将numpy数组作为列添加到Pandas数据框

Question 1

我有一个形状为（X，Y）的Pandas数据框对象，如下所示：

[[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]

还有一个形状为（X，Z）的numpy稀疏矩阵（CSC），看起来像这样

[[0, 1, 0],
[0, 0, 1],
[1, 0, 0]]

如何将矩阵中的内容添加到新命名列中的数据框中，以使数据框最终像这样：

[[1, 2, 3, [0, 1, 0]],
[4, 5, 6, [0, 0, 1]],
[7, 8, 9, [1, 0, 0]]]

请注意，数据框现在具有形状（X，Y + 1），并且矩阵中的行是数据框中的元素。

Question 2

import numpy as np
import pandas as pd
import scipy.sparse as sparse

df = pd.DataFrame(np.arange(1,10).reshape(3,3))
arr = sparse.coo_matrix(([1,1,1], ([0,1,2], [1,2,0])), shape=(3,3))
df['newcol'] = arr.toarray().tolist()
print(df)

产量

   0  1  2     newcol
0  1  2  3  [0, 1, 0]
1  4  5  6  [0, 0, 1]
2  7  8  9  [1, 0, 0]

Question 3

考虑使用更高维度的数据结构（Panel），而不是在您的列中存储数组：

In [11]: p = pd.Panel({'df': df, 'csc': csc})

In [12]: p.df
Out[12]: 
   0  1  2
0  1  2  3
1  4  5  6
2  7  8  9

In [13]: p.csc
Out[13]: 
   0  1  2
0  0  1  0
1  0  0  1
2  1  0  0

查看横截面等，等等，等等。

In [14]: p.xs(0)
Out[14]: 
   csc  df
0    0   1
1    1   2
2    0   3

有关面板的更多信息，请参见文档。

Question 4

这是其他示例：

import numpy as np
import pandas as pd

""" This just creates a list of touples, and each element of the touple is an array"""
a = [ (np.random.randint(1,10,10), np.array([0,1,2,3,4,5,6,7,8,9]))  for i in 
range(0,10) ]

""" Panda DataFrame will allocate each of the arrays , contained as a touple 
element , as column"""
df = pd.DataFrame(data =a,columns=['random_num','sequential_num'])

通常的秘密是，以a = [（（array_11，array_12，...，array_1n），...，（array_m1，array_m2，...，array_mn））的形式分配数据，熊猫DataFrame将对数据进行排序在数组的n列中。当然，可以使用数组的数组来代替touples，在这种情况下，格式为：a = [[array_11，array_12，...，array_1n]，...，[array_m1，array_m2，...，array_mn ]

如果从上面的代码中打印（df），则输出为：

                       random_num                  sequential_num
0  [7, 9, 2, 2, 5, 3, 5, 3, 1, 4]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
1  [8, 7, 9, 8, 1, 2, 2, 6, 6, 3]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
2  [3, 4, 1, 2, 2, 1, 4, 2, 6, 1]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
3  [3, 1, 1, 1, 6, 2, 8, 6, 7, 9]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
4  [4, 2, 8, 5, 4, 1, 2, 2, 3, 3]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
5  [3, 2, 7, 4, 1, 5, 1, 4, 6, 3]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
6  [5, 7, 3, 9, 7, 8, 4, 1, 3, 1]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
7  [7, 4, 7, 6, 2, 6, 3, 2, 5, 6]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
8  [3, 1, 6, 3, 2, 1, 5, 2, 2, 9]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
9  [7, 2, 3, 9, 5, 5, 8, 6, 9, 8]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

上面示例的其他变体：

b = [ (i,"text",[14, 5,], np.array([0,1,2,3,4,5,6,7,8,9]))  for i in 
range(0,10) ]
df = pd.DataFrame(data=b,columns=['Number','Text','2Elemnt_array','10Element_array'])

df的输出：

   Number  Text 2Elemnt_array                 10Element_array
0       0  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
1       1  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
2       2  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
3       3  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
4       4  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
5       5  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
6       6  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
7       7  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
8       8  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
9       9  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

如果要添加数组的其他列，则：

df['3Element_array']=[([1,2,3]),([1,2,3]),([1,2,3]),([1,2,3]),([1,2,3]),([1,2,3]),([1,2,3]),([1,2,3]),([1,2,3]),([1,2,3])]

df的最终输出将是：

   Number  Text 2Elemnt_array                 10Element_array 3Element_array
0       0  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]      [1, 2, 3]
1       1  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]      [1, 2, 3]
2       2  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]      [1, 2, 3]
3       3  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]      [1, 2, 3]
4       4  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]      [1, 2, 3]
5       5  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]      [1, 2, 3]
6       6  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]      [1, 2, 3]
7       7  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]      [1, 2, 3]
8       8  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]      [1, 2, 3]
9       9  text       [14, 5]  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]      [1, 2, 3]

Question 5

对于普通的numpy数组，要添加和从数据框中检索，可以执行此操作。它建立在先前的答案上，这使我感到困惑，因为当我只有一个普通的numpy数组时，部分稀疏。

import numpy as np
import pandas as pd

df = pd.DataFrame({'b':range(10)}) # target dataframe
a = np.random.normal(size=(10,2)) # numpy array
df['a']=a.tolist() # save array
np.array(df['a'].tolist()) # retrieve array

Question 6

df = pd.DataFrame(np.arange(1,10).reshape(3,3))
df['newcol'] = pd.Series(your_2d_numpy_array)