小编ale*_*lex的帖子

我的数据目前采用长格式。下面是一个示例：

     Stock         Date      Time     Price     Year
       AAA   2001-01-05  15:20:09     2.380     2001
       AAA   2002-02-23  10:13:24     2.440     2002
       AAA   2002-02-27  17:17:55     2.460     2002
       BBB   2006-05-13  16:03:49     2.780     2006
       BBB   2006-10-04  10:33:10     2.800     2006

我想通过“股票”和“年份”将其重塑为宽格式，如下所示：

     Stock   Year       Date1        Time1    Price1        Date2      Time2   Price2
       AAA   2001  2001-01-05     15:20:09     2.380
       AAA   2002  2002-02-23     10:13:24     2.440   2002-02-27   17:17:55    2.460
       BBB   2006  2006-05-13     16:03:49     2.780   2006-10-04   10:33:10    2.800

我尝试了此处发布的解决方案Pandas long to Wide reshape并具有以下内容：

df['idx'] = df.groupby(['Stock', 'Year']).cumcount()

df['date_idx'] = 'date_' + df.idx.astype(str)
df['time_idx'] = 'time_' …

我目前有代码模拟几何布朗运动,由http://www-math.bgsu.edu/~zirbel/sde/matlab/index.html提供.

但是,我想生成1,000个模拟,并将其显示在图表中.

我目前生成单个模拟的代码如下:

% geometric_brownian(N,r,alpha,T) simulates a geometric Brownian motion 
% on [0,T] using N normally distributed steps and parameters r and alpha

function [X] = geometric_brownian(N,r,alpha,T)

t = (0:1:N)'/N;                   % t is the column vector [0 1/N 2/N ... 1]

W = [0; cumsum(randn(N,1))]/sqrt(N); % S is running sum of N(0,1/N) variables

t = t*T;
W = W*sqrt(T);

Y = (r-(alpha^2)/2)*t + alpha * W;

X = exp(Y);

plot(t,X);          % plot the path
hold on
plot(t,exp(r*t),':');
axis([0 …