Pandas and Numpy - performance for Series vs 1d array multiplication

array_mult_pandas_vs_numpy_speed.ipynb

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   "cells": [
    {
     "cell_type": "code",
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     "input": [
      "import pandas as pd\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
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     "input": [
      "rands1 = np.random.randn(10**7)\n",
      "rands2 = np.random.randn(10**7)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
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     "input": [
      "%%timeit\n",
      "\n",
      "rands3np = rands1 * rands2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 loops, best of 3: 126 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser1 = pd.Series(rands1)\n",
      "ser2 = pd.Series(rands2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit\n",
      "\n",
      "ser3 = ser1 * ser2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 3: 208 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 13
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     "cell_type": "code",
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     "language": "python",
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