是否可以在 Pandas 中创建带有子标题的数据框? [英] Is it possible to create dataframe with sub-headers in Pandas?

问题描述

我有以下一段代码:

_tmp = {}_tmp['pre'] = {'A2': 10,'B2': 15,'C2':20}_tmp['差异'] = {'A1': 10,'B1': 15,'C1':20}_tmp['总和'] = {'A':100，'B':150，'C':200}

通过执行以下命令:

_dff = pd.DataFrame(data=_tmp.values(), index=_tmp.keys())_dff[['A', 'B', 'C', 'A1', 'B1', 'C1', 'A2', 'B2', 'C2']]

我得到了如下描述的结果:

但是我很想知道是否可以在 Pandas 中构建如下所示的结构:

解决方案

您真正看到的是三维结构，这是数据框无法做到的.(它们只有 2D.)

这给您留下了几个选择:

(1) 多个数据帧(pre/diff/sum).

dfs = {k:pd.DataFrame(v.items()) for k,v in _tmp.items()}

(2) Pandas Panel 你可以像这样构建:

pnl = pd.Panel(dfs)

这是一种对多个数据帧进行分组的方法.

(3) 一个 3d numpy 矩阵:

<预><代码>>>>pnl.as_matrix()[[['A1' 10]['C1' 20]['B1' 15]][['C2' 20]['A2' 10]['B2' 15]][['A' 100]['C' 200]['B' 150]]]

当然，您将不得不编写一个自定义打印函数来获得您所请求的确切输出，但这将是您表示数据的方式.

I have the following piece of code:

_tmp = {}
_tmp['pre'] = {
    'A2': 10,
    'B2': 15,
    'C2': 20
}
_tmp['diff'] = {
    'A1': 10,
    'B1': 15,
    'C1': 20
}
_tmp['sum'] = {
    'A': 100,
    'B': 150,
    'C': 200
}

By performing the following commands:

_dff = pd.DataFrame(data=_tmp.values(), index=_tmp.keys())
_dff[['A', 'B', 'C', 'A1', 'B1', 'C1', 'A2', 'B2', 'C2']]

I got a result described below:

However I am interested in knowing whether it is possible to build a structure in pandas like the following:

解决方案

What you're really looking at there is a three dimensional structure, which a dataframe cannot do. (They're 2D only.)

This leaves you with a couple options:

(1) Multiple dataframes (pre/diff/sum).

dfs = {k:pd.DataFrame(v.items()) for k,v in _tmp.items()}

(2) A pandas Panel which you can build like:

pnl = pd.Panel(dfs)

Which is a way of grouping multiple dataframes.

(3) A 3d numpy matrix:

>>> pnl.as_matrix()
[[['A1' 10]
  ['C1' 20]
  ['B1' 15]]

 [['C2' 20]
  ['A2' 10]
  ['B2' 15]]

 [['A' 100]
  ['C' 200]
  ['B' 150]]]

You're going to have to write a custom print function to get your exact requested output of course, but these would be the ways you can represent your data.

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