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#原始的方式

lines = [line.split(',') for line in open('iris.csv')]
df = [[float(x) for x in line[:4]] for line in lines[1:]]

#使用numpy包

import numpy as np
lines = np.loadtxt('iris.csv',delimiter=',',dtype='str')
df = lines[1:,:4].astype('float')

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import numpy as np
x = np.random.randn(100)
y = 2*x + np.random.randn(100)

%load_ext rpy2.ipython

%%R -i x,y -w 500 -h 300
df <- data.frame(x,y)
m <- lm(y~x)
inter <- m$coef[1]

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# 假设七个岛人口数分布如下
# 1:7/sum(1:7)
# 尝试10万次旅行
trajLength = 1e5
trajectory = rep( 0 , trajLength )
trajectory[1] = 3 # 第一次在第3个岛上

target = function(currentPosition,proposedJump){
  tartetposition = currentPosition+proposedJump
  p = min(1,tartetposition/currentPosition)

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myData=c(1,1,1,1,1,1,1,1,1,1,1,0,0,0)
# 尝试1万个不同的参数
tryn = 1e4
Theta = sort(runif(tryn))
pTheta = 1/tryn
z = sum( myData==1 ) 
N = length( myData ) 
# 似然函数
pDataGivenTheta = Theta^z * (1-Theta)^(N-z)
pData = sum( pDataGivenTheta * pTheta )

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# 基于《统计模拟和R实现》一书例子
import numpy as np
from numpy import random as rm
import pandas as pd

def simulation(T=4200):
    t = 0;nA =0;nD=0;n=0;A=[];D=[];N=[];S=[]
    tA = rm.exponential(10,1) # 客来时间
    tD = inf  # 客走时间
    while True:

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# 广义混合效应模型
glme <- function (){
  library(lme4)
  set.seed(820)
  ni <- c(12, 13, 14, 15, 16, 13, 11, 17, 13, 16)
  ndset <- length(ni)
  xx <- matrix(rep(0, length = max(ni) * ndset),
               ncol = ndset)
  bbi <- rnorm(ndset, mean = 0, sd = 1)
  for (ii in 1:ndset){

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# Data generation of Random Intercept and slope model
a0 <- 9.9
a1 <- 2
n <- c(12, 13, 14, 15, 16, 13)
npeople <- length(n)
set.seed(1)
si <- rnorm(npeople, mean = 0, sd = 0.5) # random slope
x <- matrix(rep(0, length = max(n) * npeople),
             ncol = npeople)
for (i in 1:npeople){

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rem2 <- function (){
  library(nlme)
  a0 <- 9.9
  a1 <- 2
  # 对6个人重复测量多次
  ni <- c(12, 13, 14, 15, 16, 13)
  nyear <- length(ni)
  set.seed(205)
  # 构造x值
  xx <- matrix(rep(0, length=max(ni) * nyear),

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# 加载库
import numpy as np
import pandas as pd
from ggplot import *

# 生成二维随机游走数据
def walk(n):
    NS = randint(0,2,size=n)
    WE = 1 - NS
    xstep = np.random.choice([1,-1],size=n,replace=True) * WE