title: How do I get rid of the \t within this python regex match object?
body: <p>The data is derived from this page: 
  <a href="https://simple.wikipedia.org/wiki/List_of_U.S._states_by_population" rel="nofollow noreferrer">https://simple.wikipedia.org/wiki/List_of_U.S._states_by_population</a></p>
  
  <pre><code>text = '''
  1   1    California 39,512,223  37,254,523  6.1%    +2,257,700  53  718,404 745,514 702,885 11.96%
  2   2    Texas  28,995,881  25,145,561  15.3%   +3,850,320  36  763,050 805,441 698,503 8.68%
  3   4    Florida    21,477,737  18,801,310  14.2%   +2,676,427  27  740,611 795,472 696,468 6.44%
  4   3    New York   19,453,561  19,378,102  0.4%    +75,459 27  670,812 720,502 717,707 5.91%
  5   6    Pennsylvania   12,801,989  12,702,379  0.8%    +99,610 18  640,099 711,222 705,715 3.87%
  6   5    Illinois   12,671,821  12,830,632  -1.2%   −158,811    18  633,591 703,990 712,864 3.85%
  7   7    Ohio   11,689,100  11,536,504  1.3%    +152,596    16  649,394 730,569 721,032 3.53%
  8   9    Georgia    10,617,423  9,687,653   9.6%    +929,770    14  663,589 758,387 691,975 3.18%
  9   10   North Carolina 10,488,084  9,535,483   10.0%   +952,601    13  699,206 806,776 733,498 3.14%
  10  8    Michigan   9,986,857   9,883,640   1.0%    +103,217    14  624,179 713,347 705,974 3.02%
  11  11   New Jersey 8,882,190   8,791,894   1.0%    +90,296 12  634,442 740,183 732,658 2.69%
  12  12   Virginia   8,535,519   8,001,024   6.7%    +534,495    11  656,578 775,956 727,366 2.58%
  13  13   Washington 7,614,893   6,724,540   13.2%   +890,353    10  634,574 751,489 672,454 2.28%
  14  16   Arizona    7,278,717   6,392,017   13.9%   +886,700    9   661,702 808,746 710,224 2.17%
  15  14   Massachusetts  6,949,503   6,547,629   5.3%    +344,874    9   626,591 765,834 727,514 2.09%
  16  17   Tennessee  6,833,174   6,346,105   7.6%    +483,069    9   620,834 758,797 705,123 2.05%
  17  15   Indiana    6,732,219   6,483,802   3.8%    +248,417    9   612,020 748,024 720,422 2.02%
  18  18   Missouri   6,137,428   5,988,927   2.5%    +148,501    8   613,743 767,179 748,615 1.85%
  19  19   Maryland   6,045,680   5,773,552   4.7%    +272,128    8   604,568 755,710 721,694 1.83%
  20  20   Wisconsin  5,822,434   5,686,986   2.4%    +135,448    8   582,243 727,804 710,873 1.76%
  21  22   Colorado   5,758,736   5,029,196   14.5%   +729,540    7   639,860 822,677 720,704 1.72%
  22  21   Minnesota  5,639,632   5,303,925   6.3%    +335,707    8   563,963 704,954 662,991 1.70%
  23  24   South Carolina 5,148,714   4,625,364   11.3%   +523,350    7   572,079 735,531 660,766 1.54%
  24  23   Alabama    4,903,185   4,779,736   2.6%    +123,449    7   544,798 700,455 682,819 1.48%
  25  25   Louisiana  4,648,794   4,533,372   2.5%    +115,422    6   581,099 774,799 755,562 1.41%
  26  26   Kentucky   4,467,673   4,339,367   3.0%    +128,306    6   558,459 744,612 723,228 1.35%
  27  27   Oregon 4,217,737   3,831,074   10.1%   +386,663    5   602,534 843,547 766,215 1.27%
  28  28   Oklahoma   3,956,971   3,751,351   5.5%    +205,620    5   565,282 791,394 750,270 1.19%
  29  30   Connecticut    3,565,287   3,574,097   -0.2%   −8,810  5   509,327 713,057 714,824 1.08%
  30  35   Utah   3,205,958   2,763,885   16.0%   +442,073    4   534,326 801,490 690,972 0.96%
  32  31   Iowa   3,155,070   3,046,355   3.6%    +108,715    4   525,845 788,768 761,717 0.95%
  31  29   Puerto Rico    3,193,694   3,725,789   -14.3%  −532,095    1 (non-voting)  —   3,193,694   3,725,789   0.97%
  33  36   Nevada 3,080,156   2,700,551   14.1%   +379,605    4   513,359 770,039 675,173 0.92%
  34  33   Arkansas   3,017,825   2,915,918   3.5%    +101,886    4   502,967 754,451 728,990 0.91%
  35  32   Mississippi    2,976,149   2,967,297   0.3%    +8,852  4   496,024 744,037 742,026 0.90%
  36  34   Kansas 2,913,314   2,853,118   2.1%    +60,196 4   485,552 728,329 713,280 0.88%
  37  37   New Mexico 2,096,829   2,059,179   1.8%    +37,650 3   419,366 698,943 686,393 0.63%
  38  39   Nebraska   1,934,408   1,826,341   5.9%    +108,067    3   386,882 644,803 608,780 0.58%
  39  40   Idaho  1,792,065   1,567,582   13.9%   +218,483    2   446,516 893,033 783,826 0.53%
  40  38   West Virginia  1,787,147   1,852,994   -3.3%   −60,820 3   358,435 597,391 617,670 0.55%
  41  41   Hawaii 1,415,872   1,360,301   4.1%    +55,571 2   353,968 707,936 680,151 0.43%
  42  43   New Hampshire  1,359,711   1,316,470   3.3%    +43,241 2   339,928 679,856 658,233 0.41%
  43  42   Maine  1,344,212   1,328,361   1.2%    +15,851 2   336,053 672,106 664,181 0.40%
  44  45   Montana    1,068,778   989,415 8.0%    +79,363 1   356,259 1,068,778   989,417 0.32%
  45  44   Rhode Island   1,059,361   1,052,567   0.6%    +6,794  2   264,840 529,681 526,466 0.32%
  46  46   Delaware   973,764 897,934 8.4%    +75,830 1   324,588 973,764 897,934 0.29%
  47  47   South Dakota   884,659 814,180 8.7%    +70,479 1   294,886 884,659 814,180 0.27%
  48  49   North Dakota   762,062 672,591 13.3%   +89,471 1   254,021 762,062 672,591 0.23%
  49  48   Alaska 731,545 710,231 3.0%    +21,314 1   243,848 731,545 710,231 0.22%
  50  51   District of Columbia   705,749 601,723 17.3%   +104,026    1 (non-voting)  235,250 —   —   0.21%
  51  50   Vermont    623,989 625,741 -0.2%   -1,752  1   207,996 623,989 625,741 0.19%
  52  52   Wyoming    578,759 563,626 2.7%    +15,133 1   192,920 578,759 563,626 0.17%
  53  53   Guam   165,718 159,358[4]  4.0%    +6,360  1 (non-voting)      —   —   0.05%
  54  54   U.S. Virgin Islands    104,914 106,405[5]  -1.4%   −1,491  1 (non-voting)      —   —   0.03%
  55  55   American Samoa 55,641  55,519[6]   0.22%   +122    1 (non-voting)      —   —   0.02%
  56  56   Northern Mariana Islands   55,194  53,883[7]   2.4%    +1,311  1 (non-voting)      —   —   0.02%
  '''
  </code></pre>
  
  <p>Then, I used re.compile() to make a match object. I only want the state/territory name, and the population estimate as of July 1, 2019 (the column directly to the right). </p>
  
  <pre><code>text_lines = text.split('\n')
  
  pattern = re.compile(r'([A-Z]\D*)'
                        '(\d*\,\d*\,\d*)')
  data = [] 
  
  for line in text_lines:
      s = re.search(pattern, line)
      if s:
          data.append(s.group(1, 2))
  
  for item in data:
      print(item)
  </code></pre>
  
  <pre><code>('California\t', '39,512,223')
  ('Texas\t', '28,995,881')
  ('Florida\t', '21,477,737')
  ('New York\t', '19,453,561')
  ('Pennsylvania\t', '12,801,989')
  ('Illinois\t', '12,671,821')
  ('Ohio\t', '11,689,100')
  ('Georgia\t', '10,617,423')
  ('North Carolina\t', '10,488,084')
  ('Michigan\t', '9,986,857')
  ('New Jersey\t', '8,882,190')
  ('Virginia\t', '8,535,519')
  ('Washington\t', '7,614,893')
  ('Arizona\t', '7,278,717')
  ('Massachusetts\t', '6,949,503')
  ('Tennessee\t', '6,833,174')
  ('Indiana\t', '6,732,219')
  ('Missouri\t', '6,137,428')
  ('Maryland\t', '6,045,680')
  ('Wisconsin\t', '5,822,434')
  ('Colorado\t', '5,758,736')
  ('Minnesota\t', '5,639,632')
  ('South Carolina\t', '5,148,714')
  ('Alabama\t', '4,903,185')
  ('Louisiana\t', '4,648,794')
  ('Kentucky\t', '4,467,673')
  ('Oregon\t', '4,217,737')
  ('Oklahoma\t', '3,956,971')
  ('Connecticut\t', '3,565,287')
  ('Utah\t', '3,205,958')
  ('Iowa\t', '3,155,070')
  ('Puerto Rico\t', '3,193,694')
  ('Nevada\t', '3,080,156')
  ('Arkansas\t', '3,017,825')
  ('Mississippi\t', '2,976,149')
  ('Kansas\t', '2,913,314')
  ('New Mexico\t', '2,096,829')
  ('Nebraska\t', '1,934,408')
  ('Idaho\t', '1,792,065')
  ('West Virginia\t', '1,787,147')
  ('Hawaii\t', '1,415,872')
  ('New Hampshire\t', '1,359,711')
  ('Maine\t', '1,344,212')
  ('Montana\t', '1,068,778')
  ('Rhode Island\t', '1,059,361')
  </code></pre>
  
  <p>How would I need to change how I wrote the pattern so that the \t is not included? 
  Also, why does it stop at Rhode Island? I'm not sure what I did wrong. </p>