024988691257348835标准差0.015328611435734359方差0.00023496632854772616偏度0.0164362832185746峰度1.1736088397060
品种oi数据长度1098均值-0.0004539513793265549标准差0.009589990427720812方差9.196791640377678e-05偏度-0.28987574371279706峰度3.871322266527967
品种rm数据长度1049均值1.458523923966432e-05标准差0.013432556545527753方差0.00018043357534880047偏度-0.053300026893851014峰度1.3938292783638
品种sm数据长度548均值-3.179600698107184e-05标准差0.020018458278106444方差0.00040073867183228846偏度-2.6734390275887647峰度31.533801188366837

#正态分布的偏度应该是0,峰度是3,所以,不满者这些的都是非标准正态分布

以上这篇python 实现检验33品种数据是否是正态分布就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持【听图阁-专注于Python设计】。

相关文章

python实现任意位置文件分割的实例

应用场景 在嵌入式开发中,常常需要将一个binary文件分割成多个文件,或者将一个binary的某块区域抓成一个单独文件。本篇blog以python为例,实现了以上需求; 实现代码...

Python3使用xml.dom.minidom和xml.etree模块儿解析xml文件封装函数的方法

总结了一下使用Python对xml文件的解析,用到的模块儿如下: 分别从xml字符串和xml文件转换为xml对象,然后解析xml内容,查询指定信息字段。 from xml.dom.m...

基于Python的文件类型和字符串详解

基于Python的文件类型和字符串详解

1. Python的文件类型 1. 源代码--直接由Python解析 vi 1.py #!/usr/bin/python print 'hello world' 这里的1.py...

python占位符输入方式实例

占位符,顾名思义就是插在输出里站位的符号。占位符是绝大部分编程语言都存在的语法, 而且大部分都是相通的, 它是一种非常常用的字符串格式化的方式。 1、常用占位符的含义 s : 获取传入...

python变量的存储原理详解

python变量的存储原理详解

变量的存储 在高级语言中,变量是对内存及其地址的抽象。 对于python而言,python的一切变量都是对象,变量的存储,采用了引用语义的方式,存储的只是一个变量的值所在的内存地址,而...