nhs-generic-branded-time-series.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NHS Prescription Costs Trend\n",
    "This is an analysis of the prescription data totals for the UK as a whole. Showing the total costs of prescription drugs and the proportion of that cost that is down to generic drugs.\n",
    "Data.gov open datasets [GP practice prescribing data - Presentation level](https://data.gov.uk/dataset/176ae264-2484-4afe-a297-d51798eb8228/gp-practice-prescribing-data-presentation-level)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Package Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import os\n",
    "import gc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find data files\n",
    "Finds all of the Practice prescribing data csv files that match the patter of the Practice prescribing data. The data can be downloaded and extracted using the included bash script.\n",
    "```shell\n",
    "#!/bin/bash\n",
    "downloads=`cat datasets.txt`\n",
    "mkdir -p ./zips\n",
    "mkdir -p ./extracted\n",
    "for url in $downloads;\n",
    "do\n",
    "    filename=`basename ${url}`\n",
    "    echo $url\n",
    "    echo $filename\n",
    "    dir_name=`basename $filename .zip`\n",
    "    full_dir=./extracted/$dir_name\n",
    "    mkdir -p $full_dir\n",
    "    curl $url > ./zips/$filename\n",
    "    unzip ./zips/$filename -d $full_dir\n",
    "done\n",
    "```\n",
    "\n",
    "datasets.txt is a list of links to the monthly data zip files.\n",
    "```\n",
    "https://files.digital.nhs.uk/94/405A94/2018_12_Dec.zip\n",
    "https://files.digital.nhs.uk/96/A7878A/2018_11_Nov.zip\n",
    "https://files.digital.nhs.uk/33/3EE982/2018_10_Oct.zip\n",
    "https://files.digital.nhs.uk/5C/FE61C4/2018_09_Sep.zip\n",
    "https://files.digital.nhs.uk/43/C6644B/2018_08_Aug.zip\n",
    "https://files.digital.nhs.uk/7E/FC3950/2018_07_Jul.zip\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "datafile_match = re.compile(r\"\\w\\d{6}PDPI\\sBNFT\\.CSV\")\n",
    "datafiles = []\n",
    "for root, dirs, files in os.walk(\"extracted\"):\n",
    "    for f in files:\n",
    "        if datafile_match.match(f.upper()):\n",
    "            datafiles.append(os.path.join(root,f))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process data files and create summary\n",
    "This is the processing section that reads all of the large CSV files in one at a time. Each one is ~10 million rows, so each one takes a while to process. Once they've been summarised the data frames are deleted to make way for the next one.\n",
    "\n",
    "The processing that occurs for each one is:\n",
    "* The column headers are cleaned up with leading and trailing spaces removed.\n",
    "* The first item in the period column is used to represent the time period of the entire file.\n",
    "* The sum of the  **actual cost** column (ACT COST) is calculated.\n",
    "* BNF Codes that matched the Generic code of 9 characters then AA, then the rest of the BNF code are filtered into a new data frame that now lists just the Generic drug entries.\n",
    "* The sum of the **actual cost** column (ACT COST) is calculated of the generic drugs.\n",
    "\n",
    "The summary is then stored in a new data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_totals = {\"PERIOD\":[],\"TOTAL\":[],\"GENERIC\":[]}\n",
    "for datafile in datafiles:\n",
    "    df = pd.read_csv(datafile, sep=\",\")\n",
    "    df.columns = [col.strip() for col in df.columns]\n",
    "    if 'PERIOD' in df and 'ACT COST' in df and 'BNF CODE' in df:\n",
    "        period = str(df['PERIOD'].iloc[0])\n",
    "        total = df['ACT COST'].sum()\n",
    "        df_generics = df[df['BNF CODE'].str.match(r\"^\\S{9}AA\")]\n",
    "        generics = df_generics['ACT COST'].sum()\n",
    "        monthly_totals[\"PERIOD\"].append(period)\n",
    "        monthly_totals[\"TOTAL\"].append(total)\n",
    "        monthly_totals[\"GENERIC\"].append(generics)\n",
    "    del df\n",
    "    gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data formatting and getting to the correct type\n",
    "The Summary data frame is now formatted with Period as a Date field and the total and generic fields scaled down to represent millions of pounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>GENERIC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>673.701184</td>\n",
       "      <td>380.864733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>629.058403</td>\n",
       "      <td>354.189755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>631.344204</td>\n",
       "      <td>352.338049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>700.264801</td>\n",
       "      <td>391.281398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>727.831844</td>\n",
       "      <td>413.664641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>707.524407</td>\n",
       "      <td>403.305150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>689.886489</td>\n",
       "      <td>387.583875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>702.632823</td>\n",
       "      <td>397.659786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>731.830218</td>\n",
       "      <td>416.085633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>708.005873</td>\n",
       "      <td>394.951544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>682.419410</td>\n",
       "      <td>378.779709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>611.150095</td>\n",
       "      <td>332.892590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>669.411818</td>\n",
       "      <td>365.634872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>643.187390</td>\n",
       "      <td>347.844577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>675.472315</td>\n",
       "      <td>368.418208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>666.803479</td>\n",
       "      <td>362.565697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>659.840430</td>\n",
       "      <td>351.500518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>679.986077</td>\n",
       "      <td>370.353950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-09-01</td>\n",
       "      <td>655.082188</td>\n",
       "      <td>348.591503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>719.904213</td>\n",
       "      <td>373.551269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018-11-01</td>\n",
       "      <td>692.682587</td>\n",
       "      <td>358.100786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>669.858940</td>\n",
       "      <td>359.004576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PERIOD       TOTAL     GENERIC\n",
       "0  2017-01-01  673.701184  380.864733\n",
       "1  2017-02-01  629.058403  354.189755\n",
       "2  2017-04-01  631.344204  352.338049\n",
       "3  2017-05-01  700.264801  391.281398\n",
       "4  2017-06-01  727.831844  413.664641\n",
       "5  2017-07-01  707.524407  403.305150\n",
       "6  2017-08-01  689.886489  387.583875\n",
       "7  2017-09-01  702.632823  397.659786\n",
       "8  2017-10-01  731.830218  416.085633\n",
       "9  2017-11-01  708.005873  394.951544\n",
       "10 2017-12-01  682.419410  378.779709\n",
       "11 2018-02-01  611.150095  332.892590\n",
       "12 2018-03-01  669.411818  365.634872\n",
       "13 2018-04-01  643.187390  347.844577\n",
       "14 2018-05-01  675.472315  368.418208\n",
       "15 2018-06-01  666.803479  362.565697\n",
       "16 2018-07-01  659.840430  351.500518\n",
       "17 2018-08-01  679.986077  370.353950\n",
       "18 2018-09-01  655.082188  348.591503\n",
       "19 2018-10-01  719.904213  373.551269\n",
       "20 2018-11-01  692.682587  358.100786\n",
       "21 2018-12-01  669.858940  359.004576"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_trends=pd.DataFrame(data=monthly_totals)\n",
    "time_trends['PERIOD'] = pd.to_datetime(time_trends['PERIOD'], format='%Y%m')\n",
    "time_trends['TOTAL'] = time_trends['TOTAL']/1000000\n",
    "time_trends['GENERIC'] = time_trends['GENERIC']/1000000\n",
    "time_trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '£ in Millions')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = time_trends.plot.area(x='PERIOD', ylim=(0,1000), stacked=False, title=\"National prescription costs by month\")\n",
    "ax.set_xlabel(\"Month\")\n",
    "ax.set_ylabel(\"£ in Millions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}