incubator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NPI,PAC ID,Professional Enrollment ID,Last Name,First Name,Middle Name,Suffix,Gender,Credential,Medical school name,Graduation year,Primary specialty,Secondary specialty 1,Secondary specialty 2,Secondary specialty 3,Secondary specialty 4,All secondary specialties,Organization legal name,Group Practice PAC ID,Number of Group Practice members,Line 1 Street Address,Line 2 Street Address,Marker of address line 2 suppression,City,State,Zip Code,Phone Number,Hospital affiliation CCN 1,Hospital affiliation LBN 1,Hospital affiliation CCN 2,Hospital affiliation LBN 2,Hospital affiliation CCN 3,Hospital affiliation LBN 3,Hospital affiliation CCN 4,Hospital affiliation LBN 4,Hospital affiliation CCN 5,Hospital affiliation LBN 5,Professional accepts Medicare Assignment,Reported Quality Measures,Used electronic health records,Committed to heart health through the Million HeartsÂ® initiative.\\n', '1487927612,4880850486,I20120726000331,HALL,ESTHER,S,,F,,LIFE CHIROPRACTIC COLLEGE - WEST,2010,CHIROPRACTIC,,,,,,,,,183 PLACERVILLE DR,SUITE A,,PLACERVILLE,CA,956673933,5306228041,,,,,,,,,,,Y,,,\\n']\n"
     ]
    }
   ],
   "source": [
    "os.chdir('c:\\\\users\\\\christopher\\\\desktop\\\\physician_compare')\n",
    "files = os.listdir()\n",
    "with open(files[4], 'r') as f:\n",
    "    print(f.readlines()[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['NPI', \n",
    "        'PAC ID', \n",
    "        'Professional Enrollment ID', \n",
    "        'Gender', \n",
    "        'Credential']\n",
    "df_nat = pd.read_csv(files[4], usecols = cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2955262 entries, 0 to 2955261\n",
      "Data columns (total 7 columns):\n",
      "NPI                           int64\n",
      "PAC ID                        int64\n",
      "Professional Enrollment ID    object\n",
      "Gender                        object\n",
      "Credential                    object\n",
      "Graduation year               float64\n",
      "State                         object\n",
      "dtypes: float64(1), int64(2), object(4)\n",
      "memory usage: 727.9 MB\n"
     ]
    }
   ],
   "source": [
    "df_nat.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['F', 'M', 'U'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nat.Gender.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2955262 entries, 0 to 2955261\n",
      "Data columns (total 7 columns):\n",
      "NPI                           int64\n",
      "PAC ID                        int64\n",
      "Professional Enrollment ID    object\n",
      "Gender                        category\n",
      "Credential                    category\n",
      "Graduation year               float64\n",
      "State                         category\n",
      "dtypes: category(3), float64(1), int64(2), object(1)\n",
      "memory usage: 279.0 MB\n"
     ]
    }
   ],
   "source": [
    "for obj in df_nat.select_dtypes(include=['object']):\n",
    "    if len(df_nat[obj].unique())/len(df_nat[obj]) < 0.25:\n",
    "        df_nat[obj] = df_nat[obj].astype('category')\n",
    "df_nat.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3073393456791762"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ratio of male:female physicians\n",
    "(df_nat['Gender'] == 'M').sum()/(df_nat['Gender'] == 'F').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of rows with both unique identifiers repeated is 1884863\n"
     ]
    }
   ],
   "source": [
    "print(\"The number of rows with both unique identifiers repeated is {}\".format(df_nat.duplicated(subset=['NPI', 'PAC ID']).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nat_uniq = df_nat.drop_duplicates(subset=['NPI', 'PAC ID']).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of physicians with NPI and PAC ID that are both unique is 1070399\n"
     ]
    }
   ],
   "source": [
    "print(\"The number of physicians with NPI and PAC ID that are both unique is {}\".format(len(df_nat)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nat_uniq.Credential.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: RuntimeWarning: divide by zero encountered in longlong_scalars\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Credential\n",
       "AA       0.897436\n",
       "AU       3.493976\n",
       "CNA      1.195792\n",
       "CNM    130.857143\n",
       "CNS     16.508772\n",
       "CP       1.175744\n",
       "CSW      3.527160\n",
       "DC       0.281076\n",
       "DDM      0.151515\n",
       "DDS      0.083333\n",
       "DO       0.371027\n",
       "DPM      0.264071\n",
       "MD       0.372612\n",
       "MNT     34.842105\n",
       "NP      10.147005\n",
       "OD       0.592168\n",
       "OT       4.415842\n",
       "PA       1.439594\n",
       "PSY      0.959459\n",
       "PT       0.932422\n",
       "SCW           inf\n",
       "Name: Gender, dtype: float64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nat_uniq.groupby([\"Credential\"])['Gender'].apply(lambda x: (x == 'F').sum()/(x == 'M').sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2728: DtypeWarning: Columns (33,27,29,31) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "#overwrite with new set of columns for memory conservation\n",
    "df_nat = pd.read_csv(files[4], usecols = ['State',\n",
    "                                          'Hospital affiliation CCN 1',\n",
    "                                         'Hospital affiliation CCN 2',\n",
    "                                         'Hospital affiliation CCN 3',\n",
    "                                         'Hospital affiliation CCN 4',\n",
    "                                         'Hospital affiliation CCN 5'])\n",
    "# for obj in df_nat.select_dtypes(include=['object']):\n",
    "#     if len(df_nat[obj].unique())/len(df_nat[obj]) < 0.25:\n",
    "#         df_nat[obj] = df_nat[obj].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State', 'HA1', 'HA2', 'HA3', 'HA4', 'HA5'], dtype='object')"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nat.rename({'Hospital affiliation CCN 1':'HA1',\n",
    "               'Hospital affiliation CCN 2':'HA2',\n",
    "               'Hospital affiliation CCN 3':'HA3',\n",
    "               'Hospital affiliation CCN 4':'HA4',\n",
    "               'Hospital affiliation CCN 5':'HA5'}, axis=1, inplace=True)\n",
    "df_nat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State          0\n",
       "HA1       769066\n",
       "HA2      1607953\n",
       "HA3      2114563\n",
       "HA4      2414805\n",
       "HA5      2596279\n",
       "dtype: int64"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nat.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State\n",
       "AK      761\n",
       "AL     4494\n",
       "AR     3309\n",
       "AZ     5972\n",
       "CA    20045\n",
       "CO     5978\n",
       "CT     3282\n",
       "DC     1192\n",
       "DE      970\n",
       "FL    15860\n",
       "GA     9215\n",
       "GU       59\n",
       "HI      877\n",
       "IA     5382\n",
       "ID     2154\n",
       "IL    16434\n",
       "IN    11727\n",
       "KS     4790\n",
       "KY     6679\n",
       "LA     5585\n",
       "MA     8412\n",
       "MD     5875\n",
       "ME     2666\n",
       "MI    13522\n",
       "MN     8476\n",
       "MO     9779\n",
       "MP        9\n",
       "MS     4079\n",
       "MT     1851\n",
       "NC     9877\n",
       "ND     1686\n",
       "NE     3775\n",
       "NH     2376\n",
       "NJ     9493\n",
       "NM     2044\n",
       "NV     2569\n",
       "NY    15970\n",
       "OH    18285\n",
       "OK     5477\n",
       "OR     4601\n",
       "PA    21023\n",
       "PR      748\n",
       "RI     1527\n",
       "SC     5641\n",
       "SD     2202\n",
       "TN     8333\n",
       "TX    24112\n",
       "UT     2780\n",
       "VA     9432\n",
       "VI       23\n",
       "VT      956\n",
       "WA     7503\n",
       "WI     9495\n",
       "WV     2869\n",
       "WY     1016\n",
       "dtype: int64"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nat.groupby(\"State\").apply(lambda g: len(g) - g.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating the performance df\n",
    "df_per = pd.read_csv(files[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [ col.replace(\" \", \"_\") for col in df_per.columns ]\n",
    "df_per.columns = cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NPI</th>\n",
       "      <th>PAC_ID</th>\n",
       "      <th>Last_Name</th>\n",
       "      <th>First_Name</th>\n",
       "      <th>Measure_Identifier</th>\n",
       "      <th>Measure_Title</th>\n",
       "      <th>Inverse_Measure</th>\n",
       "      <th>Measure_Performance_Rate</th>\n",
       "      <th>Reporting_Mechanism</th>\n",
       "      <th>Reported_on_PC_Live_Site</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1508823618</td>\n",
       "      <td>42100117</td>\n",
       "      <td>GRIFFIN</td>\n",
       "      <td>DAVID</td>\n",
       "      <td>PQRS_EP_110_1</td>\n",
       "      <td>Preventive Care and Screening: Influenza Immun...</td>\n",
       "      <td>N</td>\n",
       "      <td>21</td>\n",
       "      <td>CLM</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1508823618</td>\n",
       "      <td>42100117</td>\n",
       "      <td>GRIFFIN</td>\n",
       "      <td>DAVID</td>\n",
       "      <td>PQRS_EP_111_1</td>\n",
       "      <td>Pneumonia Vaccination Status for Older Adults</td>\n",
       "      <td>N</td>\n",
       "      <td>28</td>\n",
       "      <td>CLM</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          NPI    PAC_ID Last_Name First_Name Measure_Identifier  \\\n",
       "0  1508823618  42100117   GRIFFIN      DAVID      PQRS_EP_110_1   \n",
       "1  1508823618  42100117   GRIFFIN      DAVID      PQRS_EP_111_1   \n",
       "\n",
       "                                       Measure_Title Inverse_Measure  \\\n",
       "0  Preventive Care and Screening: Influenza Immun...               N   \n",
       "1      Pneumonia Vaccination Status for Older Adults               N   \n",
       "\n",
       "   Measure_Performance_Rate Reporting_Mechanism Reported_on_PC_Live_Site  \n",
       "0                        21                 CLM                        Y  \n",
       "1                        28                 CLM                        Y  "
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_per.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "idcs = df_per.query('Inverse_Measure == \"Y\"').index\n",
    "df_per.Measure_Performance_Rate.loc[idcs]= df_per.query('Inverse_Measure == \"Y\"')[\"Measure_Performance_Rate\"].transform(lambda x: 100 - x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_per.NPI.unique()) == len(df_per.PAC_ID.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_of_measures = df_per.groupby(['NPI', 'PAC_ID'])[\"Measure_Performance_Rate\"].count()\n",
    "mean_measures = df_per.groupby(['NPI', 'PAC_ID'])[\"Measure_Performance_Rate\"].mean()\n",
    "mean_and_count = pd.DataFrame(num_of_measures).join(mean_measures, rsuffix='_')\n",
    "mean_and_count.columns = [\"measures\", \"means\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.005684152369238"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_and_count.query('measures >= 10')['means'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>measures</th>\n",
       "      <th>means</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NPI</th>\n",
       "      <th>PAC_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1003000142</th>\n",
       "      <th>9931380672</th>\n",
       "      <td>8</td>\n",
       "      <td>46.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003000407</th>\n",
       "      <th>2769663509</th>\n",
       "      <td>1</td>\n",
       "      <td>76.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003000704</th>\n",
       "      <th>2365611035</th>\n",
       "      <td>1</td>\n",
       "      <td>100.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003000829</th>\n",
       "      <th>4587977046</th>\n",
       "      <td>2</td>\n",
       "      <td>98.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003001371</th>\n",
       "      <th>4385734086</th>\n",
       "      <td>2</td>\n",
       "      <td>91.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       measures    means\n",
       "NPI        PAC_ID                       \n",
       "1003000142 9931380672         8   46.125\n",
       "1003000407 2769663509         1   76.000\n",
       "1003000704 2365611035         1  100.000\n",
       "1003000829 4587977046         2   98.000\n",
       "1003001371 4385734086         2   91.000"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_and_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "#p-value graduation year and performance metrics\n",
    "cols = ['NPI', \n",
    "        'PAC ID', \n",
    "        'Graduation year']\n",
    "df_nat = pd.read_csv(files[4], usecols = cols)\n",
    "df_nat.columns = [col.replace(\" \", \"_\") for col in cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nat.set_index(['NPI', 'PAC_ID'], inplace=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "#join df_nat with df_per on NPI and PAC ID\n",
    "df_comb = mean_and_count.join(df_nat)\n",
    "df_comb.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>measures</th>\n",
       "      <th>means</th>\n",
       "      <th>Graduation_year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NPI</th>\n",
       "      <th>PAC_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1003000142</th>\n",
       "      <th>9931380672</th>\n",
       "      <td>8</td>\n",
       "      <td>46.125</td>\n",
       "      <td>1999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003000407</th>\n",
       "      <th>2769663509</th>\n",
       "      <td>1</td>\n",
       "      <td>76.000</td>\n",
       "      <td>2003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003000704</th>\n",
       "      <th>2365611035</th>\n",
       "      <td>1</td>\n",
       "      <td>100.000</td>\n",
       "      <td>2007.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003000829</th>\n",
       "      <th>4587977046</th>\n",
       "      <td>2</td>\n",
       "      <td>98.000</td>\n",
       "      <td>2007.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003001371</th>\n",
       "      <th>4385734086</th>\n",
       "      <td>2</td>\n",
       "      <td>91.000</td>\n",
       "      <td>1998.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       measures    means  Graduation_year\n",
       "NPI        PAC_ID                                        \n",
       "1003000142 9931380672         8   46.125           1999.0\n",
       "1003000407 2769663509         1   76.000           2003.0\n",
       "1003000704 2365611035         1  100.000           2007.0\n",
       "1003000829 4587977046         2   98.000           2007.0\n",
       "1003001371 4385734086         2   91.000           1998.0"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "grad_perf = df_comb.query('(measures >= 10) & (1973.0 <= Graduation_year <= 2003.0)').copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_means = grad_perf.groupby(\"Graduation_year\")['means'].apply(lambda x: x.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Graduation_year\n",
       "1973.0    66.875122\n",
       "1974.0    65.519294\n",
       "1975.0    67.600150\n",
       "1976.0    69.077506\n",
       "1977.0    59.334021\n",
       "1978.0    64.362933\n",
       "1979.0    65.619823\n",
       "1980.0    64.470420\n",
       "1981.0    65.390713\n",
       "1982.0    65.899130\n",
       "1983.0    66.497419\n",
       "1984.0    66.679023\n",
       "1985.0    62.469208\n",
       "1986.0    67.677605\n",
       "1987.0    66.861683\n",
       "1988.0    66.869248\n",
       "1989.0    66.388306\n",
       "1990.0    66.961101\n",
       "1991.0    67.663159\n",
       "1992.0    64.134766\n",
       "1993.0    65.762635\n",
       "1994.0    65.223057\n",
       "1995.0    68.247381\n",
       "1996.0    64.929501\n",
       "1997.0    63.889713\n",
       "1998.0    64.865963\n",
       "1999.0    60.131220\n",
       "2000.0    63.876817\n",
       "2001.0    64.406035\n",
       "2002.0    59.228166\n",
       "2003.0    59.645759\n",
       "Name: means, dtype: float64"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x253dfaab710>]"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "years = year_means.index.astype(int)\n",
    "plt.plot(years, year_means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slope is -0.11402503488354694, the r_value -0.4013497191654799, and the p_value for this model is 0.025232684316232865\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(years,year_means)\n",
    "print(\"The slope is {0}, the r_value {1}, and the p_value for this model is {2}\".format(slope, r_value, p_value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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