在numpy数组中查找行的索引

import numpy as np

# 示例数组
arr = np.array([[1, 2, 3],
                [4, 5, 6],
                [1, 2, 3]])

# 要查找的行
row_to_find = np.array([1, 2, 3])

# 使用numpy.where查找行索引
row_indices = np.where((arr == row_to_find).all(axis=1))[0]

print(row_indices)  # 输出: [0 2]

import numpy as np

# 示例数组
arr = np.array([[1, 2, 3],
                [4, 5, 6],
                [1, 2, 3]])

# 要查找的行
row_to_find = np.array([1, 2, 3])

# 使用numpy.isin和numpy.all查找行索引
matches = np.all(np.isin(arr, row_to_find).reshape(arr.shape[0], -1), axis=1)
row_indices = np.where(matches)[0]

print(row_indices)  # 输出: [0 2]

# 确保数据类型一致
arr = arr.astype(float)
row_to_find = row_to_find.astype(float)

# 确保形状一致
if row_to_find.shape[0] != arr.shape[1]:
    raise ValueError("The number of columns in 'arr' must match the length of 'row_to_find'")

from scipy.spatial.distance import cdist

# 计算距离矩阵
distances = cdist(arr, row_to_find.reshape(1, -1))

# 找到最小距离的索引
row_indices = np.where(distances == distances.min())[0]

print(row_indices)  # 输出: [0 2]