Python 将2D Panda的数据帧列表转换为3D数据帧

我试图创建一个熊猫数据框，将标签值保存到二维数据框。这就是我到目前为止所做的：

我正在使用PD.Read Tysv读取CSV文件并将它们添加到列表中，为了这个问题，我们考虑下面的代码：

import numpy as np
import pandas as pd

raw_sample = []
labels = [1,1,1,2,2,2]
samples = np.random.randn(6, 5, 4)
for contents in range(samples.shape[0]):
    raw_sample.append(pd.DataFrame(samples[contents]))

然后，我将raw_sample添加到df=d.DataFrameraw_sample。然后，我通过执行以下操作将标签添加到df：

df = df.set_index([df.index, labels])
df.index = df.index.set_names('index', level=0)
df.index = df.index.set_names('labels', level=1)

p1 = pd.Panel(samples, items=map(str, labels))

我试着打印这个，结果

                                                              0
index labels                                                   
0     1                 0         1         2         3
0  0...
1     1                 0         1         2         3
0  0...
2     1                 0         1         2         3
0  1...
3     2                 0         1         2         3
0 -0...
4     2                 0         1         2         3
0  0...
5     2                 0         1         2         3
0 -0...

我也试过打印df[0]，我还是得到了同样的结果

我想知道它是否以

index  labels         0
  0      1      1 2 3 4 5 6 7
                3 5 6 7 9 5 4
                3 4 5 6 7 8 9
  1      1      4 3 2 4 5 6 7
                3 5 6 7 4 5 6 
                2 3 4 3 4 5 3
...

我知道DataFrame不能采用2D数组，另一件事是使用pd.Panel，为此我将raw_sample的所有内容转换为numpy数组，然后将raw_sample自身转换为numpy数组，并执行了以下操作：

df = df.set_index([df.index, labels])
df.index = df.index.set_names('index', level=0)
df.index = df.index.set_names('labels', level=1)

p1 = pd.Panel(samples, items=map(str, labels))

但是当我打印这个的时候

<class 'pandas.core.panel.Panel'>
Dimensions: 6 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: 1 to 2
Major_axis axis: 0 to 4
Minor_axis axis: 0 to 3

期望输出：

index  labels      samples
  0      1      1 2 3 4 5 6 7
                3 5 6 7 9 5 4
                3 4 5 6 7 8 9
  1      1      4 3 2 4 5 6 7
                3 5 6 7 4 5 6 
                2 3 4 3 4 5 3
...

---

如果选择非唯一项，则获取另一个面板：

但如果有唯一的，则获取数据帧：

它与具有非唯一列的DataFrame中的相同：

samples = np.random.randn(6, 5)
df = pd.DataFrame(samples, columns=list('11122'))
print (df)
          1         1         1         2         2
0  0.346338 -0.855797 -0.932463 -2.289259  0.634696
1  0.272794 -0.924357 -1.898270 -0.743083 -1.587480
2 -0.519975 -0.136836  0.530178 -0.730629  2.520821
3  0.137530 -1.232763  0.508548 -0.480384 -1.213064
4 -0.157787 -1.600004 -1.287620  0.384642 -0.568072
5 -0.649427 -0.659585 -0.813359 -1.487412 -0.044206

print (df['1'])
          1         1         1
0  0.346338 -0.855797 -0.932463
1  0.272794 -0.924357 -1.898270
2 -0.519975 -0.136836  0.530178
3  0.137530 -1.232763  0.508548
4 -0.157787 -1.600004 -1.287620
5 -0.649427 -0.659585 -0.813359

编辑：

此外，对于从列表创建df，需要唯一标签无唯一上升错误，并使用参数键运行，用于面板调用：

但无法创建面板：

p1 = df.to_panel()
print (p1)

>ValueError: Can't convert non-uniquely indexed DataFrame to Panel

---

不确定你到底需要什么。“你能给我们你的输入和想要的输出吗？”Allen更新。谢谢。我不确定，但似乎您需要唯一的标签，所以将labels=[1,1,1,2,2]更改为labels=list'abcdef'，然后可以通过打印p1['a']@jezrael进行选择，但标签不能是唯一的。@akshay-是的，这是可能的。但如果测试打印p1 vs打印p1['1']，则以两种方式获取面板，只过滤第二个-尺寸：6个项目x 5个长轴x 4个短轴vs尺寸：3个项目x 5个长轴x 4个短轴请检查编辑以从列表创建数据框。问题是，标签不能唯一，每个标签都映射到一个样本。它们就像机器学习的样本。熊猫中的复制品是受支持的，但sume函数不能像reindex、concat那样工作。

print (p1.to_frame())
                    a         b         c         d         e         f
major minor                                                            
0     0      1.331587 -1.977728  0.660232 -0.232182  1.985085  0.117476
      1      0.715279 -1.743372 -0.350872 -0.501729  1.744814 -1.907457
      2     -1.545400  0.266070 -0.939433  1.128785 -1.856185 -0.922909
      3     -0.008384  2.384967 -0.489337 -0.697810 -0.222774  0.469751
1     0      0.621336  1.123691 -0.804591 -0.081122 -0.065848 -0.144367
      1     -0.720086  1.672622 -0.212698 -0.529296 -2.131712 -0.400138
      2      0.265512  0.099149 -0.339140  1.046183 -0.048831 -0.295984
      3      0.108549  1.397996  0.312170 -1.418556  0.393341  0.848209
2     0      0.004291 -0.271248  0.565153 -0.362499  0.217265  0.706830
      1     -0.174600  0.613204 -0.147420 -0.121906 -1.994394 -0.787269
      2      0.433026 -0.267317 -0.025905  0.319356  1.107708  0.292941
      3      1.203037 -0.549309  0.289094  0.460903  0.244544 -0.470807
3     0     -0.965066  0.132708 -0.539879 -0.215790 -0.061912  2.404326
      1      1.028274 -0.476142  0.708160  0.989072 -0.753893 -0.739357
      2      0.228630  1.308473  0.842225  0.314754  0.711959 -0.312829
      3      0.445138  0.195013  0.203581  2.467651  0.918269 -0.348882
4     0     -1.136602  0.400210  2.394704 -1.508321 -0.482093 -0.439026
      1      0.135137 -0.337632  0.917459  0.620601  0.089588  0.141104
      2      1.484537  1.256472 -0.112272 -1.045133  0.826999  0.273049
      3     -1.079805 -0.731970 -0.362180 -0.798009 -1.954512 -1.618571

samples = np.random.randn(6, 5)
df = pd.DataFrame(samples, columns=list('11122'))
print (df)
          1         1         1         2         2
0  0.346338 -0.855797 -0.932463 -2.289259  0.634696
1  0.272794 -0.924357 -1.898270 -0.743083 -1.587480
2 -0.519975 -0.136836  0.530178 -0.730629  2.520821
3  0.137530 -1.232763  0.508548 -0.480384 -1.213064
4 -0.157787 -1.600004 -1.287620  0.384642 -0.568072
5 -0.649427 -0.659585 -0.813359 -1.487412 -0.044206

print (df['1'])
          1         1         1
0  0.346338 -0.855797 -0.932463
1  0.272794 -0.924357 -1.898270
2 -0.519975 -0.136836  0.530178
3  0.137530 -1.232763  0.508548
4 -0.157787 -1.600004 -1.287620
5 -0.649427 -0.659585 -0.813359

np.random.seed(100)
raw_sample = []
labels = list('abcdef')
samples = np.random.randn(6, 5, 4)
for contents in range(samples.shape[0]):
    raw_sample.append(pd.DataFrame(samples[contents]))

df = pd.concat(raw_sample, keys=labels)
print (df)
            0         1         2         3
a 0 -1.749765  0.342680  1.153036 -0.252436
  1  0.981321  0.514219  0.221180 -1.070043
  2 -0.189496  0.255001 -0.458027  0.435163
  3 -0.583595  0.816847  0.672721 -0.104411
  4 -0.531280  1.029733 -0.438136 -1.118318
b 0  1.618982  1.541605 -0.251879 -0.842436
  1  0.184519  0.937082  0.731000  1.361556
  2 -0.326238  0.055676  0.222400 -1.443217
  3 -0.756352  0.816454  0.750445 -0.455947
  4  1.189622 -1.690617 -1.356399 -1.232435
c 0 -0.544439 -0.668172  0.007315 -0.612939
  1  1.299748 -1.733096 -0.983310  0.357508
  2 -1.613579  1.470714 -1.188018 -0.549746
  3 -0.940046 -0.827932  0.108863  0.507810
  4 -0.862227  1.249470 -0.079611 -0.889731
d 0 -0.881798  0.018639  0.237845  0.013549
  1 -1.635529 -1.044210  0.613039  0.736205
  2  1.026921 -1.432191 -1.841188  0.366093
  3 -0.331777 -0.689218  2.034608 -0.550714
  4  0.750453 -1.306992  0.580573 -1.104523
e 0  0.690121  0.686890 -1.566688  0.904974
  1  0.778822  0.428233  0.108872  0.028284
  2 -0.578826 -1.199451 -1.705952  0.369164
  3  1.876573 -0.376903  1.831936  0.003017
  4 -0.076023  0.003958 -0.185014 -2.487152
f 0 -1.704651 -1.136261 -2.973315  0.033317
  1 -0.248889 -0.450176  0.132428  0.022214
  2  0.317368 -0.752414 -1.296392  0.095139
  3 -0.423715 -1.185984 -0.365462 -1.271023
  4  1.586171  0.693391 -1.958081 -0.134801

p1 = df.to_panel()
print (p1)
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 6 (major_axis) x 5 (minor_axis)
Items axis: 0 to 3
Major_axis axis: a to f
Minor_axis axis: 0 to 4

np.random.seed(100)
raw_sample = []
labels = [1,1,1,2,2,2]
mux = pd.MultiIndex.from_arrays([labels, range(len(labels))])

samples = np.random.randn(6, 5, 4)
for contents in range(samples.shape[0]):
    raw_sample.append(pd.DataFrame(samples[contents]))

df = pd.concat(raw_sample, keys=mux)

df = df.reset_index(level=1, drop=True)
print (df)
            0         1         2         3
1 0 -1.749765  0.342680  1.153036 -0.252436
  1  0.981321  0.514219  0.221180 -1.070043
  2 -0.189496  0.255001 -0.458027  0.435163
  3 -0.583595  0.816847  0.672721 -0.104411
  4 -0.531280  1.029733 -0.438136 -1.118318
  0  1.618982  1.541605 -0.251879 -0.842436
  1  0.184519  0.937082  0.731000  1.361556
  2 -0.326238  0.055676  0.222400 -1.443217
  3 -0.756352  0.816454  0.750445 -0.455947
  4  1.189622 -1.690617 -1.356399 -1.232435
  0 -0.544439 -0.668172  0.007315 -0.612939
  1  1.299748 -1.733096 -0.983310  0.357508
  2 -1.613579  1.470714 -1.188018 -0.549746
  3 -0.940046 -0.827932  0.108863  0.507810
  4 -0.862227  1.249470 -0.079611 -0.889731
2 0 -0.881798  0.018639  0.237845  0.013549
  1 -1.635529 -1.044210  0.613039  0.736205
  2  1.026921 -1.432191 -1.841188  0.366093
  3 -0.331777 -0.689218  2.034608 -0.550714
  4  0.750453 -1.306992  0.580573 -1.104523
  0  0.690121  0.686890 -1.566688  0.904974
  1  0.778822  0.428233  0.108872  0.028284
  2 -0.578826 -1.199451 -1.705952  0.369164
  3  1.876573 -0.376903  1.831936  0.003017
  4 -0.076023  0.003958 -0.185014 -2.487152
  0 -1.704651 -1.136261 -2.973315  0.033317
  1 -0.248889 -0.450176  0.132428  0.022214
  2  0.317368 -0.752414 -1.296392  0.095139
  3 -0.423715 -1.185984 -0.365462 -1.271023
  4  1.586171  0.693391 -1.958081 -0.134801

p1 = df.to_panel()
print (p1)

>ValueError: Can't convert non-uniquely indexed DataFrame to Panel