IPython Notebook Example for my Usenix ;Login: Article

FileUsage.ipynb

{
 "metadata": {
  "name": "FileUsage"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# IPython Demo For Usenix ;Login:\n",
      "\n",
      "This session illustrates some simple features of the IPython notebook by performing some simple operations on files and directories."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Changing directories and directory listings\n",
      "\n",
      "You can use common shell commands such as `cd` and `ls` to move around on the file system"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd ~"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Users/beazley\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[34mDesktop\u001b[m\u001b[m/   \u001b[34mDownloads\u001b[m\u001b[m/ \u001b[34mLibrary\u001b[m\u001b[m/   \u001b[34mMusic\u001b[m\u001b[m/     \u001b[30m\u001b[43mProjects\u001b[m\u001b[m/  \u001b[34mSites\u001b[m\u001b[m/     \u001b[34mTools\u001b[m\u001b[m/\r\n",
        "\u001b[34mDocuments\u001b[m\u001b[m/ \u001b[34mJunk\u001b[m\u001b[m/      \u001b[34mMovies\u001b[m\u001b[m/    \u001b[34mPictures\u001b[m\u001b[m/  \u001b[34mPublic\u001b[m\u001b[m/    \u001b[34mTeaching\u001b[m\u001b[m/  usage.py\r\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd Pictures"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Users/beazley/Pictures\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Determine Disk Usage of Different File Types\n",
      "\n",
      "This code determines the disk usage of different types of files in the current working directory"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import collections\n",
      "import os"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "size_by_type = collections.Counter()\n",
      "for path, dirs, files in os.walk('.'):\n",
      "    for filename in files:\n",
      "        fullname = os.path.join(path, filename)\n",
      "        if os.path.exists(fullname):\n",
      "            _, ext = os.path.splitext(filename)\n",
      "            sz = os.path.getsize(fullname)\n",
      "            size_by_type[ext.upper()] += sz\n",
      "            \n",
      "for ext, sz in size_by_type.most_common(5):\n",
      "    print ext, sz\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ".JPG 50389086278\n",
        ".MOV 38328837384\n",
        ".AVI 9740373284\n",
        ".APDB 733642752\n",
        ".DATA 518045719\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Making a Pie Chart\n",
      "\n",
      "This code takes the file usage data and turns it into a pie chart.  To do this, we have to make two lists containing the percentages and associated labels.  Matplotlib is used to make the chart."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_size = sum(size_by_type.values())\n",
      "labels, fracs = zip(*size_by_type.most_common(5))\n",
      "pcts = [float(f)/total_size*100 for f in fracs]\n",
      "pcts.append(100 - sum(pcts))\n",
      "labels = labels + ('other',)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = pie(pcts, labels=labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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hxMeOHWPnzp0kJSX9plyLnLt+48aNtGvXjry8PI4fP15cugAnT56kSZMm4X+W\nMYBeXXuhpkdEVBFJrBAYCu5H4YMKUAeYw29PNSHCUzTQBLgDuAm4sWnTyy7cvXv34nQ6OXz4MKmp\nqaSmpjJx4kRmzZp1wa/V9eA5UdauXcuUKVMYPXo00dG/3Ulx6dKldO7cOTJKt2f3nsQclTGIuEqq\nQf5TGqdug4dNCh1UlZ1GZxKXZKOi0Kbzb2acL0rfvn2ZPXs2d955Z4n1gwYNYvbs2Rf8+m7dunH9\n9dczatQoOnfuzGuvvVb8uaI53ebNm7No0SJeeOGFyJheyMnJoXK1ynif9kbA4RwiovlBnQ9Ru+Bh\nVeVvmkYFozOJC+pToQIjP/qIQYMGGR3lgiKidAEaNGvAvhv3yYlvRGhkgmO2ivW0xlvACGQvh3CV\nD1SzWjly4gQVKoT/W2RETC8A9O7RG/VQxMQVka4yOJ/UyLodHjcrtFFVthqdSZzXt0C7Fi0ionAh\ngkq3V49eMq8rQq8V5E3U2dxEoz0wSlXlWJ0wsyg6mn5Dhxod46JFzPRCXl4ela+rjOcxD+X2UCJh\nrFPgSFYxndT4f8ADRNCopYzSgOo2G99v305SUmQc4B0x/2ZiY2O5pfctyGZlYZhK4BytkTsQxloU\nmqsKPxudqZzbBMQlJERM4UIElS7AqAdHEbs71ugYorxrDnnP6uy4Qacz8IBJ5ZTRmcqp/6kq/SJg\nj4WzRcz0AgQvOFepaiVyh+VCJaPTCAFkgy1ZxZyh8Q9F4RFdv8RTdYvL5Qfq2u0sWLOGVq1aGR3n\nokXUSNdsNjN82HBM2+WftQgT8eD+s0be3TDBCk0VhbVGZyonFgHVExMjqnAhwkoX4OH7HyZ6V7Qc\nqynCS2PIm6Czu7VOTwVGmExc+jUMxKV4NyaGxydONDrGJYuo6QUIHudct1Fdfr3pV8rU2ahF2ZEL\n0ckq5mMaUxWF0bouB1KWsj1A59hY0k6cIOpKr2oRYhE30lUUhRcnvUjMT7LPrghTFcDzqEb+UHg+\nSqGRovCd0ZnKmGlWKw+PGhVxhQsRONIF8Pl81Khbg8xemXJYsAhvGrAMHD/BrarKWwGN64zOFOHy\ngdrR0Wzes4daV3h5HiNE3EgXwGKx8NLkl3CscxgdRYg/pgJ9wPk0fFVNpwHwmqLgMzpXBPuPqtK1\nS5eILFyI0JEuQEFBAdfVvo7Td5xGhg4iYuyH2C9UEjw6H+o6PYzOE2GygYY2GyvWr6dp0/Ne5S3s\nReRIFyDOvjbqAAARYElEQVQqKornJz0vo10RWepB3l80DnXUuUOBO0wm0o3OFEH+YbHQb8CAiC1c\niOCRLoDb7aZarWrkDM6BqkanEeISucA6W8GcpjNJVRmvaUTeZqHQOQw0t9nYuncvNQqvAByJInak\nC2Cz2Xhl6is4ljtkv10ReezgfVDHdT/8wwZJisJSozOFsReio3nkz3+O6MKFCB/pAmiaxg2tb2BX\n7V3oLSP6jyLKuxRwrIGbFZX3Axp1jM4TRnYA3WNi2JueTnx8vNFxrkhEj3QBVFVlxkcziF4dDS6j\n0whxBbqB8xlYWUunCfCCquI2OlMY0IFn7HYmTZkS8YULZWCkW2TU6FH8d8N/8dzmMTqKEFcuHWI/\nV3Hka3ygQ3/K7+WCkoGX69Rh0549WK1Wo+NcsTJTujk5OSTWTySrf5YcMCHKjjUQk6JwIwofaBr1\njM4TYieAZjYb/1u1ihtvvNHoOKUi4qcXisTFxTHt39NwLHNAwOg0QpSSTpA/Uee7JJ0bgGdVFafR\nmUJotM3GvSNHlpnChTJUugBDhgyhXdN2WFMi/1cQIYpZwT9cxz0S3o2FROALyv4OO7OAbddcw5RX\nXrmsrz958iQWi4UPPvgAgNWrV9OhQ4cSz/H7/VStWpVjx45x//3388UXX1xp7AsqU6WrKApzPptD\nhQMVgqchEqIsqQ55YzVO9IH7TQqdVJXdRme6StKAp2w2Zi5YgN1+eRdFnDt3Ln369CE5ORmATp06\ncfjwYdLS0oqf8+2339K0aVOqVauGoigoytWfOS9TpQtQqVIlFs5biG2JDXKMTiPEVdAO8iforKuv\n0QoYp6rkGZ2pFAWA+x0Oxj777BWdoHz27Nn87W9/IzMzkyNHjqCqKnfffTezZ88u8ZyhZ11JOBSb\nuMpc6QJ06NCByRMm41go87uijLJCYCi4/wz/iYM6BLfyl4Uph2etVpRmzXhm0qTLfo309HQyMzNp\n3rw5gwcPZs6cOQAMHTq0uHQLCgpYsmQJg0J8jbUyWboAE5+ZSNuktlhWWYyOIsTVUwXyx2ic7guP\nmhXaqSo7jM50BT5VFOZXqsTnixZhMl3+ZbnmzJnD4MGDAbjrrruKpxhat25Nfn4+e/fuZcmSJbRr\n1y7k+/6W2RPaq6rK3FlzadSsEadqnIKGRicS4ipqC3ktdTZ8qXPjTnhQVXlZ04gzOtclWAeMt9tJ\nWb6cSpWu7MqzycnJZGRk8NlnnwFw7Ngx9u/fT7169YpHu7/88kuJqYVQKbMjXYBrrrmGRV8uwr7E\nDkeNTiPEVWYG/S5wPw6fVITawCcEz6Me7o4Ag2w2PkxOpkmTJlf0Wnv37sXpdHL48GFSU1NJTU1l\n4sSJxaPdoUOHMmPGDFJSUrjjjjtKIf2lKdOlC9CuXTtmfTIL21wbnDY6jRAhcC04n9DIuQOetCi0\nVlU2G53pD7iBAQ4HT0yaRP/+/a/otfr27cvs2bO58847S6wfNGhQ8Vxuo0aNiImJoXv37thsthLP\nC8XeC2XmiLQLee/99xj/4nhcf3KBnIJXlBcaKAsgehuMUFVe1TQSjM50Fg0YYbNB797MnD8/JKVn\ntHJTugATJ03kneR3cA5zghw/IcqT02BPVjGf0HgdeAjjf83VgT9HRbGzcWOW/fDDb0adZVW5Kl1d\n1xlx/wgW/LwA1yAXXP7GUSEi0zaIXaRQyw8fazptDYqhA2OiotjQoAHLfviB2NhYg5KEXrkqXQhe\nSbhX316sO70OT1+P8W/3QoSaBnwF9s1wl0nl9YDGNSH89jow3mrlu6Qklv/4Y5k4XeOlKHelC+B0\nOul5a0+25G/B01+KV5RT2WCbrWI6rvGqojBK16/6L386MMliYWmdOqxYt46EhHCaYQ6Nclm6ELy+\nWu9+vfn51M+4b3fLVIMov3ZB7EKFal74WNfpcOGvuGwvWix8UbMmKT/9xDXXhHJ8HT7KbekCeDwe\n+g7oy9rDa3EPcIMcvCbKKw1YCvYNcIdq4l+BAFVK8eV14HmLhS+uu45V69dTuXLlUnz1yFKuSxeC\nc7xDRgzhm03f4BrsgmijEwlhoFyInq1iOqoxVVF4Qtev+LBVLzAyOppfkpL4asUKqlQpzTqPPOW+\ndCF4cctRj49i5tczcQ1xQYzRiYQw2F6Ina9ybYHOR7pOl8t8mWxgkN1ObMeOzPzySxwO2UleNiER\nPE/DB9M+YNwD47B9YoNjRicSwmANIO8ZjYPtdG5TYJBJ5cglvkQacLPdTtN77+WLJUukcAvJSPcc\nn3/+OQ888gCuni5oZnQaIcJAPkTNUTCl6zyvKIzV9QseW7QZ6G+zMX7qVJ56+ulQpIwYUrrnsXXr\nVnr17UV2Ujberl75fUAIgAMQ+4VKRbfOh7pOz9952tfAA3Y77/33vwwqPL2iOENK93ecPHmSvgP7\nsuP0Dly3ywY2IYDgXg4p4Pgeuikq72pa8cW3A8AUi4WPYmKY+/XXtG/f3sCg4UtK9w/4fD5GjxnN\nZ19+Fjxs+FqjEwkRJlxgnaNgOqTzrKpyv6bxgN2O1rQpsxYupGrVqkYnDFtSuhfhw48+5MlxT+Lu\n5kZvrkPZPxGSEBfnV4hJVnAW6Dw7fjxTXnkFs7nMXhuhVEjpXqTt27czcMhAjlmO4eotp4cUAg3M\na8w4tjuY/t704svjiD8mm4guUrNmzdi5eScje47ENt0Ge41OJISBssEx08GN+o3s3r5bCvcSyEj3\nMqxevZq7ht1Fbq1cCroXyLl5RfmhgbpBJeqHKJ7/6/M8M/4ZVFXGbpdCSvcy5eTkMPKxkXy98mtc\n/VxQw+hEQlxlmeBY4qBR1UbM+mQWDRo0MDpRRJLSvULz5s3joVEPUVC/gIIuBVA+Tn4vyhM/mH8w\nE7U5itdffZ1HRj4io9srIKVbCrKysvjLs39h1pxZeLp40FvIHg6ijEgDx1IHHVt25OP/+5jrrrvO\n6EQRT0q3FG3evJn7Rt7HwdMHcfZyQjWjEwlxmfIhak0UtoM2Pnz/w99cXVdcPindUqZpGp988glj\nnxkrUw4i8vjA/JMZ809mHrj/AV6e8jIVK1Y0OlWZIqV7lZSYcujgQW+lc8UnJhXiatGArWD/3k73\nzt1587U3SUpKMjpVmSSle5Vt3bqVcRPGsW7jOlztXdACuTSQCB86cAAcqxzUv64+7//7fW666Saj\nU5VpUroh8tNPPzF2wli2/rIVV8fC00bKBmBhpCPgWOMgviCed/7fO9xxxx0oimwBvtqkdENs9erV\njJ0wlr3pe3F2dEJjZE8HETo6kAox62OIyo7ixckv8ugjj2KxyAUCQ0VK1wC6rrN8+XLGThhL2qk0\n8m/MD5avTDuIq0UHdgfLNp54pj4/lWHDhmG1yuGUoSalayBd11m8eDEvvvIiu3bvwtPSg9ZKA7vR\nyUSZEQC2g2O9gxqVavDyCy8zYMAATCZ5hzeKlG6Y2LJlC6++/ioLFy5Eb6JT0LYArjE6lYhYblC2\nKtg22mjasCkvv/AyPXr0kDnbMCClG2aOHz/O2+++zdvT3kavppPfKh/qIvO+4sJ0IB3sW+0Edge4\nre9tTBg3QfZGCDNSumHK4/Ewa9Yspv5jKidyT+Bu4ka7QYM4o5OJsOMGZZuCY5uDOGscTz3+FA/c\n/wCVKlUyOpk4DyndMKfrOuvWreODDz9g7ty5mK4zkXd9HlyPnFKyPNOBw2DbakP7RaPPrX0YO3os\nnTt3limEMCelG0E8Hg//+9//eOc/77B+3XrU61XcTdxQG5l+KA904DhYfrFg3W0lzh7HmMfG8OAD\nD3LNNbIBIFJI6Uao48ePM+OzGbz7n3c5mX0Sb0MvvgY+qI4cdFGW6EAGmH8xE7U7ihhrDPcOu5fh\n9wynefPmMqqNQFK6EU7XdbZs2cLn8z5n9rzZZGRkQENwJ7mDG+Bkn/fIc1bRRu+Nxm6yM+KeEQwf\nOpyWLVtK0UY4Kd0y5uDBgyxcuJDP5n7Gjq07sNazkp+YDw2Qi2mGMxeQCrZDNpQDCjG2GIbdPYzh\nQ4fTunVrKdoyREq3DDt16hSLFy9m5tyZrF65GmtVK87qTgJ1AlATGQUbKQAcAdMBE450B94MLzd2\nuJHB/QfTu3dv6tevL0VbRknplhMej4e1a9ey/Nvl/G/p/9i7ay+2Ojbyq+Wj1dKC13iTEr56/MBx\n4DDEHovFu99Ljdo1GNhvIH1v7Uv79u2JiooyOqUIASndciovL481a9awYtUKvln5DXt37sVW3Ya7\nmhtfVV/wqhcJyF4Rl0MHcoDDYD1mxZZhw3XYRc3EmnTu2JmeXXvSs2dPqlSpYnRSYQApXQGAy+Vi\n/fr1rPl+DWvWrWHLli3kZOVgq2HDfY0bb2UvVAWuRU7MczYdyAUyQclQiDkRgz/Nj0W10PbGtvTs\n3JP27dvTunVrYmJijE4rwoCUrvhdp0+fZsuWLWzevJnv1n3Hxk0byTiSgf06O/5r/LgquIKj4YoE\nb6MNDnw1aQRHryeDiy3bhvW0FfcRNza7jUaNG3Fjyxvp2L4j7dq1o1atWjInK85LSldckvz8fLZt\n28bOnTvZs28P237Zxv79+zly6AiqRSWqchT+eD+uWBd6RR0qADEE95yIJnynK7wESzU3uCi5CjaX\nDUu+BS1Hw53pJq5SHPXq16NFsxbc0PgGmjZtSpMmTeRwW3FJpHTDSNeuXUlNTeXQoUPF6wYMGMCK\nFSvIy8sD4Ndff+Wxxx5jz549WK1WRo0axZgxY1i9ejXPPvssP/74Y/HX+v1+qlevztatW6latepV\nza7rOhkZGezfv5/9+/ezd99etuzaQvrhdE5kniD7ZDZ+n5/ouGjMsWaIAV+0D0+0B82uBQvZcp7F\nfM5jheCoUy9ctHNui+57gQLAU3Ixe81YfBbMXjNqgYqep1OQVYDm07im2jVcd911JNZOpH5ifWrX\nqk3NmjWpUaMGSUlJOByyz524cnKpxDBTsWJFfvjhBzp27Eh2djbHjh0r8WvqHXfcwcMPP8yCBQv4\n5ZdfGD16NDVr1mTgwIEcPnyYtLQ0atWqBcC3335Ls2bNrnrhAiiKQtWqValatSo333zzeZ/jdrvJ\nzMwkIyOjxJJ2NI2s7CzyXfk4c504nU5cLhdutxuP24PH7cHr8eL1eNF1HVVVUU0qiqKgmtTix6qq\noqgKJtWEzWGjQoUKxMfHk1AxgUrVK1G5UmUqVaxEXFwc8fHxxMXFUa1aNWrUqEFCQoJMB4iQkJFu\nGOnWrRu9evXi6NGjvP3223z00UecPHmSqVOnkpeXx969e7nvvvtYu3Zt8dcsWbKEmTNn8tlnnzF+\n/HgqV67MM888A8D9999Pp06deOihh4z6IwkhziFH6YeZHj168N1336FpGnPmzGHIkCHFn/vyyy/p\n1atXied36dKFb7/9Fr/fz9ChQ5k9ezYABQUFLFmyhEGDBoU0vxDij8n0QpgxmUzcfPPNJCcn4/F4\nqF279kV/bevWrcnPz2fv3r3s2rWLdu3aER8ffxXTCiEulYx0w4yiKNxzzz2MGTOGu+++u8TnBg4c\nyPLly0usW716NT179sRsDr5/Fo1258yZw9ChQ0OWWwhxcaR0w1CnTp2YNGnSb0qzQYMGOJ1Opk2b\nhsfjYdu2bbz66qsMHDiw+DlDhw5lxowZpKSkcMcdd4Q6uhDiAqR0w0Dfvn05duxYiXXjxo0jISEB\noMRW9QULFrBo0SIaN27M3XffzYABA0rM2zZq1IiYmBi6d++OzWYLzR9ACHHRZO8FIYQIIRnpCiFE\nCEnpCiFECEnpCiFECEnpCiFECEnpCiFECEnpCiFECEnpCiFECEnpCiFECEnpCiFECEnpCiFECEnp\nCiFECEnpCiFECEnpCiFECEnpCiFECEnpCiFECEnpCiFECEnpCiFECEnpCiFECEnpCiFECEnpCiFE\nCEnpCiFECEnpCiFECP1/DYNlihOCWf0AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}