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drcjar/StatinAnalysis

Last active August 29, 2015 14:07
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StatinAnalysis
{
"metadata": {
"name": "",
"signature": "sha256:04b30ba320a353b89132e56d777ef9e8c15306435f116f4cad53b3b7ece4ccfb"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Statin prescribing in England"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Data sources:\n",
"http://www.hscic.gov.uk/gpprescribingdata - all the presentation prescribing data from here\n",
"http://www.hscic.gov.uk/media/10686/Download-glossary-of-terms-for-GP-prescribing---presentation-level/pdf/GP_Prescribing_Presentation_Level_Glossary_of_Terms.pdf\n",
"http://www.connectingforhealth.nhs.uk/systemsandservices/data/ods/ccginterim/interimpcmem_v5.zip - gp to ccg mapping here\n",
"http://www.erpho.org.uk/viewResource.aspx?id=22125 - old ccg to new ccg code mapping from here\n",
"http://www.ons.gov.uk/ons/publications/re-reference-tables.html?edition=tcm%3A77-325526 - ccg population data from here (2012)\n",
"https://geoportal.statistics.gov.uk/Docs/Boundaries/Clinical_commissioning_groups_(Eng)_Apr_2013_Boundaries_(Full_Extent).zip - maps\n",
"\n",
"\n",
"Notes:\n",
"Prescribing data is GP level and there are two types of CCG code (old and new) so this requires \n",
"1. obtaining CCG population data \n",
"2. obtaining mapping of old to new CCG codes \n",
"3. obtaining GP practice to CCG mapping\n",
"\n",
"NB: There does not appear to be an authorative list of GP practices. There are other data quality issues that should be written up another time."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import os\n",
"import datetime\n",
"import pandas as pd\n",
"import pickle\n",
"import folium\n",
"from pandas import Series, DataFrame, Panel\n",
"import matplotlib.dates as mdates\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Prepare our prescribing data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#cuts gp prescribing data for rows containing patterns of interest as dataframes\n",
"\n",
"def pattern_timeseries(pattern):\n",
" \n",
" pathtodata = '/media/mydisk/prescribing_data/bnf/'#set to the directory with bnf CSVs in\n",
" pathtopatterns = '/media/mydisk/prescribing_data/patterns/' \n",
" pathtopickles = '/media/mydisk/prescribing_data/pickles/'\n",
" \n",
" os.chdir(pathtodata)\n",
" \n",
" files = !ls T2*\n",
" \n",
" global pattern_df\n",
" \n",
" clean_filenames = []\n",
"\n",
" for f in files.l:\n",
" clean_filenames.append(f[:7]) #clean up filenames so that grep will work\n",
" \n",
" for i, item in enumerate(clean_filenames):\n",
" date = clean_filenames[i].replace('T', '')\n",
" date = pd.to_datetime(date, format=\"%Y%m\") #make pandas know the date is a date\n",
" name = \"bnf_%s_%.10s.csv\" % (pattern, date) #create a name for new csv of grep for pattern using date from file name\n",
" print \"writing %s\" % name\n",
" !fgrep $pattern {clean_filenames[i]}* > $pathtopatterns/$name #grep for pattern in csv files and write to file \n",
" \n",
" pattern_files = !ls $pathtopatterns*$pattern*\n",
" \n",
" cols = ['SHA', 'PCT', 'PRACTICE', 'BNF_CODE', 'BNF_NAME', 'ITEMS', 'NIC', 'ACT_COST', 'QUANTITY', 'DateTime', 'Index']\n",
" \n",
" practices_est = 10000 #estimated number of practices\n",
" \n",
" df_list = [pd.read_csv(file, names=cols) for file in pattern_files] \n",
" \n",
" pattern_df = pd.concat(df_list)\n",
" \n",
" pattern_df['DateTime'] = pattern_df['DateTime'].astype('|S6') \n",
" pattern_df['DateTime'] = pd.to_datetime(pattern_df['DateTime'], format=\"%Y%m\")\n",
" \n",
" os.chdir(pathtopickles)\n",
"\n",
" pattern_df.to_pickle('%s.pkl' % pattern)\n",
" \n",
" return(pattern_df)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#define our statins\n",
"Statins = ['Atorvastatin', 'Fluvastatin', 'Pravastatin', 'Rosuvastatin', 'Simvastatin']\n",
"#http://www.nhs.uk/conditions/Cholesterol-lowering-medicines-statins/Pages/Introduction.aspx\n",
"\n",
"#commented because have run to Feb 2014\n",
"for statin in Statins:\n",
" pattern_timeseries(statin)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2010-08-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2010-09-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2010-10-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2010-11-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2010-12-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2011-01-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2011-02-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2011-03-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2011-04-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2011-05-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2011-06-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2011-07-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2011-08-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2011-09-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2011-10-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2011-11-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2011-12-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2012-01-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2012-02-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2012-03-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2012-04-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2012-05-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2012-06-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2012-07-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2012-08-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2012-09-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2012-10-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2012-11-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2012-12-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2013-01-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2013-02-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2013-03-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2013-04-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2013-05-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2013-06-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2013-07-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2013-08-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2013-09-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2013-10-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2013-11-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2013-12-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2014-01-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2014-02-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2014-03-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2014-04-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2014-05-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Atorvastatin_2014-06-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2010-08-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2010-09-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2010-10-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2010-11-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2010-12-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2011-01-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2011-02-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2011-03-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2011-04-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2011-05-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2011-06-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2011-07-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2011-08-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Fluvastatin_2011-09-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Fluvastatin_2011-10-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Fluvastatin_2011-11-01.csv\n"
]
},
{
"output_type": "stream",
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"writing bnf_Fluvastatin_2011-12-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2012-01-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2012-02-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2012-03-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2012-04-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2012-05-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2012-06-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2012-07-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2012-08-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2012-09-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2012-10-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2012-11-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2012-12-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2013-01-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2013-02-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2013-03-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2013-04-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2013-05-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2013-06-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2013-07-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2013-08-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2013-09-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2013-10-01.csv\n"
]
},
{
"output_type": "stream",
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"writing bnf_Fluvastatin_2013-11-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2013-12-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Fluvastatin_2014-01-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Fluvastatin_2014-02-01.csv\n"
]
},
{
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"text": [
"writing bnf_Fluvastatin_2014-03-01.csv\n"
]
},
{
"output_type": "stream",
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"writing bnf_Fluvastatin_2014-04-01.csv\n"
]
},
{
"output_type": "stream",
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"writing bnf_Fluvastatin_2014-05-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Fluvastatin_2014-06-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Pravastatin_2010-08-01.csv\n"
]
},
{
"output_type": "stream",
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"writing bnf_Pravastatin_2010-09-01.csv\n"
]
},
{
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"writing bnf_Pravastatin_2010-10-01.csv\n"
]
},
{
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"writing bnf_Pravastatin_2010-11-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Pravastatin_2010-12-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Pravastatin_2011-01-01.csv\n"
]
},
{
"output_type": "stream",
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"writing bnf_Pravastatin_2011-02-01.csv\n"
]
},
{
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"writing bnf_Pravastatin_2011-03-01.csv\n"
]
},
{
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"writing bnf_Pravastatin_2011-04-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Pravastatin_2011-05-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Pravastatin_2011-06-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Pravastatin_2011-07-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Pravastatin_2011-08-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Pravastatin_2011-09-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Pravastatin_2011-10-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Pravastatin_2011-11-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Pravastatin_2011-12-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Pravastatin_2012-01-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Pravastatin_2012-02-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Pravastatin_2012-03-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Pravastatin_2012-04-01.csv\n"
]
},
{
"output_type": "stream",
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"writing bnf_Pravastatin_2012-05-01.csv\n"
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},
{
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"writing bnf_Pravastatin_2012-06-01.csv\n"
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},
{
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"writing bnf_Pravastatin_2012-07-01.csv\n"
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},
{
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"text": [
"writing bnf_Pravastatin_2012-08-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Pravastatin_2012-09-01.csv\n"
]
},
{
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"writing bnf_Pravastatin_2012-10-01.csv\n"
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},
{
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"writing bnf_Pravastatin_2012-11-01.csv\n"
]
},
{
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"text": [
"writing bnf_Pravastatin_2012-12-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Pravastatin_2013-01-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Pravastatin_2013-02-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Pravastatin_2013-03-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Pravastatin_2013-04-01.csv\n"
]
},
{
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"writing bnf_Pravastatin_2013-05-01.csv\n"
]
},
{
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"writing bnf_Pravastatin_2013-06-01.csv\n"
]
},
{
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"writing bnf_Pravastatin_2013-07-01.csv\n"
]
},
{
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"writing bnf_Pravastatin_2013-08-01.csv\n"
]
},
{
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"writing bnf_Pravastatin_2013-09-01.csv\n"
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},
{
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"writing bnf_Pravastatin_2013-10-01.csv\n"
]
},
{
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"writing bnf_Pravastatin_2013-11-01.csv\n"
]
},
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"writing bnf_Pravastatin_2013-12-01.csv\n"
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},
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"writing bnf_Pravastatin_2014-01-01.csv\n"
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},
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"writing bnf_Pravastatin_2014-02-01.csv\n"
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},
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"writing bnf_Pravastatin_2014-03-01.csv\n"
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},
{
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"writing bnf_Pravastatin_2014-04-01.csv\n"
]
},
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"writing bnf_Pravastatin_2014-05-01.csv\n"
]
},
{
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"text": [
"writing bnf_Pravastatin_2014-06-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Rosuvastatin_2010-08-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Rosuvastatin_2010-09-01.csv\n"
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},
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"writing bnf_Rosuvastatin_2010-10-01.csv\n"
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"writing bnf_Rosuvastatin_2010-11-01.csv\n"
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},
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"writing bnf_Rosuvastatin_2010-12-01.csv\n"
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"writing bnf_Rosuvastatin_2011-01-01.csv\n"
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"writing bnf_Rosuvastatin_2011-07-01.csv\n"
]
},
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"writing bnf_Rosuvastatin_2011-08-01.csv\n"
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"writing bnf_Rosuvastatin_2012-03-01.csv\n"
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"writing bnf_Rosuvastatin_2012-04-01.csv\n"
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"writing bnf_Rosuvastatin_2012-05-01.csv\n"
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"writing bnf_Rosuvastatin_2012-06-01.csv\n"
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"writing bnf_Rosuvastatin_2012-07-01.csv\n"
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"writing bnf_Rosuvastatin_2013-04-01.csv\n"
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"writing bnf_Rosuvastatin_2013-05-01.csv\n"
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"writing bnf_Rosuvastatin_2013-07-01.csv\n"
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"writing bnf_Rosuvastatin_2013-11-01.csv\n"
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{
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"writing bnf_Rosuvastatin_2013-12-01.csv\n"
]
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"writing bnf_Rosuvastatin_2014-01-01.csv\n"
]
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"writing bnf_Rosuvastatin_2014-02-01.csv\n"
]
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"writing bnf_Rosuvastatin_2014-03-01.csv\n"
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"writing bnf_Rosuvastatin_2014-04-01.csv\n"
]
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{
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"writing bnf_Rosuvastatin_2014-05-01.csv\n"
]
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{
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"writing bnf_Rosuvastatin_2014-06-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Simvastatin_2010-08-01.csv\n"
]
},
{
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"writing bnf_Simvastatin_2010-09-01.csv\n"
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]
},
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"writing bnf_Simvastatin_2011-11-01.csv\n"
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"writing bnf_Simvastatin_2011-12-01.csv\n"
]
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"writing bnf_Simvastatin_2012-01-01.csv\n"
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"writing bnf_Simvastatin_2012-02-01.csv\n"
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"writing bnf_Simvastatin_2012-04-01.csv\n"
]
},
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"writing bnf_Simvastatin_2012-05-01.csv\n"
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"writing bnf_Simvastatin_2012-06-01.csv\n"
]
},
{
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"text": [
"writing bnf_Simvastatin_2012-07-01.csv\n"
]
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{
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"writing bnf_Simvastatin_2012-08-01.csv\n"
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{
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"writing bnf_Simvastatin_2012-09-01.csv\n"
]
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]
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"writing bnf_Simvastatin_2012-11-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Simvastatin_2012-12-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Simvastatin_2013-01-01.csv\n"
]
},
{
"output_type": "stream",
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"text": [
"writing bnf_Simvastatin_2013-02-01.csv\n"
]
},
{
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"text": [
"writing bnf_Simvastatin_2013-03-01.csv\n"
]
},
{
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"text": [
"writing bnf_Simvastatin_2013-04-01.csv\n"
]
},
{
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"text": [
"writing bnf_Simvastatin_2013-05-01.csv\n"
]
},
{
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"text": [
"writing bnf_Simvastatin_2013-06-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Simvastatin_2013-07-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Simvastatin_2013-08-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Simvastatin_2013-09-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Simvastatin_2013-10-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Simvastatin_2013-11-01.csv\n"
]
},
{
"output_type": "stream",
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"writing bnf_Simvastatin_2013-12-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Simvastatin_2014-01-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Simvastatin_2014-02-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Simvastatin_2014-03-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Simvastatin_2014-04-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Simvastatin_2014-05-01.csv\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"writing bnf_Simvastatin_2014-06-01.csv\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#load prev outputs from above\n",
"pathtopickles = '/media/mydisk/prescribing_data/pickles/'\n",
"os.chdir(pathtopickles)\n",
"\n",
"#load dataframes\n",
"Atorvastatin = pd.read_pickle('Atorvastatin.pkl')\n",
"Fluvastatin = pd.read_pickle('Fluvastatin.pkl')\n",
"Pravastatin = pd.read_pickle('Pravastatin.pkl')\n",
"Rosuvastatin = pd.read_pickle('Rosuvastatin.pkl')\n",
"Simvastatin = pd.read_pickle('Simvastatin.pkl')\n",
"\n",
"#make a list of dataframes\n",
"df_list = [Atorvastatin, Fluvastatin, Pravastatin, Rosuvastatin, Simvastatin]\n",
"statin_lookup = {0:'Atorvastatin', 1:'Fluvastatin', 2:'Pravastatin', 3:'Rosuvastatin', 4:'Simvastatin'}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i, item in enumerate(statin_lookup):\n",
" df_list[i]['Drug'] = statin_lookup[i]\n",
"\n",
"df = pd.concat(df_list)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Analysing the data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.index = df.DateTime"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>SHA</th>\n",
" <th>PCT</th>\n",
" <th>PRACTICE</th>\n",
" <th>BNF_CODE</th>\n",
" <th>BNF_NAME</th>\n",
" <th>ITEMS</th>\n",
" <th>NIC</th>\n",
" <th>ACT_COST</th>\n",
" <th>QUANTITY</th>\n",
" <th>DateTime</th>\n",
" <th>Index</th>\n",
" <th>Drug</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DateTime</th>\n",
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" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-08-01</th>\n",
" <td> Q30</td>\n",
" <td> 5D7</td>\n",
" <td> A86003</td>\n",
" <td> 0212000B0AAAAAA</td>\n",
" <td> Atorvastatin_Tab 10mg </td>\n",
" <td> 70</td>\n",
" <td> 653.25</td>\n",
" <td> 603.35</td>\n",
" <td> 1407</td>\n",
" <td>2010-08-01</td>\n",
" <td> </td>\n",
" <td> Atorvastatin</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-08-01</th>\n",
" <td> Q30</td>\n",
" <td> 5D7</td>\n",
" <td> A86003</td>\n",
" <td> 0212000B0AAABAB</td>\n",
" <td> Atorvastatin_Tab 20mg </td>\n",
" <td> 75</td>\n",
" <td> 1595.44</td>\n",
" <td> 1470.54</td>\n",
" <td> 1813</td>\n",
" <td>2010-08-01</td>\n",
" <td> </td>\n",
" <td> Atorvastatin</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-08-01</th>\n",
" <td> Q30</td>\n",
" <td> 5D7</td>\n",
" <td> A86003</td>\n",
" <td> 0212000B0AAACAC</td>\n",
" <td> Atorvastatin_Tab 40mg </td>\n",
" <td> 208</td>\n",
" <td> 4422.88</td>\n",
" <td> 4076.64</td>\n",
" <td> 5026</td>\n",
" <td>2010-08-01</td>\n",
" <td> </td>\n",
" <td> Atorvastatin</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-08-01</th>\n",
" <td> Q30</td>\n",
" <td> 5D7</td>\n",
" <td> A86003</td>\n",
" <td> 0212000B0AAADAD</td>\n",
" <td> Atorvastatin_Tab 80mg </td>\n",
" <td> 91</td>\n",
" <td> 2157.95</td>\n",
" <td> 1988.67</td>\n",
" <td> 2142</td>\n",
" <td>2010-08-01</td>\n",
" <td> </td>\n",
" <td> Atorvastatin</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-08-01</th>\n",
" <td> Q30</td>\n",
" <td> 5D7</td>\n",
" <td> A86004</td>\n",
" <td> 0212000B0AAAAAA</td>\n",
" <td> Atorvastatin_Tab 10mg </td>\n",
" <td> 50</td>\n",
" <td> 850.11</td>\n",
" <td> 783.86</td>\n",
" <td> 1831</td>\n",
" <td>2010-08-01</td>\n",
" <td> </td>\n",
" <td> Atorvastatin</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
" SHA PCT PRACTICE BNF_CODE \\\n",
"DateTime \n",
"2010-08-01 Q30 5D7 A86003 0212000B0AAAAAA \n",
"2010-08-01 Q30 5D7 A86003 0212000B0AAABAB \n",
"2010-08-01 Q30 5D7 A86003 0212000B0AAACAC \n",
"2010-08-01 Q30 5D7 A86003 0212000B0AAADAD \n",
"2010-08-01 Q30 5D7 A86004 0212000B0AAAAAA \n",
"\n",
" BNF_NAME ITEMS NIC \\\n",
"DateTime \n",
"2010-08-01 Atorvastatin_Tab 10mg 70 653.25 \n",
"2010-08-01 Atorvastatin_Tab 20mg 75 1595.44 \n",
"2010-08-01 Atorvastatin_Tab 40mg 208 4422.88 \n",
"2010-08-01 Atorvastatin_Tab 80mg 91 2157.95 \n",
"2010-08-01 Atorvastatin_Tab 10mg 50 850.11 \n",
"\n",
" ACT_COST QUANTITY DateTime Index Drug \n",
"DateTime \n",
"2010-08-01 603.35 1407 2010-08-01 Atorvastatin \n",
"2010-08-01 1470.54 1813 2010-08-01 Atorvastatin \n",
"2010-08-01 4076.64 5026 2010-08-01 Atorvastatin \n",
"2010-08-01 1988.67 2142 2010-08-01 Atorvastatin \n",
"2010-08-01 783.86 1831 2010-08-01 Atorvastatin "
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#timeseries of statins with standardised y scale\n",
"for drug in Statins:\n",
" print drug\n",
" df[df['Drug'] == drug].groupby('DateTime')['ITEMS'].sum().plot(ylim(0, 3800000))\n",
" plt.show()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Atorvastatin\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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wZPA5cFGV/bgq01Xnq5uO+xIbKJ9E5ZjKjcCqOr+diIiIiIiIiIiI\niIiIiIiIiIiIiIiIiIiIiIiIiGTW/wHcPGGIOsRrRgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x3aeca50>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Fluvastatin\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x3ad10490>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pravastatin\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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KCksaBw/GQwbRdPx4/PpJk2xeVRVfcldW2jY6G9dubbXkEiWQQ4fiq4JBgzp+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\n",
"text": [
"<matplotlib.figure.Figure at 0xfd3d790>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Rosuvastatin\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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8D2wG7ihxX3IS0v3WoVFs/VFs/QopvqEmjJOAX2JJYzZwPXBWSXuUg9bW1lJ3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\nIiIiIiIiIqX1/wEvRizoorbxuQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x129496d0>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Simvastatin\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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U23BFKb5NDUkdBwwE3gLe9GXXYj2EEuzk/TFwqX/vXawH8C6WjxhK7TDSUGxa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\n",
"text": [
"<matplotlib.figure.Figure at 0x84662d0>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#timeseries of statins with non-standardised y scale\n",
"\n",
"for drug in Statins:\n",
" print drug\n",
" df[df['Drug'] == drug].groupby('DateTime')['ITEMS'].sum().plot()\n",
" plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Atorvastatin\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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sM9izB84/P9s1kXymHIZIHvjHP+CLL2D48Kq3FYlFOQyRPDd9OlxSfsY3kTRT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\n2/611yo+MzqaP/0JHn3UutS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"text": [
"<matplotlib.figure.Figure at 0x9262c10>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Fluvastatin\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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R9nclslzgYKTn77TrJRQlIdT5K82RvoijX4do/6uBM4GW\nwPYc112AjBdEYYf93YCkTMa138puz3PA6RHrV5RQaEpnpbmxD5Je15Fk2iM9cYCzkAcAiFTUznXd\nM8D5pDtMA5D1WrMRJiOmhYwxHAEcYJftTfotRFFiR3v+SnOgDTJwuweSZvse4Gb72G3IQh1nIcsW\nbrXLX0N647WInv9HRJ75L+LYPwJOzriPlbGdue+17bAeGSSeTnoM4jKgzvfXKYqiKIqiKIqiKIqi\nKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIpiFv8fzkmTHHHOM+cAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x5e4f450>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Pravastatin\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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IBOm+oKKgZITvvpN5COedF/uYbt3gm29g507n53USZLaff9kySQm9d29il1Mk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\nRVEURVEURVEURVEURVEURVEUJdP8P+Fw4rd072YJAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xcfe4bd0>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Rosuvastatin\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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eHowfX8gtt0i33kjWrBH3jpO1G6pVkxHoVg+kdesSxxMsUnEhrVkDo0dD//6J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\nAKWI7/9W4D1EJIqQ4PBJtvMfRhRoDRI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"text": [
"<matplotlib.figure.Figure at 0x74c3990>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Simvastatin\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Sk8Sf/uSdv8GLZ5+FCy7wfy0rMDJpAAsXyvpRbdr4b9eN3/zefs1R0PRtPheB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\n8v8BHmfZ9q4AXu4AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x8cdf390>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df[df['Drug'] == 'Simvastatin'].groupby(['SHA', 'DateTime']).ITEMS.sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"SHA DateTime \n",
"Q30 2010-08-01 222616\n",
" 2010-09-01 235678\n",
" 2010-10-01 222602\n",
" 2010-11-01 229921\n",
" 2010-12-01 251432\n",
" 2011-01-01 218033\n",
" 2011-02-01 212456\n",
" 2011-03-01 246004\n",
" 2011-04-01 222552\n",
" 2011-05-01 230218\n",
" 2011-06-01 238908\n",
" 2011-07-01 230527\n",
" 2011-08-01 239092\n",
" 2011-09-01 243873\n",
" 2011-10-01 226916\n",
"...\n",
"Q70 2013-04-01 129714\n",
" 2013-05-01 133406\n",
" 2013-06-01 122262\n",
" 2013-07-01 131550\n",
" 2013-08-01 139189\n",
" 2013-09-01 131041\n",
" 2013-10-01 139018\n",
" 2013-11-01 134873\n",
" 2013-12-01 138618\n",
" 2014-01-01 136853\n",
" 2014-02-01 123261\n",
" 2014-03-01 132240\n",
" 2014-04-01 130689\n",
" 2014-05-01 136509\n",
" 2014-06-01 126256\n",
"Name: ITEMS, Length: 788, dtype: int64"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.groupby('DateTime').ITEMS.sum().plot()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<matplotlib.axes.AxesSubplot at 0x74c3a90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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r0WVi5Up5i+/Y0d/gvS1b5I22TZuDj/XtGyw998ABmDv34AF76djf2W1SvHxa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\n",
"text": [
"<matplotlib.figure.Figure at 0x74c3490>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.groupby('DateTime').ITEMS.sum().plot(ylim(0, 5600000))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"<matplotlib.axes.AxesSubplot at 0x59f8590>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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pyLSG8ZN3uw2wNfAtljCGeMuHAB29++cBw4ANQDkwHzgaaA40wpIFwNCobaL3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\nRbv1AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x9338890>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"www.carlreynolds.net"
]
}
],
"metadata": {}
}
]
}
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