humberto-ortiz/moores-law.ipynb

## moores-law.ipynb
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "moore = pd.read_csv('moore.csv')\n",
      "moore"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name</th>\n",
        "      <th>Year</th>\n",
        "      <th>Count (1000s)</th>\n",
        "      <th>Clock (MHz)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>          MOS 65XX</td>\n",
        "      <td> 1975</td>\n",
        "      <td>       3.51</td>\n",
        "      <td>   14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>        Intel 8086</td>\n",
        "      <td> 1978</td>\n",
        "      <td>      29.00</td>\n",
        "      <td>   10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>        MIPS R3000</td>\n",
        "      <td> 1988</td>\n",
        "      <td>     120.00</td>\n",
        "      <td>   33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>         AMD Am486</td>\n",
        "      <td> 1993</td>\n",
        "      <td>    1200.00</td>\n",
        "      <td>   40</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>      NexGen Nx586</td>\n",
        "      <td> 1994</td>\n",
        "      <td>    3500.00</td>\n",
        "      <td>  111</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>        AMD Athlon</td>\n",
        "      <td> 1999</td>\n",
        "      <td>   37000.00</td>\n",
        "      <td> 1400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> Intel Pentium III</td>\n",
        "      <td> 1999</td>\n",
        "      <td>   44000.00</td>\n",
        "      <td> 1400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>       PowerPC 970</td>\n",
        "      <td> 2002</td>\n",
        "      <td>   58000.00</td>\n",
        "      <td> 2500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>     AMD Athlon 64</td>\n",
        "      <td> 2003</td>\n",
        "      <td>  243000.00</td>\n",
        "      <td> 2800</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>  Intel Core 2 Duo</td>\n",
        "      <td> 2006</td>\n",
        "      <td>  410000.00</td>\n",
        "      <td> 3330</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>        AMD Phenom</td>\n",
        "      <td> 2007</td>\n",
        "      <td>  450000.00</td>\n",
        "      <td> 2600</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>     Intel Core i7</td>\n",
        "      <td> 2008</td>\n",
        "      <td> 1170000.00</td>\n",
        "      <td> 3460</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>     Intel Core i5</td>\n",
        "      <td> 2009</td>\n",
        "      <td>  995000.00</td>\n",
        "      <td> 3600</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "                 Name  Year  Count (1000s)  Clock (MHz)\n",
        "0            MOS 65XX  1975           3.51           14\n",
        "1          Intel 8086  1978          29.00           10\n",
        "2          MIPS R3000  1988         120.00           33\n",
        "3           AMD Am486  1993        1200.00           40\n",
        "4        NexGen Nx586  1994        3500.00          111\n",
        "5          AMD Athlon  1999       37000.00         1400\n",
        "6   Intel Pentium III  1999       44000.00         1400\n",
        "7         PowerPC 970  2002       58000.00         2500\n",
        "8       AMD Athlon 64  2003      243000.00         2800\n",
        "9    Intel Core 2 Duo  2006      410000.00         3330\n",
        "10         AMD Phenom  2007      450000.00         2600\n",
        "11      Intel Core i7  2008     1170000.00         3460\n",
        "12      Intel Core i5  2009      995000.00         3600"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "moore.plot(x=\"Year\", y=\"Count (1000s)\", logy=True)\n",
      "fig = moore.plot(x=\"Year\", y=\"Clock (MHz)\", logy=True)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jICCAkSNHUlRUBIDNZiMqKoqVK1cSEhLC0KFDycvLY8CAATz11FPceuut3HTT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\nBYKvu9AAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105e9ff50>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}