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A quick test of textualising nomis (official labour market statistics) data
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"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd"
],
"language": "python",
"metadata": {
"activity": false
},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xl=pd.ExcelFile('offending-history-tables.xls')\n",
"xl.sheet_names"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"[u'Index',\n",
" u'Table Q6.1',\n",
" u'Table Q6.2',\n",
" u'Table Q6.3',\n",
" u'Table Q6.4',\n",
" u'Table Q6.5',\n",
" u'Table Q6.6',\n",
" u'Table Q6a',\n",
" u'Table Q6b',\n",
" u'Table Q6c',\n",
" u'Table Q6d',\n",
" u'Table Q6e',\n",
" u'Table Q6f',\n",
" u'Table Q6g',\n",
" u'Table Q6h',\n",
" u'Table Q6i',\n",
" u'Table A6.1',\n",
" u'Table A6.2',\n",
" u'Table A6.3',\n",
" u'Table A6.4',\n",
" u'Table A6.5',\n",
" u'Table A6.6',\n",
" u'Table A6.7',\n",
" u'Table A6.8',\n",
" u'Table A6.9',\n",
" u'Table B6.1']"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def getTable(tname):\n",
" dfx=xl.parse(tname,skiprows=4,thousands=',',index_col=0,na_values=['*','..'])\n",
" #Need to identify group by empty line then back fill (fillna, ffill) up from last row in group?\n",
" \n",
" areas=['North East','North West','Yorkshire and the Humber','East Midlands','West Midlands',\n",
" 'East of England','London','South East','South West','England','Wales','England & Wales']\n",
" dfx.dropna(how='all',axis=0,inplace=True)\n",
" dfx['area']=dfx.index.values\n",
" dfx['area'][~dfx['area'].isin(areas)]=None\n",
" dfx['area'].fillna(method='backfill',inplace=True)\n",
"\n",
" dfx=dfx.rename(columns={'Unnamed: 0':'Local Authority'})[1:]#.set_index('Local Authority')\n",
" metadata=xl.parse(tname,header=None)[0].iloc[0]\n",
" return metadata,dfx\n",
"metadata,dfx=getTable('Table Q6c')\n",
"metadata"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 382,
"text": [
"u'Table Q6c - Number of juvenile first time entrants(1)(2) residing in England and Wales to the criminal justice system by Local Authority of residence, 12 months ending December 2004 to 12 months ending December 2014(3)'"
]
}
],
"prompt_number": 382
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx[:3]"
],
"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>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>2014</th>\n",
" <th>areas</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Durham</th>\n",
" <td> 885.029775</td>\n",
" <td> 1105.851352</td>\n",
" <td> 1246.063017</td>\n",
" <td> 1367.517002</td>\n",
" <td> 789.442116</td>\n",
" <td> 389.411466</td>\n",
" <td> 329.345411</td>\n",
" <td> 299.100553</td>\n",
" <td> 255.440134</td>\n",
" <td> 210.651859</td>\n",
" <td> 211.874064</td>\n",
" <td> North East</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gateshead</th>\n",
" <td> 458.699695</td>\n",
" <td> 484.740686</td>\n",
" <td> 522.878797</td>\n",
" <td> 529.026095</td>\n",
" <td> 350.179998</td>\n",
" <td> 309.260206</td>\n",
" <td> 156.601603</td>\n",
" <td> 118.111820</td>\n",
" <td> 102.448491</td>\n",
" <td> 93.324604</td>\n",
" <td> 81.557813</td>\n",
" <td> North East</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hartlepool</th>\n",
" <td> 208.588413</td>\n",
" <td> 227.585046</td>\n",
" <td> 282.106145</td>\n",
" <td> 280.229630</td>\n",
" <td> 200.226178</td>\n",
" <td> 163.889427</td>\n",
" <td> 95.765080</td>\n",
" <td> 95.638256</td>\n",
" <td> 60.028491</td>\n",
" <td> 60.644453</td>\n",
" <td> 41.081243</td>\n",
" <td> North East</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 381,
"text": [
" 2004 2005 2006 2007 2008 \\\n",
"Durham 885.029775 1105.851352 1246.063017 1367.517002 789.442116 \n",
"Gateshead 458.699695 484.740686 522.878797 529.026095 350.179998 \n",
"Hartlepool 208.588413 227.585046 282.106145 280.229630 200.226178 \n",
"\n",
" 2009 2010 2011 2012 2013 \\\n",
"Durham 389.411466 329.345411 299.100553 255.440134 210.651859 \n",
"Gateshead 309.260206 156.601603 118.111820 102.448491 93.324604 \n",
"Hartlepool 163.889427 95.765080 95.638256 60.028491 60.644453 \n",
"\n",
" 2014 areas \n",
"Durham 211.874064 North East \n",
"Gateshead 81.557813 North East \n",
"Hartlepool 41.081243 North East "
]
}
],
"prompt_number": 381
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx.ix['Isle of Wight']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 167,
"text": [
"2004 288.775068\n",
"2005 354.839327\n",
"2006 308.111447\n",
"2007 295.057875\n",
"2008 213.333412\n",
"2009 195.211853\n",
"2010 191.285960\n",
"2011 96.341239\n",
"2012 143.427203\n",
"2013 99.158076\n",
"2014 56.257591\n",
"Name: Isle of Wight, dtype: float64"
]
}
],
"prompt_number": 167
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx.ix['South East']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 152,
"text": [
"2004 14444.076716\n",
"2005 16146.059200\n",
"2006 15218.571109\n",
"2007 15426.404272\n",
"2008 12226.012308\n",
"2009 10544.910856\n",
"2010 7328.750776\n",
"2011 5146.742147\n",
"2012 4033.554122\n",
"2013 3379.292148\n",
"2014 2844.758947\n",
"Name: South East, dtype: float64"
]
}
],
"prompt_number": 152
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#dfx.index.values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 157
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"metadata2,dfx2=getTable('Table Q6d')\n",
"metadata2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 383,
"text": [
"u'Table Q6d - Rates of juveniles receiving their first the youth caution or conviction(1)(2) per 100,000 of the 10-17 year old population(3) by Local Authority of residence, 12 months ending December 2004 to 12 months ending December 2014(4)'"
]
}
],
"prompt_number": 383
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx2.dtypes"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 164,
"text": [
"2004 object\n",
"2005 object\n",
"2006 object\n",
"2007 object\n",
"2008 object\n",
"2009 object\n",
"2010 object\n",
"2011 object\n",
"2012 object\n",
"2013 object\n",
"2014 object\n",
"dtype: object"
]
}
],
"prompt_number": 164
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx2[:3]"
],
"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>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>2014</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Durham</th>\n",
" <td> 1748.76</td>\n",
" <td> 2205</td>\n",
" <td> 2497.17</td>\n",
" <td> 2776.628</td>\n",
" <td> 1607.465</td>\n",
" <td> 799.0878</td>\n",
" <td> 689.3677</td>\n",
" <td> 641.475</td>\n",
" <td> 560.9384</td>\n",
" <td> 472.5573</td>\n",
" <td> 483.102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gateshead</th>\n",
" <td> 2340.305</td>\n",
" <td> 2499.952</td>\n",
" <td> 2733.864</td>\n",
" <td> 2784.201</td>\n",
" <td> 1853.196</td>\n",
" <td> 1637.164</td>\n",
" <td> 843.7132</td>\n",
" <td> 647.5429</td>\n",
" <td> 568.5897</td>\n",
" <td> 523.0907</td>\n",
" <td> 463.7392</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hartlepool</th>\n",
" <td> 1985.233</td>\n",
" <td> 2163.15</td>\n",
" <td> 2701.907</td>\n",
" <td> 2702.05</td>\n",
" <td> 1940.176</td>\n",
" <td> 1614.992</td>\n",
" <td> 970.3625</td>\n",
" <td> 994.7811</td>\n",
" <td> 636.7719</td>\n",
" <td> 666.0566</td>\n",
" <td> 463.2526</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 129,
"text": [
" 2004 2005 2006 2007 2008 2009 \\\n",
"Durham 1748.76 2205 2497.17 2776.628 1607.465 799.0878 \n",
"Gateshead 2340.305 2499.952 2733.864 2784.201 1853.196 1637.164 \n",
"Hartlepool 1985.233 2163.15 2701.907 2702.05 1940.176 1614.992 \n",
"\n",
" 2010 2011 2012 2013 2014 \n",
"Durham 689.3677 641.475 560.9384 472.5573 483.102 \n",
"Gateshead 843.7132 647.5429 568.5897 523.0907 463.7392 \n",
"Hartlepool 970.3625 994.7811 636.7719 666.0566 463.2526 "
]
}
],
"prompt_number": 129
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx2.ix['Isle of Wight']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 130,
"text": [
"2004 2119.761\n",
"2005 2572.231\n",
"2006 2228.977\n",
"2007 2149.941\n",
"2008 1552.758\n",
"2009 1428.136\n",
"2010 1427.933\n",
"2011 726.8294\n",
"2012 1090.204\n",
"2013 781.8804\n",
"2014 457.3416\n",
"Name: Isle of Wight, dtype: object"
]
}
],
"prompt_number": 130
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx2.ix['South East']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 131,
"text": [
"2004 1738.964\n",
"2005 1929.604\n",
"2006 1809.12\n",
"2007 1831.373\n",
"2008 1446.766\n",
"2009 1249.483\n",
"2010 871.0383\n",
"2011 614.1008\n",
"2012 483.6396\n",
"2013 410.5667\n",
"2014 347.9512\n",
"Name: South East, dtype: object"
]
}
],
"prompt_number": 131
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx2.ix['England & Wales'][2014]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 156,
"text": [
"405.4629292012915"
]
}
],
"prompt_number": 156
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import random\n",
"#Library to support natural language text generation\n",
"#!pip3 install git+https://github.com/pwdyson/inflect.py\n",
"import inflect\n",
"p = inflect.engine()\n",
"\n",
"def thous_sep(amount):\n",
" return '{:,}'.format(amount)\n",
"\n",
"def otwRiseFall(now,then,amount=False,intify=False,nformat=None):\n",
" if intify:\n",
" now =int(now)\n",
" then=int(then)\n",
" delta=now-then\n",
" if delta>0:\n",
" txt=p.a(random.choice(['rise','increase']))\n",
" elif delta<0:\n",
" txt=p.a(random.choice(['fall','decrease']))\n",
" if amount:\n",
" if nformat is not None:\n",
" #\"{:,.1f}\"\n",
" val=nformat.format(abs(delta))\n",
" else: val=thous_sep(abs(delta))\n",
" txt+=' of {0}'.format(val)\n",
" return txt\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 259
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def reporter(la,area,nation,year):\n",
" metadata,dfx=getTable('Table Q6c')\n",
" num=\"{:,}\".format(int(dfx.ix[la][year]))\n",
" txt=\"According to figures recently released by the Ministry of Justice\"\n",
" tableyear=\"for the 12 months ending December {year}\".format(year=year)\n",
" tablestat=\"the number of juvenile first time entrants to the criminal justice system residing in {LA}\".format(LA=la)\n",
" tablenum=\"was {num}\".format(num=num)\n",
" \n",
" metadata2,dfx2=getTable('Table Q6d')\n",
" \n",
" txt2=\"The rate of juveniles receiving their first the youth caution or conviction per 100,000 of the 10-17 year old population by Local Authority of residence\"\n",
" num2=\"{:,.1f}\".format(dfx2.ix[area][year])\n",
" num3=\"{:,.1f}\".format(dfx2.ix[nation][year])\n",
" num4=\"{:,.1f}\".format(dfx2.ix[la][year])\n",
" rate=\"was {num4}\".format(num4=num4)\n",
" areastat=\"This compares with a rate of {num2} in {area} and {num3} in {country}\".format(num2=num2,\n",
" num3=num3,\n",
" area=area,\n",
" country=nation)\n",
" \n",
" numLastyear=\"{:,}\".format(int(dfx.ix[la][year-1]))\n",
" rateLastyear=\"{:,.1f}\".format(dfx2.ix[la][year-1])\n",
" txt3=\"Last year, (in {lastyear}), the figure was {numLastyear}, a rate of {rateLastyear} per 100,000\".format(lastyear=year-1,\n",
" numLastyear=numLastyear,\n",
" rateLastyear=rateLastyear)\n",
" \n",
" differ=otwRiseFall(dfx.ix[la][year],dfx.ix[la][year-1],True,True)\n",
" differ2=otwRiseFall(dfx2.ix[la][year],dfx2.ix[la][year-1],True,nformat=\"{:,.1f}\")\n",
" txt3diff=\"This year's numbers in {la} thus represent {differ}, and {differ2} per 100,000 on the rate.\".format(la=la,\n",
" differ=differ,\n",
" differ2=differ2)\n",
" \n",
" tableOfficialCaveat=\"Since 8th April 2013 there have been a number of changes in out of court disposals. The previously known reprimand and warning disposal categories for juveniles have been replaced with a new out of court disposal: The Youth Caution for young offenders.\"\n",
" tableOfficialCaveat=tableOfficialCaveat+\" The figures are also estimated figures. Juveniles receiving disposals for the first time have been mapped to individual Local Authorities using the home address or postcode recorded by the police on the Police National Computer. For those with no address recorded, a model based on the patterns of offenders dealt with by police stations has been used to allocate offenders to Local Authorities. Therefore caution must be taken when using these figures.\"\n",
"\n",
" rawvals='{txt}, {tableyear} {tablestat} {tablenum}.'.format(txt=txt,\n",
" tableyear=tableyear,\n",
" tablestat=tablestat,\n",
" tablenum=tablenum)\n",
" ratevals='{txt2} {rate}. {areastat}.'.format(txt2=txt2,\n",
" rate=rate,\n",
" areastat=areastat)\n",
" \n",
" previously='{txt3}, compared with {lr} in the {r} and {ln} in {n}. {txt3diff}'.format(txt3=txt3,\n",
" lr=\"{:,.1f}\".format(dfx2.ix[area][year-1]),\n",
" r=area,\n",
" ln=\"{:,.1f}\".format(dfx2.ix[nation][year-1]),\n",
" n=nation,\n",
" txt3diff=txt3diff)\n",
" \n",
" print('{rawvals} {ratevals} {previously}\\n\\n({caveat})'.format(rawvals=rawvals,\n",
" ratevals=ratevals,\n",
" previously=previously,\n",
" caveat=tableOfficialCaveat))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 263
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#THe official reported rates per 100,000 of juveniles in an LA is not the best basis, given that LAs\n",
"# are unlikely to have more than one or two tens of thousands people in that age range?\n",
"reporter('Isle of Wight','South East','England',2014)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"According to figures recently released by the Ministry of Justice, for the 12 months ending December 2014 the number of juvenile first time entrants to the criminal justice system residing in Isle of Wight was 56. The rate of juveniles receiving their first the youth caution or conviction per 100,000 of the 10-17 year old population by Local Authority of residence was 457.3. This compares with a rate of 348.0 in South East and 409.1 in England. Last year, (in 2013), the figure was 99, a rate of 781.9 per 100,000, compared with 410.6 in the South East and 447.8 in England. This year's numbers in Isle of Wight thus represent a fall of 43, and a fall of 324.5 per 100,000 on the rate.\n",
"\n",
"(Since 8th April 2013 there have been a number of changes in out of court disposals. The previously known reprimand and warning disposal categories for juveniles have been replaced with a new out of court disposal: The Youth Caution for young offenders. The figures are also estimated figures. Juveniles receiving disposals for the first time have been mapped to individual Local Authorities using the home address or postcode recorded by the police on the Police National Computer. For those with no address recorded, a model based on the patterns of offenders dealt with by police stations has been used to allocate offenders to Local Authorities. Therefore caution must be taken when using these figures.)\n"
]
}
],
"prompt_number": 266
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Might also make sense to contextualise further eg by population estimate of number of 10-17 year olds?\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Tabular view\n",
"def table_percentChanges(area,region,nation):\n",
" dfxiw=dfx.loc[[area,region,nation]][[2014,2013]].rename(columns={2014:'2014',2013:'2013'})\n",
" dfx2iw=dfx2.loc[[area,region,nation]][[2014,2013]].rename(columns={2014:\"2014 (rate per 100,000)\",\n",
" 2013:\"2013 (rate per 100,000)\"})\n",
" pdm=pd.merge(dfxiw,dfx2iw,left_index=True,right_index=True)\n",
" pdm['% change in est. count, 2013-14']=100*(pdm['2014']-pdm['2013'])/pdm['2013']\n",
" pdm['% change in rate, 2013-14']=100*(pdm['2014 (rate per 100,000)']-pdm['2013 (rate per 100,000)'])/pdm['2013 (rate per 100,000)']\n",
"\n",
" pdm['Area % vs (est. rate), 2014']=pdm['2014 (rate per 100,000)'].apply(lambda x: 100+100*(pdm.ix[area]['2014 (rate per 100,000)']-x)/x)\n",
" pdm['Area % vs (est. rate), 2013']=pdm['2013 (rate per 100,000)'].apply(lambda x: 100+100*(pdm.ix[area]['2013 (rate per 100,000)']-x)/x)\n",
" return pdm\n",
"\n",
"table_percentChanges('Isle of Wight','South East','England')"
],
"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>2014</th>\n",
" <th>2013</th>\n",
" <th>2014 (rate per 100,000)</th>\n",
" <th>2013 (rate per 100,000)</th>\n",
" <th>% change in est. count, 2013-14</th>\n",
" <th>% change in rate, 2013-14</th>\n",
" <th>Area % vs (est. rate), 2014</th>\n",
" <th>Area % vs (est. rate), 2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Isle of Wight</th>\n",
" <td> 56.257591</td>\n",
" <td> 99.158076</td>\n",
" <td> 457.341604</td>\n",
" <td> 781.880432</td>\n",
" <td>-43.264742</td>\n",
" <td>-41.507475</td>\n",
" <td> 100.000000</td>\n",
" <td> 100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>South East</th>\n",
" <td> 2844.758947</td>\n",
" <td> 3379.292148</td>\n",
" <td> 347.951249</td>\n",
" <td> 410.566670</td>\n",
" <td>-15.817904</td>\n",
" <td>-15.250975</td>\n",
" <td> 131.438414</td>\n",
" <td> 190.439334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>England</th>\n",
" <td> 20061.806442</td>\n",
" <td> 22150.326971</td>\n",
" <td> 409.057138</td>\n",
" <td> 447.809566</td>\n",
" <td> -9.428847</td>\n",
" <td> -8.653774</td>\n",
" <td> 111.803844</td>\n",
" <td> 174.601101</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 396,
"text": [
" 2014 2013 2014 (rate per 100,000) \\\n",
"Isle of Wight 56.257591 99.158076 457.341604 \n",
"South East 2844.758947 3379.292148 347.951249 \n",
"England 20061.806442 22150.326971 409.057138 \n",
"\n",
" 2013 (rate per 100,000) % change in est. count, 2013-14 \\\n",
"Isle of Wight 781.880432 -43.264742 \n",
"South East 410.566670 -15.817904 \n",
"England 447.809566 -9.428847 \n",
"\n",
" % change in rate, 2013-14 Area % vs (est. rate), 2014 \\\n",
"Isle of Wight -41.507475 100.000000 \n",
"South East -15.250975 131.438414 \n",
"England -8.653774 111.803844 \n",
"\n",
" Area % vs (est. rate), 2013 \n",
"Isle of Wight 100.000000 \n",
"South East 190.439334 \n",
"England 174.601101 "
]
}
],
"prompt_number": 396
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Make comparisons with extremes, such as places with highest and lowest rates?\n",
"def reporter_extreme(df,col,typ='lowest',inarea=None):\n",
" if inarea is not None:\n",
" df=df[df['area']==inarea]\n",
" if typ=='lowest':\n",
" return df[df[col]==min(df[col])]\n",
" return df[df[col]==max(df[col])]\n",
"\n",
"reporter_extreme(dfx2,2014,typ='highest')"
],
"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>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>2014</th>\n",
" <th>area</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Bristol, City of</th>\n",
" <td> 1548.147349</td>\n",
" <td> 1960.606588</td>\n",
" <td> 2401.632469</td>\n",
" <td> 2214.697219</td>\n",
" <td> 1726.989042</td>\n",
" <td> 1140.030194</td>\n",
" <td> 1158.641839</td>\n",
" <td> 1135.692269</td>\n",
" <td> 957.162888</td>\n",
" <td> 771.202322</td>\n",
" <td> 808.640292</td>\n",
" <td> South West</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 389,
"text": [
" 2004 2005 2006 2007 \\\n",
"Bristol, City of 1548.147349 1960.606588 2401.632469 2214.697219 \n",
"\n",
" 2008 2009 2010 2011 \\\n",
"Bristol, City of 1726.989042 1140.030194 1158.641839 1135.692269 \n",
"\n",
" 2012 2013 2014 area \n",
"Bristol, City of 957.162888 771.202322 808.640292 South West "
]
}
],
"prompt_number": 389
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"reporter_extreme(dfx2,2014,'lowest')"
],
"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>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>2014</th>\n",
" <th>area</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Surrey</th>\n",
" <td> 1627.02416</td>\n",
" <td> 1651.815454</td>\n",
" <td> 1533.517173</td>\n",
" <td> 1599.627488</td>\n",
" <td> 1050.962444</td>\n",
" <td> 855.642886</td>\n",
" <td> 665.084912</td>\n",
" <td> 323.272197</td>\n",
" <td> 162.27229</td>\n",
" <td> 197.566553</td>\n",
" <td> 132.930965</td>\n",
" <td> South East</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 390,
"text": [
" 2004 2005 2006 2007 2008 \\\n",
"Surrey 1627.02416 1651.815454 1533.517173 1599.627488 1050.962444 \n",
"\n",
" 2009 2010 2011 2012 2013 2014 \\\n",
"Surrey 855.642886 665.084912 323.272197 162.27229 197.566553 132.930965 \n",
"\n",
" area \n",
"Surrey South East "
]
}
],
"prompt_number": 390
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"reporter_extreme(dfx2,2014,typ='highest',inarea='South East')"
],
"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>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>2014</th>\n",
" <th>area</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Portsmouth</th>\n",
" <td> 2565.390252</td>\n",
" <td> 2570.243009</td>\n",
" <td> 2101.042835</td>\n",
" <td> 2134.543166</td>\n",
" <td> 2355.628147</td>\n",
" <td> 2012.105899</td>\n",
" <td> 812.716608</td>\n",
" <td> 792.349845</td>\n",
" <td> 636.313538</td>\n",
" <td> 531.795416</td>\n",
" <td> 686.019357</td>\n",
" <td> South East</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 393,
"text": [
" 2004 2005 2006 2007 2008 \\\n",
"Portsmouth 2565.390252 2570.243009 2101.042835 2134.543166 2355.628147 \n",
"\n",
" 2009 2010 2011 2012 2013 \\\n",
"Portsmouth 2012.105899 812.716608 792.349845 636.313538 531.795416 \n",
"\n",
" 2014 area \n",
"Portsmouth 686.019357 South East "
]
}
],
"prompt_number": 393
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"reporter_extreme(dfx2,2014,typ='lowest',inarea='South East')"
],
"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>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>2014</th>\n",
" <th>area</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Surrey</th>\n",
" <td> 1627.02416</td>\n",
" <td> 1651.815454</td>\n",
" <td> 1533.517173</td>\n",
" <td> 1599.627488</td>\n",
" <td> 1050.962444</td>\n",
" <td> 855.642886</td>\n",
" <td> 665.084912</td>\n",
" <td> 323.272197</td>\n",
" <td> 162.27229</td>\n",
" <td> 197.566553</td>\n",
" <td> 132.930965</td>\n",
" <td> South East</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 394,
"text": [
" 2004 2005 2006 2007 2008 \\\n",
"Surrey 1627.02416 1651.815454 1533.517173 1599.627488 1050.962444 \n",
"\n",
" 2009 2010 2011 2012 2013 2014 \\\n",
"Surrey 855.642886 665.084912 323.272197 162.27229 197.566553 132.930965 \n",
"\n",
" area \n",
"Surrey South East "
]
}
],
"prompt_number": 394
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Some example charts?\n",
"% matplotlib inline\n",
"import seaborn\n",
"reporter_extreme(dfx2,2014,'lowest').plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 392,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x112dc7810>"
]
},
{
"metadata": {},
"output_type": "display_data",
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CvxhY7ZyrBnaZ2U5gBPBO2JGLiITQM6kPqe14/8SvEsf1x0Og1k51dTVJST0j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\n",
"text": [
"<matplotlib.figure.Figure at 0x112c584d0>"
]
}
],
"prompt_number": 392
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx2.ix['Isle of Wight'].plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 289,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1130f9410>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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rgTdExB/Q+izXXqueD+pnrJ4P6mesng/M2A3V80H9jD3Pt1jLem+0vhwcgOZJ\nfhPwc8CLe5bqGdXzQf2M1fNB/YzV84EZu6F6Pqifsef5FmtZ/zNgd/tAZh6mdZr9L/ck0bNVzwf1\nM1bPB/UzVs8HZuyG6vmgfsae51u0Z4NLkrRYLNY9a0mSFg3LWpKk4ixrSZKKs6wlSSrOspYkqbj/\nD/D389TB/e8FAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x113100610>"
]
}
],
"prompt_number": 289
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#As well as displaying the image, we can save it as an image file in its own right\n",
"dfx2.ix['Isle of Wight'].plot(kind='bar').get_figure().savefig('test.png')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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rgTdExB/Q+izXXqueD+pnrJ4P6mesng/M2A3V80H9jD3Pt1jLem+0vhwcgOZJ\nfhPwc8CLe5bqGdXzQf2M1fNB/YzV84EZu6F6Pqifsef5FmtZ/zNgd/tAZh6mdZr9L/ck0bNVzwf1\nM1bPB/UzVs8HZuyG6vmgfsae51u0Z4NLkrRYLNY9a0mSFg3LWpKk4ixrSZKKs6wlSSrOspYkqbj/\nD/D389TB/e8FAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x112e56550>"
]
}
],
"prompt_number": 293
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And so we can then embed the image file back into the notebook in a markdown cell...\n",
"![](test.png)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx2.ix[['Isle of Wight','South East','England']].T.plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 280,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x111e4d7d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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jG5Pqh9KsWydAt27Z1NRsa7J8qONk1NFp8DSI9epLXu+BTf40V8BFRKShkSMP4/nnFwGw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\n",
"text": [
"<matplotlib.figure.Figure at 0x111f202d0>"
]
}
],
"prompt_number": 280
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx2.ix[['Isle of Wight','South East','England']].plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 281,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11176f2d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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pPpO1JKmo7K0pPpO1JKno7K0pLkeDS5KUcSZrSZIyzmQtSVLGmawlSco4k7Uk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\nNz//AwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x11216c6d0>"
]
}
],
"prompt_number": 281
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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