python - 何时在 pandas 中使用多索引与 xarray

Question

pandas pivot tables documentation似乎建议使用多索引处理多于两个维度的数据:

In [1]: import pandas as pd

In [2]: import numpy as np

In [3]: import pandas.util.testing as tm; tm.N = 3

In [4]: def unpivot(frame):
   ...:         N, K = frame.shape
   ...:         data = {'value' : frame.values.ravel('F'),
   ...:                 'variable' : np.asarray(frame.columns).repeat(N),
   ...:                 'date' : np.tile(np.asarray(frame.index), K)}
   ...:         return pd.DataFrame(data, columns=['date', 'variable', 'value'])
   ...: 

In [5]: df = unpivot(tm.makeTimeDataFrame())

In [6]: df
Out[6]: 
         date variable     value    value2
0  2000-01-03        A  0.462461  0.924921
1  2000-01-04        A -0.517911 -1.035823
2  2000-01-05        A  0.831014  1.662027
3  2000-01-03        B -0.492679 -0.985358
4  2000-01-04        B -1.234068 -2.468135
5  2000-01-05        B  1.725218  3.450437
6  2000-01-03        C  0.453859  0.907718
7  2000-01-04        C -0.763706 -1.527412
8  2000-01-05        C  0.839706  1.679413
9  2000-01-03        D -0.048108 -0.096216
10 2000-01-04        D  0.184461  0.368922
11 2000-01-05        D -0.349496 -0.698993

In [7]: df['value2'] = df['value'] * 2

In [8]: df.pivot('date', 'variable')
Out[8]: 
               value                                  value2            \
variable           A         B         C         D         A         B   
date                                                                     
2000-01-03 -1.558856 -1.144732 -0.234630 -1.252482 -3.117712 -2.289463   
2000-01-04 -1.351152 -0.173595  0.470253 -1.181006 -2.702304 -0.347191   
2000-01-05  0.151067 -0.402517 -2.625085  1.275430  0.302135 -0.805035   


variable           C         D  
date                            
2000-01-03 -0.469259 -2.504964  
2000-01-04  0.940506 -2.362012  
2000-01-05 -5.250171  2.550861  

我认为 xarray 是为处理这样​​的多维数据集而设计的:

In [9]: import xarray as xr

In [10]: xr.DataArray(dict([(var, df[df.variable==var].drop('variable', 1)) for var in np.unique(df.variable)]))
Out[10]: 
<xarray.DataArray ()>
array({'A':         date     value    value2
0 2000-01-03  0.462461  0.924921
1 2000-01-04 -0.517911 -1.035823
2 2000-01-05  0.831014  1.662027, 'C':         date     value    value2
6 2000-01-03  0.453859  0.907718
7 2000-01-04 -0.763706 -1.527412
8 2000-01-05  0.839706  1.679413, 'B':         date     value    value2
3 2000-01-03 -0.492679 -0.985358
4 2000-01-04 -1.234068 -2.468135
5 2000-01-05  1.725218  3.450437, 'D':          date     value    value2
9  2000-01-03 -0.048108 -0.096216
10 2000-01-04  0.184461  0.368922
11 2000-01-05 -0.349496 -0.698993}, dtype=object)

这些方法中的一种比另一种更好吗？为什么 xarray 没有完全取代多索引？

Answer 1

似乎确实有过渡到 xarray 来处理多维数组的工作。 Pandas 将减少对 3D 面板数据结构和 documentation even suggest using xarray for working with multidemensional arrays 中的支持。 :

'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 use cases. Here is a link to the xarray panel-transition documentation.'

来自xarray documentation他们陈述了他们的目的和目标:

xarray aims to provide a data analysis toolkit as powerful as pandas but designed for working with homogeneous N-dimensional arrays instead of tabular data...

...Our target audience is anyone who needs N-dimensional labelled arrays, but we are particularly focused on the data analysis needs of physical scientists – especially geoscientists who already know and love netCDF

与使用直接 numpy 相比，xarray 的主要优势在于它使用标签的方式与 pandas 在多个维度上使用的方式相同。如果您使用多索引处理 3 维数据，则 xarray 可能可以互换。随着数据集中维数的增加，xarray 变得更易于管理。我无法评论每个人在效率或速度方面的表现。