如何用Pandas结合np.dot为12个np.array生成12×12矩阵？

1. 用np.dot生成点积矩阵 #

df.corr()的method参数可以直接传np.dot——因为np.dot对两个一维数组计算的就是你要的点积结果，完全符合method要求的「接收两个一维数组返回浮点数」的规则。只要把原来的corr = df.corr()改成：

corr = df.corr(method=np.dot)

生成的矩阵里，每个元素就是对应两列数组的点积，比如d_normal和d_redraw的交点就是你测试的11.68。

2. 强制显示完整12×12矩阵 #

Pandas默认会截断过多的列/行，只要设置几个显示选项就能让矩阵完整展示：
在代码开头加上这几行：

# 显示所有列、行，避免截断
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# 加宽显示宽度，防止内容换行挤在一起
pd.set_option('display.width', 1000)

修改后的完整代码 #

import numpy as np
import pandas as pd

# 设置Pandas显示选项，强制完整显示矩阵
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.width', 1000)

d_normal = ['1.00', '1.00', '0.90', '0.60', '0.00', '1.00', '0.00', '0.10', '0.40', '0.70', '1.00', '0.00', '0.00',
            '0.00', '1.00', '1.00', '0.00', '0.10', '0.40', '0.70', '1.00', '1.00', '0.90', '0.60', '0.00']
d_redraw = ['1.00', '1.00', '0.90', '0.60', '0.00', '1.00', '1.00', '0.20', '0.40', '0.70', '1.00', '1.00', '0.10',
            '0.00', '1.00', '1.00', '1.00', '0.20', '0.40', '0.70', '1.00', '1.00', '0.90', '0.60', '0.00']
d_noise = ['0.80', '0.80', '0.70', '0.40', '0.00', '0.80', '0.80', '0.00', '0.20', '0.50', '0.80', '0.80', '0.00',
           '0.00', '0.80', '0.80', '0.80', '0.00', '0.20', '0.50', '0.80', '0.80', '0.70', '0.40', '0.00']
d_noise_blur = ['0.96', '0.96', '0.84', '0.48', '0.00', '0.96', '0.96', '0.00', '0.24', '0.60', '0.96', '0.96', '0.00',
                '0.00', '0.96', '0.96', '0.96', '0.00', '0.24', '0.60', '0.96', '0.96', '0.84', '0.48', '0.00']
n_normal = ['1.00', '0.00', '0.00', '0.00', '1.00', '1.00', '1.00', '0.00', '0.00', '1.00', '1.00', '0.00', '1.00',
            '0.00', '1.00', '1.00', '0.00', '0.00', '1.00', '1.00', '1.00', '0.00', '0.00', '0.00', '1.00']
n_redraw = ['1.00', '0.70', '0.00', '0.00', '1.00', '1.00', '1.00', '0.70', '0.00', '1.00', '1.00', '0.70', '1.00',
            '0.70', '1.00', '1.00', '0.00', '0.70', '1.00', '1.00', '1.00', '0.00', '0.00', '0.70', '1.00']
n_noise = ['0.80', '0.50', '0.00', '0.00', '0.80', '0.80', '0.80', '0.50', '0.00', '0.80', '0.80', '0.50', '0.80',
           '0.50', '0.80', '0.80', '0.00', '0.50', '0.80', '0.80', '0.80', '0.00', '0.00', '0.50', '0.80']
n_noise_blur = ['0.96', '0.60', '0.00', '0.00', '0.96', '0.96', '0.96', '0.60', '0.00', '0.96', '0.96', '0.60', '0.96',
                '0.60', '0.96', '0.96', '0.00', '0.60', '0.96', '0.96', '0.96', '0.00', '0.00', '0.60', '0.96']
w_normal = ['1.00', '0.00', '1.00', '0.00', '1.00', '1.00', '0.00', '1.00', '0.00', '1.00', '0.80', '0.20', '0.80',
            '0.20', '0.80', '0.60', '0.40', '0.60', '0.40', '0.60', '0.00', '1.00', '0.00', '1.00', '0.00']
w_redraw = ['1.00', '0.40', '1.00', '0.40', '1.00', '1.00', '0.40', '1.00', '0.40', '1.00', '0.80', '0.40', '1.00',
            '0.40', '0.80', '0.60', '0.60', '0.80', '0.60', '0.60', '0.00', '1.00', '0.00', '1.00', '0.00']
w_noise = ['0.80', '0.20', '0.80', '0.20', '0.80', '0.80', '0.20', '0.80', '0.20', '0.80', '0.60', '0.20', '0.80',
           '0.20', '0.60', '0.40', '0.40', '0.60', '0.40', '0.40', '0.00', '0.80', '0.00', '0.80', '0.00']
w_noise_blur = ['0.96', '0.24', '0.96', '0.24', '0.96', '0.96', '0.24', '0.96', '0.24', '0.96', '0.72', '0.24', '0.96',
                '0.24', '0.72', '0.48', '0.48', '0.72', '0.48', '0.48', '0.00', '0.96', '0.00', '0.96', '0.00']

# 转换为numpy数组并整理到字典
arrays = {
    'd_normal': np.array(d_normal, dtype=float),
    'd_redraw': np.array(d_redraw, dtype=float),
    'd_noise': np.array(d_noise, dtype=float),
    'd_noise_blur': np.array(d_noise_blur, dtype=float),
    'n_normal': np.array(n_normal, dtype=float),
    'n_redraw': np.array(n_redraw, dtype=float),
    'n_noise': np.array(n_noise, dtype=float),
    'n_noise_blur': np.array(n_noise_blur, dtype=float),
    'w_normal': np.array(w_normal, dtype=float),
    'w_redraw': np.array(w_redraw, dtype=float),
    'w_noise': np.array(w_noise, dtype=float),
    'w_noise_blur': np.array(w_noise_blur, dtype=float)
}

def correlation_matrix():
    # 测试点积结果
    print(np.dot(arrays['d_normal'], arrays['d_redraw']))
    df = pd.DataFrame(arrays)
    # 使用np.dot计算点积矩阵
    corr = df.corr(method=np.dot)
    print(corr)

correlation_matrix()

小优化：简化数组管理 #

把所有数组放到字典里统一转换和管理，比逐个定义变量更简洁，后续要加/改数组也更方便。

内容的提问来源于stack exchange，提问作者AndrewRed