Python 3.1.1 (r311:74543, Aug 24 2009, 18:44:04) [GCC 4.0.1 (Apple Inc. build 5493)] on darwin Type "copyright", "credits" or "license()" for more information. >>> f = open("NST_EST2009_ALLDATA.csv", "r") >>> f <_io.TextIOWrapper name='NST_EST2009_ALLDATA.csv' encoding='US-ASCII'> >>> lines = f.readlines() >>> lines[0] 'SUMLEV,REGION,DIVISION,STATE,NAME,CENSUS2000POP,ESTIMATESBASE2000,POPESTIMATE2000,POPESTIMATE2001,POPESTIMATE2002,POPESTIMATE2003,POPESTIMATE2004,POPESTIMATE2005,POPESTIMATE2006,POPESTIMATE2007,POPESTIMATE2008,POPESTIMATE2009,NPOPCHG_2000,NPOPCHG_2001,NPOPCHG_2002,NPOPCHG_2003,NPOPCHG_2004,NPOPCHG_2005,NPOPCHG_2006,NPOPCHG_2007,NPOPCHG_2008,NPOPCHG_2009,BIRTHS2000,BIRTHS2001,BIRTHS2002,BIRTHS2003,BIRTHS2004,BIRTHS2005,BIRTHS2006,BIRTHS2007,BIRTHS2008,BIRTHS2009,DEATHS2000,DEATHS2001,DEATHS2002,DEATHS2003,DEATHS2004,DEATHS2005,DEATHS2006,DEATHS2007,DEATHS2008,DEATHS2009,NATURALINC2000,NATURALINC2001,NATURALINC2002,NATURALINC2003,NATURALINC2004,NATURALINC2005,NATURALINC2006,NATURALINC2007,NATURALINC2008,NATURALINC2009,INTERNATIONALMIG2000,INTERNATIONALMIG2001,INTERNATIONALMIG2002,INTERNATIONALMIG2003,INTERNATIONALMIG2004,INTERNATIONALMIG2005,INTERNATIONALMIG2006,INTERNATIONALMIG2007,INTERNATIONALMIG2008,INTERNATIONALMIG2009,DOMESTICMIG2000,DOMESTICMIG2001,DOMESTICMIG2002,DOMESTICMIG2003,DOMESTICMIG2004,DOMESTICMIG2005,DOMESTICMIG2006,DOMESTICMIG2007,DOMESTICMIG2008,DOMESTICMIG2009,NETMIG2000,NETMIG2001,NETMIG2002,NETMIG2003,NETMIG2004,NETMIG2005,NETMIG2006,NETMIG2007,NETMIG2008,NETMIG2009,RESIDUAL2000,RESIDUAL2001,RESIDUAL2002,RESIDUAL2003,RESIDUAL2004,RESIDUAL2005,RESIDUAL2006,RESIDUAL2007,RESIDUAL2008,RESIDUAL2009,RBIRTH2001,RBIRTH2002,RBIRTH2003,RBIRTH2004,RBIRTH2005,RBIRTH2006,RBIRTH2007,RBIRTH2008,RBIRTH2009,RDEATH2001,RDEATH2002,RDEATH2003,RDEATH2004,RDEATH2005,RDEATH2006,RDEATH2007,RDEATH2008,RDEATH2009,RNATURALINC2001,RNATURALINC2002,RNATURALINC2003,RNATURALINC2004,RNATURALINC2005,RNATURALINC2006,RNATURALINC2007,RNATURALINC2008,RNATURALINC2009,RINTERNATIONALMIG2001,RINTERNATIONALMIG2002,RINTERNATIONALMIG2003,RINTERNATIONALMIG2004,RINTERNATIONALMIG2005,RINTERNATIONALMIG2006,RINTERNATIONALMIG2007,RINTERNATIONALMIG2008,RINTERNATIONALMIG2009,RDOMESTICMIG2001,RDOMESTICMIG2002,RDOMESTICMIG2003,RDOMESTICMIG2004,RDOMESTICMIG2005,RDOMESTICMIG2006,RDOMESTICMIG2007,RDOMESTICMIG2008,RDOMESTICMIG2009,RNETMIG2001,RNETMIG2002,RNETMIG2003,RNETMIG2004,RNETMIG2005,RNETMIG2006,RNETMIG2007,RNETMIG2008,RNETMIG2009\n' >>> lines[1] '010,0,0,00,United States,281421906,281424602,282171957,285081556,287803914,290326418,293045739,295753151,298593212,301579895,304374846,307006550,747355,2909599,2722358,2522504,2719321,2707412,2840061,2986683,2794951,2631704,989020,4047314,4006985,4052799,4112637,4121160,4178113,4304907,4282972,4262897,560891,2419276,2429999,2422701,2449577,2433274,2417538,2425115,2438757,2486097,428129,1628038,1576986,1630098,1663060,1687886,1760575,1879792,1844215,1776800,319226,1200535,1078014,822079,985807,947844,1006408,866397,862955,854905,0,0,0,0,0,0,0,0,0,0,319226,1200535,1078014,822079,985807,947844,1006408,866397,862955,854905,0,81026,67358,70327,70454,71682,73078,240494,87781,-1,14.269859621,13.988782086,14.020364529,14.099531322,13.998531825,14.059522393,14.345551141,14.136276887,13.945131559,8.5297876331,8.4833675394,8.3811585931,8.3979907872,8.2652125924,8.1351149784,8.0813850928,8.0493041311,8.1327204794,5.7400719879,5.5054145465,5.639205936,5.7015405348,5.7333192323,5.9244074149,6.2641660483,6.0869727563,5.8124110796,4.2327988192,3.7634538017,2.8439227437,3.3796847798,3.2195848739,3.3866043864,2.8871570215,2.8482490246,2.7966340016,0,0,0,0,0,0,0,0,0,4.2327988192,3.7634538017,2.8439227437,3.3796847798,3.2195848739,3.3866043864,2.8871570215,2.8482490246,2.7966340016\n' >>> f.close() >>> s = 'Hi!\n' >>> s 'Hi!\n' >>> len(s) 4 >>> s2 = 'Hi!/n' >>> len(s2) 5 >>> print(s2) Hi!/n >>> print(s) Hi! >>> print('hey\nthere') hey there >>> for line in lines: data = line.split(',') print(data[4], data[5]) NAME CENSUS2000POP United States 281421906 Northeast 53594378 Midwest 64392776 South 100236820 West 63197932 Alabama 4447100 Alaska 626932 Arizona 5130632 Arkansas 2673400 California 33871648 Colorado 4301261 Connecticut 3405565 Delaware 783600 District of Columbia 572059 Florida 15982378 Georgia 8186453 Hawaii 1211537 Idaho 1293953 Illinois 12419293 Indiana 6080485 Iowa 2926324 Kansas 2688418 Kentucky 4041769 Louisiana 4468976 Maine 1274923 Maryland 5296486 Massachusetts 6349097 Michigan 9938444 Minnesota 4919479 Mississippi 2844658 Missouri 5595211 Montana 902195 Nebraska 1711263 Nevada 1998257 New Hampshire 1235786 New Jersey 8414350 New Mexico 1819046 New York 18976457 North Carolina 8049313 North Dakota 642200 Ohio 11353140 Oklahoma 3450654 Oregon 3421399 Pennsylvania 12281054 Rhode Island 1048319 South Carolina 4012012 South Dakota 754844 Tennessee 5689283 Texas 20851820 Utah 2233169 Vermont 608827 Virginia 7078515 Washington 5894121 West Virginia 1808344 Wisconsin 5363675 Wyoming 493782 Puerto Rico Commonwealth 3808610 >>> "Hello, my name is {0}. How are you {1}?".format("Max", "today") 'Hello, my name is Max. How are you today?' >>> "Hello, my name is {0:<10}. How are you {1}?".format("Max", "today") 'Hello, my name is Max . How are you today?' >>> for line in lines: data = line.split(',') print("{0:<25}{1:>10}".format(data[4], data[5])) NAME CENSUS2000POP United States 281421906 Northeast 53594378 Midwest 64392776 South 100236820 West 63197932 Alabama 4447100 Alaska 626932 Arizona 5130632 Arkansas 2673400 California 33871648 Colorado 4301261 Connecticut 3405565 Delaware 783600 District of Columbia 572059 Florida 15982378 Georgia 8186453 Hawaii 1211537 Idaho 1293953 Illinois 12419293 Indiana 6080485 Iowa 2926324 Kansas 2688418 Kentucky 4041769 Louisiana 4468976 Maine 1274923 Maryland 5296486 Massachusetts 6349097 Michigan 9938444 Minnesota 4919479 Mississippi 2844658 Missouri 5595211 Montana 902195 Nebraska 1711263 Nevada 1998257 New Hampshire 1235786 New Jersey 8414350 New Mexico 1819046 New York 18976457 North Carolina 8049313 North Dakota 642200 Ohio 11353140 Oklahoma 3450654 Oregon 3421399 Pennsylvania 12281054 Rhode Island 1048319 South Carolina 4012012 South Dakota 754844 Tennessee 5689283 Texas 20851820 Utah 2233169 Vermont 608827 Virginia 7078515 Washington 5894121 West Virginia 1808344 Wisconsin 5363675 Wyoming 493782 Puerto Rico Commonwealth 3808610 >>> f = open("populations.txt", "w") >>> for line in lines: data = line.split(',') f.write("{0:<25}{1:>10}\n".format(data[4], data[5])) 39 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 >>> f.close() >>> theSlice = lines[6:] >>> for line in theSlice: data=line.split(',') print(data[5]) 4447100 626932 5130632 2673400 33871648 4301261 3405565 783600 572059 15982378 8186453 1211537 1293953 12419293 6080485 2926324 2688418 4041769 4468976 1274923 5296486 6349097 9938444 4919479 2844658 5595211 902195 1711263 1998257 1235786 8414350 1819046 18976457 8049313 642200 11353140 3450654 3421399 12281054 1048319 4012012 754844 5689283 20851820 2233169 608827 7078515 5894121 1808344 5363675 493782 3808610 >>> populations = [] >>> for line in theSlice: data=line.split(',') populations.append(data[5]) >>> len(populations) 52 >>>