gistfile1.txt

{
 "metadata": {
  "name": "SharetribeData"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": true,
     "input": "## Init data\nimport sqlite3\n\ndef dict_factory(cursor, row):\n    d = {}\n    for idx, col in enumerate(cursor.description):\n        d[col[0]] = row[idx]\n    return d\n\ndata = sqlite3.connect('sharetribe.db' )\ndata.row_factory = dict_factory\ndata = data.cursor()\n\n## migty powers of plots and R\n%matplotlib inline\n# %load_ext rmagic\n\nprint(\"Ready to go\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Ready to go\n"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": "Descriptive statistics"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "communities = {}\n\nfor row in data.execute('select id, name from communities'):\n    communities[ row['id'] ] = row['name']\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": "Name of the community with the number of members"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "## number of people per communities\nfor row in data.execute('select count(*) \"count\", community_id as \"id\" from community_memberships group by community_id order by count desc'):\n    if row['id'] in communities:\n        print '%s %i' % ( communities[ row['id'] ], row['id'])\n    else:\n        print '%s %i' % ( row['id'], row['count'] )",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Aalto 501\nHelsingin yliopisto 19\nOtaniemi International Network 11\nKallio 295\nUniversitat Rovira i Virgili 297\nPUC 308\nUniversity of Patras 27\nIf 322\nSavonia 12\nLarun tori 473\nPikku Huopalahti 29\nHTC Keilaniemi 14\nKaristo 306\nFacultad de Econom\u00eda y Negocios de la U. de Chile 321\nHoas Kamppi 30\nOuiShare Fest 731\nStart-Up Chile 28\nOuiShare 402\nHaaga 551\nPakilan tori 445\nAnkkuri 294\nMukkula 305\nEdificio Parque Providencia 313\nPit\u00e4j\u00e4nm\u00e4ki 31\nPTSem 6\nau Comptoir Num\u00e9rique 455\nC\u00e1tedras Libres 566\nOakland Single Parents' Network 502\nRepublic of Supercellia 956\nThe Journey Exchange 719\nTeiskolaisten heimosivut 639\n\u00dcbrLocal Seattle 947\nUDD Facultad De Dise\u00f1o 314\nTrinity College Dublin 9\nZeppelin Universit\u00e4t 454\nMetropolia 299\nDarmstadt 587\nPartiokirpputori 634\nNew Hope FMC 465\nCVX-Sevilla 570\nOtaniemi  648\nMalminkartano 496\nKa\u2019ana Pono Hawaii 747\nMutualAidHarrisburg 1016\nargelaguerentransicio 352\nsllpilots 647\nHPI School of Design Thinking 360\nEtel\u00e4-Haaga 688\nOuiShareRA 488\nQuartier R\u00e9collets, Paris 788\nSummer 468\nYoruba 469\nUutisraivaaja 478\n[In.Co.Nu] 537\nReCambio 571\nHub Helsinki 351\n\u0411\u0440\u044f\u043d\u0441\u043a 4\nCentro de Estudiantes de Ingenier\u00eda El\u00e9ctrica 312\nPucon-Villarica 334\nShare-Suomi 412\nCommunaut\u00e9 des joueurs st\u00e9phanois et assimil\u00e9s :) 511\nHauhontie 346\nFlemari 369\nAthens (central) 495\nKarkku 872\nThings 383\nKing's College London 513\nBioBui(L)t 877\nBro 8\nPartio 810\nRantaharju 908\nMatinkyl\u00e4 493\nAuroville 557\n\u0392\u03b9\u03b2\u03bb\u03b9\u03cc-\u03c4\u03c3\u03b1\u03c1\u03ba\u03b5\u03c2 603\nMyyrm\u00e4ki 803\nmt airy shares 422\nNone 10\nEjemplo para Chile 296\nDemo 309\nWorld 427\nEspoo-Kauniainen 518\nLapinlahden palstaviljelij\u00e4t 528\nWijdelen 798\nUUING 828\nBay Area TCN 848\nMarket 920\nSnapbike 994\nInclusivo de Un Techo para Chile 311\nTroijan hevonen 370\nRaunon kaverit 449\nBazouges 515\nR\u00f6\u00f6peri 545\nKanervala 649\nSatorinne 779\nGalactivators Unite 882\nBuurt Talentz 894\nPuerto Varas 364\nOgeli 409\nEspoo Department 463\nCentral Hub 467\nLauttasaaren seurakunta 477\nMunkkiniemi 556\nFrankfurt am Main 580\nIda Unity 594\nKannelm\u00e4ki 629\nslo.punks 703\nSlashers rf // Yrsel 826\nCOCOBAR.Wroc\u0142aw 961\nJakom\u00e4ki 966\nReal Good Food 310\nOhana 390\nsnow 444\n\u00c9gregor 658\nHermanni 675\nDogLovers of Ibiza 732\nPortal d'Intercanvi del #Pallars 915\nLes Commandos Black Ops Strike Team 928\nSociedad de Emprendimiento e Innovaci\u00f3n UC 323\nMankkaan Er\u00e4sudet 462\nconscious living Finland 547\n\u0395\u03c1\u03c9\u03b4\u03b9\u03bf\u03af 598\n@paiania 654\nEriksn\u00e4s asukasyhdistys ry 659\nfrenchtest 18\ntest 302\nVecinos de Barcelona 316\nKasvua 327\nEdificio Plaza Italia 332\nKaivosrinteentie 338\nWazemmes La Veilleuse 357\nKaverit 395\nNational University of Singapore 470\nMonnilaiset 491\nSE Portland ShareTribe 538\nSwarthmore Presbyterian Church 723\nPopHub 733\nSeedstock Community Currency 736\nBowen 745\nTerrey Hills community 802\nJaetaan 807\nNew York University 840\nlebonhom.me 898\nCity of Caldwell, ID 916\nGear Lending test 300\nRichmond 341\nPedago Ry 349\nJakorasia 361\nMiun Siun  362\nLittoisten Verkatehdas 379\nthreeofus 380\nUOC Talent 397\nChangingChelmsford 398\nPegas  410\nAssembly 413\nTransition Highgate 417\nBoston 425\nMango 446\nTemplates 4 Transformation 453\nConsommer autrement dans la Loire 482\nN\u00f3s Coworking 486\nRecreio Vivo  505\nFriends 514\nOnmyshelf.org 539\nVernier 602\nPilpalan kyl\u00e4t 653\nPunta de Lobos 676\nIT-Innovation 697\nshare more at the well 717\nCityMakers 781\nShareHill 806\nPerrers Road Sharing Assocation  812\nHerttoniemen siirtolapuutarha 813\nCoroutine 816\nCVUU Tool Pool 829\nTucson TCN  835\nPlateforme citoyenne Lyon 844\n\u00dcbrLocal Seattle Company Test 934\nKilo 954\nEbre 298\nAvenue 61 303\nNirmal Test 307\nKaskipartio 315\nPes\u00e4, Tampere 339\naurinkoinen tulevaisuus 345\nSamraksh 348\nculturaficcion 359\nJa(h) connexion ! 365\nFalmouth 366\nGames_Maken 372\nConnected 388\nIstoryadista 391\nchow family 399\nProovih\u00f5im 407\nDougherty 418\nFund Balance 430\nHome 443\nSantaBetty 457\nintelligent life 460\nVedb\u00e6k 474\nPlaya del Carmen Shares! 479\ncheminvert 498\nFinnWill's Tribal 499\nevisovo 510\nYague4 516\nLab Cluster 1 531\nTallbo f\u00f6r\u00e4ldrar 532\nsuman 533\nGreener World Net 540\nSkintMint 577\nYorkwood Hills  583\nHesa kerho 622\nSouthgate Commons 623\ndudettes 643\nKeilaniemi  652\nsos 661\nMobile Territorial Lab 662\nBird Flu 667\nVantaan asukkaan parempaan arkeen rikkautta ilolla 674\nRental Housing in Helsinki 686\nWindsor Essex Crowd 711\n Les Pantinois 725\nBrixton Sharetribe 738\nYhteis\u00f6tori Vaasa/ Samh\u00e4lletorget Vasa 759\nPorvoo 762\nmyQute 776\nPortlandia 794\nLauneen verkkotori 831\nTribal Convergence Network 856\nThe Sustainable Gulf Coast 902\nWalo Talo 905\nPhotographers 911\nSingaporeSharetribe 938\nTest 943\nTapiola 952\nLamia 967\nOccupy National Gathering 2013 969\nPellenbergveld 980\nSuperTribu 995\nLondon Squatters 1012\nGCGH MarketPlace 1036\nTU Delft 16\nCollcons Paris 24\nCREB 301\nCREando de la Universidad del B\u00edo-B\u00edo 319\nClaro Chile 324\nAntin testiyhteis\u00f6 337\nla famiglia 340\nSavigny sur Orge 342\nBujinkan Dojo Oulu ry 343\nFiskarsin ruukki 355\nBoston University 367\nSuurpelto 368\nNet Impact Paris 377\nSoFa 378\nWitoi 381\nC\u00edrculo Liban\u00e9s Chile 384\nbadenhorst 404\nPalm Springs 405\ncurtocafe 420\nHeaton Moor 421\nWaterB.us 434\niFixit 438\nVietnam Travel 447\nDUH GORD GORD PEEPOes  451\nzero2hero 458\nERAP 461\nAmherst 476\nNNE Portland 483\ndyabe core team 484\nR\u00e4\u00e4kkyl\u00e4 489\nMin\u00e4 490\nDun Band 517\nSecond Wind 523\nruoiti 525\nyhteismaa 529\nMontreuil 530\nMurroe Shares 535\n11 Hornton Street 536\nArlington Sharing Sangha 561\ncreaexchange 565\nMurray Creek 576\nOmst\u00e4llning Tran\u00e5s 588\nHub 609\nAll Things Green 611\nHive13 613\nSapient 626\nEspoon pitsinnyplays 631\nWulff 633\nTom's tribe 635\nRiihim\u00e4en Er\u00e4tytt\u00f6jen ja Er\u00e4poikien tuki ry Reetu 638\nLeWeb 641\n\u015awierki 642\nParque Bustamante 645\nC\u00edrculo comunitario 646\n\u03a0\u03cc\u03bb\u03b7 \u03a4\u03c1\u03af\u03ba\u03b1\u03bb\u03b1 666\n\u0110\u1ea1i h\u1ecdc Qu\u1ed1c gia TP HCM (VNU-HCM) 668\nLifeCollection 671\nPHIT Lab Tribe 672\nErasmusKyamk 694\nRingv\u00e4gen 80 699\nRentez-Vous 700\nSeguidors d'Orxata 705\nMussies 718\nRahamiehen klaani 726\nKansas City Commons 729\nAbroaderview Volunteers 751\nFriends in Deed 754\nfmpreuss 757\nInner NE Portland 763\nIthaca 774\nPowderhook 782\nsharebase.inc 786\nKonsulaatin Kirpputori 795\nNew York University Gallatin 801\nZona D'Ronda 804\nLohja 811\nPARC ShareTribe 822\nNew Culture 823\nSlow Food Cache Valley 830\nValeureux 845\nPortland TCN 847\nLA TCN 861\nTestify 883\nthessaloniki 892\nPank pankerima 896\nGureganbara 903\nCasa 925\nLyddo 926\nbordeaux 927\nZeitgeist Chapter Berlin 933\nConsommation collaborative 936\nEstancias del Pilar Marketplace 940\nPunkael 955\nferhat 982\nTHD 983\nGente Linda 988\nHervanta 1001\nMazagbelleville 1009\nMagPower Magazine Literacy Marketplace 1037\nNaantalin Siniset 7\nParis 25\nAntofagasta 320\nLon 326\nEnjoy 330\nIquique 331\nTorre 18 333\nNaispurjehtijat 344\nMeksikolainen 350\nUniversidad de la Frontera 353\nULA B\u00e1squetbol Pto. Mott 354\nMustio 356\ntommi 358\nHardu 363\ncomunidad puerto varas 371\nThe New School 373\nlinyu 374\n pnch 375\nCamp Liljegrund 376\nSOYGEEK 387\nITALIANS DO IT 389\n\u0627\u062c\u062a\u0645\u0627\u0639\u064a\u0627\u062a\u064a 392\nSpirit Soul Music 393\nLattia 394\nThe Circle of W.I.C.C.A. 396\nTout (se) passe... 400\nZadco 401\nCompartirLand 403\n Middlebury 406\nSEO 408\nCDI 411\ntexploration 414\nInspiring tribe 416\ncurtocafe 419\nTransition Media Shares 423\nNewcastle upon Tyne 424\nHanoi 426\nQueensmill Road 428\nCoFED 429\nMuotiala ja Steineri 431\nIntecovietnam 432\nColucci Media 433\nkenfox 435\nGroote 436\nAn Phu Neighbours 437\nMiss Royal Boutique 439\nTrung tam SHTT&CGCN 440\nKMS Technology 441\nClayton Bruster's associates 442\nOSD-CMCSoft 448\nGentenaars 450\nKIN:D 452\nTerolankoulu 459\nasdf 464\nSpindleMedia 471\nPROJETO 5  472\nMonpaysBio 480\nPoker  481\nGenerosity Bloc 485\nPihlala-kunta 487\nTOL sharing 492\nPirkkala 494\nKisko Labs 497\nT3RC 503\nlahtelaista 506\nansoworld13 507\nSharebox 508\nShelley  509\nSFC 512\nAsociaci\u00f3n para la prevenci\u00f3n de la disbiosi intes 519\nKeskus-Kallio 520\nJussinlinja 521\nPorthaninkatu 9 522\nBinh Home 524\nPartage Ton Frigo 526\nBlue Factory 527\nlendu 534\nprivatesale.us 541\nAltrincham Community Group 542\nRUN2.CO 543\nsantab 544\nTest Jab 546\nQviflax 549\nSwallownest 550\nTorquay 552\nAltom 553\nMailMill 554\nSciencesPo Franco-Deutsch in Nanzig 555\nAJG Consultores 558\nRobbans tribe 559\nYUVENDING 560\nAnthemis Community 562\nSouthern Saratoga County Sharing Co-Op 563\nintimafaktory 564\nBeinghood 567\nCollaborate 568\narcoiris 569\nRazorblade 572\nRelieve The Stress  573\nfoo 574\nSutton-under-Whitestonecliffe 575\nsave the life 578\nxvi 579\nLoving Oslo 581\nEdgeryders 582\nHelsinki trade society 584\nPrueba 585\nHelsinki trade 586\nMoncton 589\nDas Baumhaus 590\nColumbia Heights 591\nIntegrity Buddies 592\nSue 593\nSibelius Academy / Sibelius-Akatemia 595\nLa Manchuela 596\nihuv 597\nData Tribe 599\nShiloh Oaks Neighborhood Association 600\ntribalquest 601\nEnskedef\u00e4ltet 605\n\u03b1\u03bb\u03bb\u03c5\u03bb\u03b5\u03b3\u03c5\u03b7 606\nThe Arkan Project 607\nWeToom 608\nkidup 610\nMuLondon 612\nmadrid 614\nfsh 615\nfsh2 616\nfsh3 617\nKaterina 618\nneighborhoodwatch 619\nShare n Care in Oslo 624\ngiving therapy 625\nM\u00f6kkitori 627\ncarpoola 628\nsomos santa maria 630\nglyfada 632\nOccupy Venice 636\nUllis 637\nFree The World 640\nLes insulaires de Port-Cergy 650\nRy\u00f6k\u00e4leet 657\nBeacon 660\nPancrattos 663\nOpiskelijakunta Klaani 664\nkoukaki 665\nPuya 669\nKotkan Omakotiyhdistys 670\nRRNagar 673\nWorldVideo and more 677\nAnahata Giving from the Heart 678\nAll Together 679\nTestiyhteis\u00f6 680\nprueba 681\nTENEMOSPRESIDENTE 682\nBeratung- Begleitung 683\nbdp4us 684\nFriends Share 685\nLemont 687\nJohannesburg 689\nNeev 690\njuha 691\n1180 692\ngarden geeks, et al 693\nTransition Schenectady 695\nSari 696\nCharlottle 698\nSocialMarket 701\nCommunityMarket 702\nDalston Square  704\nReaktor 706\ntalojutska 707\n24\u03bf  \u03b4\u03b7\u03bc\u03bf\u03c4\u03b9\u03ba\u03cc \u03c3\u03c7\u03bf\u03bb\u03b5\u03af\u03bf \u03a0\u03b5\u03c1\u03b9\u03c3\u03c4\u03b5\u03c1\u03af\u03bf\u03c5 708\ninoui 709\nFood 710\nCommunityShare 712\nan 713\nioannina 714\nBike Riding 715\nxariseto 716\nJustLoveLocal 720\nSeoul searching 721\nGodot Media 722\nukonek.com 724\nom 727\nHenri Kylenin tukiryhm\u00e4 728\nlolz 730\nFriends of Bootstrap Campus 734\nCollaborative Community 735\nWorldWar Programed Obsolescence 737\nOzCarsClub 739\nGoodtronix 740\nStand Up For Singapore 741\nPACCA Share 742\nLacrosse league 743\nlifestyle travelers 744\nSharing Inwood  748\nSharing South Padre 749\nPawalla Tribe 750\nhit 752\nFriends in Deed 753\nThe Third Hand 755\nas 756\nPoppoo 758\nThe Collaborative Users 760\nOhio University 761\nSharing I-90 (SD) 764\nhamznz 765\nAmhurst  766\nchloedigital 767\nBelfast Local Network 768\nFaith and Pride 769\nPimpampum 770\nMotley 771\nRabat 772\nTampa Share A Gun 773\nTribe 775\nVillage of Eldorado 777\nFirst Baptist Church of Richmond, IN 778\nGarber Electrical Contractors, Inc. 780\nSocial Media SEO 783\nTacloban 784\nIa 785\n#CiberPadres 787\nSegundaOportunidad 789\nKarlsruhe 790\nDoktors 791\nFrench connexion Berlin 792\nLuftbildaufnahmen, Foto, Fotoshooting 793\nUkko.fi 796\nVinde 797\nsharefair 799\nKring Way Neighbors 800\nThe West Coast Architects 805\nPrintie 808\nPizarron de Avisos del CRES 809\nDaily Valet 814\nBioDealer 815\ntest 817\nLa Tribu du Partage 818\nComplino 819\nMakers 820\nBatticesare 821\nOD 824\nSustainable Economies Alliance 825\nPlantZilla 827\nTest 832\nGreencove 833\nMegcsin\u00e1ljuk helyetted - Budapest 834\nESHPA 836\nFarmer's Market 837\nsouthfields 838\npolicias corruptos 839\nShared Economy 841\nClothing Swap 842\nRavensbourne 843\nselaudonien  846\nTest75 849\nTest 850\nArr\u00eat de Bus 851\nDVD Sharers 852\nelaurentpollen 853\naC3 854\n Communaut\u00e9 haitienne de Montr\u00e9al 855\nEvolver Vancouver 857\nSincronoz 858\nKanaky Online 859\ngeminiere 860\nCyprus 862\nJe dis \u00e7a je dis rien.... Alors ta gueule ! 863\nTIMGROUP 864\nKnowmads 865\n271 866\nsxb365 867\nJRDM 868\nFerritable 869\narrahib 870\nCooperative Integrale Toulouse 871\nBethelChristianFellowship (bcfHastings) 873\nSciar Treibh 874\nTradeo 875\nCadena de Favores 876\nCommunify 878\nTestify 879\nmughal 880\nshakhbazi 881\nPunaCulture 884\nEF Education First - Zurich Office 885\nOnline Marketplace for the AIESEC members & alumni 886\nizziyo 887\nthe vocalist magazine 888\nTeRento 889\nTestTwibe 890\nSammel Welt 891\nopenminded 893\nkid trade 895\nigiveyousixty 897\nFuveau 899\nTribu Campo Las Naciones 900\nMafia del pescaito 904\nKrsna Hemant 906\nKiev 907\nPennPost 909\nMajadahonda 910\nOklahoma City 912\nUrbex 913\nArunan 914\nJaipuria Sunrise Greens 917\nprueba 918\nMalillerxs Rockerxs 919\nTutoring 921\nshare&chair 922\nKwac 924\nLa Communaut\u00e9 Teufeur ! 929\nRoihuvuoren vaihtopiiri 930\nLirac 931\nLES ETANCHEURS DE L'OUEST 932\nubrlocal wallingford 935\nrandom walk 937\nAmerican Nomad 939\nComm'unes Ressources Paris 942\nTeam AUS 944\nUnion Sportive Melunaise Athl\u00e9tisme 945\nBras\u00edlia 946\nGeekeur Of The Dead 948\nSCA - Programme Clusters 949\nAz.g.zA 950\nThe Paris Expats Marketplace 951\nStart In Paris 953\nWaren M\u00fcritz 957\nSao 958\nTOUGHRUN 959\nSupernova 960\nEskidiBunlar 962\nBlindMelon 963\n#LOTE3- the unMonastery Edition 964\nasdfadsfadf 965\n\u0391Koukouselis 968\nneon 970\nUK Makers 971\ngold coast bulletin board 972\nfamilyCrescit 973\nTest 974\nwebactonodea 975\nWriting Service 976\nCopy Writer 977\nscouts helsinki 978\n801Fillmore 979\nLets Grimbergen 981\nParagliding 984\nEscapade Lesquinoise 985\nBermondsey 986\ncve4me 987\nMyCityUnsigned 989\nDigital Brick 990\nAtom - \u041a\u043b\u0443\u0431 \u0441\u043e\u0446\u0438\u0430\u043b\u044c\u043d\u043e\u0439 \u0441\u043e\u043b\u0438\u0434\u0430\u0440\u043d\u043e\u0441\u0442\u0438 991\nTesttribe 993\nChilangos 996\nRasnov 997\nBangladesh Community Marketplace 998\nantoppatrickbadmk 999\nhoyashare 1000\nalfonsoguerra 1002\nRERS du Campus Grenoble 1003\nFortunatus 1004\nBooksy Singapore 1005\nsaurabh 1006\nbarnet 1007\nPhilippe TREBAUL in PARIS 1008\nshoreo 1010\nua 1011\nThe Johnnie Exchange 1013\namericandream 1014\nUK Sharetribe 1015\nBiboo World 1017\nSOPE 1018\njnoleez 1019\nShopforcharity 1020\nTest 1022\nCoopNumancia29 08029 Barcelona 1023\nFollowingRyan 1024\nYABE 1025\n... 1026\nComunidad Makers 1027\nMural de Anuncios 1028\nTizi-Ouzou 1029\nKitchen Interiors | Modern Home Interiors 1030\nemeditation 1031\nantrapotte 1032\nWeInvest 1033\nComputer Services | Tech Repair Solutions | Laptop 1034\nNo name 1035\nEnglish Farms 1038\n"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": "Community ID by number of posts"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "a = data.execute('select community_id \"id\", count(*) \"count\" from listings left outer join communities_listings on id=listing_id group by community_id order by count desc')\ncomm_member=[]\nsample=[]\nfinal_comm=[]\nfor row in a:\n    if row['id'] in communities:\n        comm_member.append((communities[ row['id'] ], row['count'], row['id']))\n#         print '%s %i (id: %i)' % ( communities[ row['id'] ], row['count'], row['id'])\n#     else:\n#         print '%s %i' % ( row['id'], row['count'] )\nfor i in comm_member:\n    if i[1]>100:\n        sample.append(i[2])\n        final_comm.append((i[0], i[1]))\n        print i\nprint len(sample), len(final_comm)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "(u'Aalto', 9210, 501)\n(u'Otaniemi International Network', 2609, 11)\n(u'Kallio', 1569, 295)\n(u'Helsingin yliopisto', 1322, 19)\n(u'Pikku Huopalahti', 476, 29)\n(u'Universitat Rovira i Virgili', 297, 297)\n(u'University of Patras', 237, 27)\n(u'If', 231, 322)\n(u'Savonia', 207, 12)\n(u'Etel\\xe4-Haaga', 196, 688)\n(u'Larun tori', 156, 473)\n(u'Summer', 147, 468)\n(u'PUC', 145, 308)\n(u'Flemari', 122, 369)\n(u'HTC Keilaniemi', 115, 14)\n(u'Hervanta', 107, 1001)\n(u'Karisto', 106, 306)\n17 17\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "## number of items per community and per type\n\n# sample = [501, 19, 11, 295, 297, 308, 27, 322, 12, 472]\nsample_1=[]\n    for community in sample:\n        print communities[ community ]\n        for row in data.execute('select (select name from share_types where id == share_type_id) \"type\", count(*) \"count\" from listings where id in (select listing_id from communities_listings where community_id == %i) group by share_type_id'% community):\n            sample_1.append('%s, %s, %i' % ( communities[ community ], row['type'], row['count'] ))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "IndentationError",
       "evalue": "unexpected indent (<ipython-input-5-471952f43b56>, line 5)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-5-471952f43b56>\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m    for community in sample:\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "stat=[]\nfor i in sample_1:\n    i=i.split(\",\")\n    for j in final_comm:\n        if j[0]==i[0]:\n            a=float(i[2])/float(j[1])*100\n            print i[0], i[1], i[2], (\"%.2f\" %a)\n            stat.append ((i[0], i[1], i[2], a))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'sample_1' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-6-4014fb083a69>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mstat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msample_1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\",\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfinal_comm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mNameError\u001b[0m: name 'sample_1' is not defined"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": "Select all succesfully complished exchanges of favors and see the users of those"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "links = {}\nauthors = {}\nAll = {}\n\n# for row in data.execute('select * from testimonials'):\n## get it favors\nfor row in data.execute('select * from testimonials where participation_id in (select id from participations where id in (select id from conversations where listing_id in ( select id from listings where category_id == 17)))'):\n    _from = row['receiver_id']\n    _to = row['author_id']\n    if (_from, _to) not in links:\n        links[ ( _from, _to ) ] = 0\n    links[ ( _from, _to ) ] += 1\n    \n    if _from not in authors:\n        authors[ _from ] = 0\n        \n    if _to not in authors:\n        authors[ _to ] = 0\n        \n    if _from not in All:\n        All[ _from ] = 0\n    All[ _from ] += 1\n    \n    authors[ _from ] += 1\n    authors[ _to ] -= 1\n\n## relationships\nx0 = map( lambda x: x[0], links)\nx1 = x = map( lambda x: x[1], links)\n%Rpush x0\n%Rpush x1\n%R data = data.frame( x0, x1 )\n%R library(igraph)\n%R graph <- graph.data.frame( data )\n%R plot( graph )\n\n## Overall stuff\nx = map( lambda x: x, All)\ny = map( lambda x: All[x], All)\ny.sort()\n%Rpush y\n%R hist(y, main=\"Distribution of finished requests\")\n\nprint len(y)\n    \n## generate two vectors\nx = map( lambda x: x, authors)\ny = map( lambda x: authors[x], authors)\ny.sort()\n%Rpush y\n%R hist(y, main = \"Sum interaction of finished requests\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Error in library(igraph) : there is no package called \u2018igraph\u2019\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Error in eval(expr, envir, enclos) : \n  could not find function \"graph.data.frame\"\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Error in plot(graph) : object 'graph' not found\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bNm2anXbaacm86u5cdtllplu6pOC8du3adJvIQwABBBBAYL8LRCIAH3HEEbZu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\n9YVfAAAAAElFTkSuQmCC\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "53\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mLmkjgAACCCDgIEAAdoBhNQIIIIAAAm4KEIDd1CVtBBBAAAEEHAQIwA4wrEYA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\nRK5CYII=\n"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": "array([<rpy2.rinterface.SexpVector - Python:0x110d91c48 / R:0x7fa1747cb5f0>,\n       <rpy2.rinterface.SexpVector - Python:0x110d91e10 / R:0x7fa174332b88>,\n       <rpy2.rinterface.SexpVector - Python:0x110d91fd8 / R:0x7fa1747cb3f8>,\n       <rpy2.rinterface.SexpVector - Python:0x110d91cd8 / R:0x7fa1747cb158>,\n       <rpy2.rinterface.SexpVector - Python:0x110d912b8 / R:0x7fa17625a9f0>,\n       <rpy2.rinterface.SexpVector - Python:0x110d91978 / R:0x7fa176429138>,\n       <rpy2.rinterface.SexpVector - Python:0x110d91948 / R:0x7fa176523298>], dtype=object)"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": "Select all favors and examine the balance of open/closed favors per user"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "authors = {}\nAll = {}\n\n# sqlite> select * from categories where name == \"favor\";\n# 17|favor||favor|2013-04-01 21:37:44|2013-04-01 21:37:44\n\nfor row in data.execute('select * from listings where category_id == 17'):\n    author = row['author_id']\n    if author not in authors:\n        authors[ author ] = 0\n    authors[ author ] += (1 if row['open'] else -1)\n    \n    if author not in All:\n        All[ author ] = 0\n    All[ author ] += 1\n\n## generate two vectors\nx = map( lambda x: x, authors)\ny = map( lambda x: authors[x], authors)\n\ny.sort()\n\nprint len(y)\n%Rpush y\n%R plot(y)\n\n## generate two vectors\nx = map( lambda x: x, All)\ny = map( lambda x: All[x], All)\n\ny.sort()\n%Rpush y\n%R hist(y)\n\n## people who have zero\nprint y\nprint sum( ( map( lambda a: 1 if a == 0 else 0, y ) ) )",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "742\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7qXrJXH8CcDK1WRYCCCCAgCsCzZs3l9dff130OfD6vPerr77aHA92pfEkNUIA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\nWE455RSp7m9Bg4XubemQqZbRSbfRIYc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      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PEUAAAQRcFCCAXcSnagQQQAAB/woQwP4de3qOAAIIIOCiAAHsIj5VI4AAAgj4V4AA9u/Y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\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import pandas.io.sql as psql\nimport sqlite3 as lite\ncon = lite.connect(\"sharetribe.db\")\nwith con:\n    community_memberships_sql = \"SELECT * FROM community_memberships\"\n    df_community_memberships = psql.frame_query(community_memberships_sql, con)\n    print df_community_memberships.shape\n    communities_listings_sql = \"SELECT * FROM communities_listings\"\n    df_communities_listings = psql.frame_query(communities_listings_sql, con)\n    print df_communities_listings.shape\n    communities_sql = \"SELECT * FROM communities\"\n    df_communities = psql.frame_query(communities_sql, con)\n    print df_communities.shape\n    comments_sql = \"SELECT * FROM comments\"\n    df_comments = psql.frame_query(comments_sql, con)\n    print df_comments.shape\n    listings_sql = \"SELECT * FROM listings\"\n    df_listings = psql.frame_query(listings_sql, con)\n    print df_listings.shape\n    locations_sql = \"SELECT * FROM locations\"\n    df_locations = psql.frame_query(locations_sql, con)\n    print df_locations.shape\n    share_types_sql = \"SELECT * FROM share_types\"\n    df_share_types = psql.frame_query(share_types_sql, con)\n    print df_share_types.shape\n    people_sql = \"SELECT * FROM people\"\n    df_people = psql.frame_query(people_sql, con)\n    print df_people.shape\n    participations_sql = \"SELECT * FROM participations\"\n    df_participations = psql.frame_query(participations_sql, con)\n    print df_participations.shape\n    conversations_sql = \"SELECT * FROM conversations\"\n    df_conversations = psql.frame_query(conversations_sql, con)\n    print df_conversations.shape\n    payments_sql = \"SELECT * FROM payments\"\n    df_payments = psql.frame_query(payments_sql, con)\n    print df_payments.shape",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "(12647, 10)\n(21470, 2)"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n(759, 60)"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n(6401, 6)"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n(16470, 27)"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n(15705, 11)"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n(33, 7)\n(12326, 38)"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n(22117, 9)"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n(11130, 7)"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n(5, 9)\n"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import pandas as pd\na=df_locations[[\"location_type\"]]\npd.Series(a.values.ravel()).unique()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": "array([origin_loc, person, destination_loc, community, None], dtype=object)"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df_community_memberships[\"community_name\"]=\"NA\"",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "test=[]\nfor index, i in df_community_memberships.iterrows():\n    for index, j in df_communities.iterrows():\n        if i['community_id']==j['id']:\n            df_community_memberships['community_name']=j['name']\n#             test.append[i]\n            df_community_memberships.append(i)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df_community_memberships.tail()",
     "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>id</th>\n      <th>person_id</th>\n      <th>community_id</th>\n      <th>admin</th>\n      <th>created_at</th>\n      <th>updated_at</th>\n      <th>consent</th>\n      <th>invitation_id</th>\n      <th>last_page_load_date</th>\n      <th>status</th>\n      <th>community_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12642</th>\n      <td> 31623</td>\n      <td> dlfKVm8Lir4OaeXLbny1Fu</td>\n      <td> 1016</td>\n      <td> 0</td>\n      <td> 2013-07-21 22:13:39</td>\n      <td> 2013-07-21 22:13:39</td>\n      <td> SHARETRIBE1.0</td>\n      <td>   NaN</td>\n      <td> 2013-07-21 22:13:39</td>\n      <td> pending_email_confirmation</td>\n      <td> MutualAidHarrisburg</td>\n    </tr>\n    <tr>\n      <th>12643</th>\n      <td> 31624</td>\n      <td> dJdUHG8LCr4OaaXLbny1Fu</td>\n      <td> 1037</td>\n      <td> 0</td>\n      <td> 2013-07-21 22:50:06</td>\n      <td> 2013-07-21 23:44:33</td>\n      <td> SHARETRIBE1.0</td>\n      <td> 21976</td>\n      <td> 2013-07-21 23:44:33</td>\n      <td>                   accepted</td>\n      <td> MutualAidHarrisburg</td>\n    </tr>\n    <tr>\n      <th>12644</th>\n      <td> 31625</td>\n      <td> cUhvTk8L0r4OaaXLbny1Fu</td>\n      <td> 1016</td>\n      <td> 0</td>\n      <td> 2013-07-21 23:31:35</td>\n      <td> 2013-07-21 23:33:55</td>\n      <td> SHARETRIBE1.0</td>\n      <td> 21952</td>\n      <td> 2013-07-21 23:33:55</td>\n      <td>                   accepted</td>\n      <td> MutualAidHarrisburg</td>\n    </tr>\n    <tr>\n      <th>12645</th>\n      <td> 31626</td>\n      <td> dHtewc8MOr4OaeXLbny1Fu</td>\n      <td> 1016</td>\n      <td> 0</td>\n      <td> 2013-07-22 01:06:17</td>\n      <td> 2013-07-22 01:06:17</td>\n      <td> SHARETRIBE1.0</td>\n      <td>   NaN</td>\n      <td> 2013-07-22 01:06:17</td>\n      <td>                   accepted</td>\n      <td> MutualAidHarrisburg</td>\n    </tr>\n    <tr>\n      <th>12646</th>\n      <td> 31627</td>\n      <td> cdB0Ec8MWr4OaaXLbny1Fu</td>\n      <td> 1016</td>\n      <td> 0</td>\n      <td> 2013-07-22 01:18:12</td>\n      <td> 2013-07-22 01:18:12</td>\n      <td> SHARETRIBE1.0</td>\n      <td>   NaN</td>\n      <td> 2013-07-22 01:18:12</td>\n      <td>                   accepted</td>\n      <td> MutualAidHarrisburg</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": "          id               person_id  community_id  admin  \\\n12642  31623  dlfKVm8Lir4OaeXLbny1Fu          1016      0   \n12643  31624  dJdUHG8LCr4OaaXLbny1Fu          1037      0   \n12644  31625  cUhvTk8L0r4OaaXLbny1Fu          1016      0   \n12645  31626  dHtewc8MOr4OaeXLbny1Fu          1016      0   \n12646  31627  cdB0Ec8MWr4OaaXLbny1Fu          1016      0   \n\n                created_at           updated_at        consent  invitation_id  \\\n12642  2013-07-21 22:13:39  2013-07-21 22:13:39  SHARETRIBE1.0            NaN   \n12643  2013-07-21 22:50:06  2013-07-21 23:44:33  SHARETRIBE1.0          21976   \n12644  2013-07-21 23:31:35  2013-07-21 23:33:55  SHARETRIBE1.0          21952   \n12645  2013-07-22 01:06:17  2013-07-22 01:06:17  SHARETRIBE1.0            NaN   \n12646  2013-07-22 01:18:12  2013-07-22 01:18:12  SHARETRIBE1.0            NaN   \n\n       last_page_load_date                      status       community_name  \n12642  2013-07-21 22:13:39  pending_email_confirmation  MutualAidHarrisburg  \n12643  2013-07-21 23:44:33                    accepted  MutualAidHarrisburg  \n12644  2013-07-21 23:33:55                    accepted  MutualAidHarrisburg  \n12645  2013-07-22 01:06:17                    accepted  MutualAidHarrisburg  \n12646  2013-07-22 01:18:12                    accepted  MutualAidHarrisburg  "
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df_people[[\"id\", \"created_at\", \"updated_at\", ]].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>id</th>\n      <th>created_at</th>\n      <th>updated_at</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> d_8CcgNxqr4lLjabWuarwG</td>\n      <td> 2011-06-23 08:44:25</td>\n      <td> 2013-07-21 12:34:12</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> aWCO7INy8r4lLjabWuarwG</td>\n      <td> 2011-06-23 11:51:54</td>\n      <td> 2013-07-21 11:40:32</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> aE013ONKir4lLjabWuarwG</td>\n      <td> 2011-06-24 09:12:44</td>\n      <td> 2013-07-21 11:34:16</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> dhqCOaNKir4lLjabWuarwG</td>\n      <td> 2011-06-24 09:17:27</td>\n      <td> 2013-07-21 12:15:19</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> aZqiLYNKCr4kwAabWuarwG</td>\n      <td> 2011-06-24 09:49:06</td>\n      <td> 2013-07-21 11:41:54</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": "                       id           created_at           updated_at\n0  d_8CcgNxqr4lLjabWuarwG  2011-06-23 08:44:25  2013-07-21 12:34:12\n1  aWCO7INy8r4lLjabWuarwG  2011-06-23 11:51:54  2013-07-21 11:40:32\n2  aE013ONKir4lLjabWuarwG  2011-06-24 09:12:44  2013-07-21 11:34:16\n3  dhqCOaNKir4lLjabWuarwG  2011-06-24 09:17:27  2013-07-21 12:15:19\n4  aZqiLYNKCr4kwAabWuarwG  2011-06-24 09:49:06  2013-07-21 11:41:54"
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "people=df_listings[[\"id\",\"author_id\", \"share_type_id\", \"created_at\", \"updated_at\", \"last_modified\"]]\npeople.to_csv(\"people.csv\")",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df_share_types.to_csv(\"share_type.csv\")",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df_communities_listings.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>community_id</th>\n      <th>listing_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>   4</td>\n      <td> 57</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>  11</td>\n      <td> 57</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 297</td>\n      <td> 57</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>   4</td>\n      <td> 58</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>   4</td>\n      <td> 59</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": "   community_id  listing_id\n0             4          57\n1            11          57\n2           297          57\n3             4          58\n4             4          59"
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df_communities[[\"category\"]].tail()",
     "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>category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>754</th>\n      <td>        other</td>\n    </tr>\n    <tr>\n      <th>755</th>\n      <td>        other</td>\n    </tr>\n    <tr>\n      <th>756</th>\n      <td>  association</td>\n    </tr>\n    <tr>\n      <th>757</th>\n      <td>        other</td>\n    </tr>\n    <tr>\n      <th>758</th>\n      <td> neighborhood</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": "         category\n754         other\n755         other\n756   association\n757         other\n758  neighborhood"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import pandas as pd\ndata_310=pd.read_csv(\"/Users/sayantanm/Documents/Study Time/Coye/Sharetribe/analysis_3_10.csv\")",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "data_310.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>Unnamed: 0</th>\n      <th>id</th>\n      <th>author_id</th>\n      <th>auth_freq</th>\n      <th>community_id</th>\n      <th>count_in_community</th>\n      <th>share_type_id</th>\n      <th>share_type</th>\n      <th>created_at</th>\n      <th>updated_at</th>\n      <th>barter</th>\n      <th>direction</th>\n      <th>money</th>\n      <th>mon_dir</th>\n      <th>permanent</th>\n      <th>last_modified</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 3017</td>\n      <td> 20002</td>\n      <td> aBRbnaz5Kr3BIOaaWPEYjL</td>\n      <td> 55</td>\n      <td> 501</td>\n      <td> 8346</td>\n      <td> 8</td>\n      <td>   request</td>\n      <td>  2/10/09 9:38</td>\n      <td>  7/7/13 23:22</td>\n      <td> no</td>\n      <td>  in</td>\n      <td> n</td>\n      <td>  in_n</td>\n      <td> y</td>\n      <td>  2/10/09 9:38</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 3019</td>\n      <td> 20007</td>\n      <td> acnSYy-rer3BOyaaWPfx7J</td>\n      <td>  4</td>\n      <td> 501</td>\n      <td> 8346</td>\n      <td> 6</td>\n      <td> give_away</td>\n      <td> 2/12/09 20:54</td>\n      <td> 5/22/13 18:58</td>\n      <td> no</td>\n      <td> out</td>\n      <td> n</td>\n      <td> out_n</td>\n      <td> y</td>\n      <td> 2/12/09 20:54</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 3020</td>\n      <td> 20008</td>\n      <td> acnSYy-rer3BOyaaWPfx7J</td>\n      <td>  4</td>\n      <td> 501</td>\n      <td> 8346</td>\n      <td> 6</td>\n      <td> give_away</td>\n      <td> 2/12/09 20:59</td>\n      <td> 5/23/13 22:20</td>\n      <td> no</td>\n      <td> out</td>\n      <td> n</td>\n      <td> out_n</td>\n      <td> y</td>\n      <td> 2/12/09 20:59</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 3022</td>\n      <td> 20018</td>\n      <td> aBRbnaz5Kr3BIOaaWPEYjL</td>\n      <td> 55</td>\n      <td> 501</td>\n      <td> 8346</td>\n      <td> 6</td>\n      <td> give_away</td>\n      <td>   3/3/09 9:01</td>\n      <td>  4/16/13 2:15</td>\n      <td> no</td>\n      <td> out</td>\n      <td> n</td>\n      <td> out_n</td>\n      <td> y</td>\n      <td>   3/4/09 7:41</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 3023</td>\n      <td> 20019</td>\n      <td> aBRbnaz5Kr3BIOaaWPEYjL</td>\n      <td> 55</td>\n      <td> 501</td>\n      <td> 8346</td>\n      <td> 8</td>\n      <td>   request</td>\n      <td>   3/4/09 8:11</td>\n      <td>  7/6/13 20:22</td>\n      <td> no</td>\n      <td>  in</td>\n      <td> n</td>\n      <td>  in_n</td>\n      <td> y</td>\n      <td>           NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": "   Unnamed: 0     id               author_id  auth_freq community_id  \\\n0        3017  20002  aBRbnaz5Kr3BIOaaWPEYjL         55          501   \n1        3019  20007  acnSYy-rer3BOyaaWPfx7J          4          501   \n2        3020  20008  acnSYy-rer3BOyaaWPfx7J          4          501   \n3        3022  20018  aBRbnaz5Kr3BIOaaWPEYjL         55          501   \n4        3023  20019  aBRbnaz5Kr3BIOaaWPEYjL         55          501   \n\n   count_in_community  share_type_id share_type     created_at     updated_at  \\\n0                8346              8    request   2/10/09 9:38   7/7/13 23:22   \n1                8346              6  give_away  2/12/09 20:54  5/22/13 18:58   \n2                8346              6  give_away  2/12/09 20:59  5/23/13 22:20   \n3                8346              6  give_away    3/3/09 9:01   4/16/13 2:15   \n4                8346              8    request    3/4/09 8:11   7/6/13 20:22   \n\n  barter direction money mon_dir permanent  last_modified  \n0     no        in     n    in_n         y   2/10/09 9:38  \n1     no       out     n   out_n         y  2/12/09 20:54  \n2     no       out     n   out_n         y  2/12/09 20:59  \n3     no       out     n   out_n         y    3/4/09 7:41  \n4     no        in     n    in_n         y            NaN  "
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "comm=data_310.community_id\ndirec=data_310.direction\nmoney=data_310.money\nmon_dir=data_310.mon_dir\nperm=data_310.permanent",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "all_comm=[]\nfor i in comm.values:\n    if i not in all_comm:\n        all_comm.append(i)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": "Money Non Moneytary Distribution For Communities"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "yess=[]\nnoo=[]\nfor i in all_comm:\n    temp=data_310[data_310.community_id==i][['money']].values\n    ln=len(data_310[data_310.community_id==i][['money']])\n    yes=0.0\n    no=0.0\n    for j in temp:\n        if j==\"y\":\n            yes+=1\n        elif j==\"n\":\n            no+=1\n    yess.append((yes/ln)*100)\n    noo.append((no/ln)*100)\n    print i, (yes/ln)*100, (no/ln)*100, ln",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "501 81.8595734484 18.1404265516 8346\n11 88.9622641509 11.0377358491 2120\n19 91.9964028777 8.0035971223 1112\n295"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 58.8235294118 41.1764705882 731\n29 79.7136038186 20.2863961814 419\n297 96.5384615385 3.46153846154 260\n27 72.1774193548 27.8225806452 248\n322 97.4025974026 2.5974025974 231\n12 89.3401015228 10.6598984772 197\n308"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 93.7007874016 6.29921259843 127\n14 85.7142857143 14.2857142857 112\n306 70.7547169811 29.2452830189 106\n305 80.412371134 19.587628866 97\n313 86.0465116279 13.9534883721 86\n6 40.0 60.0 85\n455 58.6666666667 41.3333333333 75\n473 50.0 50.0 70\n719 13.2352941176 86.7647058824 68\n779 16.6666666667 83.3333333333 60\n294"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 79.3103448276 20.6896551724 58\n4 75.4716981132 24.5283018868 53\n30 64.1509433962 35.8490566038 53\n351 78.8461538462 21.1538461538 52\n28 71.4285714286 28.5714285714 49\n465 31.8181818182 68.1818181818 44\n352 57.1428571429 42.8571428571 42\n502 54.7619047619 45.2380952381 42\n1030 97.4358974359 2.5641025641 39\n402 36.8421052632 63.1578947368 38\n528 50.0 50.0 38\n731"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 23.5294117647 76.4705882353 34\n31 73.3333333333 26.6666666667 30\n445 26.6666666667 73.3333333333 30\n551 70.3703703704 29.6296296296 27\n296 46.1538461538 53.8461538462 26\n321 96.0 4.0 25\n314 95.8333333333 4.16666666667 24\n515 41.6666666667 58.3333333333 24\n570 29.1666666667 70.8333333333 24\n516 0.0 100.0 23\n703 22.7272727273 77.2727272727 22\n566 80.9523809524 19.0476190476 21\n639 66.6666666667 33.3333333333 21\n894"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 71.4285714286 28.5714285714 21\n299 50.0 50.0 20\n312 47.0588235294 52.9411764706 17\n413 31.25 68.75 16\n571 31.25 68.75 16\n788 31.25 68.75 16\n872 62.5 37.5 16\n360 60.0 40.0 15\n844 20.0 80.0 15\n947 60.0 40.0 15\n587 42.8571428571 57.1428571429 14\n747 85.7142857143 14.2857142857 14\n934 71.4285714286 28.5714285714 14\n9"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 76.9230769231 23.0769230769 13\n491 61.5384615385 38.4615384615 13\n483 8.33333333333 91.6666666667 12\n539 33.3333333333 66.6666666667 12\n798 0.0 100.0 12\n334 81.8181818182 18.1818181818 11\n557 9.09090909091 90.9090909091 11\n648 100.0 0.0 11\n840 27.2727272727 72.7272727273 11\n1016 36.3636363636 63.6363636364 11\n18 40.0 60.0 10\n388"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 10\n537 60.0 40.0 10\n565 80.0 20.0 10\n649 30.0 70.0 10\n725 10.0 90.0 10\n732 60.0 40.0 10\n807 80.0 20.0 10\n922 90.0 10.0 10\n969 0.0 100.0 10\n316 55.5555555556 44.4444444444 9\n781 22.2222222222 77.7777777778 9\n8 12.5 87.5 8\n309"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 37.5 62.5 8\n327 62.5 37.5 8\n723 25.0 75.0 8\n828 12.5 87.5 8\n1001 0.0 100.0 8\n311 0.0 100.0 7\n361 0.0 100.0 7\n422 28.5714285714 71.4285714286 7\n467 14.2857142857 85.7142857143 7\n482 57.1428571429 42.8571428571 7\n531 28.5714285714 71.4285714286 7\n662"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 42.8571428571 57.1428571429 7\n956 85.7142857143 14.2857142857 7\n461 50.0 50.0 6\n478 33.3333333333 66.6666666667 6\n745 66.6666666667 33.3333333333 6\n916 66.6666666667 33.3333333333 6\n946 0.0 100.0 6\n961 66.6666666667 33.3333333333 6\n326 40.0 60.0 5\n359 60.0 40.0 5\n364 80.0 20.0 5\n366 80.0 20.0 5\n398"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 60.0 40.0 5\n399 0.0 100.0 5\n470 0.0 100.0 5\n476 20.0 80.0 5\n535 40.0 60.0 5\n583 60.0 40.0 5\n711 40.0 60.0 5\n759 100.0 0.0 5\n803 0.0 100.0 5\n908 80.0 20.0 5\n909 100.0 0.0 5\n920 100.0 0.0 5\n#N/A"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 80.0 20.0 5\n301 25.0 75.0 4\n303 25.0 75.0 4\n310 75.0 25.0 4\n323 50.0 50.0 4\n513 100.0 0.0 4\n532 75.0 25.0 4\n588 100.0 0.0 4\n658 0.0 100.0 4\n686 75.0 25.0 4\n695 75.0 25.0 4\n841"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 25.0 75.0 4\n903 0.0 100.0 4\n915 75.0 25.0 4\n995 75.0 25.0 4\n1009 75.0 25.0 4\n307 0.0 100.0 3\n346 0.0 100.0 3\n390 100.0 0.0 3\n400 33.3333333333 66.6666666667 3\n439 0.0 100.0 3\n446 66.6666666667 33.3333333333 3\n450 0.0 100.0 3\n451"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 0.0 100.0 3\n474 33.3333333333 66.6666666667 3\n486 33.3333333333 66.6666666667 3\n495 100.0 0.0 3\n496 33.3333333333 66.6666666667 3\n505 66.6666666667 33.3333333333 3\n511 33.3333333333 66.6666666667 3\n563 66.6666666667 33.3333333333 3\n569 66.6666666667 33.3333333333 3\n576 0.0 100.0 3\n598 33.3333333333 66.6666666667 3\n623 33.3333333333 66.6666666667 3\n634 100.0 0.0 3\n653"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 3\n659 100.0 0.0 3\n667 0.0 100.0 3\n674 66.6666666667 33.3333333333 3\n694 33.3333333333 66.6666666667 3\n717 100.0 0.0 3\n829 0.0 100.0 3\n835 66.6666666667 33.3333333333 3\n848 33.3333333333 66.6666666667 3\n881 100.0 0.0 3\n943 100.0 0.0 3\n994 100.0 0.0 3\n1002 100.0 0.0 3\n1036"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 3\n300 50.0 50.0 2\n332 100.0 0.0 2\n341 50.0 50.0 2\n342 100.0 0.0 2\n348 50.0 50.0 2\n357 0.0 100.0 2\n362 50.0 50.0 2\n370 50.0 50.0 2\n372 100.0 0.0 2\n380 50.0 50.0 2\n391 100.0 0.0 2\n395"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 0.0 100.0 2\n397 50.0 50.0 2\n404 100.0 0.0 2\n412 100.0 0.0 2\n418 50.0 50.0 2\n430 50.0 50.0 2\n443 50.0 50.0 2\n453 50.0 50.0 2\n454 0.0 100.0 2\n463 100.0 0.0 2\n469 100.0 0.0 2\n517 50.0 50.0 2\n518 0.0 100.0 2\n541"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 2\n544 100.0 0.0 2\n568 100.0 0.0 2\n577 100.0 0.0 2\n591 100.0 0.0 2\n599 50.0 50.0 2\n637 50.0 50.0 2\n688 50.0 50.0 2\n697 100.0 0.0 2\n700 100.0 0.0 2\n714 100.0 0.0 2\n733 50.0 50.0 2\n734 0.0 100.0 2\n736"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 2\n748 100.0 0.0 2\n770 50.0 50.0 2\n786 0.0 100.0 2\n787 0.0 100.0 2\n789 50.0 50.0 2\n802 50.0 50.0 2\n808 100.0 0.0 2\n810 0.0 100.0 2\n813 50.0 50.0 2\n820 100.0 0.0 2\n869 100.0 0.0 2\n877"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 0.0 100.0 2\n884 0.0 100.0 2\n902 0.0 100.0 2\n930 100.0 0.0 2\n933 0.0 100.0 2\n938 0.0 100.0 2\n940 100.0 0.0 2\n985 100.0 0.0 2\n1018 100.0 0.0 2\n1023 50.0 50.0 2\n1037 50.0 50.0 2\n16 0.0 100.0 1\n315"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 1\n343 100.0 0.0 1\n345 100.0 0.0 1\n363 0.0 100.0 1\n373 100.0 0.0 1\n374 100.0 0.0 1\n379 100.0 0.0 1\n409 100.0 0.0 1\n410 0.0 100.0 1\n417 100.0 0.0 1\n425 100.0 0.0 1\n432 100.0 0.0 1\n434 0.0 100.0 1\n435"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 1\n436 0.0 100.0 1\n438 0.0 100.0 1\n447 0.0 100.0 1\n448 0.0 100.0 1\n457 0.0 100.0 1\n460 100.0 0.0 1\n462 100.0 0.0 1\n472 0.0 100.0 1\n477 0.0 100.0 1\n481 100.0 0.0 1\n488 0.0 100.0 1\n499"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 0.0 100.0 1\n507 100.0 0.0 1\n508 100.0 0.0 1\n509 100.0 0.0 1\n510 100.0 0.0 1\n514 0.0 100.0 1\n525 100.0 0.0 1\n547 100.0 0.0 1\n556 100.0 0.0 1\n578 0.0 100.0 1\n580 0.0 100.0 1\n581 100.0 0.0 1\n584"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 1\n592 0.0 100.0 1\n594 100.0 0.0 1\n600 100.0 0.0 1\n608 100.0 0.0 1\n622 100.0 0.0 1\n626 0.0 100.0 1\n628 100.0 0.0 1\n631 100.0 0.0 1\n635 100.0 0.0 1\n638"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 1\n647 0.0 100.0 1\n670 100.0 0.0 1\n671 0.0 100.0 1\n672 100.0 0.0 1\n676 100.0 0.0 1\n679 100.0 0.0 1\n680 0.0 100.0 1\n681 0.0 100.0 1\n682 100.0 0.0 1\n689 0.0 100.0 1\n690"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 0.0 100.0 1\n698 100.0 0.0 1\n705 100.0 0.0 1\n715 100.0 0.0 1\n716 0.0 100.0 1\n718 100.0 0.0 1\n722 100.0 0.0 1\n726 0.0 100.0 1\n729 0.0 100.0 1\n738 100.0 0.0 1\n740 0.0 100.0 1\n750 100.0 0.0 1\n751"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 1\n752 100.0 0.0 1\n760 0.0 100.0 1\n763 0.0 100.0 1\n771 100.0 0.0 1\n773 100.0 0.0 1\n777 0.0 100.0 1\n782 100.0 0.0 1\n784 100.0 0.0 1\n800 0.0 100.0 1\n801 0.0 100.0 1\n806 100.0 0.0 1\n809 0.0 100.0 1\n812"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 0.0 100.0 1\n814 0.0 100.0 1\n819 100.0 0.0 1\n822 100.0 0.0 1\n824 100.0 0.0 1\n826 0.0 100.0 1\n827 100.0 0.0 1\n830 0.0 100.0 1\n833 100.0 0.0 1\n834 100.0 0.0 1\n837 100.0 0.0 1\n857 0.0 100.0 1\n858 100.0 0.0 1\n860"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 1\n863 100.0 0.0 1\n873 0.0 100.0 1\n887 100.0 0.0 1\n896 100.0 0.0 1\n897 100.0 0.0 1\n898 0.0 100.0 1\n900 0.0 100.0 1\n907 0.0 100.0 1\n911 100.0 0.0 1\n917 100.0 0.0 1\n921"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 1\n925 0.0 100.0 1\n928 0.0 100.0 1\n936 100.0 0.0 1\n966 0.0 100.0 1\n967 100.0 0.0 1\n968 100.0 0.0 1\n970 100.0 0.0 1\n976 100.0 0.0 1\n977 100.0 0.0 1\n980 0.0 100.0 1\n987"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 1\n988 0.0 100.0 1\n991 100.0 0.0 1\n1003 100.0 0.0 1\n1004 0.0 100.0 1\n1007 0.0 100.0 1\n1011 100.0 0.0 1\n1014 100.0 0.0 1\n1015 100.0 0.0 1\n1028 100.0 0.0 1\n1033 100.0 0.0 1\n"
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import matplotlib.pyplot as plt\nimport pylab\nprint \"percentage of transaction involve money for top 25 communities per volume\"\nplt.plot(yess[:25])\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "percentage of transaction involve money for top 25 communities per volume\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VJ0kjMWIdXlLAnzx5Ehs2bMD0S5NNg4ODERYWhvz8fFitVgCA1WpFXl6eciPV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\njVYOeN/AAc9YgOAr+MDDAc9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       "text": "<matplotlib.figure.Figure at 0x118615210>"
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print \"percentage of transactions does not involve money for top 25 communities per volume\"\nplt.plot(noo[:25])\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "percentage of transactions does not involve money for top 25 communities per volume\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XXAOcPy9tHDoAnD1L5bzc3OAecthSyAe51MlALfXtCwwcCHz9tbxt8mbLFmD0\naCorqLUXYbBZuZLqnB0NH7v/frp2EcBEOJ/whhLSBBrkq1fTNPsbbpCnPf5uA3f//cCYMTSjVC/C\nIsj97ZEDzdP11bR1K/CLX4R3kC9fTutmdKRLl+ZNBhoblWsDl1WkCSTIhQCefpp643J+cM6aReWR\nujrfHr9iBX0LV3t0VKBCOshPn6ZpvoH8MmpRJ3f1yJOTgfJyGpsbTr7/nnrDnqZY/+pXVLtcvly5\ndnCQSxNIkDsc9D6XOt/Dm4QEKlOuW+f9sQcPUm985Ur3654Hq5AO8v376R8xkJlm115LpZXz5+Vr\nlydHjlBdNiGBvmZedZX6pR2tLV9OX2s7e5iuZjAAzz8P/OlPNExRCRzk0vgb5OfP00Xs7GxlNjb2\npbxSV0czQv/0J/qd05uQDvJAyyoA0KMHkJICbN4sT5u8cfXGXV8vw628IoTnskpLY8bQa/W3vynT\nFg5yafwJ8l27gPR0oH9/Gq+thFtuAdav9/zN9qmnaMLPAw8o0walhXSQBzJipSU1yyuu+rjLqFHh\nNXJl82a6wJma6tvjbTZa6rSyUv62cJBL07s3jfjwZZRXYyN9AF9/PfXElRzm17s3MHEi4G5JqIIC\n6rH/61/6vbAd0kEeyIiVltQMcleP3MXVI9di5T8tuHrjvv5CXXEF8OtfU49Kbhzk0kRE0PDQY8c8\nP+7oURqZ8t//0nt71izlA9RdeeWHHwCrlX4WHa1sG5QU8kEuR4989Ghg717g1KnAj+XJxYv0VXPE\niOb74uJo5b+SEmXPHQwuXqQVDqUO+3rySZoFumePfG1paKALzXFx8h0zHHhbzjYvj2rQY8YAn3+u\n7GYNLVkswP/+RzeXxkbqBNx5J41O07OQDfKaGtoUQI43Spcu1DPetCnwY3lSWAgMGtT+ivnIkeFR\nJ1+zBhg+XHovuHdvCvNHH5WvLZWVtEdksG7tFazc1cnPnQPuuQd4+GFalTAnx/PFbLlFRlIHoeU2\ncLm5QFWVuks+KCVkg/z772kyj1xvFjXKK1u3ti6ruITLBU9fL3J25Le/pW8tvgwz80VpqfQNDVjH\nKyBu30698AsXgJ07qTeuBVd5pbGRZnsuWkS1+chIbdojp5ANcrnKKi4TJgCffirf8TrStj7uEg4X\nPE+epNd3xgz/nh8ZCTzzDPXK6+sDbw/Xx/3Tskfe0EAXoydNoo0e7HagZ0/t2paaSt92162jKfgv\nvUS7gYXm9FSGAAARp0lEQVQCr0FeVlaG8ePHIzk5GUOHDsWSJUsAAFVVVTCbzUhMTITFYmnaEi5Y\nyDVixSU9HTh82PuFnEC4C/L0dOrJ+Do7TY/eeYfqmIHsyzllCg0he+ONwNvDQe4fV5CXlVHdee1a\n4JtvgmN3HdeU/VtuAcaOpUllocJrkEdGRuKFF17A3r17sXXrVrzyyivYv38/bDYbzGYzioqKkJGR\nAZvNpkZ7fSbXiBWXzp3pH1+p9T3Ky6muP3Bg+5/16EE9h1BeCfHNN/0vq7i4JgnNn08LXgWCg9w/\nMTEU3ldfTUP+NmwIrtdx1iz6hvBjfzRkeA3y2NhYpP44qLd79+648sorUV5ejvz8fFitVgCA1WpF\nXl6esi2VSO7SCqBsndw1ftzdMKxQrpMfOkR7IsqxWNLVV1PPfvHiwI7DQe6fpCR6D3/0Ee3wo8RM\nzUDExtKwR7n28gwWkmrkJSUlKCwsxKhRo+B0OmE0GgEARqMRTrm30A5AbS2VQQYNkve4GRnAJ58o\nM6bbXVnFJZSD/O23aVNluSaEPP008Pe/09d7f3GQ+2fMGPrm2HIILVOez2M6zp49i+nTpyM3Nxc9\nevRo9TODwQCDm65kTk5O059NJhNMJpNfDZXiwAEacSD3Jr3DhtEV7z17aJicnLZs8bwTyahRyk1F\n15IQVFb517/kO2ZcHPC739FKei2Hm0nBQc7U5HA44Aikbit8UFtbKywWi3jhhRea7ktKShIVFRVC\nCCGOHj0qkpKS2j3Px8PLbtUqITIzlTn2ww8LMX++vMe8eFGIrl2FOH3a/WPq6oTo1k2IkyflPbfW\nvv5aiIQEIRob5T3umTN03Nxc/57bpYv8bWLMV1Kz02tpRQiBuXPnYsiQIXjwwQeb7p8yZQrsdjsA\nwG63I7PtVi4aknvESkvTptGEBjnt3EkXOdt80Wmlc+fQXAnRdZFT7ina3bvTGhrPPw8sWybtubyh\nBNMbr0G+efNmLF++HBs3bkRaWhrS0tKwbt06ZGdnY/369UhMTMSGDRuQnZ2tRnt9osSFTpfRo2kI\n4oED8h3TW33cJdTq5HV1tPZzoKNV3ImPpzB/6imqw/uKyypMb7zWyK+99lo0utmGpaCgQPYGyWHf\nPuX2dIyIAKZOBd5/X75zbNlCQ6K8GTWKJlWEioICWkKhoyGXchk0iC5QZ2TQzupTp3p/Dgc50xvF\nZ3YqvdBUW/X1tDDO4MHKnWPaNODdd+U73pYtrZeudSfUVkJ8800a16u05GRax+Wee2iMszcc5Exv\nFA/yuXPVDZ5Dh2isaNeuyp1j3Dj6sAhkeJvL0aO0hnNiovfHxsVRrfzw4cDPq7UzZyhcs7LUOd9V\nV9EKiVYrsHGj58dykDO9UTzIS0rUnUUl94zOjkRG0g7vcsyB8jYRqCWDIXRWQnzvPfpA/NnP1Dvn\n6NG0uUBWFvDll+4fx0HO9EbxIH/nHZqgsXWr0mciSl7obEmu0Su+Xuh0CZULnoGsdBgIk4lKOpmZ\ntCpfRzjImd4oHuQDBgCvvUYL1Jw4ofTZlB162JLZTOuHB7qIlq/1cZdQCPLycgrRX/5Sm/PfcAPw\n6qvATTcB337b+meNjbQBNm8owfRElWVsb76Zvs7OmkW/KEpSo7QC0AiIiROB/Hz/j1FbSx8GI0f6\n/pxQWAlxxQr6RvPTn2rXhqlTgRdeoH/DoqLm+51O2qhCy7YxJpVq65EvXAicPh34YkaeNDbS4ktq\nBDkQ+OiVXbuAhARpazT37Kn/lRC1Kqu0deuttDvM9dc3b6XHZRWmR6oFeWQkTf5YskS5pWDLyqg3\nFcia1lLceCPwxRf+D7GUWh93GTlSvxtN7NlD22tdd53WLSFz5wJ//CONMy8v5yBn+qTqDkFxcTSh\n5fbbaU9EualVVnHp0YNGXnz0kX/Pl1ofd9FznXz5cvr3jwiivan+8AfgN7+hnvk333CQM/1R/dfJ\nYgHuuou+1jY0yHtstUastBTI6BV3e3R6o9cgdzpplcM779S6Je3Nm0c7xzzzDAc50x9N+kVPPUUT\nW+bPl/e4ao1YaWnyZGD9etrdR4rKSirJ+DIRqK2hQ6mMFGS763kkBM2snDtX3W9NUixYADz3HI1I\nYkxPNAnyTp2At96i3pkvU6Z9pXZpBaAJLenptJ6HFK6yij8lBj2uhLh8OXDwoPwf3nIyGIBHHqEP\nSsb0RLNKZUwMDUObPVueqe5CaFNaAYDp06WXV/ytj7vo6YJneTkFpN0u/2YfjDENgxygzYwffpjG\nmNfWBnasykraKkzNKd8umZnA6tXS/h/8rY+76KVOLgRdE/n974G0NK1bw1ho0nzswKOPUvgGupy5\nFmUVl/79abVFb4sxudTVATt2UBj7Sy8rIS5bBvzwA227xhhThuZBHhFBX7nffz+wtUu0Kqu4SBm9\nsmsXTeqRMhGorcsuo9cumFdCLCmhndTtdppHwBhThtcgnzNnDoxGI4YNG9Z0X1VVFcxmMxITE2Gx\nWFAd4PCJqChale63vwWKi/07hhYjVlqaOpVWQ/RlSGWg9XGALswFc3mlsRGYM4e+cfHFQ8aU5TXI\nZ8+ejXXr1rW6z2azwWw2o6ioCBkZGbDZbAE3ZMQImi49dizw+uvSx5hrWVoBaKp9v36el0d1CbQ+\n7iLXkrbnz9OGHHJaupSO++ij8h6XMdae1yAfO3Ys+vTp0+q+/Px8WK1WAIDVakWeHAtzg3rk+fnA\nv/9NF8bWr/f9uVqXVgDfR6/4OzW/rVGjAh+5Ul8PjB9PM1QrKgJvE0CbbuTk0PDSTp3kOSZjzD2/\nauROpxNGoxEAYDQa4XQ6ZWtQejrw2WcUBPfeS0uN7t/v+TnHj9OIkX79ZGuGX1x1ck8XIJ1O4ORJ\nICkp8PONGBH4SojPP087zlss8gxpbGgAfv1r4Mkn5fl/ZIx553XzZW8MBgMMHra3ycnJafqzyWSC\nyWTy4ZgUir/8JfDKK7TAUlYWhXt0dPvH799PZRVfdtlR0pAhQJcutNZ2enrHj9myhXrScqw10rMn\n8POf00JUV10l/fl799JMxm++oeOkptIH53PP0ZZo/njxReqF33+/f89nLBw5HA44AllNUPjg0KFD\nYujQoU1/T0pKEhUVFUIIIY4ePSqSkpI6fJ6Ph/fq+HEh7r9fiL59hXjmGSHOn2/98//3/4SYM0eW\nUwUsO1uIxx93//N584TIyZHvfLNnC7F0qfTn1dUJkZ4uxKuvtr5/714hBg6k17u2Vtox9+0T4mc/\nE6K4WHp7GGPNpGanX/3CKVOmwG63AwDsdjsyMzP9/yTxQd++QG4uXUjcvJl636tWNZcwtB6x0pJr\njXJ35RW56uMu/l7wfOYZoE8f4O67W98/ZAiVV4qKaNOF48d9O159PfXi//IX4IorpLeHMRYAb0k/\nc+ZM0a9fPxEZGSni4uLEG2+8IU6cOCEyMjLEoEGDhNlsFidPnpTlU8VXGzYIkZoqxOjRQmzZIoTZ\nLMSaNYqcSrLGRiHi4qhn21ZtrRDdugnh5uXyy44dQgweLO05u3dTz/nwYfePqa+nbw/x8ULs3On9\nmH/9K/07NDZKawtjrD2p2Wn48UmKMBgMUOrwDQ20ie6f/kQzBw8cAOLjFTmVZA88QLX8J59sff/2\n7bSE69698p2rvp420ygv921Djbo6GsN+7700dd6blStpve6XX6Z9Vzuyaxet5b1jB01UYowFRmp2\naj6z01+dOtHoiKIi4O236WJdsHA3DFGu8eMtde5MQzV9XQlx8WL6kJk717fHz5xJKzvOm0ezNNuO\n76+tpZLKM89wiDOmFd0GuUu3brQhgNYjVlq65hrqIR861Pp+uevjLr7WyXfvpmsNr78u7fVyfVBs\n3Urrr7ecyPvXv1KA//rXkpvNGJOJ7oM8GHXqBNx8c/teuRxT8zviy1T9ujoK22eeoS33pIqOpp75\noEH0wbF/Pw1bfPVV4B//CK4PUsbCDQe5QtouovXDD8CJE8osI+DLSoiLFtGEqUB6zpGR1KN//HGa\nCZqVBbzwgvYTsRgLdwFPCGIdmzCB9iWtqKCg27pVvolAbV1+OfWIS0s7vlawcyddrCwslKfnPHs2\nDVP85BP6f2SMaYt75Aq55BKaJelahkap+jjgeSXE2lrqhT/7LHDppfKdc9Qo4M9/5pIKY8GAg1xB\nLUevKFUfd3F3wfPpp+liZDDuXM8Yk4dux5HrQU0NlVUOHKBlbktLaTalEgoKaC2aL75ovm/HDuCG\nG6i00r+/MudljMlPanZykCts+nTqEX/yCS21q5RTpyisq6vpomRtLS3c9dhjwB13KHdexpj8wmZC\nkF5Mm0abLChVH3fp1at5JUSA1jwZMAC4/XZlz8sY0x6PWlHYTTfRf5Wsj7u4NppobKSx3Tt38sVI\nxsIB98gV1rs38NRTwKRJyp9r5Ejg889plAqP72YsfHCNPITs2AFcfTWQmUmjZbg3zpg+8cXOMFZX\nB8yYQdPmY2O1bg1jzF8c5IwxpnOqjlpZt24dBg8ejEGDBmHx4sWBHIoxxpif/A7yhoYG3HfffVi3\nbh327duHFStWYL+37e7DWEAbq4YYfi2a8WvRjF8L//kd5Nu2bcPAgQMRHx+PyMhIzJw5Ex988IGc\nbQsp/CZtxq9FM34tmvFr4T+/g7y8vByXtdgSJi4uDuXl5bI0ijHGmO/8DnIDj21jjLHg4O8uz1u2\nbBETJ05s+vvChQuFzWZr9ZiEhAQBgG984xvf+CbhlpCQICmP/R5+WF9fj6SkJHz66afo378/Ro4c\niRUrVuBKJbbAYYwx5pbfa6107twZL7/8MiZOnIiGhgbMnTuXQ5wxxjSg6IQgxhhjylNk0SyeKNQs\nPj4ew4cPR1paGkaOHKl1c1Q1Z84cGI1GDBs2rOm+qqoqmM1mJCYmwmKxoLq6WsMWqqej1yInJwdx\ncXFIS0tDWloa1q1bp2EL1VNWVobx48cjOTkZQ4cOxZIlSwCE53vD3Wsh+b3h78VOd+rr60VCQoI4\ndOiQqK2tFSkpKWLfvn1yn0Y34uPjxYkTJ7RuhiY+//xzsWPHDjF06NCm+/74xz+KxYsXCyGEsNls\nYt68eVo1T1UdvRY5OTni+eef17BV2qioqBCFhYVCCCHOnDkjEhMTxb59+8LyveHutZD63pC9R84T\nhdoTYVq9Gjt2LPq02dsuPz8fVqsVAGC1WpHn2p06xHX0WgDh+d6IjY1FamoqAKB79+648sorUV5e\nHpbvDXevBSDtvSF7kPNEodYMBgOuv/56pKen47XXXtO6OZpzOp0wGo0AAKPRCKfTqXGLtPXSSy8h\nJSUFc+fODYtSQlslJSUoLCzEqFGjwv694XotfvHjLjRS3huyBzlPFGpt8+bNKCwsxNq1a/HKK69g\n06ZNWjcpaBgMhrB+v9x77704dOgQdu7ciX79+uGRRx7RukmqOnv2LKZPn47c3Fz06NGj1c/C7b1x\n9uxZzJgxA7m5uejevbvk94bsQX7ppZeirKys6e9lZWWIi4uT+zS60e/HbXqio6MxdepUbNu2TeMW\nactoNKKyshIAUFFRgZiYGI1bpJ2YmJimwLrrrrvC6r1RV1eH6dOnY9asWcjMzAQQvu8N12txxx13\nNL0WUt8bsgd5eno6Dhw4gJKSEtTW1uI///kPpkyZIvdpdKGmpgZnzpwBAJw7dw6ffPJJq1EL4WjK\nlCmw2+0AALvd3vTGDUcVFRVNf37//ffD5r0hhMDcuXMxZMgQPPjgg033h+N7w91rIfm9ocCFWLFm\nzRqRmJgoEhISxMKFC5U4hS4cPHhQpKSkiJSUFJGcnBx2r8XMmTNFv379RGRkpIiLixNvvPGGOHHi\nhMjIyBCDBg0SZrNZnDx5UutmqqLta7Fs2TIxa9YsMWzYMDF8+HBx8803i8rKSq2bqYpNmzYJg8Eg\nUlJSRGpqqkhNTRVr164Ny/dGR6/FmjVrJL83eEIQY4zpnCITghhjjKmHg5wxxnSOg5wxxnSOg5wx\nxnSOg5wxxnSOg5wxxnSOg5wxxnSOg5wxxnTu/wMLlLTdBtVHggAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x11b213510>"
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": "Inbound or Outbound Distribution For Communities"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "inn=[]\nouut=[]\nfor x in all_comm:\n    temp=data_310[data_310.community_id==x][['direction']].values\n    ln=len(data_310[data_310.community_id==x][['direction']])\n    i=0.0\n    o=0.0\n    for j in temp:\n        if j==\"in\":\n            i+=1\n        elif j==\"out\":\n            o+=1\n    inn.append((i/ln)*100)\n    ouut.append((o/ln)*100)\n    print x, (i/ln)*100, (o/ln)*100, ln",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 501 20.4409297867 79.5231248502 8346\n11 19.0566037736 80.9433962264 2120\n19"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 18.1654676259 81.8345323741 1112\n295 23.6662106703 75.3761969904 731\n29 18.8544152745 80.4295942721 419\n297 23.4615384615 76.5384615385 260\n27 29.8387096774 70.1612903226 248\n322"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 13.8528138528 86.1471861472 231\n12 18.7817258883 81.2182741117 197\n308 13.3858267717 86.6141732283 127\n14 18.75 81.25 112\n306 30.1886792453 69.8113207547 106\n305 14.4329896907 85.5670103093 97\n313 20.9302325581 79.0697674419 86\n6 23.5294117647 76.4705882353 85\n455 18.6666666667 78.6666666667 75\n473 27.1428571429 72.8571428571 70\n719"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 22.0588235294 73.5294117647 68\n779 20.0 80.0 60\n294 18.9655172414 81.0344827586 58\n4 26.4150943396 73.5849056604 53\n30 35.8490566038 64.1509433962 53\n351 9.61538461538 90.3846153846 52\n28 46.9387755102 53.0612244898 49\n465 52.2727272727 40.9090909091 44\n352 33.3333333333 66.6666666667 42\n502 61.9047619048 38.0952380952 42\n1030 2.5641025641 97.4358974359 39\n402"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 34.2105263158 65.7894736842 38\n528 7.89473684211 92.1052631579 38\n731 76.4705882353 23.5294117647 34\n31 23.3333333333 76.6666666667 30\n445 20.0 80.0 30\n551 25.9259259259 70.3703703704 27\n296 57.6923076923 42.3076923077 26\n321 16.0 84.0 25\n314 12.5 87.5 24\n515 62.5 37.5 24\n570 20.8333333333 79.1666666667 24\n516 0.0 100.0 23\n703"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 18.1818181818 81.8181818182 22\n566 33.3333333333 66.6666666667 21\n639 38.0952380952 57.1428571429 21\n894 23.8095238095 76.1904761905 21\n299 35.0 65.0 20\n312 52.9411764706 47.0588235294 17\n413 0.0 100.0 16\n571 0.0 75.0 16\n788 31.25 56.25 16\n872 6.25 81.25 16\n360 40.0 60.0 15\n844 46.6666666667 46.6666666667 15\n947"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 20.0 66.6666666667 15\n587 42.8571428571 57.1428571429 14\n747 14.2857142857 85.7142857143 14\n934 14.2857142857 64.2857142857 14\n9 15.3846153846 84.6153846154 13\n491 15.3846153846 84.6153846154 13\n483 25.0 75.0 12\n539 0.0 100.0 12\n798 0.0 100.0 12\n334 27.2727272727 72.7272727273 11\n557 9.09090909091 72.7272727273 11\n648 0.0 100.0 11\n840 54.5454545455 45.4545454545 11\n1016"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 18.1818181818 81.8181818182 11\n18 40.0 60.0 10\n388 0.0 100.0 10\n537 10.0 90.0 10\n565 60.0 40.0 10\n649 10.0 90.0 10\n725 30.0 70.0 10\n732 10.0 90.0 10\n807 0.0 100.0 10\n922 50.0 50.0 10\n969 100.0 0.0 10\n316 66.6666666667 33.3333333333 9\n781 44.4444444444 55.5555555556 9\n8"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 25.0 75.0 8\n309 37.5 62.5 8\n327 12.5 87.5 8\n723 37.5 62.5 8\n828 0.0 100.0 8\n1001 0.0 100.0 8\n311 57.1428571429 42.8571428571 7\n361 28.5714285714 71.4285714286 7\n422 42.8571428571 57.1428571429 7\n467 14.2857142857 85.7142857143 7\n482 28.5714285714 71.4285714286 7\n531 57.1428571429 42.8571428571 7\n662"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 42.8571428571 57.1428571429 7\n956 14.2857142857 85.7142857143 7\n461 33.3333333333 66.6666666667 6\n478 66.6666666667 33.3333333333 6\n745 33.3333333333 66.6666666667 6\n916 16.6666666667 83.3333333333 6\n946 0.0 100.0 6\n961 16.6666666667 66.6666666667 6\n326 20.0 80.0 5\n359 40.0 60.0 5\n364 40.0 60.0 5\n366 20.0 80.0 5\n398"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 60.0 40.0 5\n399 0.0 100.0 5\n470 100.0 0.0 5\n476 20.0 80.0 5\n535 20.0 80.0 5\n583 40.0 60.0 5\n711 0.0 100.0 5\n759 40.0 60.0 5\n803 20.0 80.0 5\n908 0.0 100.0 5\n909 0.0 100.0 5\n920 0.0 100.0 5\n#N/A"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 60.0 40.0 5\n301 75.0 25.0 4\n303 50.0 50.0 4\n310 25.0 75.0 4\n323 50.0 50.0 4\n513 25.0 75.0 4\n532 25.0 75.0 4\n588 25.0 75.0 4\n658 0.0 100.0 4\n686 0.0 100.0 4\n695 25.0 75.0 4\n841"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 75.0 25.0 4\n903 50.0 50.0 4\n915 0.0 75.0 4\n995 25.0 75.0 4\n1009 25.0 75.0 4\n307 66.6666666667 33.3333333333 3\n346 66.6666666667 33.3333333333 3\n390 66.6666666667 33.3333333333 3\n400 0.0 100.0 3\n439 66.6666666667 33.3333333333 3\n446 33.3333333333 66.6666666667 3\n450 33.3333333333 66.6666666667 3\n451"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 66.6666666667 33.3333333333 3\n474 33.3333333333 66.6666666667 3\n486 33.3333333333 66.6666666667 3\n495 0.0 100.0 3\n496 0.0 100.0 3\n505 33.3333333333 66.6666666667 3\n511 33.3333333333 66.6666666667 3\n563 33.3333333333 66.6666666667 3\n569 33.3333333333 66.6666666667 3\n576 33.3333333333 66.6666666667 3\n598 0.0 100.0 3\n623"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 0.0 100.0 3\n634 33.3333333333 66.6666666667 3\n653 0.0 100.0 3\n659 0.0 100.0 3\n667 33.3333333333 66.6666666667 3\n674 0.0 100.0 3\n694 33.3333333333 66.6666666667 3\n717 0.0 100.0 3\n829 66.6666666667 33.3333333333 3\n835 33.3333333333 66.6666666667 3\n848 100.0 0.0 3\n881"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 33.3333333333 66.6666666667 3\n943 66.6666666667 33.3333333333 3\n994 0.0 100.0 3\n1002 0.0 100.0 3\n1036 0.0 100.0 3\n300 50.0 50.0 2\n332 0.0 100.0 2\n341 50.0 50.0 2\n342 0.0 100.0 2\n348 50.0 50.0 2\n357 50.0 50.0 2\n362"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 50.0 50.0 2\n370 50.0 50.0 2\n372 0.0 100.0 2\n380 50.0 50.0 2\n391 50.0 50.0 2\n395 0.0 100.0 2\n397 50.0 50.0 2\n404 0.0 100.0 2\n412 100.0 0.0 2\n418 50.0 50.0 2\n430 50.0 50.0 2\n443 100.0 0.0 2\n453 50.0 50.0 2\n454"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 50.0 50.0 2\n463 0.0 100.0 2\n469 100.0 0.0 2\n517 50.0 50.0 2\n518 0.0 100.0 2\n541 50.0 50.0 2\n544 0.0 100.0 2\n568 0.0 100.0 2\n577 0.0 100.0 2\n591 50.0 50.0 2\n599 50.0 50.0 2\n637 0.0 100.0 2\n688"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 2\n697 0.0 100.0 2\n700 50.0 50.0 2\n714 50.0 50.0 2\n733 0.0 100.0 2\n734 50.0 50.0 2\n736 0.0 100.0 2\n748 0.0 100.0 2\n770 50.0 50.0 2\n786 50.0 50.0 2\n787 0.0 100.0 2\n789 0.0 100.0 2\n802"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 50.0 50.0 2\n808 0.0 100.0 2\n810 50.0 50.0 2\n813 50.0 50.0 2\n820 0.0 100.0 2\n869 50.0 50.0 2\n877 100.0 0.0 2\n884 50.0 50.0 2\n902 0.0 100.0 2\n930 0.0 100.0 2\n933 50.0 50.0 2\n938 50.0 0.0 2\n940"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 50.0 50.0 2\n985 50.0 50.0 2\n1018 100.0 0.0 2\n1023 0.0 100.0 2\n1037 50.0 50.0 2\n16 0.0 100.0 1\n315 0.0 100.0 1\n343 0.0 100.0 1\n345 0.0 100.0 1\n363 100.0 0.0 1\n373 0.0 100.0 1\n374 100.0 0.0 1\n379"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 0.0 100.0 1\n409 0.0 100.0 1\n410 0.0 100.0 1\n417 0.0 100.0 1\n425 0.0 100.0 1\n432 0.0 100.0 1\n434 100.0 0.0 1\n435 0.0 100.0 1\n436 100.0 0.0 1\n438 0.0 100.0 1\n447 100.0 0.0 1\n448 100.0 0.0 1\n457 100.0 0.0 1\n460"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 0.0 100.0 1\n462 100.0 0.0 1\n472 0.0 100.0 1\n477 100.0 0.0 1\n481 0.0 100.0 1\n488 0.0 100.0 1\n499 0.0 100.0 1\n507 100.0 0.0 1\n508 0.0 100.0 1\n509 100.0 0.0 1\n510 0.0 100.0 1\n514 100.0 0.0 1\n525 0.0 100.0 1\n547"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 0.0 100.0 1\n556 0.0 100.0 1\n578 0.0 100.0 1\n580 0.0 100.0 1\n581 0.0 100.0 1\n584 0.0 100.0 1\n592 100.0 0.0 1\n594 0.0 100.0 1\n600 0.0 100.0 1\n608 0.0 100.0 1\n622 0.0 100.0 1\n626 0.0 100.0 1\n628 0.0 100.0 1\n631"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 0.0 100.0 1\n635 0.0 100.0 1\n638 0.0 100.0 1\n647 0.0 100.0 1\n670 0.0 100.0 1\n671 0.0 100.0 1\n672 0.0 100.0 1\n676 0.0 100.0 1\n679 0.0 100.0 1\n680 0.0 100.0 1\n681 0.0 100.0 1\n682 0.0 100.0 1\n689 100.0 0.0 1\n690 100.0 0.0 1\n698"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 0.0 100.0 1\n705 0.0 100.0 1\n715 0.0 100.0 1\n716 0.0 100.0 1\n718 0.0 100.0 1\n722 0.0 100.0 1\n726 100.0 0.0 1\n729 0.0 100.0 1\n738 0.0 100.0 1\n740 100.0 0.0 1\n750 0.0 100.0 1\n751 0.0 100.0 1\n752 0.0 100.0 1\n760"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 1\n763 0.0 100.0 1\n771 0.0 100.0 1\n773 0.0 100.0 1\n777 0.0 100.0 1\n782 0.0 100.0 1\n784 0.0 100.0 1\n800 0.0 100.0 1\n801 100.0 0.0 1\n806 0.0 100.0 1\n809 0.0 100.0 1\n812 0.0 100.0 1\n814"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 100.0 0.0 1\n819 0.0 100.0 1\n822 0.0 100.0 1\n824 100.0 0.0 1\n826 0.0 100.0 1\n827 0.0 100.0 1\n830 0.0 100.0 1\n833 100.0 0.0 1\n834 0.0 100.0 1\n837 0.0 100.0 1\n857 100.0 0.0 1\n858"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 0.0 100.0 1\n860 0.0 100.0 1\n863 0.0 100.0 1\n873 0.0 100.0 1\n887 0.0 100.0 1\n896 0.0 100.0 1\n897 0.0 100.0 1\n898 100.0 0.0 1\n900 0.0 100.0 1\n907 0.0 100.0 1\n911 0.0 100.0 1\n917"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 0.0 100.0 1\n921 0.0 100.0 1\n925 0.0 100.0 1\n928 100.0 0.0 1\n936 0.0 100.0 1\n966 0.0 0.0 1\n967 0.0 100.0 1\n968 0.0 100.0 1\n970 0.0 100.0 1\n976 0.0 100.0 1\n977 0.0 100.0 1\n980 0.0 100.0 1\n987"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 0.0 100.0 1\n988 0.0 100.0 1\n991 0.0 100.0 1\n1003 0.0 100.0 1\n1004 0.0 100.0 1\n1007 0.0 100.0 1\n1011 0.0 100.0 1\n1014 0.0 100.0 1\n1015 0.0 100.0 1\n1028 0.0 100.0 1\n1033 0.0 100.0 1\n"
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print \"percentage of transactions are inward for top 25 communities per volume\"\nplt.plot(inn[:25])\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "percentage of transactions are inward for top 25 communities per volume\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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G8thYHbXt2WNNv0LBV18BH39sdS/MUd2649VhaoUs8/e/A3/5C9ClS+W/T07WmWaHDgW3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\n",
       "text": "<matplotlib.figure.Figure at 0x1186e4e10>"
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print \"percentage of transactions are outward for top 25 communities per volume\"\nplt.plot(ouut[:25])\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "percentage of transactions are outward for top 25 communities per volume\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dNE1eHtChg/ffeBs0ANq29X7ZYiNoDvDHjx9HdnY2/vrXv1bclpGRgU8//RSx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\nSTHAExGZFAM8EZFJ/T8kvWG2GL8YMwAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x1186a59d0>"
      }
     ],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "in_y=[]\nin_n=[]\nout_y=[]\nout_n=[]\nprint \"community\", \"in_money\", \"in_no_money\", \"out_money\", \"out_no_money\", \"total-list\"\nfor x in all_comm:\n    temp=data_310[data_310.community_id==x][['mon_dir']].values\n    ln=len(data_310[data_310.community_id==x][['mon_dir']])\n    i1=0.0\n    i2=0.0\n    o1=0.0\n    o2=0.0\n    for j in temp:\n        if j==\"in_y\":\n            i1+=1\n        elif j==\"in_n\":\n            i2+=1\n        elif j==\"out_y\":\n            o1+=1\n        elif j==\"out_n\":\n            o2+=1\n    in_y.append((i1/ln)*100)\n    in_n.append((i2/ln)*100)\n    out_y.append((o1/ln)*100)\n    out_n.append((o2/ln)*100)\n    print x, (i1/ln)*100, (i2/ln)*100,(o1/ln)*100,  (o2/ln)*100, ln",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "community in_money in_no_money out_money out_no_money total-list\n501"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 13.8269829859 6.61394680086 68.0325904625 11.4905343877 8346\n11"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 15.8018867925 3.25471698113 73.1603773585 7.78301886792 2120\n19 15.8273381295 2.3381294964 76.1690647482 5.6654676259 1112\n295 11.2175102599 12.4487004104 47.6060191518 27.7701778386 731\n29 12.8878281623 5.96658711217 66.8257756563 13.6038186158 419\n297"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 20.3846153846 3.07692307692 76.1538461538 0.384615384615 260\n27 8.87096774194 20.9677419355 63.3064516129 6.85483870968 248\n322 12.5541125541 1.2987012987 84.8484848485 1.2987012987 231\n12 16.7512690355 2.03045685279 72.5888324873 8.62944162437 197\n308 7.87401574803 5.51181102362 85.8267716535 0.787401574803 127\n14 9.82142857143 8.92857142857 75.8928571429 5.35714285714 112\n306 12.2641509434 17.9245283019 58.4905660377 11.320754717 106\n305 4.12371134021 10.3092783505 76.2886597938 9.27835051546 97\n313"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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       "text": " 0.0 0.0 0.0 100.0 1\n987 0.0 0.0 100.0 0.0 1\n988 0.0 0.0 0.0 100.0 1\n991 0.0 0.0 100.0 0.0 1\n1003 0.0 0.0 100.0 0.0 1\n1004 0.0 0.0 0.0 100.0 1\n1007 0.0 0.0 0.0 100.0 1\n1011 0.0 0.0 100.0 0.0 1\n1014 0.0 0.0 100.0 0.0 1\n1015 0.0 0.0 100.0 0.0 1\n1028 0.0 0.0 100.0 0.0 1\n1033 0.0 0.0 100.0 0.0 1\n"
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print \"percentage of transactions are inward and does not involve money for top 25 communities per volume\"\nplt.plot(in_n[:25])\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "percentage of transactions are inward and does not involve money for top 25 communities per volume\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Wb6zef6dZB/JAZuNuY8bIgumWLeqMSc1AHh0NHDigbx91TxRF+me0aQO8/rq1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\nHAM5EZHFMZATEVkcAzkRkcX9P5w9jkr+g6WNAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x116595990>"
      }
     ],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print \"percentage of transactions are inward and involve money for top 25 communities per volume\"\nplt.plot(in_y[:25])\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "percentage of transactions are inward and involve money for top 25 communities per volume\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fFjAZ8VHa19a1vnTrJsHop5/0H4NezKqPq4Kd8DSi/RCwprzSYiAfN24crr32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\nyOEYyImIHO7/AUbecaCVyD0UAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x118dcbe10>"
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print \"percentage of transactions are outward and does not involve money for top 25 communities per volume\"\nplt.plot(out_n[:25])\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "percentage of transactions are outward and does not involve money for top 25 communities per volume\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+4kSZ6hwuRnWstBYXJ4vllJQYex4CKipkLYshQ4C8PGDqVPk3NhrLKtr5y8jn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\nAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x118dbf990>"
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print \"percentage of transactions are outward and involve money for top 25 communities per volume\"\nplt.plot(out_y[:25])\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "percentage of transactions are outward and involve money for top 25 communities per volume\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VgM5NXbjQ+6pcjYwVEbPmkoeVkAOe7RW2VRgpqJW5Iq7GAeAXvwBKS6+8SzQD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\nIb9bKP8zagAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x1165e97d0>"
      }
     ],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "usr=[]\nfor i in da",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "temp_usr=data_310.author_id\nusr=[]\nfor i in temp_usr:\n    if i not in usr:\n        usr.append(i)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(usr)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 127,
       "text": "5086"
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "user_money_no_money=[]\nuser_in_out=[]\nuser_in_out_mon_no_mon=[]\nfor x in usr:\n    temp=data_310[data_310.author_id==x][['mon_dir']].values\n    temp_dir=data_310[data_310.author_id==x][['direction']].values\n    temp_mon=data_310[data_310.author_id==x][['money']].values\n    ln=len(data_310[data_310.author_id==x][['mon_dir']])\n    money=0\n    no_money=0\n    inward=0\n    outward=0\n    in_mon=0\n    in_no_mon=0\n    out_mon=0\n    out_no_mon=0\n    for j in temp_dir:\n        if j==\"in\":\n            inward+=1\n        if j==\"out\":\n            outward+=1\n    for j in temp_mon:\n        if j==\"y\":\n            money+=1\n        elif j==\"n\":\n            no_money+=1\n    for j in temp:\n        if j==\"in_y\":\n            in_mon+=1\n        elif j==\"in_n\":\n            in_no_mon+=1\n        elif j==\"out_y\":\n            out_mon+=1\n        elif j==\"out_n\":\n            out_no_mon+=1\n    user_money_no_money.append(( x,( float(money)/float(ln))*100, float(no_money)/float(ln)*100, money, no_money,ln))\n    user_in_out.append((x,(float(inward)/float(ln))*100, float(outward)/float(ln)*100, inward, outward, ln))\n    user_in_out_mon_no_mon.append((x, float(in_mon)/float(ln)*100, float(in_no_mon)/float(ln)*100, float(out_mon)/float(ln)*100, float(out_no_mon)/float(ln)*100, in_mon, in_no_mon, out_mon, out_no_mon, ln)) \n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 183
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "temp_in=[]\ntemp_out=[]\ntemp_in_number=[]\ntemp_out_number=[]\nfor i in user_in_out:\n    temp_in.append(i[1])\n    temp_out.append(i[2])\n    temp_in_number.append(i[3])\n    temp_out_number.append(i[4])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 184
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.hist(temp_in)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 180,
       "text": "(array([ 3152.,    96.,   149.,   145.,    54.,   294.,    83.,    31.,\n          18.,  1064.]),\n array([   0.,   10.,   20.,   30.,   40.,   50.,   60.,   70.,   80.,\n         90.,  100.]),\n <a list of 10 Patch objects>)"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x11a858e90>"
      }
     ],
     "prompt_number": 180
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.hist(temp_out)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 181,
       "text": "(array([ 1073.,     5.,    47.,    70.,    28.,   302.,   171.,   107.,\n         138.,  3145.]),\n array([   0.,   10.,   20.,   30.,   40.,   50.,   60.,   70.,   80.,\n         90.,  100.]),\n <a list of 10 Patch objects>)"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x11a9b0090>"
      }
     ],
     "prompt_number": 181
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.scatter(temp_in_number,temp_out_number)\nplt.xlabel(\"inward\")\nplt.ylabel(\"outward\")\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 186,
       "text": "<matplotlib.text.Text at 0x11a753850>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qcuy8aJFITExk8+bNbNu2DTc3N/r168eNN97I4MGDLyvZy6FFQimlaq9BisRZP/30E+vW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\n/h/IKIQ+Bonk6QAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x11a745a90>"
      }
     ],
     "prompt_number": 186
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "temp_m=[]\ntemp_nm=[]\ntemp_m_number=[]\ntemp_nm_number=[]\nfor i in user_money_no_money:\n    temp_m.append(i[1])\n    temp_nm.append(i[2])\n    temp_m_number.append(i[3])\n    temp_nm_number.append(i[4])\nplt.scatter(temp_m_number,temp_nm_number)\nplt.xlabel(\"money\")\nplt.ylabel(\"no money\")\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 187,
       "text": "<matplotlib.text.Text at 0x11a727450>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UlU1A09bg7r6Lbt26GR2aEKISKmzD6N69Oz/88ENNxeMUacOo/Y4fP87o0Q+xadPPtGzZ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\n",
       "text": "<matplotlib.figure.Figure at 0x11ac3c050>"
      }
     ],
     "prompt_number": 187
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.hist(temp_m)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 203,
       "text": "(array([  676.,    21.,    55.,    69.,    45.,   259.,   198.,    96.,\n         135.,  3532.]),\n array([   0.,   10.,   20.,   30.,   40.,   50.,   60.,   70.,   80.,\n         90.,  100.]),\n <a list of 10 Patch objects>)"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x11ac44110>"
      }
     ],
     "prompt_number": 203
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.hist(temp_nm)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 207,
       "text": "(array([ 3524.,   100.,   131.,   177.,    43.,   261.,    97.,    43.,\n          32.,   678.]),\n array([   0.,   10.,   20.,   30.,   40.,   50.,   60.,   70.,   80.,\n         90.,  100.]),\n <a list of 10 Patch objects>)"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8AAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x11b093e90>"
      }
     ],
     "prompt_number": 207
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "data_501=data_310[data_310.community_id==\"501\"]\ntemp_usr_501=data_501.author_id\nusr_501=[]\nfor i in temp_usr_501:\n    if i not in usr_501:\n        usr_501.append(i)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 212
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(usr_501)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 213,
       "text": "2021"
      }
     ],
     "prompt_number": 213
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "user_money_no_money=[]\nuser_in_out=[]\nuser_in_out_mon_no_mon=[]\nfor x in usr_501:\n    temp=data_501[data_501.author_id==x][['mon_dir']].values\n    temp_dir=data_501[data_310.author_id==x][['direction']].values\n    temp_mon=data_501[data_310.author_id==x][['money']].values\n    ln=len(data_501[data_501.author_id==x][['mon_dir']])\n    money=0\n    no_money=0\n    inward=0\n    outward=0\n    in_mon=0\n    in_no_mon=0\n    out_mon=0\n    out_no_mon=0\n    for j in temp_dir:\n        if j==\"in\":\n            inward+=1\n        if j==\"out\":\n            outward+=1\n    for j in temp_mon:\n        if j==\"y\":\n            money+=1\n        elif j==\"n\":\n            no_money+=1\n    for j in temp:\n        if j==\"in_y\":\n            in_mon+=1\n        elif j==\"in_n\":\n            in_no_mon+=1\n        elif j==\"out_y\":\n            out_mon+=1\n        elif j==\"out_n\":\n            out_no_mon+=1\n    user_money_no_money.append(( x,( float(money)/float(ln))*100, float(no_money)/float(ln)*100, money, no_money,ln))\n    user_in_out.append((x,(float(inward)/float(ln))*100, float(outward)/float(ln)*100, inward, outward, ln))\n    user_in_out_mon_no_mon.append((x, float(in_mon)/float(ln)*100, float(in_no_mon)/float(ln)*100, float(out_mon)/float(ln)*100, float(out_no_mon)/float(ln)*100, in_mon, in_no_mon, out_mon, out_no_mon, ln)) \n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "/Library/Python/2.7/site-packages/pandas/core/frame.py:2021: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n  \"DataFrame index.\", UserWarning)\n"
      }
     ],
     "prompt_number": 214
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "temp_in=[]\ntemp_out=[]\ntemp_in_number=[]\ntemp_out_number=[]\nfor i in user_in_out:\n    temp_in.append(i[1])\n    temp_out.append(i[2])\n    temp_in_number.append(i[3])\n    temp_out_number.append(i[4])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 215
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "authors=[]\n# authors=(data_310.author_id, data_310.mon_dir)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 251
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "for i in range(len(data_310)):\n    authors.append((data_310.author_id[i],data_310.mon_dir[i], data_310.created_at))",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 258
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "author=data_310.author_id\nauthors[100]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 260,
       "text": "('dopEXwTamr3PY0aaWPfx7J', 'out_y')"
      }
     ],
     "prompt_number": 260
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "auth_state=[]\nfor i in usr:\n    temp_state=[]\n    for j in authors:\n        if j[0]==i:\n            temp_state.append(j[1])\n    auth_state.append((i, temp_state, len(temp_state)))",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 265
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "auth_state[1]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 429,
       "text": "('acnSYy-rer3BOyaaWPfx7J',\n ['out_n', 'out_n', 'out_n', 'out_n', 'out_n', 'out_n', 'out_n', 'out_n'],\n 8)"
      }
     ],
     "prompt_number": 429
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "temp_auth_state=auth_state[103][1]\ntemp_auth_state",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 370,
       "text": "['in_n',\n 'in_n',\n 'in_n',\n 'out_n',\n 'in_n',\n 'in_n',\n 'in_n',\n 'in_n',\n 'in_n',\n 'out_n',\n 'in_n',\n 'in_n']"
      }
     ],
     "prompt_number": 370
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def state(i):\n    temp_auth_state=auth_state[i][1]\n    prev_letter=0\n    prev_count=0\n    tmp=[]\n    state=[]\n    for i in range(len(temp_auth_state)):\n        if i==0:\n            tmp.append((temp_auth_state[0], 0))\n        if i>0:\n            if temp_auth_state[i]==temp_auth_state[i-1]:\n                prev_letter=temp_auth_state[i]\n                prev_count+=1\n            else:\n                prev_letter=temp_auth_state[i]\n                prev_count+=1\n                tmp.append((prev_letter, prev_count))\n        if i==len(temp_auth_state)-1:\n            tmp.append((temp_auth_state[i], i))\n                \n    for i in range(1,len(tmp)+1):\n        if i<len(tmp):\n            state.append((tmp[i-1][0], (tmp[i][1]-tmp[i-1][1])))\n        if i==len(tmp):\n            state.append((tmp[i-1][0], (tmp[i-1][1]-tmp[i-2][1])))\n    print auth_state[i][0],state[:-1]\n    return state[:-1]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 433
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "for i in range(10):\n    state(i)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "bXeUScR0ar3QO0aaWPfx7J [('in_n', 1), ('out_n', 1), ('in_n', 9), ('out_y', 1), ('out_n', 1), ('in_n', 3), ('out_y', 1), ('in_n', 1), ('out_y', 1), ('in_n', 1), ('out_y', 1), ('in_n', 2), ('out_n', 1), ('in_n', 3), ('out_y', 1), ('in_n', 1), ('out_y', 1), ('in_n', 1), ('out_n', 14), ('out_y', 5), ('in_y', 1), ('in_n', 5), ('out_n', 1), ('in_n', 9), ('out_y', 1), ('out_n', 1), ('in_n', 3), ('out_y', 1), ('in_n', 1), ('out_y', 1), ('in_n', 1), ('out_y', 1), ('in_n', 2), ('out_n', 1), ('in_n', 3), ('out_y', 1), ('in_n', 1), ('out_y', 1), ('in_n', 1), ('out_n', 14), ('out_y', 5), ('in_y', 1), ('in_n', 3)]\nd4e6Gypx8r3PExaaWPfx7J [('out_n', 7)]\nd4e6Gypx8r3PExaaWPfx7J [('out_n', 1)]\naCDLmUyimr3RJHaaWPfx7J [('in_n', 1), ('out_n', 1), ('out_y', 1), ('in_n', 1), ('out_n', 1), ('out_y', 0)]\na5es-et0er3RJHaaWPfx7J [('out_y', 1), ('out_n', 3), ('out_y', 3), ('out_n', 3), ('out_y', 1)]\najgQjUuA4r3RM4aaWPfx7J [('in_n', 1), ('out_y', 1), ('in_n', 1), ('out_y', 0)]\naCDLmUyimr3RJHaaWPfx7J [('in_n', 1), ('out_n', 2), ('out_y', 2), ('in_n', 1), ('out_n', 2), ('out_y', 1)]\najgQjUuA4r3RM4aaWPfx7J [('out_n', 5), ('out_y', 2), ('out_n', 5), ('out_y', 1)]\nd4e6Gypx8r3PExaaWPfx7J [('out_n', 11)]\na5es-et0er3RJHaaWPfx7J [('out_y', 1), ('out_n', 3), ('out_y', 2), ('out_n', 3), ('out_y', 0)]\n"
      }
     ],
     "prompt_number": 434
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "a=[1,2,3]\na[:-1]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 432,
       "text": "[1, 2]"
      }
     ],
     "prompt_number": 432
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from datetime import datetime\nfrom dateutil import parser\ncreated=data_310.created_at\nlen(created)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 455,
       "text": "16470"
      }
     ],
     "prompt_number": 455
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "dt=(parser.parse(created[2])-parser.parse(created[1]))\ndt.",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 454,
       "text": "300"
      }
     ],
     "prompt_number": 454
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "authtime=[]\nfor i in range(len(data_310)):\n    authtime.append((data_310.author_id[i], data_310.created_at[i]))",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 459
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "at=[]\nfor i in usr:\n    temp_at=[]\n    for j in range(len(authtime)):\n        if i==authtime[j][0]:\n            temp_at.append(authtime[j][1])\n    if len(temp_at)>1:\n        temp_ath=[]\n        for k in range(len(temp_at)-1):\n            temp_ath.append((parser.parse(temp_at[k+1])-parser.parse(temp_at[k])).seconds/86400)\n    else:\n        at.append((i, \"NA\"))\n    at.append((i,temp_ath))",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 479
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "at[210]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 478,
       "text": "('d6p1oWMRKr34RiaaWPfx7J', [47220, 63600])"
      }
     ],
     "prompt_number": 478
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import pandas.io.sql as psql\nimport sqlite3 as lite\ncon = lite.connect(\"sharetribe.db\")\nwith con:\n    user_log_sql = \"SELECT * FROM user_log\"\n    df_user_log = psql.frame_query(user_log_sql, con)\n    print df_user_log.shape",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "(1084292, 6)\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df_user_log.to_csv(\"user_log.csv\")",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "with con:\n    user_act_freq_sql = \"SELECT * FROM user_act_freq\"\n    df_user_act_freq = psql.frame_query(user_act_freq_sql, con)\n    print df_user_act_freq.shape\n    user_act_freq_sql = \"SELECT * FROM user_act_freq\"\n    df_user_act_freq = psql.frame_query(user_act_freq_sql, con)\n    print df_user_act_freq.shape\n    visitfreq_sql = \"SELECT * FROM visitfreq1\"\n    df_visitfreq = psql.frame_query(visitfreq_sql, con)\n    print df_visitfreq.shape\n    post_freq_sql = \"SELECT * FROM post_freq1\"\n    df_post_freq = psql.frame_query(post_freq_sql, con)\n    print df_post_freq.shape\ndf_user_act_freq.to_csv(\"user_act_freq.csv\")\ndf_visitfreq.to_csv(\"userfreq.csv\")\ndf_post_freq.to_csv(\"post_freq.csv\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "(97837, 4)\n(97837, 4)"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n(797, 2)\n(782, 2)\n"
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df_user_act_freq.head(10)",
     "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>user_id</th>\n      <th>date(created_at)</th>\n      <th>count(id)</th>\n      <th>community_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> a--1rI380r4Oac3LC_sCEq</td>\n      <td> 2013-06-28</td>\n      <td>  4</td>\n      <td>  19</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> a-0Beu_nSr4B_IabWuarwG</td>\n      <td> 2012-09-12</td>\n      <td>  8</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> a-0Beu_nSr4B_IabWuarwG</td>\n      <td> 2012-09-13</td>\n      <td>  7</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> a-0Beu_nSr4B_IabWuarwG</td>\n      <td> 2012-09-19</td>\n      <td>  1</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> a-0Beu_nSr4B_IabWuarwG</td>\n      <td> 2012-10-24</td>\n      <td>  3</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td> a-1j5GZ4yr4ARnabWuarwG</td>\n      <td> 2012-07-16</td>\n      <td>  3</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td> a-1j5GZ4yr4ARnabWuarwG</td>\n      <td> 2012-07-24</td>\n      <td> 11</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td> a-1j5GZ4yr4ARnabWuarwG</td>\n      <td> 2013-05-23</td>\n      <td>  1</td>\n      <td> 297</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td> a-659UU9Cr4OaaHRwIatgc</td>\n      <td> 2013-05-13</td>\n      <td>  9</td>\n      <td> 297</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td> a-7mnM8Iqr4OaaXLbny1Fu</td>\n      <td> 2013-07-21</td>\n      <td>  5</td>\n      <td> NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": "                  user_id date(created_at)  count(id)  community_id\n0  a--1rI380r4Oac3LC_sCEq       2013-06-28          4            19\n1  a-0Beu_nSr4B_IabWuarwG       2012-09-12          8           NaN\n2  a-0Beu_nSr4B_IabWuarwG       2012-09-13          7           NaN\n3  a-0Beu_nSr4B_IabWuarwG       2012-09-19          1           NaN\n4  a-0Beu_nSr4B_IabWuarwG       2012-10-24          3           NaN\n5  a-1j5GZ4yr4ARnabWuarwG       2012-07-16          3           NaN\n6  a-1j5GZ4yr4ARnabWuarwG       2012-07-24         11           NaN\n7  a-1j5GZ4yr4ARnabWuarwG       2013-05-23          1           297\n8  a-659UU9Cr4OaaHRwIatgc       2013-05-13          9           297\n9  a-7mnM8Iqr4OaaXLbny1Fu       2013-07-21          5           NaN"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df_community_memberships.head(10)",
     "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>id</th>\n      <th>person_id</th>\n      <th>community_id</th>\n      <th>admin</th>\n      <th>created_at</th>\n      <th>updated_at</th>\n      <th>consent</th>\n      <th>invitation_id</th>\n      <th>last_page_load_date</th>\n      <th>status</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 147</td>\n      <td> d_8CcgNxqr4lLjabWuarwG</td>\n      <td> 4</td>\n      <td> 1</td>\n      <td> 2011-06-23 08:44:25</td>\n      <td> 2013-02-21 11:13:38</td>\n      <td> KASSI_CO1.0</td>\n      <td>NaN</td>\n      <td> 2013-02-21 11:13:38</td>\n      <td> accepted</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 148</td>\n      <td> aWCO7INy8r4lLjabWuarwG</td>\n      <td> 4</td>\n      <td> 0</td>\n      <td> 2011-06-23 11:51:54</td>\n      <td> 2012-04-24 10:01:44</td>\n      <td> KASSI_CO1.0</td>\n      <td>NaN</td>\n      <td> 2012-04-24 10:01:44</td>\n      <td> accepted</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 150</td>\n      <td> aE013ONKir4lLjabWuarwG</td>\n      <td> 4</td>\n      <td> 0</td>\n      <td> 2011-06-24 09:12:44</td>\n      <td> 2012-08-03 12:17:59</td>\n      <td> KASSI_CO1.0</td>\n      <td>NaN</td>\n      <td> 2012-08-03 12:17:59</td>\n      <td> accepted</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 151</td>\n      <td> dhqCOaNKir4lLjabWuarwG</td>\n      <td> 4</td>\n      <td> 0</td>\n      <td> 2011-06-24 09:17:27</td>\n      <td> 2011-11-21 16:25:44</td>\n      <td> KASSI_CO1.0</td>\n      <td>NaN</td>\n      <td> 2011-06-24 09:17:28</td>\n      <td> accepted</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 152</td>\n      <td> aZqiLYNKCr4kwAabWuarwG</td>\n      <td> 4</td>\n      <td> 0</td>\n      <td> 2011-06-24 09:49:06</td>\n      <td> 2012-08-27 15:35:15</td>\n      <td> KASSI_CO1.0</td>\n      <td>NaN</td>\n      <td> 2012-08-27 15:35:15</td>\n      <td> accepted</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td> 157</td>\n      <td> cPhMgeOiKr4ib_abWuarwG</td>\n      <td> 4</td>\n      <td> 0</td>\n      <td> 2011-06-27 06:49:53</td>\n      <td> 2011-11-21 16:25:44</td>\n      <td> KASSI_CO1.0</td>\n      <td>NaN</td>\n      <td> 2011-06-30 07:24:05</td>\n      <td> accepted</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td> 171</td>\n      <td> a97Kp8Q34r4iZKabWuarwG</td>\n      <td> 4</td>\n      <td> 0</td>\n      <td> 2011-07-11 05:25:52</td>\n      <td> 2011-11-21 16:25:44</td>\n      <td> KASSI_CO1.0</td>\n      <td>NaN</td>\n      <td> 2011-07-11 05:25:56</td>\n      <td> accepted</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td> 172</td>\n      <td> c1c0aEQ4ar4iZKabWuarwG</td>\n      <td> 4</td>\n      <td> 0</td>\n      <td> 2011-07-11 05:43:32</td>\n      <td> 2011-11-21 16:25:44</td>\n      <td> KASSI_CO1.0</td>\n      <td>NaN</td>\n      <td> 2011-07-27 13:46:03</td>\n      <td> accepted</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td> 183</td>\n      <td> c_LuL0U_qr4lv1abWuarwG</td>\n      <td> 4</td>\n      <td> 0</td>\n      <td> 2011-08-01 04:14:28</td>\n      <td> 2011-11-21 16:25:44</td>\n      <td> KASSI_CO1.0</td>\n      <td>NaN</td>\n      <td> 2011-10-26 10:21:22</td>\n      <td> accepted</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td> 186</td>\n      <td> bYQ5AuVMSr4knrabWuarwG</td>\n      <td> 7</td>\n      <td> 0</td>\n      <td> 2011-08-04 07:29:11</td>\n      <td> 2011-11-21 16:25:44</td>\n      <td> KASSI_CO1.0</td>\n      <td>NaN</td>\n      <td> 2011-08-04 07:29:16</td>\n      <td> accepted</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": "    id               person_id  community_id  admin           created_at  \\\n0  147  d_8CcgNxqr4lLjabWuarwG             4      1  2011-06-23 08:44:25   \n1  148  aWCO7INy8r4lLjabWuarwG             4      0  2011-06-23 11:51:54   \n2  150  aE013ONKir4lLjabWuarwG             4      0  2011-06-24 09:12:44   \n3  151  dhqCOaNKir4lLjabWuarwG             4      0  2011-06-24 09:17:27   \n4  152  aZqiLYNKCr4kwAabWuarwG             4      0  2011-06-24 09:49:06   \n5  157  cPhMgeOiKr4ib_abWuarwG             4      0  2011-06-27 06:49:53   \n6  171  a97Kp8Q34r4iZKabWuarwG             4      0  2011-07-11 05:25:52   \n7  172  c1c0aEQ4ar4iZKabWuarwG             4      0  2011-07-11 05:43:32   \n8  183  c_LuL0U_qr4lv1abWuarwG             4      0  2011-08-01 04:14:28   \n9  186  bYQ5AuVMSr4knrabWuarwG             7      0  2011-08-04 07:29:11   \n\n            updated_at      consent  invitation_id  last_page_load_date  \\\n0  2013-02-21 11:13:38  KASSI_CO1.0            NaN  2013-02-21 11:13:38   \n1  2012-04-24 10:01:44  KASSI_CO1.0            NaN  2012-04-24 10:01:44   \n2  2012-08-03 12:17:59  KASSI_CO1.0            NaN  2012-08-03 12:17:59   \n3  2011-11-21 16:25:44  KASSI_CO1.0            NaN  2011-06-24 09:17:28   \n4  2012-08-27 15:35:15  KASSI_CO1.0            NaN  2012-08-27 15:35:15   \n5  2011-11-21 16:25:44  KASSI_CO1.0            NaN  2011-06-30 07:24:05   \n6  2011-11-21 16:25:44  KASSI_CO1.0            NaN  2011-07-11 05:25:56   \n7  2011-11-21 16:25:44  KASSI_CO1.0            NaN  2011-07-27 13:46:03   \n8  2011-11-21 16:25:44  KASSI_CO1.0            NaN  2011-10-26 10:21:22   \n9  2011-11-21 16:25:44  KASSI_CO1.0            NaN  2011-08-04 07:29:16   \n\n     status  \n0  accepted  \n1  accepted  \n2  accepted  \n3  accepted  \n4  accepted  \n5  accepted  \n6  accepted  \n7  accepted  \n8  accepted  \n9  accepted  "
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "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>community_id</th>\n      <th>listing_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>   4</td>\n      <td> 57</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>  11</td>\n      <td> 57</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 297</td>\n      <td> 57</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>   4</td>\n      <td> 58</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>   4</td>\n      <td> 59</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>   4</td>\n      <td> 60</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>   4</td>\n      <td> 61</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>  11</td>\n      <td> 61</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td> 297</td>\n      <td> 61</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>   4</td>\n      <td> 62</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": "   community_id  listing_id\n0             4          57\n1            11          57\n2           297          57\n3             4          58\n4             4          59\n5             4          60\n6             4          61\n7            11          61\n8           297          61\n9             4          62"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}