{
"metadata": {
"name": "epcidata_analysis"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analyse des donn\u00e9es EPCI scrap\u00e9es en 2012\n",
"\n",
"source des donn\u00e9es: [http://www.collectivites-locales.gouv.fr/](http://www.collectivites-locales.gouv.fr/)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Checks:\n",
"\n",
"* nombre d'EPCI \u00e0 fiscalit\u00e9 propre au 2012/01/01: 2583 ([wikipedia](http://fr.wikipedia.org/wiki/%C3%89tablissement_public_de_coop%C3%A9ration_intercommunale))\n",
"* selon l'insee, il y a 2456 EPCI \u00e0 fiscalit\u00e9 propre au 2013/01/01 ([insee](http://www.insee.fr/fr/methodes/default.asp?page=zonages/intercommunalite.htm))\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import os\n",
"curdir = os.path.abspath('./..')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pd.read_csv(os.path.join(curdir, 'scraped_data', 'epci_all.csv'))\n",
"df[['year', 'net_profit', 'staff_costs', 'financial_costs', 'debt_repayments', 'allocation']].head(n=20)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" year | \n",
" net_profit | \n",
" staff_costs | \n",
" financial_costs | \n",
" debt_repayments | \n",
" allocation | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 2007 | \n",
" 1451000 | \n",
" 4684000 | \n",
" 367000 | \n",
" 211000 | \n",
" 2540000 | \n",
"
\n",
" \n",
" 1 | \n",
" 2007 | \n",
" 2460000 | \n",
" 2497000 | \n",
" 0 | \n",
" 0 | \n",
" 4334000 | \n",
"
\n",
" \n",
" 2 | \n",
" 2007 | \n",
" 364000 | \n",
" 2000 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
" 3 | \n",
" 2007 | \n",
" 411000 | \n",
" 239000 | \n",
" 26000 | \n",
" 663000 | \n",
" 248000 | \n",
"
\n",
" \n",
" 4 | \n",
" 2007 | \n",
" 95000 | \n",
" 23000 | \n",
" 0 | \n",
" 0 | \n",
" 101000 | \n",
"
\n",
" \n",
" 5 | \n",
" 2007 | \n",
" 87000 | \n",
" 36000 | \n",
" 24000 | \n",
" 11000 | \n",
" 13000 | \n",
"
\n",
" \n",
" 6 | \n",
" 2007 | \n",
" 1444000 | \n",
" 2966000 | \n",
" 166000 | \n",
" 471000 | \n",
" 2172000 | \n",
"
\n",
" \n",
" 7 | \n",
" 2007 | \n",
" 148000 | \n",
" 134000 | \n",
" 4000 | \n",
" 12000 | \n",
" 304000 | \n",
"
\n",
" \n",
" 8 | \n",
" 2007 | \n",
" 580000 | \n",
" 1571000 | \n",
" 72000 | \n",
" 177000 | \n",
" 1171000 | \n",
"
\n",
" \n",
" 9 | \n",
" 2007 | \n",
" 733000 | \n",
" 983000 | \n",
" 12000 | \n",
" 90000 | \n",
" 282000 | \n",
"
\n",
" \n",
" 10 | \n",
" 2007 | \n",
" 96000 | \n",
" 102000 | \n",
" 12000 | \n",
" 14000 | \n",
" 106000 | \n",
"
\n",
" \n",
" 11 | \n",
" 2007 | \n",
" 510000 | \n",
" 303000 | \n",
" 21000 | \n",
" 52000 | \n",
" 157000 | \n",
"
\n",
" \n",
" 12 | \n",
" 2007 | \n",
" 317000 | \n",
" 165000 | \n",
" 2000 | \n",
" 0 | \n",
" 2658000 | \n",
"
\n",
" \n",
" 13 | \n",
" 2007 | \n",
" 367000 | \n",
" 66000 | \n",
" 66000 | \n",
" 125000 | \n",
" 35000 | \n",
"
\n",
" \n",
" 14 | \n",
" 2007 | \n",
" 86000 | \n",
" 27000 | \n",
" 0 | \n",
" 0 | \n",
" 50000 | \n",
"
\n",
" \n",
" 15 | \n",
" 2007 | \n",
" 298000 | \n",
" 47000 | \n",
" 24000 | \n",
" 16000 | \n",
" 161000 | \n",
"
\n",
" \n",
" 16 | \n",
" 2007 | \n",
" 104000 | \n",
" 250000 | \n",
" 0 | \n",
" 0 | \n",
" 90000 | \n",
"
\n",
" \n",
" 17 | \n",
" 2007 | \n",
" 3933000 | \n",
" 10301000 | \n",
" 0 | \n",
" 0 | \n",
" 23165000 | \n",
"
\n",
" \n",
" 18 | \n",
" 2007 | \n",
" 219000 | \n",
" 97000 | \n",
" 0 | \n",
" 0 | \n",
" 233000 | \n",
"
\n",
" \n",
" 19 | \n",
" 2007 | \n",
" 989000 | \n",
" 451000 | \n",
" 93000 | \n",
" 98000 | \n",
" 1730000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"output_type": "pyout",
"prompt_number": 44,
"text": [
" year net_profit staff_costs financial_costs debt_repayments allocation\n",
"0 2007 1451000 4684000 367000 211000 2540000\n",
"1 2007 2460000 2497000 0 0 4334000\n",
"2 2007 364000 2000 0 0 0\n",
"3 2007 411000 239000 26000 663000 248000\n",
"4 2007 95000 23000 0 0 101000\n",
"5 2007 87000 36000 24000 11000 13000\n",
"6 2007 1444000 2966000 166000 471000 2172000\n",
"7 2007 148000 134000 4000 12000 304000\n",
"8 2007 580000 1571000 72000 177000 1171000\n",
"9 2007 733000 983000 12000 90000 282000\n",
"10 2007 96000 102000 12000 14000 106000\n",
"11 2007 510000 303000 21000 52000 157000\n",
"12 2007 317000 165000 2000 0 2658000\n",
"13 2007 367000 66000 66000 125000 35000\n",
"14 2007 86000 27000 0 0 50000\n",
"15 2007 298000 47000 24000 16000 161000\n",
"16 2007 104000 250000 0 0 90000\n",
"17 2007 3933000 10301000 0 0 23165000\n",
"18 2007 219000 97000 0 0 233000\n",
"19 2007 989000 451000 93000 98000 1730000"
]
}
],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 7,
"text": [
"Index([u'surplus', u'home_tax_rate', u'additionnal_land_property_tax_value', u'property_tax_rate', u'business_property_contribution_basis', u'financing_capacity', u'facilities_expenses', u'business_property_contribution_value', u'compensation_2010_rate', u'operating_revenues', u'business_tax_value', u'property_tax_cuts_on_deliberation', u'property_tax_value', u'land_property_tax_basis', u'received_subsidies', u'business_network_tax_value', u'net_profit', u'business_profit_contribution_basis', u'land_property_tax_cuts_on_deliberation', u'retail_land_tax_cuts_on_deliberation', u'business_property_contribution_rate', u'home_tax_cuts_on_deliberation', u'retail_land_tax_basis', u'thirdparty_balance', u'business_tax_cuts_on_deliberation', u'paid_subsidies', u'business_tax_rate', u'additionnal_land_property_tax_cuts_on_deliberation', u'population', u'name', u'business_profit_contribution_cuts_on_deliberation', u'business_profit_contribution_value', u'business_profit_contribution_rate', u'compensation_2010_basis', u'zone_type', u'land_property_tax_value', u'staff_costs', u'investment_ressources', u'localtax', u'financial_costs', u'purchases_and_external_costs', u'fctva', u'operating_costs', u'debt_repayments', u'tax_refund', u'year', u'residual_financing_capacity', u'siren', u'debt_at_end_year', u'business_network_tax_cuts_on_deliberation', u'additionnal_land_property_tax_rate', u'global_profit', u'business_tax_basis', u'compensation_2010_cuts_on_deliberation', u'property_tax_basis', u'retail_land_tax_rate', u'other_tax', u'home_tax_basis', u'business_network_tax_rate', u'allocation', u'home_tax_value', u'loans', u'compensation_2010_value', u'investments_usage', u'self_financing_capacity', u'land_property_tax_rate', u'url', u'debt_repayment_capacity', u'debt_annual_costs', u'business_network_tax_basis', u'additionnal_land_property_tax_basis', u'retail_land_tax_value', u'business_property_contribution_cuts_on_deliberation'], dtype=object)"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['debt_ratio'] = df['debt_annual_costs']/df['operating_revenues']\n",
"df['staff_costs_ratio'] = df['staff_costs']/df['operating_revenues']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"Nombre d'EPCI crawl\u00e9s par an\"\n",
"df.groupby('year').year.count()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Nombre d'EPCI crawl\u00e9s par an\n"
]
},
{
"output_type": "pyout",
"prompt_number": 16,
"text": [
"year\n",
"2007 2154\n",
"2008 2171\n",
"2009 2202\n",
"2010 2247\n",
"2011 2272\n",
"2012 2279\n",
"dtype: int64"
]
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sur les 2456 epci r\u00e9pertori\u00e9s au 2013/01/01 dans le fichier insee ([insee](??)), on note qu'il en manque 177 en 2012.\n",
"\n",
"Visiblement, certains codes d'EPCI d\u00e9finis dans le fichier insee ne sont pas les m\u00eames que ceux utilis\u00e9 dans l'url sur le site des collectivit\u00e9s.\n",
"\n",
"Exemple:\n",
"\n",
" * L'insee identifie l'EPCI de Bar le duc ainsi: 55029\tBar-le-Duc\t200033025\tCA Bar-le-Duc - Sud Meuse\tCA\n",
" * L'url qui identifie cette EPCI est http://alize2.finances.gouv.fr/communes/eneuro/tableau_gfp.php?siren=245500061&dep=055&nomdep=MEUSE&icom=029&type=BPS¶m=0\n",
"\n",
"On constate donc un code siren diff\u00e9rent."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xls = pd.ExcelFile(os.path.join(curdir, 'data', 'epci-au-01-01-2013.xls')\n",
"data = xls.parse('Composition communale des EPCI')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data['siren'] = data[u'\u00c9tablissement public \u00e0 fiscalit\u00e9 propre'][1:]\n",
"data['siren'].dropna().unique().size # there is a strange epci ZZZZZZZZZZZZZZ"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 27,
"text": [
"2457"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(set(df['siren'].apply(unicode).unique()).symmetric_difference(data['siren'].unique()))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 37,
"text": [
"168"
]
}
],
"prompt_number": 37
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ratio d'endettement et des charges de personnel"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure(figsize=(12,12));\n",
"df[['debt_ratio', 'staff_costs_ratio']].boxplot()\n",
"df[['debt_ratio', 'staff_costs_ratio', 'name']].head(20)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" debt_ratio | \n",
" staff_costs_ratio | \n",
" name | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 0.043450 | \n",
" 0.383275 | \n",
" GFP : CC FAUCIGNY-GLIERES | \n",
"
\n",
" \n",
" 1 | \n",
" 0.000000 | \n",
" 0.151673 | \n",
" GFP : CC DES DEUX RIVES DE LA SEINE | \n",
"
\n",
" \n",
" 2 | \n",
" 0.000000 | \n",
" 0.000574 | \n",
" GFP : CC LES COTEAUX DE SEINE | \n",
"
\n",
" \n",
" 3 | \n",
" 0.672515 | \n",
" 0.232943 | \n",
" GFP : CC LA LOUGE ET TOUCH | \n",
"
\n",
" \n",
" 4 | \n",
" 0.000000 | \n",
" 0.039792 | \n",
" GFP : CC SUD MORVAN | \n",
"
\n",
" \n",
" 5 | \n",
" 0.058431 | \n",
" 0.060100 | \n",
" GFP : CC DES 2 RIVES DE LA MOSELLE | \n",
"
\n",
" \n",
" 6 | \n",
" 0.066271 | \n",
" 0.308573 | \n",
" GFP : CC PAYS PONTCHATEAU SAINT-GILDAS | \n",
"
\n",
" \n",
" 7 | \n",
" 0.014147 | \n",
" 0.118479 | \n",
" GFP : CC DU PLATEAU DE LOMMOYE | \n",
"
\n",
" \n",
" 8 | \n",
" 0.047931 | \n",
" 0.302406 | \n",
" GFP : CC PRESQU'ILE RHUYS | \n",
"
\n",
" \n",
" 9 | \n",
" 0.020540 | \n",
" 0.197946 | \n",
" GFP : CC PORTES ROMILLY | \n",
"
\n",
" \n",
" 10 | \n",
" 0.043253 | \n",
" 0.176471 | \n",
" GFP : CC ENTRE LOIRE ET ALLIER | \n",
"
\n",
" \n",
" 11 | \n",
" 0.032531 | \n",
" 0.135027 | \n",
" GFP : CC LE MINERVOIS | \n",
"
\n",
" \n",
" 12 | \n",
" 0.001750 | \n",
" 0.144357 | \n",
" GFP : CC RHONE-LEZ-PROVENCE | \n",
"
\n",
" \n",
" 13 | \n",
" 0.267606 | \n",
" 0.092958 | \n",
" GFP : CC CEVENNE ET MONTAGNE ARDECHOISE | \n",
"
\n",
" \n",
" 14 | \n",
" 0.000000 | \n",
" 0.102662 | \n",
" GFP : CC LES GRANDS SITES GORGES ARDECHE | \n",
"
\n",
" \n",
" 15 | \n",
" 0.050761 | \n",
" 0.059645 | \n",
" GFP : CC DE LA VIADENE | \n",
"
\n",
" \n",
" 16 | \n",
" 0.000000 | \n",
" 0.486381 | \n",
" GFP : CC DU NEBBIU | \n",
"
\n",
" \n",
" 17 | \n",
" 0.000000 | \n",
" 0.436280 | \n",
" GFP : CA AGENTEUIL-BEZONS | \n",
"
\n",
" \n",
" 18 | \n",
" 0.000000 | \n",
" 0.235437 | \n",
" GFP : CC COEUR DE SOLOGNE | \n",
"
\n",
" \n",
" 19 | \n",
" 0.021709 | \n",
" 0.051262 | \n",
" GFP : CC PAYS DE MONTMELIAN CCPM | \n",
"
\n",
" \n",
"
\n",
"
"
],
"output_type": "pyout",
"prompt_number": 40,
"text": [
" debt_ratio staff_costs_ratio name\n",
"0 0.043450 0.383275 GFP : CC FAUCIGNY-GLIERES\n",
"1 0.000000 0.151673 GFP : CC DES DEUX RIVES DE LA SEINE\n",
"2 0.000000 0.000574 GFP : CC LES COTEAUX DE SEINE\n",
"3 0.672515 0.232943 GFP : CC LA LOUGE ET TOUCH\n",
"4 0.000000 0.039792 GFP : CC SUD MORVAN\n",
"5 0.058431 0.060100 GFP : CC DES 2 RIVES DE LA MOSELLE\n",
"6 0.066271 0.308573 GFP : CC PAYS PONTCHATEAU SAINT-GILDAS\n",
"7 0.014147 0.118479 GFP : CC DU PLATEAU DE LOMMOYE\n",
"8 0.047931 0.302406 GFP : CC PRESQU'ILE RHUYS\n",
"9 0.020540 0.197946 GFP : CC PORTES ROMILLY\n",
"10 0.043253 0.176471 GFP : CC ENTRE LOIRE ET ALLIER\n",
"11 0.032531 0.135027 GFP : CC LE MINERVOIS\n",
"12 0.001750 0.144357 GFP : CC RHONE-LEZ-PROVENCE\n",
"13 0.267606 0.092958 GFP : CC CEVENNE ET MONTAGNE ARDECHOISE\n",
"14 0.000000 0.102662 GFP : CC LES GRANDS SITES GORGES ARDECHE\n",
"15 0.050761 0.059645 GFP : CC DE LA VIADENE\n",
"16 0.000000 0.486381 GFP : CC DU NEBBIU\n",
"17 0.000000 0.436280 GFP : CA AGENTEUIL-BEZONS\n",
"18 0.000000 0.235437 GFP : CC COEUR DE SOLOGNE\n",
"19 0.021709 0.051262 GFP : CC PAYS DE MONTMELIAN CCPM"
]
},
{
"output_type": "display_data",
"png": 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nn+r2888PbB90UHEzAZTRX446vOgRoGFUSqAGr3hFxJNPbvv+gQdGPPHErp8H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\nAjcAAOTo/wMhtWlsU+Mm1wAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Biggest property tax rate\n",
"_df = df.sort(columns='debt_ratio', ascending=False)\n",
"_df[['year', 'debt_ratio', 'name']].head(n=20)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" year | \n",
" debt_ratio | \n",
" name | \n",
"
\n",
" \n",
" \n",
" \n",
" 4826 | \n",
" 2009 | \n",
" 2.108333 | \n",
" GFP : CC AGHJA NOVA | \n",
"
\n",
" \n",
" 3373 | \n",
" 2008 | \n",
" 1.856364 | \n",
" GFP : CC DE LA VALLEE DE LA COOLE | \n",
"
\n",
" \n",
" 5115 | \n",
" 2009 | \n",
" 1.634201 | \n",
" GFP : CC ARRATS GIMONE | \n",
"
\n",
" \n",
" 3258 | \n",
" 2008 | \n",
" 1.517297 | \n",
" GFP : CC DE CHEMILLE | \n",
"
\n",
" \n",
" 10119 | \n",
" 2011 | \n",
" 1.505529 | \n",
" GFP : CC BASSIN DE LANDRES | \n",
"
\n",
" \n",
" 4184 | \n",
" 2008 | \n",
" 1.483489 | \n",
" GFP : CC VAL VERT DU CLAIN | \n",
"
\n",
" \n",
" 822 | \n",
" 2007 | \n",
" 1.462203 | \n",
" GFP : CC DU PAYS DE SAINT-AUBIN-DU-CORMIER | \n",
"
\n",
" \n",
" 739 | \n",
" 2007 | \n",
" 1.412834 | \n",
" GFP : CC VAL DE GERS | \n",
"
\n",
" \n",
" 7822 | \n",
" 2010 | \n",
" 1.346433 | \n",
" GFP : CC PAYS DU DER | \n",
"
\n",
" \n",
" 10017 | \n",
" 2011 | \n",
" 1.338164 | \n",
" GFP : CC ENTRE PLAGE ET BOCAGE | \n",
"
\n",
" \n",
" 10342 | \n",
" 2011 | \n",
" 1.308844 | \n",
" GFP : CC NOEUX ET ENVIRONS | \n",
"
\n",
" \n",
" 5030 | \n",
" 2009 | \n",
" 1.292761 | \n",
" GFP : CC DU BONNEVALAIS | \n",
"
\n",
" \n",
" 11736 | \n",
" 2012 | \n",
" 1.292339 | \n",
" GFP : CC DE CAUSSES ET VEZERE | \n",
"
\n",
" \n",
" 11410 | \n",
" 2012 | \n",
" 1.158621 | \n",
" GFP : CC REGION ARCIS-SUR-AUBE | \n",
"
\n",
" \n",
" 4276 | \n",
" 2008 | \n",
" 1.157179 | \n",
" GFP : CA EVRY CENTRE ESSONNE | \n",
"
\n",
" \n",
" 4372 | \n",
" 2009 | \n",
" 1.060797 | \n",
" GFP : CC ISLE MANOIRE | \n",
"
\n",
" \n",
" 2104 | \n",
" 2007 | \n",
" 1.042221 | \n",
" GFP : CA EVRY CENTRE ESSONNE | \n",
"
\n",
" \n",
" 3925 | \n",
" 2008 | \n",
" 1.036251 | \n",
" GFP : CC BOCAGE CENOMANS | \n",
"
\n",
" \n",
" 3069 | \n",
" 2008 | \n",
" 1.029687 | \n",
" GFP : CC PAYS ST MARCELLIN | \n",
"
\n",
" \n",
" 4627 | \n",
" 2009 | \n",
" 1.025945 | \n",
" GFP : CC DES RIVIERES | \n",
"
\n",
" \n",
"
\n",
"
"
],
"output_type": "pyout",
"prompt_number": 39,
"text": [
" year debt_ratio name\n",
"4826 2009 2.108333 GFP : CC AGHJA NOVA\n",
"3373 2008 1.856364 GFP : CC DE LA VALLEE DE LA COOLE\n",
"5115 2009 1.634201 GFP : CC ARRATS GIMONE\n",
"3258 2008 1.517297 GFP : CC DE CHEMILLE\n",
"10119 2011 1.505529 GFP : CC BASSIN DE LANDRES\n",
"4184 2008 1.483489 GFP : CC VAL VERT DU CLAIN\n",
"822 2007 1.462203 GFP : CC DU PAYS DE SAINT-AUBIN-DU-CORMIER\n",
"739 2007 1.412834 GFP : CC VAL DE GERS\n",
"7822 2010 1.346433 GFP : CC PAYS DU DER\n",
"10017 2011 1.338164 GFP : CC ENTRE PLAGE ET BOCAGE\n",
"10342 2011 1.308844 GFP : CC NOEUX ET ENVIRONS\n",
"5030 2009 1.292761 GFP : CC DU BONNEVALAIS\n",
"11736 2012 1.292339 GFP : CC DE CAUSSES ET VEZERE\n",
"11410 2012 1.158621 GFP : CC REGION ARCIS-SUR-AUBE\n",
"4276 2008 1.157179 GFP : CA EVRY CENTRE ESSONNE\n",
"4372 2009 1.060797 GFP : CC ISLE MANOIRE\n",
"2104 2007 1.042221 GFP : CA EVRY CENTRE ESSONNE\n",
"3925 2008 1.036251 GFP : CC BOCAGE CENOMANS\n",
"3069 2008 1.029687 GFP : CC PAYS ST MARCELLIN\n",
"4627 2009 1.025945 GFP : CC DES RIVIERES"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}