Antibiotics from Paul

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os, re\n",
    "\n",
    "# local libs\n",
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "\n",
    "from lib.df_tools import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usage of Antibiotics \n",
    "===\n",
    "\n",
    "Based on sumarrise date of: \n",
    "- __Prescription Cost Analysis Data__\n",
    "    - _Pharmacy & Appliance Contractor_\n",
    "    - _Dispensing Docs & PADM items_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "FILE_ALL_DRUGS_GROUPS = \"../data/cleaned/ALL_DRUGS_GROUPS.csv\"\n",
    "FILE_ALL_DRUGS        = \"../data/cleaned/ALL_DRUGS.csv\"\n",
    "FILE_ALL_DRUGS_TS     = \"../data/cleaned/ALL_DRUGS_TS.csv\"\n",
    "\n",
    "# ANTIBIOTICSS\n",
    "FILE_ANTIBIOTICS      = \"../data/cleaned/ANTIBIOTICS.csv\"\n",
    "FILE_ANTIBIOTICS_TS   = \"../data/cleaned/ANTIBIOTICS_TS.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top Antibiotics Usage\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "_df = pd.read_csv(FILE_ANTIBIOTICS, index_col=None, low_memory=False)\n",
    "df_antibiotics = _df[[\"Items Dispensed\",\"group\"]].groupby(\"group\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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A93fs/7/AOcAbU58WArcRn8k8YAdgw9TvS4CnA1sDTyISGzcQ00I6zz3SMul6ngbsDXwz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\nVG+uPn8+9rzqV6q3VE+tzpvvv7N6WXX2fN7+6unV26r3VrevXl79RfW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x108e923d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title_ = \"Top Antibiotics Usage\"\n",
    "\n",
    "_dfp = df_antibiotics.sort(columns=[\"Items Dispensed\"], ascending=True)\n",
    "_dfp.plot( kind='barh', \n",
    "                       figsize=[15,12], \n",
    "                       title=title_,\n",
    "                       legend=False)\n",
    "# save\n",
    "savefig(\"../visualizations/\"+title_+'.png')\n",
    "_dfp.to_csv(\"../visualizations/\"+title_+'.csv')\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antibiotics Usage Over Time\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "_df = pd.read_csv(FILE_ANTIBIOTICS_TS, index_col=None, low_memory=False)\n",
    "f = lambda s:bool(re.search(r\"20[0-9]{2}\\.[\\0-9]{2}\", s))\n",
    "cols_dt = filter(f, _df.columns)\n",
    "col_gr  = [\"group\"]\n",
    "style_ = [\n",
    "\"b-\",\"g-\",\"r-\",\"c-\",\"m-\",\"y-\",\"k-\",\n",
    "\"b.-\",\"g.-\",\"r.-\",\"c.-\",\"m.-\",\"y.-\",\"k.-\",\n",
    "\"b+-\",\"g+-\",\"r+-\",\"c+-\",\"m+-\",\"y+-\",\"k+-\",\n",
    "]\n",
    "df_antibiotics_ts = _df[col_gr+cols_dt].groupby(col_gr).sum()\n",
    "_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "title_ = \"Antibiotics Usage Over Time\"\n",
    "\n",
    "_dfp = df_antibiotics_ts.T.copy()\n",
    "_dfp[\"TOTAL\"] = _dfp.sum(1)\n",
    "\n",
    "_dfp.iloc[:,:10].plot(figsize=[15,10],\n",
    "          title=title_,\n",
    "          grid=False,\n",
    "          style=style_,\n",
    "          rot=45)\n",
    "# save\n",
    "savefig(\"../visualizations/\"+title_+'.png')\n",
    "_dfp.to_csv(\"../visualizations/\"+title_+'.csv')\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalized and Smoothed Top Antibiotics Usage Over Time\n",
    "---\n",
    "Plot showing Antibiotics usage trend over time (data normalised).\n",
    "- Data normalised (converted to z-score)\n",
    "- Than ploted as a 12 months weighted average "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5+PhwR8AdLB+4nPtr30/zb5rzybpPcFgOu0MTERERkeu1fDl07w7ffaeEzUvk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SAAAg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o9Wk4Or6Bo2N3ypR5QbuSx8fx9ZVX0ZYuhdde0zoao0rPSefVVa/iXtGdeZ3nGbbzZ1CQTNiu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108eb4bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title_ = \"Normalized and Smoothed Top Antibiotics Usage Over Time\"\n",
    "\n",
    "# zscore ----\n",
    "def zscore(x):\n",
    "    r = (x - x.mean()) / x.std()\n",
    "    return r\n",
    "\n",
    "def smooth(x,window_len=12,window='flat'):\n",
    "    \"\"\"\n",
    "    \"\"\"\n",
    "    if x.ndim != 1:\n",
    "        raise ValueError, \"smooth only accepts 1 dimension arrays.\"\n",
    "    if x.size < window_len:\n",
    "        raise ValueError, \"Input vector needs to be bigger than window size.\"\n",
    "    if window_len<3:\n",
    "        return x\n",
    "    if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n",
    "        raise ValueError, \"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\"\n",
    "    s=r_[2*x[0]-x[window_len-1::-1],x,2*x[-1]-x[-1:-window_len:-1]]\n",
    "    if window == 'flat': #moving average\n",
    "        w=ones(window_len,'d')\n",
    "    else:  \n",
    "        w=eval('numpy.'+window+'(window_len)')\n",
    "    y=convolve(w/w.sum(),s,mode='same')\n",
    "    return y[window_len:-window_len+1]\n",
    "\n",
    "_df = df_antibiotics_ts.T.copy()\n",
    "_df[\"TOTAL\"] = _df.sum(1)\n",
    "\n",
    "_df = _df.apply(zscore, 0)\n",
    "_dfp = _df.apply(smooth, 0)\n",
    "_dfp.iloc[:,:10].plot(figsize=[15,15], \n",
    "          title=title_,\n",
    "          grid = False,\n",
    "          style = style_,\n",
    "          rot=45)\n",
    "# save\n",
    "savefig(\"../visualizations/\"+title_+'.png')\n",
    "_dfp.to_csv(\"../visualizations/\"+title_+'.csv')\n",
    "\n",
    "legend(loc = 'lower center')\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prescribtion of Antibiotics per Practise\n",
    "===\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# PATHS\n",
    "FILE_PRACTICE         = \"../data/cleaned/gp-reg-patients-04-2014.csv\"\n",
    "FILE_PRESCRIBTIONS    = \"../data/cleaned/PRESCRIBTIONS.csv\"\n",
    "FILE_PRESCRIBTIONS_TS = \"../data/cleaned/PRESCRIBTIONS_TS.csv\"\n",
    "FILE_ANTIBIOTICS_TS   = \"../data/cleaned/ANTIBIOTICS_TS.csv\"\n",
    "# FILE_GP_SURVEY        = \"../data/cleaned/GP_Patient_Survey.csv\"\n",
    "GP_RATING_SURVEY      = \"../data/cleaned/GP_Rating_Survey.csv\"\n",
    "FILE_POSTCODES = \"../data/raw/postcodes.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "TIME = [ \n",
    "#     u'2010.08', \n",
    "#     u'2010.09', \n",
    "#     u'2010.10', \n",
    "#     u'2010.11', \n",
    "#     u'2010.12', \n",
    "#     u'2011.01', \n",
    "#     u'2011.02', \n",
    "#     u'2011.03', \n",
    "#     u'2011.04',\n",
    "    u'2011.05', \n",
    "    u'2011.06', \n",
    "    u'2011.07', \n",
    "    u'2011.08', \n",
    "    u'2011.09', \n",
    "    u'2011.10', \n",
    "    u'2011.11', \n",
    "    u'2011.12', \n",
    "    u'2012.01', \n",
    "    u'2012.02', \n",
    "    u'2012.03', \n",
    "    u'2012.04',\n",
    "    u'2012.05', \n",
    "    u'2012.06', \n",
    "    u'2012.07', \n",
    "    u'2012.08', \n",
    "    u'2012.09', \n",
    "    u'2012.10', \n",
    "    u'2012.11', \n",
    "    u'2012.12', \n",
    "    u'2013.01', \n",
    "    u'2013.02', \n",
    "    u'2013.03', \n",
    "    u'2013.04',\n",
    "    u'2013.05', \n",
    "    u'2013.06', \n",
    "    u'2013.07', \n",
    "    u'2013.08', \n",
    "    u'2013.09', \n",
    "    u'2013.10', \n",
    "    u'2013.11', \n",
    "    u'2013.12', \n",
    "    u'2014.01', \n",
    "    u'2014.02', \n",
    "    u'2014.03', \n",
    "    u'2014.04'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antibiotics Prescribed Item Levels per Practice\n",
    "---\n",
    "Data: __GP Practice Prescribing Presentation level Data__\n",
    "\n",
    "- data aggregated over last 12 months on recors\n",
    "- calculated average per 1000 patients\n",
    "- normalised (log)\n",
    "- calculated extreeme p levels values per number of patients\n",
    "    - 1.65 sigma (p=.05)\n",
    "    - 2.33 sigma (p=.01)\n",
    "    - 3.1  sigma (p=.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load and aggr 12 of Prescribtions\n",
    "_df = pd.read_csv(FILE_PRESCRIBTIONS_TS, index_col=None)\n",
    "_df = _df.groupby(\"PRACTICE\")[TIME].sum()\n",
    "df_prescribtins = pd.DataFrame(_df.sum(1), columns=['GP Prescribtions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7950:rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GP Prescribtions</th>\n",
       "      <th>TOTAL_ALL</th>\n",
       "      <th>days</th>\n",
       "      <th>daily_prescribtions</th>\n",
       "      <th>daily prescribtions per patient</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRACTICE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A81001</th>\n",
       "      <td>  7964</td>\n",
       "      <td>  4157</td>\n",
       "      <td> 1094</td>\n",
       "      <td>  7.279707</td>\n",
       "      <td> 0.001751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81002</th>\n",
       "      <td> 39603</td>\n",
       "      <td> 19709</td>\n",
       "      <td> 1094</td>\n",
       "      <td> 36.200183</td>\n",
       "      <td> 0.001837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          GP Prescribtions  TOTAL_ALL  days  daily_prescribtions  \\\n",
       "PRACTICE                                                           \n",
       "A81001                7964       4157  1094             7.279707   \n",
       "A81002               39603      19709  1094            36.200183   \n",
       "\n",
       "          daily prescribtions per patient  \n",
       "PRACTICE                                   \n",
       "A81001                           0.001751  \n",
       "A81002                           0.001837  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add number of patients\n",
    "_df = pd.read_csv(FILE_PRACTICE, index_col='GP_PRACTICE_CODE')['TOTAL_ALL']\n",
    "_df = df_prescribtins.join(_df)[[\"GP Prescribtions\",\"TOTAL_ALL\"]]\n",
    "_df['days'] = round(len(TIME)*30.4)\n",
    "_df['daily_prescribtions'] = _df[\"GP Prescribtions\"] / _df['days']\n",
    "_df['daily prescribtions per patient'] = _df['daily_prescribtions'] / _df['TOTAL_ALL']\n",
    "_df = _df.dropna()\n",
    "df_data = _df.copy()\n",
    "df_info(df_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute margin of error (ME): \n",
    "---\n",
    "\n",
    "    ME = critical value * standard error \n",
    "\n",
    "\n",
    "Find standard error. The standard error (SE) of the mean is:\n",
    "SE = s / sqrt( n ) = 30 / sqrt(1000) = 30/31.62 = 0.95\n",
    "\n",
    "Find critical value. The critical value is a factor used to compute the margin of error. To express the critical value as a t score (t*), follow these steps.\n",
    "\n",
    "Compute alpha (α): α = 1 - (confidence level / 100) = 0.05\n",
    "Find the critical probability (p*): p* = 1 - α/2 = 1 - 0.05/2 = 0.975\n",
    "Find the degrees of freedom (df): df = n - 1 = 1000 - 1 = 999\n",
    "The critical value is the t score having 999 degrees of freedom and a cumulative probability equal to 0.975. From the t Distribution Calculator, we find that the critical value is 1.96."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7950:rows\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/paulgesiak/.virtualenvs/ipython/lib/python2.7/site-packages/pandas/core/indexing.py:121: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the 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"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GP Prescribtions</th>\n",
       "      <th>TOTAL_ALL</th>\n",
       "      <th>days</th>\n",
       "      <th>daily_prescribtions</th>\n",
       "      <th>daily prescribtions per patient</th>\n",
       "      <th>dppp_expected</th>\n",
       "      <th>dppp_margin_of_error</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRACTICE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A81001</th>\n",
       "      <td>  7964</td>\n",
       "      <td>  4157</td>\n",
       "      <td> 1094</td>\n",
       "      <td>  7.279707</td>\n",
       "      <td> 0.001751</td>\n",
       "      <td> 0.001701</td>\n",
       "      <td> 0.001253</td>\n",
       "      <td> average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81002</th>\n",
       "      <td> 39603</td>\n",
       "      <td> 19709</td>\n",
       "      <td> 1094</td>\n",
       "      <td> 36.200183</td>\n",
       "      <td> 0.001837</td>\n",
       "      <td> 0.001701</td>\n",
       "      <td> 0.000575</td>\n",
       "      <td> average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          GP Prescribtions  TOTAL_ALL  days  daily_prescribtions  \\\n",
       "PRACTICE                                                           \n",
       "A81001                7964       4157  1094             7.279707   \n",
       "A81002               39603      19709  1094            36.200183   \n",
       "\n",
       "          daily prescribtions per patient  dppp_expected  \\\n",
       "PRACTICE                                                   \n",
       "A81001                           0.001751       0.001701   \n",
       "A81002                           0.001837       0.001701   \n",
       "\n",
       "          dppp_margin_of_error    group  \n",
       "PRACTICE                                 \n",
       "A81001                0.001253  average  \n",
       "A81002                0.000575  average  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# z = 1.65  # 90%\n",
    "z = 1.96  # 95%\n",
    "# z = 2.58  # 99%\n",
    "# z = 3.29  # 99.9%\n",
    "\n",
    "n = df_data[\"TOTAL_ALL\"]\n",
    "p = _df['daily prescribtions per patient']\n",
    "p_mean = df_data['daily_prescribtions'].sum() / df_data['TOTAL_ALL'].sum()\n",
    "\n",
    "\n",
    "df_data['dppp_expected'] = p_mean\n",
    "df_data['dppp_margin_of_error'] = (z * sqrt((p_mean*(1-p_mean)) / n))\n",
    "\n",
    "\n",
    "df_data['group'] = 'average'\n",
    "\n",
    "i  = df_data['daily prescribtions per patient'] > (df_data['dppp_expected'] + df_data['dppp_margin_of_error'])\n",
    "df_data['group'].ix[i] = 'overprescribing'\n",
    "\n",
    "i  = df_data['daily prescribtions per patient'] < (df_data['dppp_expected'] - df_data['dppp_margin_of_error'])\n",
    "df_data['group'].ix[i] = 'underprescribing'\n",
    "\n",
    "df_info(df_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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CJ57Qf27qVNi+HVDmGEauWsW37ywnctUqEg3wj4cQQgghxL2uMTrd6lvH0qVLmT59OrNm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89thj+CwjA+uUlqYGfmcnrhEBOOjrA/AtL8e+9PUBvIoARD33HF7X1Ihs2IBxc3PxeOAB6ENQ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wShGvozwnf4KvBID5+vilB5/AqwEvhY8nmuS66RISSHvVVAe4u3NYl3WXK8mZB4PtXl/BcJ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\nSZIkSZIkSZJUyv4fkmO4v9/xLqUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1086e2810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title_ = \"Prescribtion Levels per GP Practice (England)\"\n",
    "\n",
    "figure(1, figsize=[15,10])\n",
    "\n",
    "\n",
    "plot(n, p, \"+\", alpha=.5)\n",
    "\n",
    "\n",
    "i = df_data['group']=='overprescribing'\n",
    "plot(n[i], p[i], \"ro\", alpha=.5, )\n",
    "\n",
    "i = df_data['group']=='underprescribing'\n",
    "plot(n[i], p[i], \"go\", alpha=.5)\n",
    "\n",
    "# funnel\n",
    "x_seq = arange(n.min(), n.max(), 10)\n",
    "lo_95  = p_mean - (1.96 * sqrt((p_mean*(1-p_mean)) / x_seq)) \n",
    "hi_95  = p_mean + (1.96 * sqrt((p_mean*(1-p_mean)) / x_seq)) \n",
    "lo_999 = p_mean - (3.29 * sqrt((p_mean*(1-p_mean)) / x_seq)) \n",
    "hi_999 = p_mean + (3.29 * sqrt((p_mean*(1-p_mean)) / x_seq)) \n",
    "hlines(p_mean,x_seq.min(),x_seq.max(), colors=\"b\")\n",
    "plot(x_seq, hi_95, \"r--\")\n",
    "plot(x_seq, lo_95, \"g--\")\n",
    "plot(x_seq, hi_999, \"r:\")\n",
    "plot(x_seq, lo_999, \"g:\")\n",
    "\n",
    "\n",
    "legend([\"GP Practices\", \n",
    "            \"significantly above average\",\n",
    "            \"significantly below average\",\n",
    "            \"95%   (p<0.05)\",\n",
    "            \"95%   (p<0.05)\",\n",
    "            \"99.9% (p<0.001)\",\n",
    "            \"99.9% (p<0.001)\",\n",
    "            \"average %.4f\" % p_mean\n",
    "            ])\n",
    "\n",
    "xlabel(\"Number of Registered Patients\")\n",
    "ylabel(\"Daily Antibiotic Prescribtion per Patient\")\n",
    "title(title_)\n",
    "\n",
    "\n",
    "ylim([-0.002,0.008])\n",
    "xlim(-1000, 40000)\n",
    "\n",
    "savefig(\"../visualizations/\"+title_+'.png')\n",
    "\n",
    "show(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List of GP Practices\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7950:rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GP Prescribtions</th>\n",
       "      <th>TOTAL_ALL</th>\n",
       "      <th>days</th>\n",
       "      <th>daily_prescribtions</th>\n",
       "      <th>daily prescribtions per patient</th>\n",
       "      <th>dppp_expected</th>\n",
       "      <th>dppp_margin_of_error</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRACTICE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A81001</th>\n",
       "      <td>  7964</td>\n",
       "      <td>  4157</td>\n",
       "      <td> 1094</td>\n",
       "      <td>  7.279707</td>\n",
       "      <td> 0.001751</td>\n",
       "      <td> 0.001701</td>\n",
       "      <td> 0.001253</td>\n",
       "      <td> average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81002</th>\n",
       "      <td> 39603</td>\n",
       "      <td> 19709</td>\n",
       "      <td> 1094</td>\n",
       "      <td> 36.200183</td>\n",
       "      <td> 0.001837</td>\n",
       "      <td> 0.001701</td>\n",
       "      <td> 0.000575</td>\n",
       "      <td> average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          GP Prescribtions  TOTAL_ALL  days  daily_prescribtions  \\\n",
       "PRACTICE                                                           \n",
       "A81001                7964       4157  1094             7.279707   \n",
       "A81002               39603      19709  1094            36.200183   \n",
       "\n",
       "          daily prescribtions per patient  dppp_expected  \\\n",
       "PRACTICE                                                   \n",
       "A81001                           0.001751       0.001701   \n",
       "A81002                           0.001837       0.001701   \n",
       "\n",
       "          dppp_margin_of_error    group  \n",
       "PRACTICE                                 \n",
       "A81001                0.001253  average  \n",
       "A81002                0.000575  average  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_info(df_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GP Prescribtions</th>\n",
       "      <th>expected above</th>\n",
       "      <th>expected below</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRACTICE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A81001</th>\n",
       "      <td>  7964</td>\n",
       "      <td>  2038</td>\n",
       "      <td> 13431</td>\n",
       "      <td> average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81002</th>\n",
       "      <td> 39603</td>\n",
       "      <td> 24267</td>\n",
       "      <td> 49075</td>\n",
       "      <td> average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81003</th>\n",
       "      <td>  7840</td>\n",
       "      <td>  1305</td>\n",
       "      <td> 11786</td>\n",
       "      <td> average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81004</th>\n",
       "      <td> 17077</td>\n",
       "      <td>  8220</td>\n",
       "      <td> 24888</td>\n",
       "      <td> average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81005</th>\n",
       "      <td> 14959</td>\n",
       "      <td>  6875</td>\n",
       "      <td> 22602</td>\n",
       "      <td> average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          GP Prescribtions  expected above  expected below    group\n",
       "PRACTICE                                                           \n",
       "A81001                7964            2038           13431  average\n",
       "A81002               39603           24267           49075  average\n",
       "A81003                7840            1305           11786  average\n",
       "A81004               17077            8220           24888  average\n",
       "A81005               14959            6875           22602  average"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_df = df_data\n",
    "_df['expected above'] = ((_df['dppp_expected'] - _df['dppp_margin_of_error']) * _df['days'] * _df['TOTAL_ALL']).apply(round)\n",
    "i = _df['expected above'] < 0\n",
    "_df['expected above'].ix[i] = 0\n",
    "    \n",
    "_df['expected below'] = ((_df['dppp_expected'] + _df['dppp_margin_of_error']) * _df['days'] * _df['TOTAL_ALL']).apply(round)\n",
    "i = _df['expected below'] < 0\n",
    "_df['expected below'].ix[i] = 0\n",
    "df_appp = _df[['GP Prescribtions','expected above','expected below','group']]\n",
    "df_appp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7088:rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GP Prescribtions</th>\n",
       "      <th>expected above</th>\n",
       "      <th>expected below</th>\n",
       "      <th>group</th>\n",
       "      <th>TOTAL_ALL</th>\n",
       "      <th>POSTCODE</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A81001</th>\n",
       "      <td>  7964</td>\n",
       "      <td>  2038</td>\n",
       "      <td> 13431</td>\n",
       "      <td> average</td>\n",
       "      <td>  4157</td>\n",
       "      <td> TS18 1HU</td>\n",
       "      <td> 54.561218</td>\n",
       "      <td>-1.318807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81002</th>\n",
       "      <td> 39603</td>\n",
       "      <td> 24267</td>\n",
       "      <td> 49075</td>\n",
       "      <td> average</td>\n",
       "      <td> 19709</td>\n",
       "      <td> TS18 2AW</td>\n",
       "      <td> 54.569019</td>\n",
       "      <td>-1.313928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        GP Prescribtions  expected above  expected below    group  TOTAL_ALL  \\\n",
       "A81001              7964            2038           13431  average       4157   \n",
       "A81002             39603           24267           49075  average      19709   \n",
       "\n",
       "        POSTCODE   latitude  longitude  \n",
       "A81001  TS18 1HU  54.561218  -1.318807  \n",
       "A81002  TS18 2AW  54.569019  -1.313928  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add postcode and Lon/Lat\n",
    "title_ = \"Prescribtion Levels per GP Practice (England)\"\n",
    "\n",
    "# postcodes\n",
    "_df = pd.read_csv(FILE_POSTCODES, index_col=None)\n",
    "_df = _df.drop_duplicates(subset=['postcode']).dropna(subset=['postcode'])\n",
    "_df.index = _df['postcode']\n",
    "_df_pc = _df[[\"latitude\",\"longitude\"]]\n",
    "\n",
    "# practice postcodes\n",
    "_df = pd.read_csv(FILE_PRACTICE, index_col=None)\n",
    "_df = _df.drop_duplicates(subset=['POSTCODE']).dropna(subset=['POSTCODE'])\n",
    "f = lambda s: re.sub(r\"\\s+\",r\"\",s)\n",
    "_df.index = _df[\"POSTCODE\"].apply(f)\n",
    "\n",
    "_dfx = _df.join(_df_pc)\n",
    "_dfx.index = _df['GP_PRACTICE_CODE']\n",
    "\n",
    "cols = ['TOTAL_ALL','POSTCODE',\"latitude\",\"longitude\"]\n",
    "_dfw = df_appp.join(_dfx[cols], how=\"inner\")\n",
    "\n",
    "_dfw.to_csv(\"../visualizations/\"+title_+'.csv')\n",
    "df_info(_dfw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x108eb5810>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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79fpvLBfS+mbl/YBnsCcr5AN134mItFMmuu/2ZfeABPYsu/07uiIiIiLpglIU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\niIiIiIiIiIiIiIiIiHSs/w/YSkTTd+xFjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108eb5f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k=10\n",
    "(df_appp[['expected below','expected above']][:k]).plot(kind='barh')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Hipothesis test\n",
    "===\n",
    "\n",
    "### null Hipothesis:\n",
    "\n",
    "All GP practices are rated by patients __equally__.\n",
    "\n",
    "---\n",
    "\n",
    "### Hipothesis A:\n",
    "\n",
    "GP practices which __overprescribe__ antibiotics are rated __higher__ by patients than preactices with average level prescribtions.\n",
    "\n",
    "---\n",
    "\n",
    "### Hipothesis B:\n",
    "\n",
    "GP practices which __underprescribe__ antibiotics are rated __lower__ by patients than preactices with average level prescribtions.\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "load and re-populate GP_RATING_SURVEY\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7765:rows\n",
      "               Patients rating of GP  Patients rating of GP (zscore)\n",
      "Practice code                                                       \n",
      "A81001                       0.92100                        1.278779\n",
      "A81002                       0.91375                        1.163219\n",
      "7727:rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GP Prescribtions</th>\n",
       "      <th>TOTAL_ALL</th>\n",
       "      <th>days</th>\n",
       "      <th>daily_prescribtions</th>\n",
       "      <th>daily prescribtions per patient</th>\n",
       "      <th>dppp_expected</th>\n",
       "      <th>dppp_margin_of_error</th>\n",
       "      <th>group</th>\n",
       "      <th>expected above</th>\n",
       "      <th>expected below</th>\n",
       "      <th>Patients rating of GP</th>\n",
       "      <th>Patients rating of GP (zscore)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A81001</th>\n",
       "      <td>  7964</td>\n",
       "      <td>  4157</td>\n",
       "      <td> 1094</td>\n",
       "      <td>  7.279707</td>\n",
       "      <td> 0.001751</td>\n",
       "      <td> 0.001701</td>\n",
       "      <td> 0.001253</td>\n",
       "      <td> average</td>\n",
       "      <td>  2038</td>\n",
       "      <td> 13431</td>\n",
       "      <td> 0.92100</td>\n",
       "      <td> 1.278779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81002</th>\n",
       "      <td> 39603</td>\n",
       "      <td> 19709</td>\n",
       "      <td> 1094</td>\n",
       "      <td> 36.200183</td>\n",
       "      <td> 0.001837</td>\n",
       "      <td> 0.001701</td>\n",
       "      <td> 0.000575</td>\n",
       "      <td> average</td>\n",
       "      <td> 24267</td>\n",
       "      <td> 49075</td>\n",
       "      <td> 0.91375</td>\n",
       "      <td> 1.163219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        GP Prescribtions  TOTAL_ALL  days  daily_prescribtions  \\\n",
       "A81001              7964       4157  1094             7.279707   \n",
       "A81002             39603      19709  1094            36.200183   \n",
       "\n",
       "        daily prescribtions per patient  dppp_expected  dppp_margin_of_error  \\\n",
       "A81001                         0.001751       0.001701              0.001253   \n",
       "A81002                         0.001837       0.001701              0.000575   \n",
       "\n",
       "          group  expected above  expected below  Patients rating of GP  \\\n",
       "A81001  average            2038           13431                0.92100   \n",
       "A81002  average           24267           49075                0.91375   \n",
       "\n",
       "        Patients rating of GP (zscore)  \n",
       "A81001                        1.278779  \n",
       "A81002                        1.163219  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_df = pd.read_csv(GP_RATING_SURVEY, index_col=\"Practice code\")\n",
    "_df = _df.dropna()\n",
    "_df = _df.select(lambda x: x in TIME, axis=1)\n",
    "_df = pd.DataFrame(_df.mean(1), columns=['Patients rating of GP'])\n",
    "\n",
    "zscore = lambda v: (v - v.mean()) / v.std()\n",
    "_df['Patients rating of GP (zscore)'] = _df[['Patients rating of GP']].apply(zscore)\n",
    "\n",
    "df_about_GPs = _df.copy()\n",
    "print df_info(df_about_GPs)\n",
    "\n",
    "# join\n",
    "df_ht = df_data.join(df_about_GPs, how='inner')\n",
    "df_info(df_ht)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Hipothesis A:\n",
    "\n",
    "GP practices which __overprescribe__ antibiotics are rated __higher__ by patients than preactices with average level prescribtions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "GP practices overprescribing\n",
      "t-test 2-tail\n",
      "pvalue : 0.745732212214498\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "i = df_ht['group']=='average'\n",
    "s0 = df_ht.ix[i]['Patients rating of GP']\n",
    "\n",
    "i = df_ht['group']=='overprescribing'\n",
    "s1 = df_ht.ix[i]['Patients rating of GP']\n",
    "\n",
    "t, pvalue = stats.ttest_ind(s0, s1, equal_var=False)\n",
    "\n",
    "print\n",
    "print \"GP practices overprescribing\"\n",
    "print \"t-test 2-tail\" \n",
    "print \"pvalue : %.15f\" % pvalue\n",
    "print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hipothesis A: FAIL\n",
    "GP practices which __overprescribe__ antibiotics are __NOT__ rated __higher__ by patients than preactices with average level prescribtions.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Hipothesis B:\n",
    "\n",
    "GP practices which __underprescribe__ antibiotics are rated __lower__ by patients than preactices with average level prescribtions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "GP practices underprescribing\n",
      "t-test 2-tail\n",
      "pvalue : 0.000000000010447\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "i = df_ht['group']=='average'\n",
    "s0 = df_ht.ix[i]['Patients rating of GP']\n",
    "\n",
    "i = df_ht['group']=='underprescribing'\n",
    "s2 = df_ht.ix[i]['Patients rating of GP']\n",
    "\n",
    "t, pvalue = stats.ttest_ind(s0, s2, equal_var=False)\n",
    "\n",
    "print\n",
    "print \"GP practices underprescribing\"\n",
    "print \"t-test 2-tail\" \n",
    "print \"pvalue : %.15f\" % pvalue\n",
    "print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.79955685358255446, 0.84144487553610436)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.mean(), s0.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hipothesis B: CONFIRMED\n",
    "GP practices which __underprescribe__ antibiotics are rated __lower__ by patients than preactices with average level prescribtions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IRK1Qz/gexVZ1rsI+2wPIAPoCGtBPRESiUk1FxBTgaqBi4qVd2MYtkW7qpqpO\nEREJEG5VZ7QMEqfEJyIiASL5jA9sx3YREZGoE27ie+6URiEiIhIhquoUEZGoFOmqzj+F+TkREZE6\nFao7Q0o16z3A/7oQi4iIiOtCJb4C7Nx7wTRzIRYRERHXhUp824GfEDz57QyyTkRE5LQX6hnfdOy4\nnME84UIsIiIirlOrThERiUqRatWp1pwiIhLVapv4fuxKFCIiIhFS28T3jStRiIiIRIie8YmISFQK\n9xlfqO4MDYBfANcDbXzr8oGlwCzg+9qeTEREpK6FypQLsPPuzcbOwweQDgzDdnO41d3QAqjEJyIi\nAdyYj+8LoFMY29ygxCciIgHc6M6wDxhYZZ962JLevtqeSERE5HQQKvENAm4GvsaW8L7wvb/Jt01E\nRCTqOCkieoBU3/vvgLqoc1RVp4iIBHDjGV8oLYG9YX42HEp8IiISINIT0c4K83MiIiJ1Sh3YRUQk\nKrnRgb1CC2z/PYPtz/d1bU8iIiJyugiV+LoDzwJJ2BFbwCbA/cA9wEZ3QxMRETn1QhURNwN3Au9V\nWd8LeA64wK2gglBVp4iIBHCjcUsTTkx6AO8CTWt7IhERkdNBqKrON4DXsWN17sRm1Qzg58Df3Q9N\nRETk1AtVRPQAPwOu4/jsDLuAZdiEGEmq6hQRkQCR7sAeaUp8YfB6UygpKazrMKJOQkIyxcUajlbk\ndOfGM74XgB+H2H4x8JfanlAixyY9o1ctX/pjQeSHLVSmPB8Yi23F+Tmwx7d/S6AzsBaYCmxxOUZQ\niS8s9q8hXbfa86D7TeT052ZVZzy2T9+Z2N+iO7BdHY7U9mQnQYkvDEp84VLiE4kGesYnJ1DiC5cS\nn0g0iPQg1U71Az7DzuU3Lsj2NGzXiE3YKtNsl+MREZEY52aJLw77bPCn2G4Q7wNZwKeV9snBVqWO\nxybBz7Fjg5ZWOZZKfGFQiS9cKvGJRINIlPia1PLYPYE84Evge2ABMKDKPnsAr++9FzvRbdWkJyIi\ncso4SXx9gE+wpTGAbsAzDj7XBjviS4V8jneEr/A8cB6wG9tgZrSD44qIiITNSeKbjn1WV+Bb3gRc\n4eBzTuqKfuM7XmtsQv0jkODgcyIiImFxMh8fwFdVlp1UR+7Cju1ZIYPj0xtV6AM86nu/Dfgvto/g\nhqoHy8nJ8b/PzMwkMzPTQQgiEineJC8lRSV1HUbUSUhMoHh/cV2HERVyc3PJzc096eM4eSi4CHgK\nmIEdreVb518jAAAQ80lEQVSXQA9gUA2fq4+tHv0JtipzPSc2bnkSKAImYRu1fAB0BaqOF6XGLWFQ\n45ZwqXFLODwej22uJrWTg+63MLk5A/vdwO+xz+d2AauAex18rhQYBazEtvCchU16d/m2PwdMxg57\nthlb7foAJyY9ERGRU0Yd2H/AVOILl0p84VCJL0w5KvGFy83uDHOApErLydgBrEVERKKOk8TXFdhf\nabkQuNCdcERERNzlJPF5gJRKyynYZ3YiIiJRx0njlmnAOuBv2CR4C8e7IIiIiEQVJ4lvDrabwVXY\nlhI3YEdyERERiTqhEp8XKMZWbe4B5vvWG986dTsQEZGoEyrxvQz8L7CR4G3i27sSkYiIiItCJb7/\n9f3bLgJxiIiIRISTVp1vOlwnIiJy2gtV4muMnYOvGYHdGbycOL2QiIhIVAiV+O7Czo/XGtuqs0IJ\ndsBqERGRqBMq8U33ve4D/hCZcERERNzl5BmfwY7PWSEZuMedcERERNzlJPGNwI7PWaEQuNOdcERE\nRNzlJPHVq7JfHNDAnXBERETc5WTIspXAAuzEsR5so5e/uxmUiIiIW5wkvnHYqs27fcv/AP7sWkQi\nIiIucpL4yoBnfS8REZGo5iTxnQ1MBrpgO7WDbenZwa2gRERE3OKkcctfgJlAKXAlMBuY52ZQIiIi\nbnGS+BoD/8Q2bPkSyOH4ANYiIiJRxUlV5xFsF4Y8YBSwG2jqZlAiIiJucZL4RmMHq/4l8Ah2kOph\nbgYlIiLilpoSXxxwK/Br7ODU2W4HJCIi4qaanvGVAZdin++JiIhEPSdVnZuApcBC4JBvnQEWuxWU\niIiIW5wkvkbAPuCqKuuV+EREJOrUlPiaAX/EtugsrGFfERGR016oZ3zDgf8ATwOfAQMiEpGIiIiL\nQpX4fgWcB3yLHZ5sPvZZn4iISNQKVeI7hk16ANuBePfDERERcVeoEl86tpqzoitDm0rLBtuhXURE\nJKqESnxjsQmuwge+ZU+V9SIiIlEjVOJ7MVJBiIiIRIqT2RlERER+MJT4REQkpjhJfGmuRyEiIhIh\noRLftdjuDB8D+cAlEYlIRETERaES32TgMqAVcBPwWBjH74cd9eULYFw1+2QCHwJbgNwwziEiIuJY\nqFadpdikBfAekFDLY8cBM4CfAruA94FlwKeV9knCjgV6NbZUqWpVERFxVajE1wwYw/EO7JWXDfBk\nDcfuiR3c+kvf8gLseJ+VE99g4BVs0gMocBi3iIhIWEJVdf4ZW8o7w/eqWK541aQNsLPScr5vXWWd\ngBTgX8AGYKijqEVERMIUqsSXc5LHdjK6SwPgQuAnQBNgHfAu9plgYDA5x8PJzMwkMzPzJMMTEZFo\nkpubS25u7kkfxxNi24+AjhyfkWE6kIhNaDOAjTUcuxc2efbzLY8HyoHHK+0zDmjM8ST7Z+DvwKIq\nxzLGaJS02vJ4NLpceDzofqs9j8dz8n8ux6IcdL+Fyf6OC5nHggpV1fk7Ap+59QVWYFtePuTg2Buw\nVZntgIbArdjGLZUtBS7FNoRpAlwMfOLg2CIiImEJVdXZClhTabkE2xAF4C4Hxy4FRgErsYltFrZh\nS8Vnn8O2Gv078BG2NPg8SnwiIuKiUEXErcDZ1Wz7AluaixRVdYZBVZ3hUlVnOFTVGaYcVXWGy42q\nzt3Y53RV9cb2yxMREYk6oao6HwD+ip2eaCM2q14IZGOf14mIiESdUCW+9dgSX31sshvm2/9i7Egu\nIiIiUSdUiQ/ga2BCJAIRERGJBM3HJyIiMUWJT0REYkqoxPeS79/7IxGIiIhIJIRKfBcBrYE7sANJ\nV32JiIhEnVCNW2YCbwIdgA+qbDO+9SIiIlElVInvaeBc4C9A+yovJT0REYlKNXVnABgJXABcji3p\nrQY2uxmUiIiIW5y06hwNzMPOwN4CmAv80s2gRERE3OKkxDccO1rLQd/y77CTxT7tVlAiIiJucdqP\nr7ya9yIiIlHFSYnvL9ixORdjB6q+HnjBzaBERETc4iTxPQm8jZ0p3WAHrP7QxZhERERc4yTxge3H\nV7Uvn4iISNTRWJ0iIhJTlPhERCSmKPGJiEhMcZL4SoK88oFX0dBlIiISZZw0bvk9sBN42bc8COiI\nbdn5ApDpSmQiIiIucFLiuw54Dij2vf4EXA0sAJLdC01EROTUc5L4DgG3+vatBwwEjvi2GZfiEhER\ncYWTxDcEGAp843v9HLgNaAyMci80ERGRU8/JM75tQP9qtv37FMYiIiLiOieJrzkwAmhXaX8D3OFS\nTCIiIq5xkviWAu8A/+D4zAx6ticiIlHJSeJrDIxzOxAREZFIcNK4ZQXwv24HIiIiEglOEt/9wHJs\nF4aKkVuK3QxKRETELU6qOs9wPQoREZEICZX4zgU+BS6sZvvGUx+OiIiIu0IlvjHYbgxPErwV55Wu\nRCQiIuKiUIlvhO/ffhwfoqxCI3fCERERcZeTxi1rHa4TERE57YUq8bUCWgNNsM/5PNgqT69vnYiI\nSNQJlfj6AtlAG2BapfUlwG8cHr8fMB2IA/4MPF7Nfj8G1mFnfljs8NgiIiK1Firxzfa9bgYWhXHs\nOGAG8FNgF/A+sAzbUrTqfo8Df8eWKkVERFzjpB/fIuzsDF0IbNTycA2f6wnkAV/6lhcAAzgx8d3n\nO8ePHcQiIiJyUpw0bnkOWwX5S2yJbCBwpoPPtQF2VlrO962rus8A4Fnfsga/FhERVzlJfH2wk8/u\nAyYBvYDODj7nJIlNB/7Pt68HVXWKiIjLnFR1Hvb9ewhbQvsOaOngc7uAjErLGdhSX2UXYatAAdKA\nnwHfY58FBsjJyfG/z8zMJDMz00EIIiLyQ5Gbm0tubu5JH8dJCWsCtpHKVcAffeue960PpT7wOfAT\nYDewHsjixGd8Ff6CHQw7WKtOY4xqQWvL46nogSK140H3W+15PB7IqesoolAOut/CZH/H1b6msKYS\nXz3gLaAQeAV4DdvAZb+DY5cCo4CV2Jabs7BJ7y7f9udqG6yIiMjJcpIpNwHd3A6kBirxhUElvnCp\nxBcOlfjClKMSX7jCLfE5adzyT2xfPjU8ERGRqOck8Y0E/gYcQxPRiohIlNNEtCIiElOclPhERER+\nMJT4REQkpijxiYhITHGS+M7i+ODUV2LH7ExyLSIREREXOUl8r2A7o5+F7XSeAcx3MygRERG3OEl8\n5djEdyPwB2AsdnZ2ERGRqOMk8X0PDMbO0LDCt66BaxGJiIi4yEniux07FdGjwH+B9sBLbgYlIiLi\nFicd2H+KbdBS4b/AUXfCERERcZeTEl+2w3UiIiKnvVAlvizss7322HnyKiRgJ6MVERGJOqES31pg\nD9AMmMrx2RlKgM0uxyUiIuKKUIlvh+/VK0KxiIiIuM7JM76bgC+wUxFpWiIREYlqTlp1TgH6A5+6\nHIuIiIjrnJT49qKkJyIiPxBOSnwbgL8CS7CzsAMYYLFbQYmIiLjFSeJLBA4DfausV+ITEZGo4yTx\nZbsdhIiISKQ4ecbXGXgT+I9vuSvwW9ciEhERcZGTxPc88BuOP9/7GDuqi4iISNRxkviaAO9VWjbY\nqYpERESijpPE9y129vUKN2OHMhMREYk6Thq3jAL+BJwD7MZOSzTEzaBERETc4iTxbQN+AjTFlhBL\nXI1IRETERaES31DsTOv/D/tcr4LHt/yki3GJiIi4IlTia+L7N4HAxCciIhK1QiW+53z//hP4d5Vt\nl7oTjoiIiLuctOr8Q5B1T5/qQERERCIhVImvN9AHOwP7GI7PwJ4AxLkcl4iIiCtCJb6GHE9yCZXW\nF2P78omIiESdUInvbd/rReDLSAQjIiLiNif9+A4BU4EuQGPfOgNc5VZQIiIibnHSuGUe8BnQAcjB\nlv42uBeSiIiIe5wkvlTgz9jZGd4GbkelPRERiVJOEl/FdER7gf7AhUByLc7RD1ti/AIYF2T7EGAz\n8BGwBjvfn4iIiCucPOP7/4Ak7NBlfwC8wK8cHj8OmAH8FNgFvA8sAz6ttM924HKgCJsk/wT0cnh8\nERGRWgmV+BoDI7FTErUBZgGZtTx+TyCP461CFwADCEx86yq9fw9Ir+U5REREHAtV1TkbuAhbBXkN\nMC2M47cBdlZazvetq84vgNfDOI+IiIgjoUp85wLn+97PwlZT1lZtBre+ErgDuCTYxpycHP/7zMxM\nMjMzwwhHRESiVW5uLrm5uSd9HE+IbR8C3UMsO9EL2wWin295PFAOPF5lv67AYt9+eUGOY4zRBBG1\n5fFUzCAlteNB91vteTwe+79daicH3W9hsr/jQuaxoEKV+LoSOOls40rLBtvIpSYbgE5AO+zs7bcC\nWVX2aYtNercRPOmJiIicMqES36kYiLoUGAWs9B1vFrZhy12+7c8BD2G7RzzrW/c9tlGMiIjIKVfr\nImIdUVVnGFTVGS5VdYZDVZ1hylFVZ7jCrep00oFdRETkB0OJT0REYooSn4iIxBQlPhERiSlKfCIi\nElOU+EREJKYo8YmISExR4hMRkZiixCciIjFFiU9ERGKKEp+IiMQUJT4REYkpSnwiIhJTlPhERCSm\nKPGJiEhMUeITEZGYosQnIiIxRYlPRERiihKfiIjEFCU+ERGJKUp8IiISU5T4REQkpijxiYhITFHi\nExGRmKLEJyIiMUWJT0REYooSn4iIxBQlPhERiSlKfCIiElOU+EREJKYo8YmISExR4hMRkZiixCci\nIjFFiU9ERGKKEp+IiMQUJT4REYkpSnwiIhJT3E58/YDPgC+AcdXs87Rv+2agu8vxiIhIjHMz8cUB\nM7DJrwuQBZxbZZ9rgLOATsCdwLMuxiMiIuJq4usJ5AFfAt8DC4ABVfa5Dpjte/8ekAS0cDEmERGJ\ncW4mvjbAzkrL+b51Ne2T7mJMIiIS49xMfMbhfp4wPyciIlJr9V089i4go9JyBrZEF2qfdN+6qrZ5\nPJ6Opza8WFH17wpxwuPRdQtLTl0HEJ10v4VtW10HUFV9bFDtgIbAJoI3bnnd974X8G6kghMREXHD\nz4DPsY1cxvvW3eV7VZjh274ZuDCi0YmIiIiIiEjk1NThPRMoAj70vX4bschOXy8AXwMfh9hHgwSc\nqKbrlonutWAygH8B/wG2AL+sZj/dc4GcXLdMdM9V1Qjb1W0T8AnwWDX7Re39Foet8mwHNCD4M8FM\nYFlEozr9XYb9oqv7BV75OerF6DlqhZquWya614JpCXTzvT8D+ygj1LN73XOWk+uWie65YJr4/q2P\nvZcurbK91vfb6TRWp5MO76BmilWtBgpDbNcgAcHVdN1A91owe7F/lAIcAD4FWlfZR/fciZxcN9A9\nF8wh378NsQWkfVW21/p+O50Sn5MO7wbogy3Ovo4dCk1C0yAB4dG9VrN22FLze1XW654LrR3Br5vu\nueDqYf9o+BpbXfxJle21vt/c7MdXW046rm/E1pUfwrYYXQKc7WZQPxAaJKD2dK+FdgawCBiNLcFU\npXsuuFDXTfdccOXYauJEYCW2Sji3yj61ut9OpxKfkw7vJRwv9r6BfRaY4n5oUc3pIAESSPda9RoA\nrwBzsb+cq9I9F1xN1033XGhFwGtAjyrro/p+c9LhvQXHM3tP7PNAsdfMSeMWDRIQqB3VXzfda8F5\ngDnAUyH20T13IifXTffcidKwz+wAGgPvAD+psk/U3281dXi/F9sUeBOwFvtDxrqXgd3AMWw99x1o\nkAAnarpuuteCuxRb9bSJ483uf4buuZo4uW665050PrYKeBPwETDWt173m4iIiIiIiIiIiIiIiIiI\niIiIiIiIiIiIiIiIiIiIiNS9/x9lYg4J0djCXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c82b0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=[7,5])\n",
    "bins_=10\n",
    "\n",
    "bar([1],[s0.mean()], color='b', width=0.5)\n",
    "bar([2],[s2.mean()], color='g', width=0.5)\n",
    "xlim([.5,3])\n",
    "ylim([.0,1.2])\n",
    "\n",
    "title(\"Means\")\n",
    "# xlabel(\"Patients rating of GP Practice (0.0-1.0)\")\n",
    "ylabel(\"Patients rating of GP Practice (0.0-1.0)\")\n",
    "xticks()\n",
    "legend([\"GP prescibing at average level\", \"GP practices underprescribing\"])\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BG9332GOPzY033phbbrkla9asyYoVKzJr1qzMnz8/f/nLXzJz5swsX748O+yw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\nIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c82b350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=[15,10])\n",
    "bins_=10\n",
    "\n",
    "hist(s0, bins=bins_, alpha=.5, color='b', normed=True)\n",
    "axvline(s0.mean(), color='b', linewidth=3)\n",
    "\n",
    "hist(s2, bins=bins_, alpha=.3, color='g', normed=True)\n",
    "axvline(s2.mean(), color='g', linewidth=3)\n",
    "\n",
    "ylim([0,10])\n",
    "title(\"Distributions\")\n",
    "xlabel(\"Patients rating of GP Practice (0.0-1.0)\")\n",
    "ylabel(\"Percentage of ratings (%)\")\n",
    "\n",
    "legend([\"GP prescibing at average level\", \"GP practices underprescribing\"])\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "_df_1 = pd.DataFrame(df_about_GPs.mean(1), columns=[\"patients_about_GPs\"])\n",
    "_df_1['patients_about_GPs(z)'] = (_df_1 - _df_1.mean()) / _df_1.std()\n",
    "_df = df_data.copy().join(_df_1, how=\"inner\")\n",
    "df_info(_df)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "1 \t 0.001\n",
    "16 \t 0.01\n",
    "63 \t 0.05\n",
    "81 \t 0.05\n",
    "29 \t 0.01\n",
    "17 \t 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "overprescribing\n",
      "Patients rating of GP (zscore) \t0.0481300939676 \tPatients rating of GP \t0.843791152263\n",
      "\n",
      "average\n",
      "Patients rating of GP (zscore) \t0.0107322852932 \tPatients rating of GP \t0.841444875536\n",
      "\n",
      "underprescribing\n",
      "Patients rating of GP (zscore) \t-0.656929893069 \tPatients rating of GP \t0.799556853583\n"
     ]
    }
   ],
   "source": [
    "col = \"Patients rating of GP (zscore)\"\n",
    "col2 = \"Patients rating of GP\"\n",
    "_df = df_ht\n",
    "\n",
    "\n",
    "print\n",
    "i = _df['group']=='overprescribing'\n",
    "s1_above   = _df.ix[i][col2]\n",
    "s1_above_z = _df.ix[i][col]\n",
    "print 'overprescribing'\n",
    "print col, \"\\t\",mean(s1_above_z),\"\\t\" ,col2, \"\\t\",mean(s1_above)\n",
    "\n",
    "print\n",
    "i = _df['group']=='average'\n",
    "s0_base   = _df[col2].ix[i]\n",
    "s0_base_z = _df.ix[i][col]\n",
    "print 'average'\n",
    "print col, \"\\t\",mean(s0_base_z),\"\\t\" ,col2, \"\\t\",mean(s0_base)\n",
    "\n",
    "print\n",
    "i = _df['group']=='underprescribing'\n",
    "s2_below   = _df.ix[i][col2]\n",
    "s2_below_z = _df.ix[i][col]\n",
    "print 'underprescribing'\n",
    "print col, \"\\t\",mean(s2_below_z),\"\\t\" ,col2, \"\\t\",mean(s2_below)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EJZ3VeCaYmGL4+IALX3+qBuF057lo3oEDvAeAXoPY2qqLtezbx1e3m3N5+rla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kfDNmzbxn2lGIvk44nTJgtacfb++T+/j5r23ehPCRMEC4GcjcW48ToHnnrJFfc4g2FVcEgWdOZe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\n2L34QTfefGNXv/3qjlxw5PttR94+HAuEAADTYJoobKO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1075daed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_x = \"TOTAL_ALL\"\n",
    "col_y = \"patients_about_GPs(z)\"\n",
    "x = _df[col_x]\n",
    "y = _df[col_y]\n",
    "# x = _df['daily_prescribtions'] / _df['TOTAL_ALL']\n",
    "\n",
    "figure(figsize=[15,10])\n",
    "plot(x, y, \"b+\", alpha=.3)\n",
    "hlines(y.mean(), x.min(), x.max(), colors=\"b\")\n",
    "\n",
    "i = df_data['group']=='overprescribing'\n",
    "plot(x[i], y[i], \"ro\")\n",
    "hlines(y[i].mean(), x.min(), x.max(), colors=\"r\")\n",
    "\n",
    "i = df_data['group']=='underprescribing'\n",
    "plot(x[i], y[i], \"go\")\n",
    "hlines(y[i].mean(), x.min(), x.max(), colors=\"g\")\n",
    "\n",
    "xlabel(col_x)\n",
    "ylabel(col_y)\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X5OIkl85Y5/QkP0nrbNtb26+9KM6SFwYaAADAIJt5UmrdunVdP9Mt0I4kuSLJ7Wnd0fGG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\nuoH9Mz/WpHX56o+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x108349e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=[15,10])\n",
    "\n",
    "x = _df[\"patients_about_GPs(z)\"]\n",
    "# x = _df[\"patients_about_GPs\"]\n",
    "\n",
    "i = df_data['group']=='overprescribing'\n",
    "s1 = x[i].dropna()\n",
    "\n",
    "i = df_data['group']=='underprescribing'\n",
    "s2 = x[i].dropna()\n",
    "\n",
    "i = df_data['group']=='average'\n",
    "s0 = x[i].dropna()\n",
    "\n",
    "bins_=10\n",
    "\n",
    "# hist(s0, bins=bins_, alpha=.2, color='k', normed=True)\n",
    "# axvline(s0.mean(), color='k', linewidth=1)\n",
    "\n",
    "hist(s1, bins=bins_, alpha=.5, color='r', normed=True)\n",
    "axvline(s1.mean(), color='r', linewidth=3)\n",
    "\n",
    "hist(s2, bins=bins_, alpha=.3, color='g', normed=True)\n",
    "axvline(s2.mean(), color='g', linewidth=3)\n",
    "\n",
    "legend([\"a\", \"b\"])\n",
    "# i = _df['below_average_p.05']\n",
    "# plot(x[i], y[i], \"go\")\n",
    "\n",
    "\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 's1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-70-8a2a0b6b562a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mttest_ind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m# t_statistic, p_value = scipy.stats.ttest_ind( sample_one, sample_two )\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 's1' is not defined"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "stats.ttest_ind(s1, s2)\n",
    "# t_statistic, p_value = scipy.stats.ttest_ind( sample_one, sample_two )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(0.3354630045568321), 0.73728508505288592)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(s1_above_z, s0_base_z, equal_var=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(0.32537189041623327), 0.74573221221449248)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(s1_above_z, s0_base_z, equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0447459585053907e-11"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t, pvalue = stats.ttest_ind(s2_below_z, s0_base_z, equal_var=False)\n",
    "pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# PATHS\n",
    "DIR_PATIENT_SURVEY    = \"../data/raw/GP_Patient_Survey/\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_survey = pd.read_csv(GP_RATING_SURVEY, index_col=\"Practice code\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2010.06</th>\n",
       "      <th>2010.09</th>\n",
       "      <th>2010.12</th>\n",
       "      <th>2011.03</th>\n",
       "      <th>2011.06</th>\n",
       "      <th>2012.06</th>\n",
       "      <th>2012.12</th>\n",
       "      <th>2013.06</th>\n",
       "      <th>2013.12</th>\n",
       "      <th>2014.07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Practice code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A81001</th>\n",
       "      <td> 0.92000</td>\n",
       "      <td> 0.92125</td>\n",
       "      <td> 0.91625</td>\n",
       "      <td> 0.91875</td>\n",
       "      <td> 0.92000</td>\n",
       "      <td> 0.920000</td>\n",
       "      <td> 0.903333</td>\n",
       "      <td> 0.933333</td>\n",
       "      <td> 0.928333</td>\n",
       "      <td> 0.923333</td>\n",
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       "    <tr>\n",
       "      <th>A81002</th>\n",
       "      <td> 0.91625</td>\n",
       "      <td> 0.90500</td>\n",
       "      <td> 0.89875</td>\n",
       "      <td> 0.89375</td>\n",
       "      <td> 0.91875</td>\n",
       "      <td> 0.911667</td>\n",
       "      <td> 0.923333</td>\n",
       "      <td> 0.928333</td>\n",
       "      <td> 0.886667</td>\n",
       "      <td> 0.848333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81003</th>\n",
       "      <td> 0.78375</td>\n",
       "      <td> 0.77500</td>\n",
       "      <td> 0.76500</td>\n",
       "      <td> 0.77375</td>\n",
       "      <td> 0.78125</td>\n",
       "      <td> 0.796667</td>\n",
       "      <td> 0.791667</td>\n",
       "      <td> 0.771667</td>\n",
       "      <td> 0.790000</td>\n",
       "      <td> 0.843333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81004</th>\n",
       "      <td> 0.88375</td>\n",
       "      <td> 0.87875</td>\n",
       "      <td> 0.86875</td>\n",
       "      <td> 0.86625</td>\n",
       "      <td> 0.86250</td>\n",
       "      <td> 0.860000</td>\n",
       "      <td> 0.878333</td>\n",
       "      <td> 0.903333</td>\n",
       "      <td> 0.883333</td>\n",
       "      <td> 0.846667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81005</th>\n",
       "      <td> 0.87750</td>\n",
       "      <td> 0.86250</td>\n",
       "      <td> 0.85375</td>\n",
       "      <td> 0.85750</td>\n",
       "      <td> 0.86125</td>\n",
       "      <td> 0.888333</td>\n",
       "      <td> 0.883333</td>\n",
       "      <td> 0.843333</td>\n",
       "      <td> 0.833333</td>\n",
       "      <td> 0.851667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81006</th>\n",
       "      <td> 0.92875</td>\n",
       "      <td> 0.92750</td>\n",
       "      <td> 0.90750</td>\n",
       "      <td> 0.91500</td>\n",
       "      <td> 0.90125</td>\n",
       "      <td> 0.945000</td>\n",
       "      <td> 0.936667</td>\n",
       "      <td> 0.891667</td>\n",
       "      <td> 0.898333</td>\n",
       "      <td> 0.885000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81007</th>\n",
       "      <td> 0.89750</td>\n",
       "      <td> 0.89250</td>\n",
       "      <td> 0.89750</td>\n",
       "      <td> 0.87500</td>\n",
       "      <td> 0.86625</td>\n",
       "      <td> 0.895000</td>\n",
       "      <td> 0.891667</td>\n",
       "      <td> 0.868333</td>\n",
       "      <td> 0.856667</td>\n",
       "      <td> 0.896667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81008</th>\n",
       "      <td> 0.82750</td>\n",
       "      <td> 0.80375</td>\n",
       "      <td> 0.78875</td>\n",
       "      <td> 0.77875</td>\n",
       "      <td> 0.79625</td>\n",
       "      <td> 0.873333</td>\n",
       "      <td> 0.833333</td>\n",
       "      <td> 0.830000</td>\n",
       "      <td> 0.780000</td>\n",
       "      <td> 0.765000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81009</th>\n",
       "      <td> 0.89375</td>\n",
       "      <td> 0.87625</td>\n",
       "      <td> 0.87000</td>\n",
       "      <td> 0.85125</td>\n",
       "      <td> 0.87375</td>\n",
       "      <td> 0.845000</td>\n",
       "      <td> 0.851667</td>\n",
       "      <td> 0.885000</td>\n",
       "      <td> 0.833333</td>\n",
       "      <td> 0.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81010</th>\n",
       "      <td> 0.85375</td>\n",
       "      <td> 0.87250</td>\n",
       "      <td> 0.85250</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81011</th>\n",
       "      <td> 0.82250</td>\n",
       "      <td> 0.80250</td>\n",
       "      <td> 0.79875</td>\n",
       "      <td> 0.79375</td>\n",
       "      <td> 0.78875</td>\n",
       "      <td> 0.920000</td>\n",
       "      <td> 0.880000</td>\n",
       "      <td> 0.870000</td>\n",
       "      <td> 0.886667</td>\n",
       "      <td> 0.841667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81012</th>\n",
       "      <td> 0.80000</td>\n",
       "      <td> 0.80250</td>\n",
       "      <td> 0.75250</td>\n",
       "      <td> 0.74125</td>\n",
       "      <td> 0.72000</td>\n",
       "      <td> 0.775000</td>\n",
       "      <td> 0.828333</td>\n",
       "      <td> 0.771667</td>\n",
       "      <td> 0.811667</td>\n",
       "      <td> 0.865000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81013</th>\n",
       "      <td> 0.89500</td>\n",
       "      <td> 0.87750</td>\n",
       "      <td> 0.89375</td>\n",
       "      <td> 0.90750</td>\n",
       "      <td> 0.91750</td>\n",
       "      <td> 0.931667</td>\n",
       "      <td> 0.913333</td>\n",
       "      <td> 0.901667</td>\n",
       "      <td> 0.891667</td>\n",
       "      <td> 0.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81014</th>\n",
       "      <td> 0.80875</td>\n",
       "      <td> 0.79875</td>\n",
       "      <td> 0.78875</td>\n",
       "      <td> 0.78625</td>\n",
       "      <td> 0.79500</td>\n",
       "      <td> 0.748333</td>\n",
       "      <td> 0.793333</td>\n",
       "      <td> 0.836667</td>\n",
       "      <td> 0.828333</td>\n",
       "      <td> 0.851667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81015</th>\n",
       "      <td> 0.92250</td>\n",
       "      <td> 0.92625</td>\n",
       "      <td> 0.92375</td>\n",
       "      <td> 0.92500</td>\n",
       "      <td> 0.92875</td>\n",
       "      <td> 0.931667</td>\n",
       "      <td> 0.896667</td>\n",
       "      <td> 0.876667</td>\n",
       "      <td> 0.881667</td>\n",
       "      <td> 0.911667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81016</th>\n",
       "      <td> 0.85625</td>\n",
       "      <td> 0.83250</td>\n",
       "      <td> 0.83000</td>\n",
       "      <td> 0.83625</td>\n",
       "      <td> 0.84375</td>\n",
       "      <td> 0.828333</td>\n",
       "      <td> 0.800000</td>\n",
       "      <td> 0.788333</td>\n",
       "      <td> 0.805000</td>\n",
       "      <td> 0.776667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81017</th>\n",
       "      <td> 0.75875</td>\n",
       "      <td> 0.76375</td>\n",
       "      <td> 0.75125</td>\n",
       "      <td> 0.76250</td>\n",
       "      <td> 0.75125</td>\n",
       "      <td> 0.766667</td>\n",
       "      <td> 0.758333</td>\n",
       "      <td> 0.775000</td>\n",
       "      <td> 0.723333</td>\n",
       "      <td> 0.713333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81018</th>\n",
       "      <td> 0.91500</td>\n",
       "      <td> 0.90875</td>\n",
       "      <td> 0.90625</td>\n",
       "      <td> 0.90250</td>\n",
       "      <td> 0.90125</td>\n",
       "      <td> 0.898333</td>\n",
       "      <td> 0.913333</td>\n",
       "      <td> 0.881667</td>\n",
       "      <td> 0.831667</td>\n",
       "      <td> 0.861667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81019</th>\n",
       "      <td> 0.83875</td>\n",
       "      <td> 0.83125</td>\n",
       "      <td> 0.83125</td>\n",
       "      <td> 0.83625</td>\n",
       "      <td> 0.83375</td>\n",
       "      <td> 0.813333</td>\n",
       "      <td> 0.860000</td>\n",
       "      <td> 0.883333</td>\n",
       "      <td> 0.858333</td>\n",
       "      <td> 0.851667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81020</th>\n",
       "      <td> 0.81125</td>\n",
       "      <td> 0.81625</td>\n",
       "      <td> 0.81000</td>\n",
       "      <td> 0.81250</td>\n",
       "      <td> 0.83125</td>\n",
       "      <td> 0.896667</td>\n",
       "      <td> 0.876667</td>\n",
       "      <td> 0.866667</td>\n",
       "      <td> 0.843333</td>\n",
       "      <td> 0.805000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81021</th>\n",
       "      <td> 0.88250</td>\n",
       "      <td> 0.89375</td>\n",
       "      <td> 0.89125</td>\n",
       "      <td> 0.89625</td>\n",
       "      <td> 0.87500</td>\n",
       "      <td> 0.861667</td>\n",
       "      <td> 0.871667</td>\n",
       "      <td> 0.883333</td>\n",
       "      <td> 0.865000</td>\n",
       "      <td> 0.821667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81022</th>\n",
       "      <td> 0.88625</td>\n",
       "      <td> 0.87250</td>\n",
       "      <td> 0.86125</td>\n",
       "      <td> 0.85625</td>\n",
       "      <td> 0.83375</td>\n",
       "      <td> 0.828333</td>\n",
       "      <td> 0.871667</td>\n",
       "      <td> 0.885000</td>\n",
       "      <td> 0.841667</td>\n",
       "      <td> 0.861667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81023</th>\n",
       "      <td> 0.90375</td>\n",
       "      <td> 0.88625</td>\n",
       "      <td> 0.89750</td>\n",
       "      <td> 0.89125</td>\n",
       "      <td> 0.88625</td>\n",
       "      <td> 0.891667</td>\n",
       "      <td> 0.913333</td>\n",
       "      <td> 0.908333</td>\n",
       "      <td> 0.926667</td>\n",
       "      <td> 0.931667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81025</th>\n",
       "      <td> 0.86125</td>\n",
       "      <td> 0.87250</td>\n",
       "      <td> 0.89625</td>\n",
       "      <td> 0.91125</td>\n",
       "      <td> 0.89750</td>\n",
       "      <td> 0.873333</td>\n",
       "      <td> 0.840000</td>\n",
       "      <td> 0.831667</td>\n",
       "      <td> 0.858333</td>\n",
       "      <td> 0.821667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81026</th>\n",
       "      <td> 0.90750</td>\n",
       "      <td> 0.89000</td>\n",
       "      <td> 0.90625</td>\n",
       "      <td> 0.89500</td>\n",
       "      <td> 0.88375</td>\n",
       "      <td> 0.931667</td>\n",
       "      <td> 0.940000</td>\n",
       "      <td> 0.941667</td>\n",
       "      <td> 0.935000</td>\n",
       "      <td> 0.948333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81027</th>\n",
       "      <td> 0.89250</td>\n",
       "      <td> 0.89250</td>\n",
       "      <td> 0.90000</td>\n",
       "      <td> 0.88875</td>\n",
       "      <td> 0.89625</td>\n",
       "      <td> 0.930000</td>\n",
       "      <td> 0.911667</td>\n",
       "      <td> 0.900000</td>\n",
       "      <td> 0.903333</td>\n",
       "      <td> 0.906667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81029</th>\n",
       "      <td> 0.78000</td>\n",
       "      <td> 0.76750</td>\n",
       "      <td> 0.73375</td>\n",
       "      <td> 0.72500</td>\n",
       "      <td> 0.73625</td>\n",
       "      <td> 0.783333</td>\n",
       "      <td> 0.720000</td>\n",
       "      <td> 0.766667</td>\n",
       "      <td> 0.833333</td>\n",
       "      <td> 0.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81030</th>\n",
       "      <td> 0.89125</td>\n",
       "      <td> 0.90500</td>\n",
       "      <td> 0.92500</td>\n",
       "      <td> 0.91500</td>\n",
       "      <td> 0.92000</td>\n",
       "      <td> 0.900000</td>\n",
       "      <td> 0.900000</td>\n",
       "      <td> 0.905000</td>\n",
       "      <td> 0.928333</td>\n",
       "      <td> 0.945000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81031</th>\n",
       "      <td> 0.85000</td>\n",
       "      <td> 0.86625</td>\n",
       "      <td> 0.87125</td>\n",
       "      <td> 0.86125</td>\n",
       "      <td> 0.87750</td>\n",
       "      <td> 0.896667</td>\n",
       "      <td> 0.908333</td>\n",
       "      <td> 0.875000</td>\n",
       "      <td> 0.846667</td>\n",
       "      <td> 0.828333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81032</th>\n",
       "      <td> 0.90625</td>\n",
       "      <td> 0.89375</td>\n",
       "      <td> 0.89250</td>\n",
       "      <td> 0.89250</td>\n",
       "      <td> 0.90125</td>\n",
       "      <td> 0.885000</td>\n",
       "      <td> 0.873333</td>\n",
       "      <td> 0.903333</td>\n",
       "      <td> 0.933333</td>\n",
       "      <td> 0.918333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03052</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td> 0.841667</td>\n",
       "      <td> 0.831667</td>\n",
       "      <td> 0.876667</td>\n",
       "      <td> 0.878333</td>\n",
       "      <td> 0.843333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03054</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.808333</td>\n",
       "      <td> 0.843333</td>\n",
       "      <td> 0.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03063</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td> 0.78125</td>\n",
       "      <td> 0.780000</td>\n",
       "      <td> 0.795000</td>\n",
       "      <td> 0.718333</td>\n",
       "      <td> 0.713333</td>\n",
       "      <td> 0.828333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03079</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td> 0.83000</td>\n",
       "      <td> 0.840000</td>\n",
       "      <td> 0.813333</td>\n",
       "      <td> 0.810000</td>\n",
       "      <td> 0.776667</td>\n",
       "      <td> 0.808333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03112</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.835000</td>\n",
       "      <td> 0.751667</td>\n",
       "      <td> 0.738333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03124</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.926667</td>\n",
       "      <td> 0.898333</td>\n",
       "      <td> 0.915000</td>\n",
       "      <td> 0.935000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03135</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td> 0.755000</td>\n",
       "      <td> 0.698333</td>\n",
       "      <td> 0.770000</td>\n",
       "      <td> 0.770000</td>\n",
       "      <td> 0.755000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03222</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td> 0.683333</td>\n",
       "      <td> 0.696667</td>\n",
       "      <td> 0.691667</td>\n",
       "      <td> 0.666667</td>\n",
       "      <td> 0.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03296</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.808333</td>\n",
       "      <td> 0.831667</td>\n",
       "      <td> 0.751667</td>\n",
       "      <td> 0.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03303</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td> 0.858333</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03362</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.848333</td>\n",
       "      <td> 0.788333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03363</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.771667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03364</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td> 0.691667</td>\n",
       "      <td> 0.695000</td>\n",
       "      <td> 0.715000</td>\n",
       "      <td> 0.758333</td>\n",
       "      <td> 0.825000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03366</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td> 0.848333</td>\n",
       "      <td> 0.875000</td>\n",
       "      <td> 0.896667</td>\n",
       "      <td> 0.903333</td>\n",
       "      <td> 0.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03402</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.691667</td>\n",
       "      <td> 0.655000</td>\n",
       "      <td> 0.668333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03430</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.760000</td>\n",
       "      <td> 0.793333</td>\n",
       "      <td> 0.873333</td>\n",
       "      <td> 0.905000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03441</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.818333</td>\n",
       "      <td> 0.755000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03445</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.865000</td>\n",
       "      <td> 0.880000</td>\n",
       "      <td> 0.905000</td>\n",
       "      <td>      NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03506</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.611667</td>\n",
       "      <td> 0.630000</td>\n",
       "      <td> 0.638333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03516</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.883333</td>\n",
       "      <td> 0.885000</td>\n",
       "      <td>      NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03587</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.633333</td>\n",
       "      <td> 0.706667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03595</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.825000</td>\n",
       "      <td> 0.773333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03597</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.861667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03602</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.900000</td>\n",
       "      <td> 0.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03656</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03661</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03663</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.863333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03664</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03671</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y03755</th>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>     NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> 0.760000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8467 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               2010.06  2010.09  2010.12  2011.03  2011.06   2012.06  \\\n",
       "Practice code                                                          \n",
       "A81001         0.92000  0.92125  0.91625  0.91875  0.92000  0.920000   \n",
       "A81002         0.91625  0.90500  0.89875  0.89375  0.91875  0.911667   \n",
       "A81003         0.78375  0.77500  0.76500  0.77375  0.78125  0.796667   \n",
       "A81004         0.88375  0.87875  0.86875  0.86625  0.86250  0.860000   \n",
       "A81005         0.87750  0.86250  0.85375  0.85750  0.86125  0.888333   \n",
       "A81006         0.92875  0.92750  0.90750  0.91500  0.90125  0.945000   \n",
       "A81007         0.89750  0.89250  0.89750  0.87500  0.86625  0.895000   \n",
       "A81008         0.82750  0.80375  0.78875  0.77875  0.79625  0.873333   \n",
       "A81009         0.89375  0.87625  0.87000  0.85125  0.87375  0.845000   \n",
       "A81010         0.85375  0.87250  0.85250      NaN      NaN       NaN   \n",
       "A81011         0.82250  0.80250  0.79875  0.79375  0.78875  0.920000   \n",
       "A81012         0.80000  0.80250  0.75250  0.74125  0.72000  0.775000   \n",
       "A81013         0.89500  0.87750  0.89375  0.90750  0.91750  0.931667   \n",
       "A81014         0.80875  0.79875  0.78875  0.78625  0.79500  0.748333   \n",
       "A81015         0.92250  0.92625  0.92375  0.92500  0.92875  0.931667   \n",
       "A81016         0.85625  0.83250  0.83000  0.83625  0.84375  0.828333   \n",
       "A81017         0.75875  0.76375  0.75125  0.76250  0.75125  0.766667   \n",
       "A81018         0.91500  0.90875  0.90625  0.90250  0.90125  0.898333   \n",
       "A81019         0.83875  0.83125  0.83125  0.83625  0.83375  0.813333   \n",
       "A81020         0.81125  0.81625  0.81000  0.81250  0.83125  0.896667   \n",
       "A81021         0.88250  0.89375  0.89125  0.89625  0.87500  0.861667   \n",
       "A81022         0.88625  0.87250  0.86125  0.85625  0.83375  0.828333   \n",
       "A81023         0.90375  0.88625  0.89750  0.89125  0.88625  0.891667   \n",
       "A81025         0.86125  0.87250  0.89625  0.91125  0.89750  0.873333   \n",
       "A81026         0.90750  0.89000  0.90625  0.89500  0.88375  0.931667   \n",
       "A81027         0.89250  0.89250  0.90000  0.88875  0.89625  0.930000   \n",
       "A81029         0.78000  0.76750  0.73375  0.72500  0.73625  0.783333   \n",
       "A81030         0.89125  0.90500  0.92500  0.91500  0.92000  0.900000   \n",
       "A81031         0.85000  0.86625  0.87125  0.86125  0.87750  0.896667   \n",
       "A81032         0.90625  0.89375  0.89250  0.89250  0.90125  0.885000   \n",
       "...                ...      ...      ...      ...      ...       ...   \n",
       "Y03052             NaN      NaN      NaN      NaN      NaN  0.841667   \n",
       "Y03054             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03063             NaN      NaN      NaN      NaN  0.78125  0.780000   \n",
       "Y03079             NaN      NaN      NaN      NaN  0.83000  0.840000   \n",
       "Y03112             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03124             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03135             NaN      NaN      NaN      NaN      NaN  0.755000   \n",
       "Y03222             NaN      NaN      NaN      NaN      NaN  0.683333   \n",
       "Y03296             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03303             NaN      NaN      NaN      NaN      NaN  0.858333   \n",
       "Y03362             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03363             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03364             NaN      NaN      NaN      NaN      NaN  0.691667   \n",
       "Y03366             NaN      NaN      NaN      NaN      NaN  0.848333   \n",
       "Y03402             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03430             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03441             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03445             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03506             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03516             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03587             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03595             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03597             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03602             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03656             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03661             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03663             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03664             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03671             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "Y03755             NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "\n",
       "                2012.12   2013.06   2013.12   2014.07  \n",
       "Practice code                                          \n",
       "A81001         0.903333  0.933333  0.928333  0.923333  \n",
       "A81002         0.923333  0.928333  0.886667  0.848333  \n",
       "A81003         0.791667  0.771667  0.790000  0.843333  \n",
       "A81004         0.878333  0.903333  0.883333  0.846667  \n",
       "A81005         0.883333  0.843333  0.833333  0.851667  \n",
       "A81006         0.936667  0.891667  0.898333  0.885000  \n",
       "A81007         0.891667  0.868333  0.856667  0.896667  \n",
       "A81008         0.833333  0.830000  0.780000  0.765000  \n",
       "A81009         0.851667  0.885000  0.833333  0.810000  \n",
       "A81010              NaN       NaN       NaN       NaN  \n",
       "A81011         0.880000  0.870000  0.886667  0.841667  \n",
       "A81012         0.828333  0.771667  0.811667  0.865000  \n",
       "A81013         0.913333  0.901667  0.891667  0.883333  \n",
       "A81014         0.793333  0.836667  0.828333  0.851667  \n",
       "A81015         0.896667  0.876667  0.881667  0.911667  \n",
       "A81016         0.800000  0.788333  0.805000  0.776667  \n",
       "A81017         0.758333  0.775000  0.723333  0.713333  \n",
       "A81018         0.913333  0.881667  0.831667  0.861667  \n",
       "A81019         0.860000  0.883333  0.858333  0.851667  \n",
       "A81020         0.876667  0.866667  0.843333  0.805000  \n",
       "A81021         0.871667  0.883333  0.865000  0.821667  \n",
       "A81022         0.871667  0.885000  0.841667  0.861667  \n",
       "A81023         0.913333  0.908333  0.926667  0.931667  \n",
       "A81025         0.840000  0.831667  0.858333  0.821667  \n",
       "A81026         0.940000  0.941667  0.935000  0.948333  \n",
       "A81027         0.911667  0.900000  0.903333  0.906667  \n",
       "A81029         0.720000  0.766667  0.833333  0.870000  \n",
       "A81030         0.900000  0.905000  0.928333  0.945000  \n",
       "A81031         0.908333  0.875000  0.846667  0.828333  \n",
       "A81032         0.873333  0.903333  0.933333  0.918333  \n",
       "...                 ...       ...       ...       ...  \n",
       "Y03052         0.831667  0.876667  0.878333  0.843333  \n",
       "Y03054              NaN  0.808333  0.843333  0.830000  \n",
       "Y03063         0.795000  0.718333  0.713333  0.828333  \n",
       "Y03079         0.813333  0.810000  0.776667  0.808333  \n",
       "Y03112              NaN  0.835000  0.751667  0.738333  \n",
       "Y03124         0.926667  0.898333  0.915000  0.935000  \n",
       "Y03135         0.698333  0.770000  0.770000  0.755000  \n",
       "Y03222         0.696667  0.691667  0.666667  0.670000  \n",
       "Y03296         0.808333  0.831667  0.751667  0.760000  \n",
       "Y03303              NaN       NaN       NaN       NaN  \n",
       "Y03362              NaN       NaN  0.848333  0.788333  \n",
       "Y03363              NaN       NaN       NaN  0.771667  \n",
       "Y03364         0.695000  0.715000  0.758333  0.825000  \n",
       "Y03366         0.875000  0.896667  0.903333  0.870000  \n",
       "Y03402              NaN  0.691667  0.655000  0.668333  \n",
       "Y03430         0.760000  0.793333  0.873333  0.905000  \n",
       "Y03441              NaN       NaN  0.818333  0.755000  \n",
       "Y03445         0.865000  0.880000  0.905000       NaN  \n",
       "Y03506              NaN  0.611667  0.630000  0.638333  \n",
       "Y03516              NaN  0.883333  0.885000       NaN  \n",
       "Y03587              NaN       NaN  0.633333  0.706667  \n",
       "Y03595              NaN       NaN  0.825000  0.773333  \n",
       "Y03597              NaN       NaN       NaN  0.861667  \n",
       "Y03602              NaN       NaN  0.900000  0.883333  \n",
       "Y03656              NaN       NaN       NaN  0.916667  \n",
       "Y03661              NaN       NaN       NaN  0.733333  \n",
       "Y03663              NaN       NaN       NaN  0.863333  \n",
       "Y03664              NaN       NaN       NaN  0.866667  \n",
       "Y03671              NaN       NaN       NaN  0.830000  \n",
       "Y03755              NaN       NaN       NaN  0.760000  \n",
       "\n",
       "[8467 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "questions\n",
    "---\n",
    "- which practices are prescribing significantly over an average?\n",
    "- what is it corelated with?\n",
    "    - average age\n",
    "    - age group size\n",
    "    - antibiotic?...\n",
    "    - region\n",
    "    - postcode\n",
    "- london (include/exclude)\n",
    "- corr \n",
    "    - age\n",
    "    - gender\n",
    "    - location (r/c)\n",
    "    - practice score\n",
    "- actionable information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>daily prescribtions per patient</th>\n",
       "      <th>Patients rating of GP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GP Prescribtions</th>\n",
       "      <td> 0.261711</td>\n",
       "      <td> 0.166301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_ALL</th>\n",
       "      <td>-0.057380</td>\n",
       "      <td> 0.098007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days</th>\n",
       "      <td>      NaN</td>\n",
       "      <td>      NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daily_prescribtions</th>\n",
       "      <td> 0.261711</td>\n",
       "      <td> 0.166301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daily prescribtions per patient</th>\n",
       "      <td> 1.000000</td>\n",
       "      <td> 0.175222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patients rating of GP</th>\n",
       "      <td> 0.175222</td>\n",
       "      <td> 1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patients rating of GP (zscore)</th>\n",
       "      <td> 0.175222</td>\n",
       "      <td> 1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_MALE</th>\n",
       "      <td>-0.066636</td>\n",
       "      <td> 0.082793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_FEMALES</th>\n",
       "      <td>-0.048176</td>\n",
       "      <td> 0.112274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_0-4</th>\n",
       "      <td>-0.067677</td>\n",
       "      <td>-0.004196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_5-9</th>\n",
       "      <td>-0.031511</td>\n",
       "      <td> 0.032438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_10-14</th>\n",
       "      <td> 0.002225</td>\n",
       "      <td> 0.081210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_15-19</th>\n",
       "      <td>-0.059452</td>\n",
       "      <td> 0.065235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_20-24</th>\n",
       "      <td>-0.155138</td>\n",
       "      <td>-0.028972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_25-29</th>\n",
       "      <td>-0.214243</td>\n",
       "      <td>-0.087736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_30-34</th>\n",
       "      <td>-0.226196</td>\n",
       "      <td>-0.102066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_35-39</th>\n",
       "      <td>-0.198606</td>\n",
       "      <td>-0.058608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_40-44</th>\n",
       "      <td>-0.099941</td>\n",
       "      <td> 0.050391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_45-49</th>\n",
       "      <td>-0.027965</td>\n",
       "      <td> 0.123504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_50-54</th>\n",
       "      <td> 0.013810</td>\n",
       "      <td> 0.167534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_55-59</th>\n",
       "      <td> 0.050182</td>\n",
       "      <td> 0.190046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_60-64</th>\n",
       "      <td> 0.089251</td>\n",
       "      <td> 0.225987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_65-69</th>\n",
       "      <td> 0.120368</td>\n",
       "      <td> 0.257728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_70-74</th>\n",
       "      <td> 0.128493</td>\n",
       "      <td> 0.250704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_75-79</th>\n",
       "      <td> 0.127445</td>\n",
       "      <td> 0.245526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_80-84</th>\n",
       "      <td> 0.116396</td>\n",
       "      <td> 0.238049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_85-89</th>\n",
       "      <td> 0.107696</td>\n",
       "      <td> 0.236945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_90-94</th>\n",
       "      <td> 0.096748</td>\n",
       "      <td> 0.220042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE_95+</th>\n",
       "      <td> 0.058705</td>\n",
       "      <td> 0.159788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_0-4</th>\n",
       "      <td>-0.067714</td>\n",
       "      <td>-0.008250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_5-9</th>\n",
       "      <td>-0.035407</td>\n",
       "      <td> 0.028219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_10-14</th>\n",
       "      <td> 0.001161</td>\n",
       "      <td> 0.079902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_15-19</th>\n",
       "      <td>-0.088268</td>\n",
       "      <td> 0.052387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_20-24</th>\n",
       "      <td>-0.174609</td>\n",
       "      <td>-0.035597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_25-29</th>\n",
       "      <td>-0.216370</td>\n",
       "      <td>-0.076812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_30-34</th>\n",
       "      <td>-0.194151</td>\n",
       "      <td>-0.059565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_35-39</th>\n",
       "      <td>-0.142253</td>\n",
       "      <td> 0.010216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_40-44</th>\n",
       "      <td>-0.040853</td>\n",
       "      <td> 0.117015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_45-49</th>\n",
       "      <td> 0.013193</td>\n",
       "      <td> 0.170514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_50-54</th>\n",
       "      <td> 0.041958</td>\n",
       "      <td> 0.201762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_55-59</th>\n",
       "      <td> 0.071028</td>\n",
       "      <td> 0.218532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_60-64</th>\n",
       "      <td> 0.096553</td>\n",
       "      <td> 0.241443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_65-69</th>\n",
       "      <td> 0.123505</td>\n",
       "      <td> 0.262332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_70-74</th>\n",
       "      <td> 0.124026</td>\n",
       "      <td> 0.255349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_75-79</th>\n",
       "      <td> 0.124224</td>\n",
       "      <td> 0.247156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_80-84</th>\n",
       "      <td> 0.121783</td>\n",
       "      <td> 0.243228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_85-89</th>\n",
       "      <td> 0.112887</td>\n",
       "      <td> 0.237975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_90-94</th>\n",
       "      <td> 0.109633</td>\n",
       "      <td> 0.234231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEMALE_95+</th>\n",
       "      <td> 0.091596</td>\n",
       "      <td> 0.206937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 daily prescribtions per patient  \\\n",
       "GP Prescribtions                                        0.261711   \n",
       "TOTAL_ALL                                              -0.057380   \n",
       "days                                                         NaN   \n",
       "daily_prescribtions                                     0.261711   \n",
       "daily prescribtions per patient                         1.000000   \n",
       "Patients rating of GP                                   0.175222   \n",
       "Patients rating of GP (zscore)                          0.175222   \n",
       "TOTAL_MALE                                             -0.066636   \n",
       "TOTAL_FEMALES                                          -0.048176   \n",
       "MALE_0-4                                               -0.067677   \n",
       "MALE_5-9                                               -0.031511   \n",
       "MALE_10-14                                              0.002225   \n",
       "MALE_15-19                                             -0.059452   \n",
       "MALE_20-24                                             -0.155138   \n",
       "MALE_25-29                                             -0.214243   \n",
       "MALE_30-34                                             -0.226196   \n",
       "MALE_35-39                                             -0.198606   \n",
       "MALE_40-44                                             -0.099941   \n",
       "MALE_45-49                                             -0.027965   \n",
       "MALE_50-54                                              0.013810   \n",
       "MALE_55-59                                              0.050182   \n",
       "MALE_60-64                                              0.089251   \n",
       "MALE_65-69                                              0.120368   \n",
       "MALE_70-74                                              0.128493   \n",
       "MALE_75-79                                              0.127445   \n",
       "MALE_80-84                                              0.116396   \n",
       "MALE_85-89                                              0.107696   \n",
       "MALE_90-94                                              0.096748   \n",
       "MALE_95+                                                0.058705   \n",
       "FEMALE_0-4                                             -0.067714   \n",
       "FEMALE_5-9                                             -0.035407   \n",
       "FEMALE_10-14                                            0.001161   \n",
       "FEMALE_15-19                                           -0.088268   \n",
       "FEMALE_20-24                                           -0.174609   \n",
       "FEMALE_25-29                                           -0.216370   \n",
       "FEMALE_30-34                                           -0.194151   \n",
       "FEMALE_35-39                                           -0.142253   \n",
       "FEMALE_40-44                                           -0.040853   \n",
       "FEMALE_45-49                                            0.013193   \n",
       "FEMALE_50-54                                            0.041958   \n",
       "FEMALE_55-59                                            0.071028   \n",
       "FEMALE_60-64                                            0.096553   \n",
       "FEMALE_65-69                                            0.123505   \n",
       "FEMALE_70-74                                            0.124026   \n",
       "FEMALE_75-79                                            0.124224   \n",
       "FEMALE_80-84                                            0.121783   \n",
       "FEMALE_85-89                                            0.112887   \n",
       "FEMALE_90-94                                            0.109633   \n",
       "FEMALE_95+                                              0.091596   \n",
       "\n",
       "                                 Patients rating of GP  \n",
       "GP Prescribtions                              0.166301  \n",
       "TOTAL_ALL                                     0.098007  \n",
       "days                                               NaN  \n",
       "daily_prescribtions                           0.166301  \n",
       "daily prescribtions per patient               0.175222  \n",
       "Patients rating of GP                         1.000000  \n",
       "Patients rating of GP (zscore)                1.000000  \n",
       "TOTAL_MALE                                    0.082793  \n",
       "TOTAL_FEMALES                                 0.112274  \n",
       "MALE_0-4                                     -0.004196  \n",
       "MALE_5-9                                      0.032438  \n",
       "MALE_10-14                                    0.081210  \n",
       "MALE_15-19                                    0.065235  \n",
       "MALE_20-24                                   -0.028972  \n",
       "MALE_25-29                                   -0.087736  \n",
       "MALE_30-34                                   -0.102066  \n",
       "MALE_35-39                                   -0.058608  \n",
       "MALE_40-44                                    0.050391  \n",
       "MALE_45-49                                    0.123504  \n",
       "MALE_50-54                                    0.167534  \n",
       "MALE_55-59                                    0.190046  \n",
       "MALE_60-64                                    0.225987  \n",
       "MALE_65-69                                    0.257728  \n",
       "MALE_70-74                                    0.250704  \n",
       "MALE_75-79                                    0.245526  \n",
       "MALE_80-84                                    0.238049  \n",
       "MALE_85-89                                    0.236945  \n",
       "MALE_90-94                                    0.220042  \n",
       "MALE_95+                                      0.159788  \n",
       "FEMALE_0-4                                   -0.008250  \n",
       "FEMALE_5-9                                    0.028219  \n",
       "FEMALE_10-14                                  0.079902  \n",
       "FEMALE_15-19                                  0.052387  \n",
       "FEMALE_20-24                                 -0.035597  \n",
       "FEMALE_25-29                                 -0.076812  \n",
       "FEMALE_30-34                                 -0.059565  \n",
       "FEMALE_35-39                                  0.010216  \n",
       "FEMALE_40-44                                  0.117015  \n",
       "FEMALE_45-49                                  0.170514  \n",
       "FEMALE_50-54                                  0.201762  \n",
       "FEMALE_55-59                                  0.218532  \n",
       "FEMALE_60-64                                  0.241443  \n",
       "FEMALE_65-69                                  0.262332  \n",
       "FEMALE_70-74                                  0.255349  \n",
       "FEMALE_75-79                                  0.247156  \n",
       "FEMALE_80-84                                  0.243228  \n",
       "FEMALE_85-89                                  0.237975  \n",
       "FEMALE_90-94                                  0.234231  \n",
       "FEMALE_95+                                    0.206937  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_df = pd.read_csv(FILE_PRACTICE, index_col='GP_PRACTICE_CODE')\n",
    "\n",
    "# \n",
    "_df = _df.select(lambda x: (\"MALE_\" in x) | (\"TOTAL_MALE\" in x)| (\"TOTAL_FEMALE\" in x), 1)\n",
    "_df = df_ht.join(_df, how='inner')\n",
    "_df.corr()[['daily prescribtions per patient', 'Patients rating of GP']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare last x months of PCA and Prescribtions\n",
    "---\n",
    "Data quality test. Last 12 months on record compared between:\n",
    "- __Prescription Cost Analysis Data__\n",
    "and\n",
    "- __GP Practice Prescribing Presentation level Data__"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# load and aggr\n",
    "_df = pd.read_csv(FILE_PRESCRIBTIONS_TS, index_col=\"PRACTICE\")\n",
    "_df = _df.groupby(\"group\")[TIME].sum()\n",
    "df_prescribtins_gr = pd.DataFrame(_df.sum(1), columns=['GP Prescribtions'])\n",
    "\n",
    "_df = pd.read_csv(FILE_ANTIBIOTICS_TS, index_col=None)\n",
    "_df = _df.groupby(\"group\")[TIME].sum()\n",
    "df_pca_gr = pd.DataFrame(_df.sum(1), columns=['PCA'])\n",
    "\n",
    "df_joined = df_prescribtins_gr.join(df_pca_gr)\n",
    "\n",
    "title_ = \"PCA and GP Prescribtions %s - %s\" % (TIME[0], TIME[-1])\n",
    "\n",
    "\n",
    "_dfp = df_joined.sort(columns=['PCA'], ascending=True)\n",
    "\n",
    "_dfp.plot( kind='barh', \n",
    "           figsize=[15,12], \n",
    "           title=title_,\n",
    "           legend=True)\n",
    "# save\n",
    "savefig(\"../visualizations/\"+title_+'.png')\n",
    "_dfp.to_csv(\"../visualizations/\"+title_+'.csv')\n",
    "\n",
    "show()"
   ]
  }
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