gistfile1.txt

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "from numpy.random import randn\n",
      "import pandas as pd\n",
      "import seaborn as sns\n",
      "import matplotlib as mpl\n",
      "import matplotlib.pyplot as plt\n",
      "import scipy as sp\n",
      "import scipy.stats as st"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def percentile(i, n):\n",
      "    return (i - .5) / n\n",
      "\n",
      "def percentile_from_data(data):\n",
      "    n = len(data)\n",
      "    for i in range(1, n + 1):\n",
      "        yield percentile(i, n)\n",
      "\n",
      "def lmplot_compare(series, comparison_name, comparison_function, series_function=lambda _: _):\n",
      "    data =  pd.DataFrame({\n",
      "        'data': series_function(series),\n",
      "        comparison_name: comparison_function(np.array(list(percentile_from_data(series))))\n",
      "    })\n",
      "    sns.lmplot(comparison_name, 'data', data, line_kws={\"linewidth\": 1},  markers=[\".\"])\n",
      "    sns.residplot(comparison_name, 'data', data)\n",
      "\n",
      "def is_normal(series):\n",
      "    series.sort()\n",
      "    sns.distplot(series)\n",
      "    lmplot_compare(series, u'normal', st.norm.ppf)\n",
      "    #lmplot_compare(series, u'weiball', lambda series: np.log(-np.log(1-series)), lambda series: np.log(series))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PATH = '/host/Users/saul/Downloads/Day3Excel.xlsx'\n",
      "excel = pd.io.excel.read_excel(PATH, header=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "IOError",
       "evalue": "[Errno 2] No such file or directory: '/host/Users/saul/Downloads/Day3Excel.xlsx'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-3-0daa4dee57c5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mPATH\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/host/Users/saul/Downloads/Day3Excel.xlsx'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mexcel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexcel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPATH\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/io/excel.pyc\u001b[0m in \u001b[0;36mread_excel\u001b[0;34m(io, sheetname, **kwds)\u001b[0m\n\u001b[1;32m    111\u001b[0m     \u001b[0mengine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'engine'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msheetname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msheetname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/io/excel.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, io, **kwds)\u001b[0m\n\u001b[1;32m    148\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_contents\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'xlrd'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/lib/python2.7/dist-packages/xlrd/__init__.pyc\u001b[0m in \u001b[0;36mopen_workbook\u001b[0;34m(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows)\u001b[0m\n\u001b[1;32m    392\u001b[0m         \u001b[0mpeek\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfile_contents\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mpeeksz\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 394\u001b[0;31m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    395\u001b[0m         \u001b[0mpeek\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpeeksz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m         \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: '/host/Users/saul/Downloads/Day3Excel.xlsx'"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = u'''54.3 54.7 54.9\n",
      "55\n",
      "56.7\n",
      "57\n",
      "57.3\n",
      "58\n",
      "58\n",
      "58.2\n",
      "58.2\n",
      "59.3\n",
      "59.7\n",
      "59.8\n",
      "60\n",
      "60\n",
      "60\n",
      "60\n",
      "60.1\n",
      "60.1\n",
      "60.3\n",
      "60.4\n",
      "60.8\n",
      "61.1\n",
      "62.2\n",
      "62.7\n",
      "63'''\n",
      "\n",
      "data = pd.Series(map(float, d.split()), name='data')\n",
      "is_normal(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/lib/pymodules/python2.7/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to Bitstream Vera Sans\n",
        "  (prop.get_family(), self.defaultFamily[fontext]))\n",
        "/usr/lib/pymodules/python2.7/matplotlib/font_manager.py:1246: UserWarning: findfont: Could not match :family=Bitstream Vera Sans:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. Returning /usr/share/matplotlib/mpl-data/fonts/ttf/cmb10.ttf\n",
        "  UserWarning)\n",
        "/usr/lib/pymodules/python2.7/matplotlib/font_manager.py:1246: UserWarning: findfont: Could not match :family=Bitstream Vera Sans:style=normal:variant=normal:weight=normal:stretch=normal:size=11.0. Returning /usr/share/matplotlib/mpl-data/fonts/ttf/cmb10.ttf\n",
        "  UserWarning)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pS67kLaUlfuqqw3QPjDKQGLO7HCkyCmkpepZl8f3HzzI+keHjd27UYDG5zKZV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\nc4Z0JmNzRZJrCmmH6B8a4ze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CWqQIGYbhITs7m4jkMa/dBYhI7hiG4QP+ARgCBicfrjMM\n42XgIeBdwB+TXUbyfuCCYRhJ0zS/ZhjGF8kuUJIATNM0//OyN0BE3kZH0iLO8m+Ae03T/FPg7yYf\nKyc7R/B/I7v+7pdM0zxIdiGPfzVN82uT2xlkV+X5e+AvDcMolLWTRRxLIS3iLDt4a8nLKWeArwB/\nQXY1nuDk4y54s+sb4K/IBvp9k/dDS1qpiMxJIS3iLCfIHjXDW/v3fuD/Bv4z2WXxpla/mwA8wJcN\nwwgDx8iue/udyeeXdZU8EbmcZ+5NRKRQVFVVvQrsrKqquhW4jew55hCQApJABDhQVVX1NNBH9qj5\nJPAMsIfscpluYA1Q1tvb+9vlboOIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiOTY\n/w+ZDw0DbnsNvQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0d0ab06210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mS1bTnE0B4Hk+ruuE3CqpVZEKZmPM7cBGYJ+19u6w65Hw7OscYnisyMaVjdSn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\nRERERERERKTm/H/OHWsU1BJAyAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0d0a88b990>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import random\n",
      "print np.mean(data)\n",
      "get = lambda: random.choice(data)\n",
      "series = lambda: pd.Series([get() for _ in range(0, 30)])\n",
      "all_series = [series() for _ in range(0 ,10000)]\n",
      "means = map(np.mean, all_series)\n",
      "print np.percentile(means, 2.5)\n",
      "print np.percentile(means, 97.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "58.9555555556\n",
        "58.0965833333"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "59.7833333333\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.std(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "2.3760889424430229"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_random = lambda: np.random.normal(np.mean(data), np.std(data), len(data))\n",
      "all_samples = [get_random() for _ in range(100000)]\n",
      "means = pd.Series(data=map(np.mean, all_samples))\n",
      "stds = pd.Series(data=map(np.std, all_samples))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#is_normal(means)\n",
      "np.mean(means)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "3.4198273218830093"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy.stats as ss"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ss.norm.ppf(1-0.025)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "1.959963984540054"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}