blocks|key|579192|text|您可以将3D数组转换为Pandas+Panel，然后将其展平为2D+DataFrame+(使用.to_frame())：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|579193|import+numpy+as+np
import+pandas+as+pd
np.random.seed(2016)

arr+=+np.random.randint(0,+1000,+(5,+4,+3))
pan+=+pd.Panel(arr)
df+=+pan.swapaxes(0,+2).to_frame()
df.index+=+df.index.droplevel('minor')
df.index.name+=+'Date'
df.index+=+df.index%2B1
df.columns+=+list('ABC')|code-block|syntax|javascript|579194|收益率|579195|++++++++A++++B++++C
Date+++++++++++++++
1+++++875++702++266
1+++++940++180++971
1+++++254++649++353
1+++++824++677++745
...
4+++++675++488++939
4+++++382++238++225
4+++++923++926++633
4+++++664++639++616
4+++++770++274++378|579196|579197|或者，您可以重塑数组以形成(20,+3)，像往常一样形成DataFrame，然后修复索引：|579198|import+numpy+as+np
import+pandas+as+pd
np.random.seed(2016)

arr+=+np.random.randint(0,+1000,+(5,+4,+3))
df+=+pd.DataFrame(arr.reshape(-1,+3),+columns=list('ABC'))
df.index+=+np.repeat(np.arange(arr.shape[0]),+arr.shape[1])+%2B+1
df.index.name+=+'Date'
print(df)|579199|产生相同的结果。|579200|entityMap|0|LINK|mutability|MUTABLE|url|http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Panel.to_frame.html^0|1B|B|1B|B|0|0|0|0|0|0|D|7|0|0|0^^$0|@$1|2|3|4|5|6|7|13|8|@$9|14|A|15|B|C]]|D|@$9|16|A|17|1|18]]|E|$]]|$1|F|3|G|5|H|7|19|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1A|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|1B|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|1C|8|@]|D|@]|E|$]]|$1|P|3|Q|5|6|7|1D|8|@$9|1E|A|1F|B|C]]|D|@]|E|$]]|$1|R|3|S|5|H|7|1G|8|@]|D|@]|E|$I|J]]|$1|T|3|U|5|6|7|1H|8|@]|D|@]|E|$]]|$1|V|3|-4|5|6|7|1I|8|@]|D|@]|E|$]]]|W|$X|$5|Y|Z|10|E|$11|12]]]]

You could convert your 3D array to a Pandas Panel, then flatten it to a 2D DataFrame (using <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Panel.to_frame.html" rel="nofollow noreferrer"><code>.to_frame()</code></a>):

<pre><code>import numpy as np
import pandas as pd
np.random.seed(2016)

arr = np.random.randint(0, 1000, (5, 4, 3))
pan = pd.Panel(arr)
df = pan.swapaxes(0, 2).to_frame()
df.index = df.index.droplevel('minor')
df.index.name = 'Date'
df.index = df.index+1
df.columns = list('ABC')
</code></pre>

yields

<pre><code> A B C
Date 
1 875 702 266
1 940 180 971
1 254 649 353
1 824 677 745
...
4 675 488 939
4 382 238 225
4 923 926 633
4 664 639 616
4 770 274 378
</code></pre>

<hr>

Alternatively, you could reshape the array to shape <code>(20, 3)</code>, form the DataFrame as usual, and then fix the index:

<pre><code>import numpy as np
import pandas as pd
np.random.seed(2016)

arr = np.random.randint(0, 1000, (5, 4, 3))
df = pd.DataFrame(arr.reshape(-1, 3), columns=list('ABC'))
df.index = np.repeat(np.arange(arr.shape[0]), arr.shape[1]) + 1
df.index.name = 'Date'
print(df)
</code></pre>

yields the same result.

blocks|key|104129|text|ThreeD_Arrays+=+np.random.randint(0,+1000,+(5,+4,+3))
df+=+pd.DataFrame([list(l)+for+l+in+ThreeD_Arrays]).stack().apply(pd.Series).reset_index(1,+drop=True)
df.index.name+=+'Date'
df.columns+=+list('ABC')|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|104130|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>ThreeD_Arrays = np.random.randint(0, 1000, (5, 4, 3))
df = pd.DataFrame([list(l) for l in ThreeD_Arrays]).stack().apply(pd.Series).reset_index(1, drop=True)
df.index.name = 'Date'
df.columns = list('ABC')
</code></pre>

blocks|key|575998|text|根据对this+question的回答，我们可以使用MultiIndex。首先，创建MultiIndex和一个扁平化的DataFrame。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|575999|A+=+np.random.randint(0,+1000,+(5,+4,+3))

names+=+['x',+'y',+'z']
index+=+pd.MultiIndex.from_product([range(s)for+s+in+A.shape],+names=names)
df+=+pd.DataFrame({'A':+A.flatten()},+index=index)['A']|code-block|syntax|javascript|576000|现在我们可以随心所欲地重塑它：|576001|df+=+df.unstack(level='x').swaplevel().sort_index()
df.columns+=+['A',+'B',+'C']
df.index.names+=+['DATE',+'i']|576002|这就是结果：|576003|++++++++++A++++B++++C
DATE+i+++++++++++
0++++0++715++226++632
+++++1++895++837++431
+++++2++520++692++230
+++++3++286++358++462
+++++4+++44++119++757
1++++0++305+++97++534
+++++1++649++717+++39
+++++2++452++816++887
+++++3++831+++26++332
+++++4++908++937++728
2++++0+++88++592++902
+++++1++363++121++274
+++++2++688++509++770
+++++3++424++178++642
+++++4++809+++28++442
3++++0++172++932++263
+++++1++334++359++816
+++++2++290++856++584
+++++3++955+++42++938
+++++4++832++220++348|576004|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/q/46134827/512652^0|3|D|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@]|9|@$A|Z|B|10|1|11]]|C|$]]|$1|D|3|E|5|F|7|12|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|13|8|@]|9|@]|C|$]]|$1|K|3|L|5|F|7|14|8|@]|9|@]|C|$G|H]]|$1|M|3|N|5|6|7|15|8|@]|9|@]|C|$]]|$1|O|3|P|5|F|7|16|8|@]|9|@]|C|$G|H]]|$1|Q|3|-4|5|6|7|17|8|@]|9|@]|C|$]]]|R|$S|$5|T|U|V|C|$W|X]]]]

Based on the answer to <a href="https://stackoverflow.com/q/46134827/512652">this question</a>, we can use a MultiIndex. First, create the MultiIndex and a flattened DataFrame.

<pre><code>A = np.random.randint(0, 1000, (5, 4, 3))

names = ['x', 'y', 'z']
index = pd.MultiIndex.from_product([range(s)for s in A.shape], names=names)
df = pd.DataFrame({'A': A.flatten()}, index=index)['A']
</code></pre>

Now we can reshape it however we like:

<pre><code>df = df.unstack(level='x').swaplevel().sort_index()
df.columns = ['A', 'B', 'C']
df.index.names = ['DATE', 'i']
</code></pre>

This is the result:

<pre><code> A B C
DATE i 
0 0 715 226 632
 1 895 837 431
 2 520 692 230
 3 286 358 462
 4 44 119 757
1 0 305 97 534
 1 649 717 39
 2 452 816 887
 3 831 26 332
 4 908 937 728
2 0 88 592 902
 1 363 121 274
 2 688 509 770
 3 424 178 642
 4 809 28 442
3 0 172 932 263
 1 334 359 816
 2 290 856 584
 3 955 42 938
 4 832 220 348
</code></pre>

I have a 3d array as follows:
<pre class="lang-py prettyprint-override"><code> ThreeD_Arrays = np.random.randint(0, 1000, (5, 4, 3))
 
 array([[[715, 226, 632],
 [305, 97, 534],
 [ 88, 592, 902],
 [172, 932, 263]],

 [[895, 837, 431],
 [649, 717, 39],
 [363, 121, 274],
 [334, 359, 816]],

 [[520, 692, 230],
 [452, 816, 887],
 [688, 509, 770],
 [290, 856, 584]],

 [[286, 358, 462],
 [831, 26, 332],
 [424, 178, 642],
 [955, 42, 938]], 

 [[ 44, 119, 757],
 [908, 937, 728],
 [809, 28, 442],
 [832, 220, 348]]])
</code></pre>
Now I would like to have it into a <code>DataFrame</code> like this:
<a href="https://i.stack.imgur.com/5UvY0.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/5UvY0.png" alt="the expected results is shown here" /></a>
Add a <code>Date</code> column like indicated and the column names <code>A</code>, <code>B</code>, <code>C</code>.
How to do this transformation? Thanks!

How to transform a 3d arrays into a dataframe in python

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我有一个3d数组，如下所示：    ThreeD_Arrays = np.random.randint(0, 1000, (5, 4, 3))        array([[[715, 226, 632],            [305,  97, 534],            [ 88, 592, 902], ...

问如何在python中将3d数组转换为数据帧
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问如何在python中将3d数组转换为数据帧EN