三维数字数组到多索引熊猫数据存储器

I have a 3 dimensional numpy array, (z, x, y). z is a time dimension and x and y are coordinates.

我有一个三维的numpy数组，(z, x, y) z是时间维度，x和y是坐标。

I want to convert this to a multiindexed pandas.DataFrame. I want the row index to be the z dimension and each column to have values from a unique x, y coordinate (and so, each column would be multi-indexed).

我想把它转换成一个多索引的pandas.DataFrame。我希望行索引是z维，并且每一列都有来自唯一的x、y坐标的值(因此，每一列都是多索引的)。

The simplest case (not multi-indexed):

最简单的例子(不是多索引的):

>>> array.shape
(500L, 120L, 100L)

>>> df = pd.DataFrame(array[:,0,0])

>>> df.shape
(500, 1)

I've been trying to pass the whole array into a multiindex dataframe using pd.MultiIndex.from_arrays but I'm getting an error: NotImplementedError: > 1 ndim Categorical are not supported at this time

我一直试图将整个数组传递到一个多索引的dataframe中，使用pd.multiindex.from_array，但我得到一个错误:NotImplementedError: > 1 ndim Categorical在这个时候不被支持。

Looks like it should be fairly simple but I cant figure it out.

看起来应该很简单，但我搞不清楚。

2 个解决方案

#1

I think you can use panel - and then for Multiindex DataFrame add to_frame:

我认为你可以使用面板-然后对于多索引的DataFrame添加to_frame:

np.random.seed(10)
arr = np.random.randint(10, size=(5,3,2))
print (arr)
[[[9 4]
  [0 1]
  [9 0]]

 [[1 8]
  [9 0]
  [8 6]]

 [[4 3]
  [0 4]
  [6 8]]

 [[1 8]
  [4 1]
  [3 6]]

 [[5 3]
  [9 6]
  [9 1]]]

df = pd.Panel(arr).to_frame()
print (df)
             0  1  2  3  4
major minor               
0     0      9  1  4  1  5
      1      4  8  3  8  3
1     0      0  9  0  4  9
      1      1  0  4  1  6
2     0      9  8  6  3  9
      1      0  6  8  6  1

Also transpose can be useful:

转置也是有用的:

df = pd.Panel(arr).transpose(1,2,0).to_frame()
print (df)
             0  1  2
major minor         
0     0      9  0  9
      1      1  9  8
      2      4  0  6
      3      1  4  3
      4      5  9  9
1     0      4  1  0
      1      8  0  6
      2      3  4  8
      3      8  1  6
      4      3  6  1

Another possible solution with concat:

concat的另一个可能解决方案是:

arr = arr.transpose(1,2,0)
df = pd.concat([pd.DataFrame(x) for x in arr], keys=np.arange(arr.shape[2]))
print (df)
    0  1  2  3  4
0 0  9  1  4  1  5
  1  4  8  3  8  3
1 0  0  9  0  4  9
  1  1  0  4  1  6
2 0  9  8  6  3  9
  1  0  6  8  6  1

np.random.seed(10)
arr = np.random.randint(10, size=(500,120,100))
df = pd.Panel(arr).transpose(2,0,1).to_frame()
print (df.shape)
(60000, 100)

print (df.index.max())
(499, 119)

#2

I find that a Series with a Multiindex is the most analagous pandas datatype for a numpy array with arbitrarily many dimensions (presumably 3 or more).

我发现具有多索引的序列是任意多个维数的numpy数组(大概是3个或更多)的最前后矛盾的熊猫数据类型。

Here is some example code:

下面是一些示例代码:

import pandas as pd
import numpy as np

time_vals = np.linspace(1, 50, 50)
x_vals = np.linspace(-5, 6, 12)
y_vals = np.linspace(-4, 5, 10)

measurements = np.random.rand(50,12,10)

#setup multiindex
mi = pd.MultiIndex.from_product([time_vals, x_vals, y_vals], names=['time', 'x', 'y'])

#connect multiindex to data and save as multiindexed Series
sr_multi = pd.Series(index=mi, data=measurements.flatten())

#pull out a dataframe of x, y at time=22
sr_multi.xs(22, level='time').unstack(level=0)

#pull out a dataframe of y, time at x=3
sr_multi.xs(3, level='x').unstack(level=1)

#1