将2D Panda的DataFrame列表转换为3D DataFrame
[英]Converting list of 2D Panda's DataFrame to 3D DataFrame
问题描述
I am trying to create a Pandas DataFrame that holds label values to a 2D DataFrame. 
我正在尝试创建一个Pandas DataFrame，它将标签值保存到2D DataFrame。 This is what I have done so far: 这是我到目前为止所做的：
I am reading csv files using pd.read_csv and appending them to list, for the purpose of this question let's consider the following code: 我正在使用pd.read_csv读取csv文件并将它们附加到列表中，出于这个问题的目的，让我们考虑以下代码：
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]))
Then, I added raw_sample to df=d.DataFrame(raw_sample) . 然后，我将raw_sample添加到df=d.DataFrame(raw_sample) 。 Then I added the labels to df by doing the following: 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)
I tried printing this and I got 我尝试打印这个，我得到了
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...
I have also tried printing df[0] , I still got the same thing. 我也试过打印df[0] ，我仍然有同样的事情。
I wanted to know if it is in the form of 我想知道它是否是以形式出现的
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
...
I know that a DataFrame cannot take 2D array, the other thing was to use pd.Panel , for this I converted all the contents of raw_sample to numpy array and then converted raw_sample itself to numpy array and did the following: 我知道DataFrame不能使用2D数组，另一件事是使用pd.Panel ，为此我将raw_sample所有内容转换为numpy数组，然后将raw_sample本身转换为numpy数组并执行以下操作：
p1 = pd.Panel(samples, items=map(str, labels))
but when I print this, I get 但是当我打印出来时，我明白了
<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
Looking at the Items , it looks like all the common values are grouped together. 查看Items ，看起来所有常见值都组合在一起。
I am not sure what to do at this point. 我不知道此时该怎么做。 Help!! 救命！！
Update 更新
Inputs: 输入：
labels = [1,1,1,2,2,2]
samples = [5x4 pd.DataFrame, 5x4 pd.DataFrame, 5x4 pd.DataFrame, 5x4 pd.DataFrame, 5x4 pd.DataFrame, 5x4 pd.DataFrame]
Desired Output: 期望的输出：
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
...
1 个解决方案
解决方案1
2 已采纳 2017-04-12 06:50:05
If select with not unique items, get another Panel : 如果选择不是唯一的项目，请获取另一个Panel ：
np.random.seed(10)
labels = [1,1,1,2,2,2]
samples = np.random.randn(6, 5, 4)
p1 = pd.Panel(samples, items=map(str, labels))
print (p1)
<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
print (p1['1'])
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: 1 to 1
Major_axis axis: 0 to 4
Minor_axis axis: 0 to 3
print (p1.to_frame())
1 1 1 2 2 2
major minor
0 0 1.331587 1.331587 1.331587 -0.232182 -0.232182 -0.232182
1 0.715279 0.715279 0.715279 -0.501729 -0.501729 -0.501729
2 -1.545400 -1.545400 -1.545400 1.128785 1.128785 1.128785
3 -0.008384 -0.008384 -0.008384 -0.697810 -0.697810 -0.697810
1 0 0.621336 0.621336 0.621336 -0.081122 -0.081122 -0.081122
1 -0.720086 -0.720086 -0.720086 -0.529296 -0.529296 -0.529296
2 0.265512 0.265512 0.265512 1.046183 1.046183 1.046183
3 0.108549 0.108549 0.108549 -1.418556 -1.418556 -1.418556
2 0 0.004291 0.004291 0.004291 -0.362499 -0.362499 -0.362499
1 -0.174600 -0.174600 -0.174600 -0.121906 -0.121906 -0.121906
2 0.433026 0.433026 0.433026 0.319356 0.319356 0.319356
3 1.203037 1.203037 1.203037 0.460903 0.460903 0.460903
3 0 -0.965066 -0.965066 -0.965066 -0.215790 -0.215790 -0.215790
1 1.028274 1.028274 1.028274 0.989072 0.989072 0.989072
2 0.228630 0.228630 0.228630 0.314754 0.314754 0.314754
3 0.445138 0.445138 0.445138 2.467651 2.467651 2.467651
4 0 -1.136602 -1.136602 -1.136602 -1.508321 -1.508321 -1.508321
1 0.135137 0.135137 0.135137 0.620601 0.620601 0.620601
2 1.484537 1.484537 1.484537 -1.045133 -1.045133 -1.045133
3 -1.079805 -1.079805 -1.079805 -0.798009 -0.798009 -0.798009
But if have unique one, get DataFrame : 但如果有唯一的，请获取DataFrame ：
np.random.seed(10)
labels = list('abcdef')
samples = np.random.randn(6, 5, 4)
p1 = pd.Panel(samples, items=labels)
print (p1)
<class 'pandas.core.panel.Panel'>
Dimensions: 6 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: a to f
Major_axis axis: 0 to 4
Minor_axis axis: 0 to 3
print (p1['a'])
0 1 2 3
0 1.331587 0.715279 -1.545400 -0.008384
1 0.621336 -0.720086 0.265512 0.108549
2 0.004291 -0.174600 0.433026 1.203037
3 -0.965066 1.028274 0.228630 0.445138
4 -1.136602 0.135137 1.484537 -1.079805
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
It is same as in DataFrame with non unique columns: 它与DataFrame中的非唯一列相同：
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
EDIT: 编辑：
Also for creating df from list need unique labels (no unique raise error) and function concat with parameter keys , for Panel call to_panel : 另外，对于从列表中创建df ，需要使用唯一labels （没有唯一的引发错误）和带参数keys函数concat ，用于Panel调用to_panel ：
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
EDIT1: EDIT1：
If need MultiIndex DataFrame is possible create helper range for unique values, use concat and last remove helper level of MultiIndex : 如果需要MultiIndex DataFrame可以为唯一值创建辅助范围，请使用concat和last删除MultiIndex帮助程序级别：
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
But create panel is not possible: 但是创建面板是不可能的：
p1 = df.to_panel()
print (p1)
>ValueError: Can't convert non-uniquely indexed DataFrame to Panel
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