弃用面板(Deprecate Panel)

Over the last few years, pandas has increased in both breadth and depth, with new features, datatype support, and manipulation routines. As a result, supporting efficient indexing and functional routines for Series, DataFrame and Panel has contributed to an increasingly fragmented and difficult-to-understand codebase.

The 3-D structure of a Panel is much less common for many types of data analysis, than the 1-D of the Series or the 2-D of the DataFrame. Going forward it makes sense for pandas to focus on these areas exclusively.

Oftentimes, one can simply use a MultiIndex DataFrame for easily working with higher dimensional data.

In addition, the xarray package was built from the ground up, specifically in order to support the multi-dimensional analysis that is one of Panel s main usecases. Here is a link to the xarray panel-transition documentation.

  1. In [147]: p = tm.makePanel()
  3. In [148]: p
  4. Out[148]: 
  5. <class 'pandas.core.panel.Panel'>
  6. Dimensions: 3 (items) x 30 (major_axis) x 4 (minor_axis)
  7. Items axis: ItemA to ItemC
  8. Major_axis axis: 2000-01-03 00:00:00 to 2000-02-11 00:00:00
  9. Minor_axis axis: A to D

Convert to a MultiIndex DataFrame.

  1. In [149]: p.to_frame()
  2. Out[149]: 
  3.                      ItemA     ItemB     ItemC
  4. major      minor                              
  5. 2000-01-03 A     -0.390201 -1.624062 -0.605044
  6.            B      1.562443  0.483103  0.583129
  7.            C     -1.085663  0.768159 -0.273458
  8.            D      0.136235 -0.021763 -0.700648
  9. 2000-01-04 A      1.207122 -0.758514  0.878404
  10.            B      0.763264  0.061495 -0.876690
  11.            C     -1.114738  0.225441 -0.335117
  12.            D      0.886313 -0.047152 -1.166607
  13. 2000-01-05 A      0.178690 -0.560859 -0.921485
  14.            B      0.162027  0.240767 -1.919354
  15.            C     -0.058216  0.543294 -0.476268
  16.            D     -1.350722  0.088472 -0.367236
  17. 2000-01-06 A     -1.004168 -0.589005 -0.200312
  18.            B     -0.902704  0.782413 -0.572707
  19.            C     -0.486768  0.771931 -1.765602
  20.            D     -0.886348 -0.857435  1.296674
  21. 2000-01-07 A     -1.377627 -1.070678  0.522423
  22.            B      1.106010  0.628462 -1.736484
  23.            C      1.685148 -0.968145  0.578223
  24.            D     -1.013316 -2.503786  0.641385
  25. 2000-01-10 A      0.499281 -1.681101  0.722511
  26.            B     -0.199234 -0.880627 -1.335113
  27.            C      0.112572 -1.176383  0.242697
  28.            D      1.920906 -1.058041 -0.779432
  29. 2000-01-11 A     -1.405256  0.403776 -1.702486
  30.            B      0.458265  0.777575 -1.244471
  31.            C     -1.495309 -3.192716  0.208129
  32.            D     -0.388231 -0.657981  0.602456
  33. 2000-01-12 A      0.162565  0.609862 -0.709535
  34.            B      0.491048 -0.779367  0.347339
  35. ...                    ...       ...       ...
  36. 2000-02-02 C     -0.303961 -0.463752 -0.288962
  37.            D      0.104050  1.116086  0.506445
  38. 2000-02-03 A     -2.338595 -0.581967 -0.801820
  39.            B     -0.557697 -0.033731 -0.176382
  40.            C      0.625555 -0.055289  0.875359
  41.            D      0.174068 -0.443915  1.626369
  42. 2000-02-04 A     -0.374279 -1.233862 -0.915751
  43.            B      0.381353 -1.108761 -1.970108
  44.            C     -0.059268 -0.360853 -0.614618
  45.            D     -0.439461 -0.200491  0.429518
  46. 2000-02-07 A     -2.359958 -3.520876 -0.288156
  47.            B      1.337122 -0.314399 -1.044208
  48.            C      0.249698  0.728197  0.565375
  49.            D     -0.741343  1.092633  0.013910
  50. 2000-02-08 A     -1.157886  0.516870 -1.199945
  51.            B     -1.531095 -0.860626 -0.821179
  52.            C      1.103949  1.326768  0.068184
  53.            D     -0.079673 -1.675194 -0.458272
  54. 2000-02-09 A     -0.551865  0.343125 -0.072869
  55.            B      1.331458  0.370397 -1.914267
  56.            C     -1.087532  0.208927  0.788871
  57.            D     -0.922875  0.437234 -1.531004
  58. 2000-02-10 A      1.592673  2.137827 -1.828740
  59.            B     -0.571329 -1.761442 -0.826439
  60.            C      1.998044  0.292058 -0.280343
  61.            D      0.303638  0.388254 -0.500569
  62. 2000-02-11 A      1.559318  0.452429 -1.716981
  63.            B     -0.026671 -0.899454  0.124808
  64.            C     -0.244548 -2.019610  0.931536
  65.            D     -0.917368  0.479630  0.870690
  67. [120 rows x 3 columns]

Alternatively, one can convert to an xarray DataArray.

  1. In [150]: p.to_xarray()
  2. Out[150]: 
  3. <xarray.DataArray (items: 3, major_axis: 30, minor_axis: 4)>
  4. array([[[-0.390201,  1.562443, -1.085663,  0.136235],
  5.         [ 1.207122,  0.763264, -1.114738,  0.886313],
  6.         ..., 
  7.         [ 1.592673, -0.571329,  1.998044,  0.303638],
  8.         [ 1.559318, -0.026671, -0.244548, -0.917368]],
  10.        [[-1.624062,  0.483103,  0.768159, -0.021763],
  11.         [-0.758514,  0.061495,  0.225441, -0.047152],
  12.         ..., 
  13.         [ 2.137827, -1.761442,  0.292058,  0.388254],
  14.         [ 0.452429, -0.899454, -2.01961 ,  0.47963 ]],
  16.        [[-0.605044,  0.583129, -0.273458, -0.700648],
  17.         [ 0.878404, -0.87669 , -0.335117, -1.166607],
  18.         ..., 
  19.         [-1.82874 , -0.826439, -0.280343, -0.500569],
  20.         [-1.716981,  0.124808,  0.931536,  0.87069 ]]])
  21. Coordinates:
  22.   * items       (items) object 'ItemA' 'ItemB' 'ItemC'
  23.   * major_axis  (major_axis) datetime64[ns] 2000-01-03 2000-01-04 2000-01-05 ...
  24.   * minor_axis  (minor_axis) object 'A' 'B' 'C' 'D'

You can see the full-documentation for the xarray package.