构造3D Pandas DataFrame [英] Constructing 3D Pandas DataFrame
问题描述
我在Pandas中构建3D DataFrame有困难.我想要这样的东西
I'm having difficulty constructing a 3D DataFrame in Pandas. I want something like this
A B C
start end start end start end ...
7 20 42 52 90 101
11 21 213 34
56 74 9 45
45 12
其中A
,B
等是顶级描述符,而start
和end
是子描述符.接下来的数字是成对的,并且A
,B
等的对数不相同.观察到A
有四个这样的对,B
只有1,而C
有3
Where A
, B
, etc are the top-level descriptors and start
and end
are subdescriptors. The numbers that follow are in pairs and there aren't the same number of pairs for A
, B
etc. Observe that A
has four such pairs, B
has only 1, and C
has 3.
我不确定如何继续构建此DataFrame.修改此示例并没有为我提供设计的输出:
I'm not sure how to proceed in constructing this DataFrame. Modifying this example didn't give me the designed output:
import numpy as np
import pandas as pd
A = np.array(['one', 'one', 'two', 'two', 'three', 'three'])
B = np.array(['start', 'end']*3)
C = [np.random.randint(10, 99, 6)]*6
df = pd.DataFrame(zip(A, B, C), columns=['A', 'B', 'C'])
df.set_index(['A', 'B'], inplace=True)
df
屈服:
C
A B
one start [22, 19, 16, 20, 63, 54]
end [22, 19, 16, 20, 63, 54]
two start [22, 19, 16, 20, 63, 54]
end [22, 19, 16, 20, 63, 54]
three start [22, 19, 16, 20, 63, 54]
end [22, 19, 16, 20, 63, 54]
有什么方法可以将C中的列表分解成自己的列?
Is there any way of breaking up the lists in C into their own columns?
我的C
的结构很重要.看起来如下:
The structure of my C
is important. It looks like the following:
C = [[7,11,56,45], [20,21,74,12], [42], [52], [90,213,9], [101, 34, 45]]
所需的输出是顶部的输出.它表示某个序列(A
,B
.C
是不同的序列)内子序列的起点和终点.根据序列本身,有不同数量的子序列可以满足我要寻找的给定条件.结果,A
,B
等
And the desired output is the one at the top. It represents the starting and ending points of subsequences within a certain sequence (A
, B
. C
are the different sequences). Depending on the sequence itself, there are a differing number of subsequences that satisfy a given condition I'm looking for. As a result, there are a differing number of start:end pairs for A
, B
, etc
推荐答案
首先,我认为您需要填充C来表示缺失值
First, I think you need to fill C to represent missing values
In [341]: max_len = max(len(sublist) for sublist in C)
In [344]: for sublist in C:
...: sublist.extend([np.nan] * (max_len - len(sublist)))
In [345]: C
Out[345]:
[[7, 11, 56, 45],
[20, 21, 74, 12],
[42, nan, nan, nan],
[52, nan, nan, nan],
[90, 213, 9, nan],
[101, 34, 45, nan]]
然后,将其转换为numpy数组,进行转置,并与列一起传递给DataFrame构造函数.
Then, convert to a numpy array, transpose, and pass to the DataFrame constructor along with the columns.
In [288]: C = np.array(C)
In [289]: df = pd.DataFrame(data=C.T, columns=pd.MultiIndex.from_tuples(zip(A,B)))
In [349]: df
Out[349]:
one two three
start end start end start end
0 7 20 42 52 90 101
1 11 21 NaN NaN 213 34
2 56 74 NaN NaN 9 45
3 45 12 NaN NaN NaN NaN
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