markov_data.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
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   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "size = 1000\n",
    "x = np.random.normal(size=size)\n",
    "y = x + np.random.normal(size=size)\n",
    "z = y + np.random.normal(size=size)\n",
    "X = pd.DataFrame({'x': x, 'y': y, 'z': z})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>z</th>\n",
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       "      <th>0</th>\n",
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       "      <td>-0.578115</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2.034245</td>\n",
       "      <td>2.362531</td>\n",
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       "          x         y         z\n",
       "0 -0.078186 -0.578115 -0.892278\n",
       "1  0.129325  1.005127 -0.894835\n",
       "2  2.440264  2.034245  2.362531\n",
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       "4  0.664560 -1.410155 -0.845570"
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "X.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": []
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