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  1. 0 1 2 3 4 5 6 7
  2. 0 ADN.L 20130220 437.4 442.37 436.5000 441.9000 2775364 2013-02-20 18:47:42
  3. 1 ADM.L 20130220 1279.0 1300.00 1272.0000 1285.0000 967730 2013-02-20 18:47:42
  4. 2 AGK.L 20130220 1717.0 1749.00 1709.0000 1739.0000 834534 2013-02-20 18:47:43
  5. 3 AMEC.L 20130220 1030.0 1040.00 1024.0000 1035.0000 1972517 2013-02-20 18:47:43
  6. 4 AAL.L 20130220 1998.0 2014.50 1942.4999 1951.0000 3666033 2013-02-20 18:47:44
  7. 5 ANTO.L 20130220 1093.0 1097.00 1064.7899 1068.0000 2183931 2013-02-20 18:47:44
  8. 6 ARM.L 20130220 941.5 965.10 939.4250 951.5001 2994652 2013-02-20 18:47:45
  9.  
  10. 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7
  11. 0 NaN NaN NaN NaN NaN NaN NaN NaN ADN.L 20130220 437.4 442.37 436.5000 441.9000 2775364 2013-02-20 18:47:42
  12. 1 NaN NaN NaN NaN NaN NaN NaN NaN ADM.L 20130220 1279.0 1300.00 1272.0000 1285.0000 967730 2013-02-20 18:47:42
  13. 2 NaN NaN NaN NaN NaN NaN NaN NaN AGK.L 20130220 1717.0 1749.00 1709.0000 1739.0000 834534 2013-02-20 18:47:43
  14. 3 NaN NaN NaN NaN NaN NaN NaN NaN AMEC.L 20130220 1030.0 1040.00 1024.0000 1035.0000 1972517 2013-02-20 18:47:43
  15. 4 NaN NaN NaN NaN NaN NaN NaN NaN AAL.L 20130220 1998.0 2014.50 1942.4999 1951.0000 3666033 2013-02-20 18:47:44
  16. 5 NaN NaN NaN NaN NaN NaN NaN NaN ANTO.L 20130220 1093.0 1097.00 1064.7899 1068.0000 2183931 2013-02-20 18:47:44
  17. 6 NaN NaN NaN NaN NaN NaN NaN NaN ARM.L 20130220 941.5 965.10 939.4250 951.5001 2994652 2013-02-20 18:47:45
  18. 0 NaN NaN NaN NaN NaN NaN NaN NaN ADN.L 20130220 437.4 442.37 436.5000 441.9000 2775364 2013-02-20 18:47:42
  19. 1 NaN NaN NaN NaN NaN NaN NaN NaN ADM.L 20130220 1279.0 1300.00 1272.0000 1285.0000 967730 2013-02-20 18:47:42
  20. 2 NaN NaN NaN NaN NaN NaN NaN NaN AGK.L 20130220 1717.0 1749.00 1709.0000 1739.0000 834534 2013-02-20 18:47:43
  21. 3 NaN NaN NaN NaN NaN NaN NaN NaN
  22.  
  23. Frame = Frame.append(pandas.DataFrame(data = SomeNewLineOfData))
  24.  
  25. Frame = Frame.append(pandas.DataFrame(data = SomeNewLineOfData), ignore_index=True)
  26.  
  27. import numpy as np
  28. import pandas as pd
  29.  
  30. dates = np.asarray(pd.date_range('1/1/2000', periods=8))
  31. df1 = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
  32. df2 = df1.copy()
  33. df = df1.append(df2)
  34.  
  35. A B C D
  36. 2000-01-01 -0.327208 0.552500 0.862529 0.493109
  37. 2000-01-02 1.039844 -2.141089 -0.781609 1.307600
  38. 2000-01-03 -0.462831 0.066505 -1.698346 1.123174
  39. 2000-01-04 -0.321971 -0.544599 -0.486099 -0.283791
  40. 2000-01-05 0.693749 0.544329 -1.606851 0.527733
  41. 2000-01-06 -2.461177 -0.339378 -0.236275 0.155569
  42. 2000-01-07 -0.597156 0.904511 0.369865 0.862504
  43. 2000-01-08 -0.958300 -0.583621 -2.068273 0.539434
  44. 2000-01-01 -0.327208 0.552500 0.862529 0.493109
  45. 2000-01-02 1.039844 -2.141089 -0.781609 1.307600
  46. 2000-01-03 -0.462831 0.066505 -1.698346 1.123174
  47. 2000-01-04 -0.321971 -0.544599 -0.486099 -0.283791
  48. 2000-01-05 0.693749 0.544329 -1.606851 0.527733
  49. 2000-01-06 -2.461177 -0.339378 -0.236275 0.155569
  50. 2000-01-07 -0.597156 0.904511 0.369865 0.862504
  51. 2000-01-08 -0.958300 -0.583621 -2.068273 0.539434
  52.  
  53. import pandas as pd
  54. import numpy as np
  55.  
  56. array = np.random.randint( 0,10, size = (2,4) )
  57. df = pd.DataFrame(array, columns = ['A','B', 'C', 'D'],
  58. index = ['10aa', '20bb'] ) ### some crazy indexes
  59. df
  60.  
  61. A B C D
  62. 10aa 4 2 4 6
  63. 20bb 5 1 0 2
  64.  
  65. List = [i**3 for i in range(df.shape[1]) ]
  66. List
  67. [0, 1, 8, 27]
  68.  
  69. Dict = dict( zip(df.columns, List) )
  70. Dict
  71. {'A': 0, 'B': 1, 'C': 8, 'D': 27}
  72.  
  73. df = df.append(Dict, ignore_index=True)
  74. df
  75. A B C D
  76. 0 7 5 5 4
  77. 1 5 8 4 1
  78. 2 0 1 8 27
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