{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "import pandas as pd\n",
    "from scipy.stats import boxcox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"sample_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f50737beef0>]], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wBAwAAGA4AgYAADAcAQMAABiOgAEAAAzHOhiICP7LmSs2Tq7YuOANCACucwQMRAa/5cxj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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f50832b46d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 原始分布\n",
    "df[['AIR_TIME']].hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f50a44cbdd8>]], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAAwhTAAAAEMMX80BAOHIFGWWubnp6gLmUAB+I0wAGJo6O9Sx9kF3kzkUgP84zQEAAAwh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\ngKDr7TRJqAlamPj973+vn/3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f50716338d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BoxCox变换后的分布\n",
    "df[['AIR_TIME']].apply(lambda x: boxcox(x)[0]).hist(bins=100)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "11px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
