结构化二维 Numpy 数组：设置列名和行名

Question

我正在尝试找到一种很好的方法来获取 2d numpy 数组并将列名和行名附加为结构化数组。例如：

import numpy as np

column_names = ['a', 'b', 'c']
row_names    = ['1', '2', '3']

matrix = np.reshape((1, 2, 3, 4, 5, 6, 7, 8, 9), (3, 3))

# TODO: insert magic here

matrix['3']['a']  # 7

我已经能够像这样设置列：

matrix.dtype = [(n, matrix.dtype) for n in column_names]

这让我可以 matrix[2]['a'] 但现在我想重命名这些行以便我可以 matrix['3']['a'].

Answer 1

据我所知，不可能 "name" 具有纯结构化 NumPy 数组的行。

但如果您有 pandas，则可以提供 "index"（本质上就像 "row name"）：

>>> import pandas as pd
>>> import numpy as np
>>> column_names = ['a', 'b', 'c']
>>> row_names    = ['1', '2', '3']

>>> matrix = np.reshape((1, 2, 3, 4, 5, 6, 7, 8, 9), (3, 3))
>>> df = pd.DataFrame(matrix, columns=column_names, index=row_names)
>>> df
   a  b  c
1  1  2  3
2  4  5  6
3  7  8  9

>>> df['a']['3']      # first "column" then "row"
7

>>> df.loc['3', 'a']  # another way to index "row" and "column"
7

结构化二维 Numpy 数组：设置列名和行名

Structured 2D Numpy Array: setting column and row names

python

arrays

numpy

structured-array