scipy.integrate time series overhead

{
 "metadata": {
  "name": "knowledge.scipy.integrate.benchmark"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from scipy import integrate\nimport numpy as np\nimport pandas as pd",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "x = np.abs(np.random.randn(10000))\nprint(x)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "[ 0.66290476  1.35351935  1.47110616 ...,  1.98089506  0.4121001\n  0.32761199]\n"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%timeit integrate.trapz(x)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "10000 loops, best of 3: 72.2 \u00b5s per loop\n"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ts = pd.Series(x, pd.date_range(start='2013-05-03', periods=len(x), freq='s'))\nprint(ts)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "2013-05-03 00:00:00    0.662905\n2013-05-03 00:00:01    1.353519\n2013-05-03 00:00:02    1.471106\n2013-05-03 00:00:03    0.636451\n2013-05-03 00:00:04    0.400131\n2013-05-03 00:00:05    0.438203\n2013-05-03 00:00:06    0.283670\n2013-05-03 00:00:07    1.390324\n2013-05-03 00:00:08    0.589401\n2013-05-03 00:00:09    0.698161\n2013-05-03 00:00:10    0.263194\n2013-05-03 00:00:11    2.247498\n2013-05-03 00:00:12    0.612423\n2013-05-03 00:00:13    0.033452\n2013-05-03 00:00:14    0.531537\n...\n2013-05-03 02:46:25    0.888961\n2013-05-03 02:46:26    0.617411\n2013-05-03 02:46:27    1.515751\n2013-05-03 02:46:28    0.682397\n2013-05-03 02:46:29    2.362538\n2013-05-03 02:46:30    0.314756\n2013-05-03 02:46:31    0.620458\n2013-05-03 02:46:32    1.009591\n2013-05-03 02:46:33    0.706764\n2013-05-03 02:46:34    0.279825\n2013-05-03 02:46:35    1.002564\n2013-05-03 02:46:36    0.210033\n2013-05-03 02:46:37    1.980895\n2013-05-03 02:46:38    0.412100\n2013-05-03 02:46:39    0.327612\nFreq: S, Length: 10000\n"
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Integrate\n=========="
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%timeit integrate.trapz(ts, ts.index.astype(np.int64) / 10**9)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "10 loops, best of 3: 58.4 ms per loop\n"
      }
     ],
     "prompt_number": 20
    }
   ],
   "metadata": {}
  }
 ]
}