outlier_visual.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier Visualization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11470b510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#!/usr/bin/python\n",
    "\n",
    "import pickle\n",
    "import sys\n",
    "import matplotlib.pyplot\n",
    "sys.path.append(\"../tools/\")\n",
    "from feature_format import featureFormat, targetFeatureSplit\n",
    "\n",
    "\n",
    "### read in data dictionary, convert to numpy array\n",
    "data_dict = pickle.load( open(\"../final_project/final_project_dataset.pkl\", \"r\") )\n",
    "features = [\"salary\", \"bonus\"]\n",
    "#data_dict.pop('TOTAL',0)\n",
    "\n",
    "data = featureFormat(data_dict, features)\n",
    "\n",
    "### your code below\n",
    "for point in data:\n",
    "    salary = point[0]\n",
    "    bonus = point[1]\n",
    "    matplotlib.pyplot.scatter( salary, bonus )\n",
    "\n",
    "matplotlib.pyplot.xlabel(\"salary\")\n",
    "matplotlib.pyplot.ylabel(\"bonus\")\n",
    "matplotlib.pyplot.show()\n",
    "\n",
    "salaryList = []\n",
    "bonusList = []\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at the data and figure out that one outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "salary:  [365788.0, 267102.0, 170941.0, 243293.0, 267093.0, 370448.0, 197091.0, 130724.0, 288589.0, 248546.0, 257486.0, 288542.0, 251654.0, 288558.0, 63744.0, 357091.0, 271442.0, 304110.0, 187922.0, 213625.0, 249201.0, 231330.0, 182245.0, 211788.0, 224305.0, 273746.0, 339288.0, 216582.0, 210500.0, 272880.0, 477.0, 269076.0, 428780.0, 211844.0, 206121.0, 174246.0, 510364.0, 365038.0, 365163.0, 162779.0, 236457.0, 1072321.0, 261516.0, 329078.0, 184899.0, 192008.0, 263413.0, 262663.0, 374125.0, 278601.0, 199157.0, 96840.0, 80818.0, 213999.0, 210692.0, 222093.0, 440698.0, 240189.0, 420636.0, 275101.0, 314288.0, 94941.0, 239502.0, 1111258.0, 6615.0, 655037.0, 404338.0, 26704229.0, 259996.0, 317543.0, 201955.0, 248146.0, 76399.0, 262788.0, 261809.0, 248017.0, 229284.0, 231946.0, 221003.0, 158403.0, 250100.0, 492375.0, 1060932.0, 261879.0, 239671.0, 304588.0, 309946.0, 85274.0, 247338.0, 349487.0, 330546.0, 415189.0, 265214.0, 278601.0, 274975.0]\n",
      "max salary; 26704229.0\n",
      "employe with max salary is ; TOTAL\n"
     ]
    }
   ],
   "source": [
    "for point in data:\n",
    "    salaryList.append(point[0])\n",
    "    bonusList.append(point[1])\n",
    "print \"salary: \", salaryList\n",
    "max_sal = max(salaryList)\n",
    "print \"max salary;\", max_sal\n",
    "\n",
    "for k,v in data_dict.iteritems():\n",
    "    if (float(v[\"salary\"]) == max_sal):\n",
    "        print \"employe with max salary is ;\",k\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove the outlier\n",
    "## using \n",
    "### data_dict.pop('TOTAL',0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wrh07QklHRLpFp5KQmZ0O3ApMSt7H3S9rZ59PAZXuvtbMPp4IpyjqHWxrK56qF9de+Y7q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\n9x9lqKf+49zWrKTtldtc8zH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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11470b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### read in data dictionary, convert to numpy array\n",
    "data_dict = pickle.load( open(\"../final_project/final_project_dataset.pkl\", \"r\") )\n",
    "features = [\"salary\", \"bonus\"]\n",
    "\n",
    "data_dict.pop('TOTAL',0) ## Remove the outlier\n",
    "\n",
    "data = featureFormat(data_dict, features)\n",
    "\n",
    "### your code below\n",
    "for point in data:\n",
    "    salary = point[0]\n",
    "    bonus = point[1]\n",
    "    matplotlib.pyplot.scatter( salary, bonus )\n",
    "\n",
    "matplotlib.pyplot.xlabel(\"salary\")\n",
    "matplotlib.pyplot.ylabel(\"bonus\")\n",
    "matplotlib.pyplot.show()\n",
    "\n",
    "salaryList = []\n",
    "bonusList = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can still remove observe two outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LAY KENNETH L\n",
      "SKILLING JEFFREY K\n"
     ]
    }
   ],
   "source": [
    "for employee in data_dict:\n",
    "    if (data_dict[employee][\"salary\"] != 'NaN') and (data_dict[employee][\"bonus\"] != 'NaN'):\n",
    "        if float(data_dict[employee][\"salary\"]) >1000000 and float(data_dict[employee][\"bonus\"])>5000000:\n",
    "            print employee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}