
company_strength_before_2005 = 14210

period = [u'2005-01',
 u'2005-02',
 u'2005-03',
 u'2005-04',
 u'2005-05',
 u'2005-06',
 u'2005-07',
 u'2005-08',
 u'2005-09',
 u'2005-10',
 u'2005-11',
 u'2005-12',
 u'2006-01',
 u'2006-02',
 u'2006-03',
 u'2006-04',
 u'2006-05',
 u'2006-06',
 u'2006-07',
 u'2006-08',
 u'2006-09',
 u'2006-10',
 u'2006-11',
 u'2006-12',
 u'2007-01',
 u'2007-02',
 u'2007-03',
 u'2007-04',
 u'2007-05',
 u'2007-06',
 u'2007-07',
 u'2007-08',
 u'2007-09',
 u'2007-10',
 u'2007-11',
 u'2007-12',
 u'2008-01',
 u'2008-02',
 u'2008-03',
 u'2008-04',
 u'2008-05',
 u'2008-06',
 u'2008-07',
 u'2008-08',
 u'2008-09',
 u'2008-10',
 u'2008-11',
 u'2008-12',
 u'2009-01',
 u'2009-02',
 u'2009-03',
 u'2009-04',
 u'2009-05',
 u'2009-06',
 u'2009-07',
 u'2009-08',
 u'2009-09',
 u'2009-10',
 u'2009-11',
 u'2009-12',
 u'2010-01',
 u'2010-02',
 u'2010-03',
 u'2010-04',
 u'2010-05',
 u'2010-06',
 u'2010-07',
 u'2010-08',
 u'2010-09',
 u'2010-10',
 u'2010-11',
 u'2010-12',
 u'2011-01',
 u'2011-02',
 u'2011-03',
 u'2011-04',
 u'2011-05',
 u'2011-06',
 u'2011-07',
 u'2011-08',
 u'2011-09',
 u'2011-10',
 u'2011-11',
 u'2011-12',
 u'2012-01',
 u'2012-02',
 u'2012-03',
 u'2012-04',
 u'2012-05',
 u'2012-06',
 u'2012-07',
 u'2012-08',
 u'2012-09',
 u'2012-10',
 u'2012-11',
 u'2012-12',
 u'2013-01',
 u'2013-02',
 u'2013-03',
 u'2013-04',
 u'2013-05',
 u'2013-06',
 u'2013-07',
 u'2013-08',
 u'2013-09',
 u'2013-10',
 u'2013-11',
 u'2013-12',
 u'2014-01',
 u'2014-02',
 u'2014-03',
 u'2014-04',
 u'2014-05',
 u'2014-06',
 u'2014-07',
 u'2014-08',
 u'2014-09',
 u'2014-10',
 u'2014-11',
 u'2014-12']

employee_intake = [2696, 501, 415, 563, 956, 1389, 1533, 1710, 1487, 882, 911, 849, 2908, 689, 710, 618, 1102, 2814, 2328, 1669, 1347, 1271, 940, 809, 2906, 666, 728, 627, 1102, 2354, 2452, 1452, 864, 1602, 1489, 1126, 2321, 448, 539, 655, 889, 2119, 1874, 1082, 2169, 471, 1268, 958, 1666, 846, 991, 431, 537, 1663, 1274, 1298, 715, 1585, 1134, 1147, 2905, 1175, 933, 832, 760, 2525, 1912, 1764, 1961, 1246, 1182, 1264, 1949, 1349, 952, 734, 781, 2011, 1778, 2171, 1698, 1146, 757, 932, 1641, 1204, 702, 437, 526, 722, 990, 807, 631, 585, 231, 242, 1057, 476, 365, 441, 396, 531, 449, 433, 417, 530, 109, 105, 546, 1042, 164, 172, 236, 364, 469, 261, 240, 290, 151, 173]

employee_transfer = [1668, 353, 489, 418, 547, 600, 572, 470, 441, 443, 416, 633, 2291, 576, 664, 592, 665, 894, 751, 743, 614, 597, 534, 903, 2606, 631, 804, 748, 839, 985, 980, 845, 832, 750, 595, 958, 2347, 714, 789, 795, 946, 1236, 1030, 855, 758, 704, 484, 751, 1860, 509, 637, 867, 1036, 1168, 1083, 890, 1302, 945, 736, 1192, 2505, 1086, 1524, 1423, 1560, 1919, 1548, 1306, 1386, 1268, 1059, 1483, 2100, 1048, 1411, 1271, 1381, 1735, 1366, 1010, 1197, 1085, 791, 1149, 2088, 821, 913, 958, 1341, 1922, 1395, 919, 716, 644, 573, 1473, 1795, 697, 810, 783, 1058, 1101, 1076, 814, 922, 764, 511, 693, 1196, 583, 721, 589, 733, 845, 619, 485, 463, 387, 282, 324]

total_employees = [15238, 15386, 15312, 15457, 15866, 16655, 17616, 18856, 19902, 20341, 20836, 21052, 21669, 21782, 21828, 21854, 22291, 24211, 25788, 26714, 27447, 28121, 28527, 28433, 28733, 28768, 28692, 28571, 28834, 30203, 31675, 32282, 32314, 33166, 34060, 34228, 34202, 33936, 33686, 33546, 33489, 34372, 35216, 35443, 36854, 36621, 37405, 37612, 37418, 37755, 38109, 37673, 37174, 37669, 37860, 38268, 37681, 38321, 38719, 38674, 39074, 39163, 38572, 37981, 37181, 37787, 38151, 38609, 39184, 39162, 39285, 39066, 38915, 39216, 38757, 38220, 37620, 37896, 38308, 39469, 39970, 40031, 39997, 39780, 39333, 39716, 39505, 38984, 38169, 36969, 36564, 36452, 36367, 36308, 35966, 34735, 33997, 33776, 33331, 32989, 32327, 31757, 31130, 30749, 30244, 30010, 29608, 29020, 28370, 28829, 28272, 27855, 27358, 26877, 26727, 26503, 26280, 26183, 26052, 25901]
monthly_employee = []


print len(period),len(employee_intake), len(employee_transfer)
# exit()
# 'manager,', 'officer','director','head,','manager-',
[  'engineer',
 'analyst', 'associate', 'sap', 'technology', 'software',
 'project', 'process', 'business', 'executive', 'technical', 'developer',
 'systems', 'test', 'services', 'system',
 'management', 'support',  'principal', 'team',
    'practice', 'oracle', 'development',  'operations',
  'client', 'quality', 'consultant,', 'global', 'solution', 'service', 'design',
 'delivery', 'product', 'program', 'solutions', 'data', 'finance', 'functional', 'financial', 'marketing', 'group',
 'research', 'security', 'banking', 'leader', 'administrator', 'programmer', 'testing', 'finacle', 'infrastructure',
 'sales', 'engineering', 'trainee', 'application', 'partner', 'admin', 'designer', 'enterprise', 'tech', 'domain',
 'network', 'experience', 'intern', 'customer',  'account', 'crm', 'web', 'lead,', 'planning',
 'assurance', 'accounts',  'engg', 'sourcing', 'integration',  'engagement', 'unit', 'corporate',
  'dba',  'years', 'digital', 'java', 'technologies', 'procurement', 'president',  'talent',
 'engineer,',  'insurance', 'information', 'human', 'analyst,', 'capital', 'transformation',
 'training', 'retail', 'coordinator', 'education', 'abap', 'markets',  'basis', 'industry', 'consultant-',
 'manufacturing', 'cloud', 'computer', 'onsite', 'recruitment',  'performance', 'risk',
 'bpo', 'accounting', 'automation', 'working', 'apps', 'facilities', 'strategy', 'strategic', 'fico', 'life',
 'implementation', 'operation', 'limited', 'recruiter',  'member', 'logistics', 'telecom',
 'acquisition', 'chain', 'resources', 'siebel',  'module', 'junior', 'expert', 'lead-', 'intelligence',
 'supply', 'engg.', 'resource', 'media', 'legal', 'erp', 'cpg', 'instep', 'database', 'microsoft',
 'analytics', 'healthcare', 'regional', 'hardware',  'compliance', 'mainframe', 'retail,', 'hcm', 'tester', 'etl',
 'relations', 'center', 'america', 'content', 'user', 'sciences', 'accountant', 'server', 'principal,', 'avp', 'office',
 'ui', '(sap', 'eng', 'analysis', 'production', 'administration', 'mobility', 'platform',
 'transition', 'supervisor', 'knowledge', 'level', 'communications', 'services,', 'company',
 'applications', 'internship', 'enginner', 'co-ordinator', 'learning', 'advisor', 'employee',
 'benefits', 'compensation', 'core', 's/w', 'relationship', 'lead)', 'hrd', 'payroll', 'certified', 'presales',
 'immigration', 'portfolio', 'offshore', 'energy', 'payable', 'exp', 'excellence',
 'excutive', 'order', 'mobile', 'networking', 'analyst)', 'on', 'communication', 'sharepoint', 'matter','freshers']



top_level = ['senior','sr.', 'vice','professional','sr ','architect','head','specialist',]
mid_level = ['consultant', 'lead','hr','consulting','assistant','asst.', 'asst', 'trainer']


# top_level = [
# 'ceo','owner','manager',''
# ]
# for num,i in enumerate(period):
#     employees = employee_intake[num] - employee_transfer[num]
#     employe_present = company_strength_before_2005 + employees
#     print employees
#     monthly_employee.append(employe_present)
#     company_strength_before_2005 = employe_present
#
# print monthly_employee


# fo = open('infosys.csv','w')
# #
# for num,i in enumerate(period):
#     fo.write(i+','+str(employee_intake[num])+','+str(employee_transfer[num])+','+str(total_employees[num])+'\n')
#
# fo.close()