面板(Series)

Warning: In 0.20.0, Panel is deprecated and will be removed in a future version. See the section Deprecate Panel.

Panel is a somewhat less-used, but still important container for 3-dimensional data. The term panel data is derived from econometrics and is partially responsible for the name pandas: pan(el)-da(ta)-s. The names for the 3 axes are intended to give some semantic meaning to describing operations involving panel data and, in particular, econometric analysis of panel data. However, for the strict purposes of slicing and dicing a collection of DataFrame objects, you may find the axis names slightly arbitrary:

  • items: axis 0, each item corresponds to a DataFrame contained inside
  • major_axis: axis 1, it is the index (rows) of each of the DataFrames
  • minor_axis: axis 2, it is the columns of each of the DataFrames

Construction of Panels works about like you would expect:

From 3D ndarray with optional axis labels

  1. In [121]: wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
  2.    .....:               major_axis=pd.date_range('1/1/2000', periods=5),
  3.    .....:               minor_axis=['A', 'B', 'C', 'D'])
  4.    .....: 
  6. In [122]: wp
  7. Out[122]: 
  8. <class 'pandas.core.panel.Panel'>
  9. Dimensions: 2 (items) x 5 (major_axis) x 4 (minor_axis)
  10. Items axis: Item1 to Item2
  11. Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
  12. Minor_axis axis: A to D

From dict of DataFrame objects

  1. In [123]: data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)),
  2.    .....:         'Item2' : pd.DataFrame(np.random.randn(4, 2))}
  3.    .....: 
  5. In [124]: pd.Panel(data)
  6. Out[124]: 
  7. <class 'pandas.core.panel.Panel'>
  8. Dimensions: 2 (items) x 4 (major_axis) x 3 (minor_axis)
  9. Items axis: Item1 to Item2
  10. Major_axis axis: 0 to 3
  11. Minor_axis axis: 0 to 2

Note that the values in the dict need only be convertible to DataFrame. Thus, they can be any of the other valid inputs to DataFrame as per above.

One helpful factory method is Panel.from_dict, which takes a dictionary of DataFrames as above, and the following named parameters:

ParameterDefaultDescription
intersectFalsedrops elements whose indices do not align
orientitemsuse minor to use DataFrames’ columns as panel items

For example, compare to the construction above:

  1. In [125]: pd.Panel.from_dict(data, orient='minor')
  2. Out[125]: 
  3. <class 'pandas.core.panel.Panel'>
  4. Dimensions: 3 (items) x 4 (major_axis) x 2 (minor_axis)
  5. Items axis: 0 to 2
  6. Major_axis axis: 0 to 3
  7. Minor_axis axis: Item1 to Item2

Orient is especially useful for mixed-type DataFrames. If you pass a dict of DataFrame objects with mixed-type columns, all of the data will get upcasted to dtype=object unless you pass orient='minor':

  1. In [126]: df = pd.DataFrame({'a': ['foo', 'bar', 'baz'],
  2.    .....:                    'b': np.random.randn(3)})
  3.    .....: 
  5. In [127]: df
  6. Out[127]: 
  7.      a         b
  8. 0  foo -0.308853
  9. 1  bar -0.681087
  10. 2  baz  0.377953
  12. In [128]: data = {'item1': df, 'item2': df}
  14. In [129]: panel = pd.Panel.from_dict(data, orient='minor')
  16. In [130]: panel['a']
  17. Out[130]: 
  18.   item1 item2
  19. 0   foo   foo
  20. 1   bar   bar
  21. 2   baz   baz
  23. In [131]: panel['b']
  24. Out[131]: 
  25.       item1     item2
  26. 0 -0.308853 -0.308853
  27. 1 -0.681087 -0.681087
  28. 2  0.377953  0.377953
  30. In [132]: panel['b'].dtypes
  31. Out[132]: 
  32. item1    float64
  33. item2    float64
  34. dtype: object

Note: Panel, being less commonly used than Series and DataFrame, has been slightly neglected feature-wise. A number of methods and options available in DataFrame are not available in Panel.

From DataFrame using to_panel method

to_panel converts a DataFrame with a two-level index to a Panel.

  1. In [133]: midx = pd.MultiIndex(levels=[['one', 'two'], ['x','y']], labels=[[1,1,0,0],[1,0,1,0]])
  3. In [134]: df = pd.DataFrame({'A' : [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=midx)
  5. In [135]: df.to_panel()
  6. Out[135]: 
  7. <class 'pandas.core.panel.Panel'>
  8. Dimensions: 2 (items) x 2 (major_axis) x 2 (minor_axis)
  9. Items axis: A to B
  10. Major_axis axis: one to two
  11. Minor_axis axis: x to y

Item selection / addition / deletion

Similar to DataFrame functioning as a dict of Series, Panel is like a dict of DataFrames:

  1. In [136]: wp['Item1']
  2. Out[136]: 
  3.                    A         B         C         D
  4. 2000-01-01  1.588931  0.476720  0.473424 -0.242861
  5. 2000-01-02 -0.014805 -0.284319  0.650776 -1.461665
  6. 2000-01-03 -1.137707 -0.891060 -0.693921  1.613616
  7. 2000-01-04  0.464000  0.227371 -0.496922  0.306389
  8. 2000-01-05 -2.290613 -1.134623 -1.561819 -0.260838
  10. In [137]: wp['Item3'] = wp['Item1'] / wp['Item2']

The API for insertion and deletion is the same as for DataFrame. And as with DataFrame, if the item is a valid Python identifier, you can access it as an attribute and tab-complete it in IPython.

Transposing

A Panel can be rearranged using its transpose method (which does not make a copy by default unless the data are heterogeneous):

  1. In [138]: wp.transpose(2, 0, 1)
  2. Out[138]: 
  3. <class 'pandas.core.panel.Panel'>
  4. Dimensions: 4 (items) x 3 (major_axis) x 5 (minor_axis)
  5. Items axis: A to D
  6. Major_axis axis: Item1 to Item3
  7. Minor_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00

Indexing / Selection

OperationSyntaxResult
Select itemwp[item]DataFrame
Get slice at major_axis labelwp.major_xs(val)DataFrame
Get slice at minor_axis labelwp.minor_xs(val)DataFrame

For example, using the earlier example data, we could do:

  1. In [139]: wp['Item1']
  2. Out[139]: 
  3.                    A         B         C         D
  4. 2000-01-01  1.588931  0.476720  0.473424 -0.242861
  5. 2000-01-02 -0.014805 -0.284319  0.650776 -1.461665
  6. 2000-01-03 -1.137707 -0.891060 -0.693921  1.613616
  7. 2000-01-04  0.464000  0.227371 -0.496922  0.306389
  8. 2000-01-05 -2.290613 -1.134623 -1.561819 -0.260838
  10. In [140]: wp.major_xs(wp.major_axis[2])
  11. Out[140]: 
  12.       Item1     Item2     Item3
  13. A -1.137707  0.800193 -1.421791
  14. B -0.891060  0.782098 -1.139320
  15. C -0.693921 -1.069094  0.649074
  16. D  1.613616 -1.099248 -1.467927
  18. In [141]: wp.minor_axis
  19. Out[141]: Index(['A', 'B', 'C', 'D'], dtype='object')
  21. In [142]: wp.minor_xs('C')
  22. Out[142]: 
  23.                Item1     Item2     Item3
  24. 2000-01-01  0.473424 -0.902937 -0.524316
  25. 2000-01-02  0.650776 -1.144073 -0.568824
  26. 2000-01-03 -0.693921 -1.069094  0.649074
  27. 2000-01-04 -0.496922  0.661084 -0.751678
  28. 2000-01-05 -1.561819 -1.056652  1.478083

Squeezing

Another way to change the dimensionality of an object is to squeeze a 1-len object, similar to wp['Item1'].

  1. In [143]: wp.reindex(items=['Item1']).squeeze()
  2. Out[143]: 
  3.                    A         B         C         D
  4. 2000-01-01  1.588931  0.476720  0.473424 -0.242861
  5. 2000-01-02 -0.014805 -0.284319  0.650776 -1.461665
  6. 2000-01-03 -1.137707 -0.891060 -0.693921  1.613616
  7. 2000-01-04  0.464000  0.227371 -0.496922  0.306389
  8. 2000-01-05 -2.290613 -1.134623 -1.561819 -0.260838
  10. In [144]: wp.reindex(items=['Item1'], minor=['B']).squeeze()
  11. Out[144]: 
  12. 2000-01-01    0.476720
  13. 2000-01-02   -0.284319
  14. 2000-01-03   -0.891060
  15. 2000-01-04    0.227371
  16. 2000-01-05   -1.134623
  17. Freq: D, Name: B, dtype: float64

Conversion to DataFrame

A Panel can be represented in 2D form as a hierarchically indexed DataFrame. See the section hierarchical indexing for more on this. To convert a Panel to a DataFrame, use the to_frame method:

  1. In [145]: panel = pd.Panel(np.random.randn(3, 5, 4), items=['one', 'two', 'three'],
  2.    .....:                  major_axis=pd.date_range('1/1/2000', periods=5),
  3.    .....:                  minor_axis=['a', 'b', 'c', 'd'])
  4.    .....: 
  6. In [146]: panel.to_frame()
  7. Out[146]: 
  8.                        one       two     three
  9. major      minor                              
  10. 2000-01-01 a      0.493672  1.219492 -1.290493
  11.            b     -2.461467  0.062297  0.787872
  12.            c     -1.553902 -0.110388  1.515707
  13.            d      2.015523 -1.184357 -0.276487
  14. 2000-01-02 a     -1.833722 -0.558081 -0.223762
  15.            b      1.771740  0.077849  1.397431
  16.            c     -0.670027  0.629498  1.503874
  17.            d      0.049307 -1.035260 -0.478905
  18. 2000-01-03 a     -0.521493 -0.438229 -0.135950
  19.            b     -3.201750  0.503703 -0.730327
  20.            c      0.792716  0.413086 -0.033277
  21.            d      0.146111 -1.139050  0.281151
  22. 2000-01-04 a      1.903247  0.660342 -1.298915
  23.            b     -0.747169  0.464794 -2.819487
  24.            c     -0.309038 -0.309337 -0.851985
  25.            d      0.393876 -0.649593 -1.106952
  26. 2000-01-05 a      1.861468  0.683758 -0.937731
  27.            b      0.936527 -0.643834 -1.537770
  28.            c      1.255746  0.421287  0.555759
  29.            d     -2.655452  1.032814 -2.277282