MEPS_luke.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda/lib/python3.4/site-packages/pandas/io/parsers.py:1170: DtypeWarning: Columns (59,62,65,68,504,505,1792,1793,1820,1826) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data = self._reader.read(nrows)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DUID</th>\n",
       "      <th>PID</th>\n",
       "      <th>DUPERSID</th>\n",
       "      <th>PANEL</th>\n",
       "      <th>FAMID31</th>\n",
       "      <th>FAMID42</th>\n",
       "      <th>FAMID53</th>\n",
       "      <th>FAMID12</th>\n",
       "      <th>FAMIDYR</th>\n",
       "      <th>CPSFAMID</th>\n",
       "      <th>...</th>\n",
       "      <th>RXOSR12</th>\n",
       "      <th>RXPTR12</th>\n",
       "      <th>RXOTH12</th>\n",
       "      <th>PERWT12F</th>\n",
       "      <th>FAMWT12F</th>\n",
       "      <th>FAMWT12C</th>\n",
       "      <th>SAQWT12F</th>\n",
       "      <th>DIABW12F</th>\n",
       "      <th>VARSTR</th>\n",
       "      <th>VARPSU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20004</td>\n",
       "      <td>101</td>\n",
       "      <td>20004101</td>\n",
       "      <td>17 PANEL 17</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5278.8731</td>\n",
       "      <td>5507.4827</td>\n",
       "      <td>5507.4827</td>\n",
       "      <td>6821.5242</td>\n",
       "      <td>0</td>\n",
       "      <td>1109</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20004</td>\n",
       "      <td>102</td>\n",
       "      <td>20004102</td>\n",
       "      <td>17 PANEL 17</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5487.2298</td>\n",
       "      <td>5507.4827</td>\n",
       "      <td>5507.4827</td>\n",
       "      <td>6842.4318</td>\n",
       "      <td>0</td>\n",
       "      <td>1109</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20004</td>\n",
       "      <td>103</td>\n",
       "      <td>20004103</td>\n",
       "      <td>17 PANEL 17</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4393.8235</td>\n",
       "      <td>5507.4827</td>\n",
       "      <td>5507.4827</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1109</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20005</td>\n",
       "      <td>101</td>\n",
       "      <td>20005101</td>\n",
       "      <td>17 PANEL 17</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>4218.8032</td>\n",
       "      <td>3566.9810</td>\n",
       "      <td>3566.9810</td>\n",
       "      <td>4164.1973</td>\n",
       "      <td>0</td>\n",
       "      <td>1109</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20005</td>\n",
       "      <td>102</td>\n",
       "      <td>20005102</td>\n",
       "      <td>17 PANEL 17</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5666.9517</td>\n",
       "      <td>3566.9810</td>\n",
       "      <td>3566.9810</td>\n",
       "      <td>5537.4505</td>\n",
       "      <td>0</td>\n",
       "      <td>1109</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1883 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    DUID  PID  DUPERSID        PANEL FAMID31 FAMID42 FAMID53 FAMID12 FAMIDYR  \\\n",
       "0  20004  101  20004101  17 PANEL 17       A       A       A       A       A   \n",
       "1  20004  102  20004102  17 PANEL 17       A       A       A       A       A   \n",
       "2  20004  103  20004103  17 PANEL 17       A       A       A       A       A   \n",
       "3  20005  101  20005101  17 PANEL 17       A       A       A       A       A   \n",
       "4  20005  102  20005102  17 PANEL 17       A       A       A       A       A   \n",
       "\n",
       "  CPSFAMID  ...    RXOSR12 RXPTR12 RXOTH12   PERWT12F   FAMWT12F   FAMWT12C  \\\n",
       "0        A  ...          0       0       0  5278.8731  5507.4827  5507.4827   \n",
       "1        A  ...          0       0       0  5487.2298  5507.4827  5507.4827   \n",
       "2        A  ...          0       0       0  4393.8235  5507.4827  5507.4827   \n",
       "3        A  ...          0       0      53  4218.8032  3566.9810  3566.9810   \n",
       "4        A  ...          0       0       0  5666.9517  3566.9810  3566.9810   \n",
       "\n",
       "    SAQWT12F DIABW12F VARSTR VARPSU  \n",
       "0  6821.5242        0   1109      1  \n",
       "1  6842.4318        0   1109      1  \n",
       "2     0.0000        0   1109      1  \n",
       "3  4164.1973        0   1109      2  \n",
       "4  5537.4505        0   1109      2  \n",
       "\n",
       "[5 rows x 1883 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Take a look at how the data is formatted.\n",
    "MEPS = pd.DataFrame.from_csv('DATA/H155.CSV', index_col=None)\n",
    "MEPS.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DUID',\n",
       " 'PID',\n",
       " 'DUPERSID',\n",
       " 'PANEL',\n",
       " 'FAMID31',\n",
       " 'FAMID42',\n",
       " 'FAMID53',\n",
       " 'FAMID12',\n",
       " 'FAMIDYR',\n",
       " 'CPSFAMID',\n",
       " 'FCSZ1231',\n",
       " 'FCRP1231',\n",
       " 'RULETR31',\n",
       " 'RULETR42',\n",
       " 'RULETR53',\n",
       " 'RULETR12',\n",
       " 'RUSIZE31',\n",
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       " 'FAMSZEYR',\n",
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       " 'DOBMM',\n",
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       " 'SELFCM53',\n",
       " 'DISVW31X',\n",
       " 'DISVW42X',\n",
       " 'DISVW53X',\n",
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       " 'INDCAT42',\n",
       " 'INDCAT53',\n",
       " 'NUMEMP31',\n",
       " 'NUMEMP42',\n",
       " 'NUMEMP53',\n",
       " 'MORE31',\n",
       " 'MORE42',\n",
       " 'MORE53',\n",
       " 'UNION31',\n",
       " 'UNION42',\n",
       " 'UNION53',\n",
       " 'NWK31',\n",
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       " 'CHGJ3142',\n",
       " 'CHGJ4253',\n",
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       " 'STJBDD31',\n",
       " 'STJBYY31',\n",
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       " 'SICPAY31',\n",
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       " 'CLMDEP12',\n",
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       " 'EICRDT12',\n",
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       " 'SALEP12X',\n",
       " 'SALIMP12',\n",
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       " 'OTHIMP12',\n",
       " 'HIEUIDX',\n",
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       " 'TRIFE12X',\n",
       " 'TRIMA12X',\n",
       " 'TRIAP12X',\n",
       " 'TRIMY12X',\n",
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       " 'TRIJL12X',\n",
       " 'TRIAU12X',\n",
       " 'TRISE12X',\n",
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       " 'TRIDE12X',\n",
       " 'MCRJA12',\n",
       " 'MCRFE12',\n",
       " 'MCRMA12',\n",
       " 'MCRAP12',\n",
       " 'MCRMY12',\n",
       " 'MCRJU12',\n",
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       " 'OPBAP12',\n",
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       " 'STAJA12',\n",
       " 'STAFE12',\n",
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       " 'STAJL12',\n",
       " 'STAAU12',\n",
       " 'STASE12',\n",
       " 'STAOC12',\n",
       " 'STANO12',\n",
       " 'STADE12',\n",
       " 'PUBJA12X',\n",
       " 'PUBFE12X',\n",
       " 'PUBMA12X',\n",
       " 'PUBAP12X',\n",
       " 'PUBMY12X',\n",
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       " 'PUBAU12X',\n",
       " 'PUBSE12X',\n",
       " 'PUBOC12X',\n",
       " 'PUBNO12X',\n",
       " 'PUBDE12X',\n",
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       " 'PEGJU12',\n",
       " 'PEGJL12',\n",
       " 'PEGAU12',\n",
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       " 'PEGOC12',\n",
       " 'PEGNO12',\n",
       " 'PEGDE12',\n",
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       " 'PDKFE12',\n",
       " 'PDKMA12',\n",
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       " 'PDKMY12',\n",
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       " 'PDKSE12',\n",
       " 'PDKOC12',\n",
       " 'PDKNO12',\n",
       " 'PDKDE12',\n",
       " 'PNGJA12',\n",
       " 'PNGFE12',\n",
       " 'PNGMA12',\n",
       " 'PNGAP12',\n",
       " 'PNGMY12',\n",
       " 'PNGJU12',\n",
       " 'PNGJL12',\n",
       " 'PNGAU12',\n",
       " 'PNGSE12',\n",
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       " 'PNGNO12',\n",
       " 'PNGDE12',\n",
       " 'POGJA12',\n",
       " 'POGFE12',\n",
       " 'POGMA12',\n",
       " 'POGAP12',\n",
       " 'POGMY12',\n",
       " 'POGJU12',\n",
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       " 'POGSE12',\n",
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       " 'POGNO12',\n",
       " 'POGDE12',\n",
       " 'PRSJA12',\n",
       " 'PRSFE12',\n",
       " 'PRSMA12',\n",
       " 'PRSAP12',\n",
       " 'PRSMY12',\n",
       " 'PRSJU12',\n",
       " 'PRSJL12',\n",
       " 'PRSAU12',\n",
       " 'PRSSE12',\n",
       " 'PRSOC12',\n",
       " 'PRSNO12',\n",
       " 'PRSDE12',\n",
       " 'POUJA12',\n",
       " 'POUFE12',\n",
       " 'POUMA12',\n",
       " 'POUAP12',\n",
       " 'POUMY12',\n",
       " 'POUJU12',\n",
       " 'POUJL12',\n",
       " 'POUAU12',\n",
       " 'POUSE12',\n",
       " 'POUOC12',\n",
       " 'POUNO12',\n",
       " 'POUDE12',\n",
       " 'PRIJA12',\n",
       " 'PRIFE12',\n",
       " 'PRIMA12',\n",
       " 'PRIAP12',\n",
       " 'PRIMY12',\n",
       " 'PRIJU12',\n",
       " 'PRIJL12',\n",
       " 'PRIAU12',\n",
       " 'PRISE12',\n",
       " 'PRIOC12',\n",
       " 'PRINO12',\n",
       " 'PRIDE12',\n",
       " 'HPEJA12',\n",
       " 'HPEFE12',\n",
       " 'HPEMA12',\n",
       " 'HPEAP12',\n",
       " 'HPEMY12',\n",
       " 'HPEJU12',\n",
       " 'HPEJL12',\n",
       " 'HPEAU12',\n",
       " 'HPESE12',\n",
       " 'HPEOC12',\n",
       " 'HPENO12',\n",
       " 'HPEDE12',\n",
       " 'HPDJA12',\n",
       " 'HPDFE12',\n",
       " 'HPDMA12',\n",
       " 'HPDAP12',\n",
       " 'HPDMY12',\n",
       " 'HPDJU12',\n",
       " 'HPDJL12',\n",
       " 'HPDAU12',\n",
       " 'HPDSE12',\n",
       " 'HPDOC12',\n",
       " 'HPDNO12',\n",
       " 'HPDDE12',\n",
       " 'HPNJA12',\n",
       " 'HPNFE12',\n",
       " 'HPNMA12',\n",
       " 'HPNAP12',\n",
       " 'HPNMY12',\n",
       " 'HPNJU12',\n",
       " 'HPNJL12',\n",
       " 'HPNAU12',\n",
       " 'HPNSE12',\n",
       " 'HPNOC12',\n",
       " 'HPNNO12',\n",
       " 'HPNDE12',\n",
       " 'HPOJA12',\n",
       " 'HPOFE12',\n",
       " 'HPOMA12',\n",
       " 'HPOAP12',\n",
       " 'HPOMY12',\n",
       " 'HPOJU12',\n",
       " 'HPOJL12',\n",
       " 'HPOAU12',\n",
       " 'HPOSE12',\n",
       " 'HPOOC12',\n",
       " 'HPONO12',\n",
       " 'HPODE12',\n",
       " 'HPSJA12',\n",
       " 'HPSFE12',\n",
       " 'HPSMA12',\n",
       " 'HPSAP12',\n",
       " 'HPSMY12',\n",
       " 'HPSJU12',\n",
       " ...]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What columns are there? Make sure to reference the crosswalk.\n",
    "list(MEPS.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5175, 1883)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This checks how many (rows, columns).\n",
    "np.shape(MEPS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10c963b38>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Wk5zSN+hazZ4mxl2ejcPR+44zNzu/Qd9xqtqOb78bhqFvwouI9BC2+AQSEpP8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\nnyKHBKmrfsoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a1becc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Take a brief look at which age groups we are looking at. A histogram.\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "year_of_birth = MEPS.DOBYY\n",
    "age = 2015 - year_of_birth\n",
    "sns.distplot(age, kde=False)\n",
    "plt.xlabel('age')\n",
    "plt.ylabel('observations')\n",
    "plt.title('Age Groups')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5175.000000\n",
       "mean       36.382802\n",
       "std        21.823397\n",
       "min         3.000000\n",
       "25%        18.000000\n",
       "50%        34.000000\n",
       "75%        53.000000\n",
       "max        88.000000\n",
       "Name: DOBYY, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Now let's look at the medical expenditures column.\n",
    "t = MEPS.TOTEXP12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "total_exp = MEPS.TOTEXP12.order()\n",
    "t = total_exp.reset_index().TOTEXP12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11873ce48>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SIr3IMAwqahvZV1LH+q3tx6BnTx6AJTbc5GQi4o46nUlv3LiRmTNnMn36dFat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\nqf8fLTKXm75gGiYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118bdd908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We would like to see a cumulative distribution of health care spending.\n",
    "cdf = np.cumsum(t) / 15221120\n",
    "index1 = cdf.reset_index().index / 5175\n",
    "cdf = cdf.reset_index()\n",
    "cdf['x'] = index1\n",
    "cdf.plot(x='x', y='TOTEXP12')\n",
    "#y = cdf.index / 5175\n",
    "\n",
    "#cdf.plot(x='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4.2566120289657263,\n",
       " 89.256612028965748,\n",
       " -13658.959918043447,\n",
       " 286837.9599180434)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I+VLT1x+B6931WuDnwaFu4O+q+rrUC1sIM3kZhmMkpqNGj8lodPrjkSyTVymn\n/C+BB4BZwOlB+FPA72srnmEYrUqjp0EZjfRNaeWj1Hoo96jqZlU9GPhfnDO+G7hX3VTxhmGMEQYH\nj6GzcxVueaF1fjqRYxotVlXU2hcU9STbtGk5mzYtZ8WKAfMxZZFmB9O4n+HtwD24ha3Ox82/9c/l\nrmuFH+ZDMcYZpWbprWQG30bO9luKevhUbHbhYsjwoeQpdG8Edgn2ZwE3lruuFX6mUIzxRK0K22Z2\nhNej8M8T52gr2EYr9JEolK348Sp+v41gfZNW/plCMcYTtSpsK4lntAu+eiiUcgp0tBVsMyj0LIVS\naj2Uz6rqJ3CLWA2JyIW4EfKHAT+pndHNMIyxSNoo9pNP/hBXX/07oD7O7XpMLd/f38/69esCp3x8\nJH58jXvYvt2F1freoo4B1133+1FJryrStIxTQG78CU6JrATO8L8VWde02g9roRjjiNE2eaW1Ftra\nZtS9Zj0WWkVJ4nl+cMn0RuP+qdTkhfOd9AS/Gf7XA/RkXddKP1MoxnijVoVNnnjSClpXGNav4G0E\no2GCiuflBoWZqemNljmsGoWyA7gr4/fHrOta6WcKxTDqR7Jwa2ubrjDYtAplJMq2Hq2CMM6FC5ck\nlPOg9vQsKEpvtHqkZSmUUgMbb1bVhbUxrBmGMd5I+h6WLBnkM5/5Mtu3HwA019K5I521uNaDK5Py\ndHR8mI6Oj7Fjhzve2fkdLryw+WZVLjX1yvVjXaHY1CuGMbo064jzZpu1OE2ehQu/wcyZs4HivIvy\n9eGHt3Hzzb9nx44vAU5p12M6/2qmXjmrphIYhjHuKVWTb1Zl0yzMnDk7VcEVt2Y+Nqx8kj3S6k6a\nHWy8/DAfyrii0YPBjGw2bNigHR2zhv0tHR2zxvWsxZXI04iR/FQ7sHEs/0yhjB+arcAw4hQ7nc/T\nhQuXjKoMzVbhyCuPKZQm+ZlCGT/YfEzNTU/PgqLn09OzoNFitQSVVpZqoTizFErJFRtF5FAROVpE\n5ifC31MH65thGOOUefN2BU4gmu0YTvBhlTPeVp6MetMtXXoZS5deVtIJX/eZk9O0jFNAfA74H+BL\nwB+A44JjRas4tuKPKloozdYsNvJhJq/mxvlQpvmBjwdrR8e0MTdxZTNQq5Y6VQxsvAmY6Len4ebv\n+hJuKpZxqVDsZW1trDLQ+Dyo1fT5WRQXmOkDAMcrjVQotyb224FvAf+FG/TYcIUw0l+lCsXs8EYr\n0+gKUTOnCLJcAAAgAElEQVRNUTJeqdUzqEah/BhYkhK+Bng+67pW+plCMcYTjX5/m20SxfFKo5zy\nbwO2pPhcPgnsUaGrpggRuVtEbhSR60Vkiw/rEZFNInK7iGwUkWnB+SeJyB0icpuILAvCF4nIVn/s\nzCB8kohc5MOvFZF5I5V5LC2TasQZb47csUrooO7p+WujxWlK+vv72bjxEjZuvKT2gx7TtEy5H7Bv\nNdcl4riLxKzFwBeBj/vtVcDn/fZ+wA3ARGA+cCeFaWO2AK/021cAh/rtY4Gv+u3DgO+nyFCxZm60\nDdqojDzPq9GmoNGiEfcZ5v+aNWvKLlSV17+S59yFC5d4R38hvTVr1tj3WwOo5TgU4N5qrkvEcRcw\nIxF2GzDbb+8K3Oa3TwJWBedtAA4Gegl8PcDhwNnBOQf57Xbgryky1DqfjSZiJOt2jFXTyGhWiNLy\nP6tAL/Wsksc6OmYVKYpS5y5cuDiXQjPyU7FCAb5c4vdk1nV5f8AfgeuB3wLv82GPBscl2vdpviM4\n9k3col+LgE1B+GuBy/32VmC34NidKS2ieuW30QTkVRRjWaE0skVdSb6WOreSdVUqjWesPOfRJkuh\nlJoc8l9wI42eATQIF+DIEtflZbGqPiAis4BNInJbeFBVVUQ041rDqBn1WDa2GRjplOz1JpwM8uGH\nH6rw6sdwdUqAPUuct5Xrrvs9y5atrCKN5qIlJs9M0zJOAXEVrtBPO3Z31nXV/IBTgEGcyWtXH9ZL\nweR1InBicP4G4CCcWSw0eR0BfC0452AtY/I65ZRThn9XXXVVbdW40VAq8RmMRk2+lZemrUb2epmx\n2tunKkwZ3ocpumbNmuFzC5NMDsbOK5VGs9NoP99VV10VKyupwuTVA0zOOj6SHzAZ6PbbOwO/AJbh\nnPKrtKBEkk75Dlx15A8UnPK/9spFKHbKR8rlcGrklDdGh9FcqnY0aESBUE6h5M2bkcielUaabAsX\nLsnllE+bSDK6r/iI+zkVpdHMNJu5rmKFUs+fVwo3+N9NwElaUGJXArcDG4FpwTWfwPlBbgP6g/BF\nOH/JncBZQfgk4GLgDuBaYH6KHPXIa2OENLo2NhIqKUDrUSCsWbNGe3oWaE/PAh0YGMjdQhjt6dFH\nEmfatdFo+IULFwfHmqsQHgktr1Bw06183hfgjwKP+O3PhwV9K/9MoTQnzfbx5KVUIT3Se8rTmliz\nZk2RKWhgYGDECq5WLZ3k/Yyk1RNe6+55UCG5bv3YGSnfbJWsahTKRtxYkF0D81KvN0VtzLqulX6m\nUJqTVlUopeSuZQGadW0lU8BXkseV+EJGYg6rRDFF57p7Hozdh0i3RpNMtrfvPGzmalVlEtEs5lvV\n6hTK7dUca6WfKZTqqPeL3Wy1sbzUoyafJ96IShRKpXlcb1Netc+8OP1BFSk43kd75cdWoJ5Tr5Qq\nbDcBH8cPNPRhu/pWy5VZ17XSzxRK5YxWYd9MtbG81Ctv8hbaaSavqPdTlrx5R6aPVLZa3WPaPYR5\n3tY2oyVbtyOlkg4WtVgmoNpeXl8MfCiP+u0vkhgg2Ko/UyiV06rmqNGiHoqwEkU1MDCg7e27aHv7\nLjowMFCRfKWmLCnV3bra6VRC3Hs1qM6R7rbzvld5e4CNVSp5P1ynhYJfCWbqwoWLK06zqXp5NcvP\nFErlmEJpDHkK5jyFe15fiCt0NmQ+41CeaqZTSTIwMFDUukpTiHnyqRXNpSOhElNrV1dvbrNoKapS\nKMC+wOuArkT4oaWua5WfKZTKGY8fbLOR5cxO86GEBUupgiftmGsppBdQpcwmeeUJSTu3u3uPqlp7\nrWguVa1e7rRW2cKFS4bjDL9X518aTD23ErIUSubUKyJyHPAB4FbgWyJyvKpe6g9/DjcS3RhnRNOD\nF6aAaJ6pPMYDyelUrr76XcCz7NjxJeDPuCFZeaYkycOfgXV0dHyMJUuOZ9kyF+8f/3gHO3a0A+8H\nYMeOEzjppNPo7+9naGiI5cvfxY4d/+Gvr54nn3yKTZuWA8XTxpSahqS/v7/l3sm0aXJOPvlDXH31\n74ByU608h5slK+IE4IUArF17jo9zAHAqBD6CG0sO7e238rnPXVS7G0nTMk4BcRO+ZYKbMv63wIf9\n/rhcAtgYPSpxMtajNtqstdz0lkQ0UWJpp3w5k1dhypJobMe+GnW9jR/ryTSbuNpy5AtZHJxbujWb\n1qEAVgZpFJbyHUuzBmd3fx70Y2ryDjxN9z+lvS8ihWdSbS84qnDK35zY7wKGgP8Ebsi6rpV+402h\nNGshmSSvWS3PeaM96K7elFYo5f1bWfmRNGO5Aj3yoSRXPtw/08TibPSh03eKTpgwIzX/w1H9a9as\niXUo6O3dI0hjQ0wxFZttWnPd+NJ+q+LVJhcs2C+WX1nxlPKNxQd+pr8jeahGoVwFHJgImwiczzhd\nArie2NiOAnkd/3mckbUZ29A8HQ+S9xSf8LD6JW9L+1CS8WaP9ejunlsUT3f33KL0ilskk7W9fUbG\nfRUrMJjn5VsSi6fSXmaNrGRlVw7Suj+v1HKtzzxdwONT09ReoZSavv4o4NmEeexZERkAzhmJmc2I\nMxrTjCdtqdu3u7DmtTWP3BfQevdcnmIf1gWAu6+HH57AzTd/jB073Llp0/CHvoclS142bKN/+OFt\nKalFPpTbeP75QZ57zoV2dJzPpz99AldffdmwDFGe7rXXC7j++viz22uvFxTFfMYZ3wbOIno2cDbP\nPff+4f0dO2Dhwm8zc+ZlXHXVX4bTdmzFjWJY7vdvxg2R6489Y+fPOZwdO/YF4OqrD+eyy74f88NU\n+90lfThAlVPLx/Oqp+evLFp0GUuWfITPfGbV8JIKrn4f5hecccZpnHzyyUB+v9HKla/ntttW1W+p\nhjQtM15+NEkLZTRqxLVMo961urTa64IFB+bqlhp2Ya12TEIrteaSlKupZs2B1dExLeYnaW+fqt3d\nc4cnmcw7GC7v4MqCzyCy+++b+ayKx04U+3DSeqSVG3NRq8GU1U6LXy6vwmfZ3b1HkaylZkEIu3SH\nz7WjY1ZNlkHGxqGMb4UykkKy3Ata68I2nh+lJ/grNbtuspCsxbTrrUy5rsELFhygPT0LtKurN2Z+\nKmd3r2ZgYdq4k7a27tTKwcKFi/2xyL8zLeU+Di56xuW6IpeTNesdKO3Hyh9Pqe6+SfIq6uJuwsXK\nt5puwklMoTSxQhmtGnEtHNQi0+vygobEP9j8Ey6mFXxZjsxq86OVKadQCnb7Yl9MVoFZ/AzyTX2S\nJku4Vkm8J9egwuRhhdLWNrmocF2w4IBcBXbh/S1d4Uj2egsrTpUolFLfdiVzr6kWd2LI94yLn2U1\nAxmTmEJpYoWi2ryFW/ELWrxoUS1e0JD4R5jtaC7/YWd3vcwzqryZBtWFPaAOOeSQqmXLN+17WlfT\n9Hwsfgb5uruWUyhxZVB8btSaKlW4lu+KfJ4uXLi44tZDJSavUtaHWk2DElIs96DC1Fgavb3zzeRV\nj18zKZRmpfiD2Df3R1BJ4Zo1+nvhwsWZtci0j7VQQy5dWy71oVfbYizn06m2S3OaeciN8ai8NZs0\nX6b3/ombGWHmcAsgj/mn2sK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An/3+lbh34D+C48nvLnxfT/Bx3+jvN+JnFNZ7v4zyzMLl\n87F+P8rnn1AoP/D3Gj3PqPw4G2d0CdeXP9vfY5gfpwG/AtZ5Wd8XyPruQM53A+fmkDkno91yGK0f\n8DbgG8H+O4EvJ86pUCsna0ZhzSDZgyNZW0zWSLNsy1GN64CM2lBYC0rzk0S1rDwmpzlBejtnXDe1\nxH6aHXxyiWPJGnFk2036YkqZzsKOEGnO2rD2FT6f0Mmb9Fkk5zdKM79E5o4897VE0595aLJM869E\n+ZE2DUtWB4LwHUg6crs0e/R5FG/oFI/GdpRbwz3tnZzut8PvIC2vIhNTWisjbM2FHTOSJtpS3bHD\nNEuZ9aI0uxLHI9Ndmom0RwvvTthqD7+ttI4qyVbqAT6dLH9g1nu1q78uz/MIWzqRX/etmfleKYxD\nk9fK2iuUUrO3luvBUapHR1qzOHropeZGSvtgoniShXS5UeMzM65Lky0rjbzHwg80VJBpeZUWz5wc\nx8o9n/DcpDmsVF7lNWlknXugFhe8Uf5HnR2yehjleQdC23qkNKPCJMuPFBZmed7lNHNUVJEJFWXa\n84nSSFMK0zTdxJOWfp7utmlzdyUd1D0Z+ZH23e0SbIfvWbKjRtSV2zn6Cz62Ho13jEl7ztM1XsFL\nHjtPixVRWieBsLIamuvS871SshTKWDZ53Q/MDfbn4tqQMVavXj283dfXR19fX4kotcSxOYn9xcRN\nLLeVuDYrrcuA/RLxnAB8p8K4AA6i2OTz8WB/QhVxVsr/4prg4O6jG3ePE3Cmioi8eXUMrp4QEZnR\n8vK0l6crkf7/pZz7jD83Tbbbid/XzBLnvsD/TyNuGhrAmR7W4cyFSdNVXg4AvkXBVHk1BVPiB4jf\n50eBF3kZwvQ7E3Euxpm5Ir6JM69F5tQdXuaz/X8e2jLCLvHb63Cm0LNxfWpCDsCZXk/w6f0NeNBf\nczzwXtw9DwFHBtdFx0Keo/DsjgOm+zSfofgZ9GbcSxfwJAVz9t9x78A9Pu7jcCbJx4CrgvSeoRjF\nmUSvo/h7fRL3zLZTyO/oHqDwLs0Bvo3Lz6eBW4I0JSXNtLA4mzdvZvPmzWXPq6jW30o/nFH/Dzij\nZAc1ccqTUhMpZf4Jndtp3WbDWkSyWbwyJZ7ZifPSTF6lTE5owek7KXFsUonrkrKVSqPUsaRzP4x3\nsjrTU7LbcCmzSXTd7JTrks8nzYw1wcsSTZiX3ee/0HMprXNFsndfd45nnjZeAS2MKyk93ijd5JV8\nBpMT8ZTrDRTdx7wy95jsZTVZC50mwm8k6x04T53DO+15hPtRN+K0Gnj0LiV7VybzIxwEmpZX0be1\nq8bfz7R7DL/X5DuY7ERSONeNW4rewXLvmRtTJZIcND05mPJnniaXMxAJl0+eql1dvcPrwcSXdEiW\nYVO0p6d266GIL1jHJCLyepxnbwJwrqp+LnFcK71/EcE50wEe8f/hfqljlZw7HtJoJVktP8L9trY2\nnn9+WtG5XV07eOaZZ3j22e7UeHbaaSf+9rfJRcdEHmX69Ok84pPq6YEdO3bw1FMdRed2de2go6Nj\n+NzwWG/vJJ588snh69raHuP556cDcMghCznhhBNYu/YcAAYHj+H000/nyiuv92kqjzwiw+f29fVx\nxhnfBmD6dPjDHx4bPrb77rvz3e/+BIB3vOP17L333sPnzps3ha1b7x8+dsQRRwynudtu3Vx++TUA\nfPSj7+aOO+4Yjqev7yWITBmWDYjJGu4n43n5y18eO7e/vz/KHIaGhmLHjjzyyFg+b9u2jUoREVS1\nqGkzphVKOapRKIZhGOOdLIWSZsg0DMMwjIoxhWIYhmHUBFMoVZKrx8M4xvInG8ub0lj+lKaZ88cU\nSpU080NtBix/srG8KY3lT2maOX9MoRiGYRg1wRSKYRiGURPGfbfhRstgGIbRitg4FMMwDKNumMnL\nMAzDqAmmUAzDMIyaYAqlCkTkUBG5TUTuEJFVjZankYjIXBG5SkRuFpGbROQ4H94jIptE5HYR2Sgi\n08rFNZYRkQkicr2IXO73LX8AEZkmIv8lIreKyC0icpDlTQEROcl/W1tF5EIRmdTM+WMKpUJEZAJu\nmcRDcXPLHyEiL2qsVA3lWeAjqvpi4GDgAz4/TgQ2qeo+wE/9/njmeNw84pHT0vLHcSZwhaq+CLd0\n4W1Y3gAgIvNxSy2+TFUPwE1yezhNnD+mUCpneGlhVX0WiJYWHpeo6oOqeoPffgq3xPLuuDVOo0UY\n1gFvaYyEjUdE5gBvwC0kEvWMGff5IyJTgdeq6rcAVPU5VX0cy5uIJ3AVtski0o5bz/jPNHH+mEKp\nnN2Be4P9+3zYuMfXqBYCvwZmq+pD/tBDFBaXH4/8J/Ax4qtPWf64xev/KiLfFpHficg3RGRnLG8A\nUNVHgLXAn3CK5DFV3UQT548plMqxftYpiEgXbrm941X1yfBYYXGy8YeI/D/gL6p6PRlL443j/GkH\nXj6IAq8AAALjSURBVAZ8VVVfhlteMGa+Gcd5g4gswC2VOR/YDegSkXCJ0qbLH1MolZNraeHxhIhM\nxCmTC1T1Uh/8kIjs6o/3An9plHwN5tXAchG5C/ge8E8icgGWP+C+m/tU9Td+/79wCuZByxsAXg78\nUlW3qepzwH8Dr6KJ88cUSuX8FthbROaLSAdwGIXFnMcd4pawPBe4RVW/FByKFirH/1+avHY8oKqf\nUNW5qronzqH6M1V9F5Y/qOqDwL0iso8POgS4GbiccZ43ntuAg0Wk039nh+A6djRt/thI+Soot7Tw\neEJEXgP8D3Ajhab3ScAW4GJgD+Bu4O2q+lgjZGwWRGQJMKiqy0WkB8sfROSluM4KHcAfgHfjvqtx\nnzcAIvJxnNJ4Hvgd8F6gmybNH1MohmEYRk0wk5dhGIZRE0yhGIZhGDXBFIphGIZRE0yhGIZhGDXB\nFIphGIZRE0yhGIZhGDXBFIphNAgReYuIPC8iL2y0LIZRC0yhGEbjOAL4kf83jJbHFIphNAA/meZB\nwAdx0/cgIm0i8lW/2NRGEfmxiKz0xxaJyGYR+a2IbIjmcjKMZsIUimE0hjcDG1T1T7gp3F8GvBWY\n5xebehduIkD1k29+GVipqi8Hvg18pkFyG0Ym7Y0WwDDGKUfg1kkB+IHfb8fN0YSqPiQiV/njLwRe\nDFzp5ghkAm59DMNoKkyhGMYo4yeG/EdgfxFRnIJQYD0Za6YAN6vqq0dJRMOoCjN5Gcbo8zbgfFWd\nr6p7quoewF3AI8BKccwG+vz5/wvMEpGDwa0/IyL7NUJwwyiFKRTDGH0Ox7VGQi4BdsUtOnULcAFu\nuvLHVfVZnBL6gojcAFyP868YRlNh09cbRhMhIjur6tMiMgP4NfBqVW2aFfkMoxTmQzGM5uJHIjIN\nt+DUv5syMVoJa6EYhmEYNcF8KIZhGEZNMIViGIZh1ARTKIZhGEZNMIViGIZh1ARTKIZhGEZNMIVi\nGIZh1IT/H0dpyFEDdxxpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116cdb6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now we would like to weave the two together - age and medical expenses.\n",
    "age_exp = MEPS.groupby(['DOBYY', 'TOTEXP12']).size()\n",
    "plt.scatter(x=(2012 - MEPS.DOBYY), y=MEPS.TOTEXP12)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('2012 Total Medical Expenditure')\n",
    "plt.title('Age vs. Medical Expenditure')\n",
    "plt.axis('tight')\n",
    "\n",
    "#Run a regression? Ask Prof Backus about this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x10e0bbb00>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2vPvuu3j22WchCAIAYPXq1Vi7di127tyJkpIS7Nu3DwCQm5sLhUKBnTt3YuHC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\noIiIOJZCUEREHEshKCIijvUfN8lTEbuMUMgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e0bbda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age = 2012 - MEPS.DOBYY\n",
    "exp = MEPS.TOTEXP12\n",
    "\n",
    "sns.jointplot(x=age, y=exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e7df668>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BhIgI/VuApHKs2EJ1ZtFEjpyot54fjVXvgzFUNraG50Krn68vnE5+3ri476k+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\nEzt9c+wokaxMJ0u+dDmunEw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I1xGwRrrEG9WSaORNb4SvJUH8AfNv0Oqo6vMH8fuD+AJB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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11213d470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Try running a regression?\n",
    "sns.regplot(x=age, y=exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1122e9898>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11215dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#How about gender? Quick look at gender distribution. There are more female observations.\n",
    "MEPS.SEX.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1125eeeb8>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B0f4ZhONVfvSG//GDhAeFBBEREbGkkCAiIqfE09rGpIvG9xrPvjgDT2tbECqSQNGaBBER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\n6f8DC23EncRyOJ8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11273c198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gender and Expenditures\n",
    "sns.stripplot(x=MEPS.SEX, y=MEPS.TOTEXP12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def bottom_percent(list, percent):\n",
    "    total = 0\n",
    "    for x in list:\n",
    "        if x < np.percentile(list, percent):\n",
    "            total = total + x\n",
    "    return total\n",
    "\n",
    "def top_percent(list, percent):\n",
    "    total = 0\n",
    "    for x in list:\n",
    "        if x > np.percentile(list, percent):\n",
    "            total = total + x\n",
    "    return total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1195d6a20>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ts2dPvv/+e3vWKCIiInZkcxAki8XCtm3bjOcJCQm4urqWaVEiIiLiODaHT548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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119f29668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Mean spending of portions of sample\n",
    "def bottom_mean_spending (list, percent):\n",
    "    return np.mean(list[list <= np.percentile(list, percent)])\n",
    "\n",
    "def top_mean_spending (list, percent):\n",
    "    return np.mean(list[list >= np.percentile(list, percent)])\n",
    "\n",
    "Means = (bottom_mean_spending (exp, 50),\n",
    "         top_mean_spending (exp, 50),\n",
    "         top_mean_spending (exp, 70),\n",
    "         top_mean_spending (exp, 90),\n",
    "         top_mean_spending (exp, 95),\n",
    "         top_mean_spending (exp, 99),\n",
    "         top_mean_spending (exp, 99.9))\n",
    "\n",
    "bins = (\"Bottom 50%\", \"Top 50%\", \"Top 30%\", \n",
    "        \"Top 10%\", \"Top 5%\", \"Top 1%\", \"Top 0.1%\")\n",
    "\n",
    "sns.barplot(bins, Means)\n",
    "plt.title('Mean Medical Expenditure 2012')\n",
    "plt.xlabel('Percent of Population, Ordered')\n",
    "plt.ylabel('Mean Annual Expenditure per Person')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "382.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(exp, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AnKXW4H744Yfl7++vzMxM/fjjj3r44Yf13HPPNURtAADgLLUGd3l5uRISEvTx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\ny8vdVMmvq+EKTxfRCDSGXkj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1121623c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##THIS IS JUST A SAMPLE CODE FROM THE WEB.\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "N = 5\n",
    "menMeans   = (20, 35, 30, 35, 27)\n",
    "womenMeans = (25, 32, 34, 20, 25)\n",
    "menStd     = (2, 3, 4, 1, 2)\n",
    "womenStd   = (3, 5, 2, 3, 3)\n",
    "ind = np.arange(N)    # the x locations for the groups\n",
    "width = 0.35       # the width of the bars: can also be len(x) sequence\n",
    "\n",
    "p1 = plt.bar(ind, menMeans,   width, color='r', yerr=womenStd)\n",
    "p2 = plt.bar(ind, womenMeans, width, color='y',\n",
    "             bottom=menMeans, yerr=menStd)\n",
    "\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Scores by group and gender')\n",
    "plt.xticks(ind+width/2., ('G1', 'G2', 'G3', 'G4', 'G5') )\n",
    "plt.yticks(np.arange(0,81,10))\n",
    "plt.legend( (p1[0], p2[0]), ('Men', 'Women') )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}