Comprehensive Guide to Common NumPy Array Operations

NumPy provides a rich set of high‑level operations for creating and manipulating arrays, which are essential for scientific computing and data processing in Python.

1. Array creation

import numpy as np
# 创建一个全零数组
zeros_array = np.zeros((3, 3))
print("全零数组:")
print(zeros_array)
# 创建一个全一数组
ones_array = np.ones((2, 2))
print("全一数组:")
print(ones_array)
# 创建一个单位矩阵
identity_array = np.eye(2)
print("单位矩阵:")
print(identity_array)
# 创建一个随机数组
np.random.seed(0)
random_array = np.random.rand(2, 2)
print("随机数组:")
print(random_array)

2. Array indexing

arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
element = arr[1, 2]
print("访问单个元素:", element)
sub_array = arr[0:2, 1:3]
print("访问子数组:")
print(sub_array)

3. Shape manipulation

arr = np.array([[1, 2, 3], [4, 5, 6]])
reshaped_arr = arr.reshape(3, 2)
print("改变形状后的数组:")
print(reshaped_arr)

4. Concatenation

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
concatenated_arr = np.hstack((arr1, arr2))
print("水平拼接后的数组:", concatenated_arr)
concatenated_arr = np.vstack((arr1, arr2))
print("垂直拼接后的数组:")
print(concatenated_arr)

5. Splitting

# Horizontal split
arr = np.array([1, 2, 3, 4, 5, 6])
split_arr = np.hsplit(arr, 2)
print("水平分割后的数组:")
print(split_arr)
# Vertical split
arr = np.array([[1, 2], [3, 4], [5, 6]])
split_arr = np.vsplit(arr, 2)
print("垂直分割后的数组:")
print(split_arr)

6. Copy

arr = np.array([1, 2, 3])
deep_copy_arr = arr.copy()
print("深拷贝后的数组:", deep_copy_arr)

7. Slicing

arr = np.array([1, 2, 3, 4, 5, 6])
sliced_arr = arr[1:4]
print("切片后的数组:", sliced_arr)

8. Statistics

arr = np.array([1, 2, 3, 4, 5, 6])
min_value = arr.min()
print("最小值:", min_value)
max_value = arr.max()
print("最大值:", max_value)
sum_value = arr.sum()
print("总和:", sum_value)
mean_value = arr.mean()
print("平均值:", mean_value)

9. Boolean indexing

bool_index = arr > 3
print("布尔索引:", bool_index)
selected_elements = arr[bool_index]
print("选择的元素:", selected_elements)

10. Sorting and reversing

sorted_arr = np.sort(arr)
print("排序后的数组:", sorted_arr)
reversed_arr = np.flip(arr)
print("逆序后的数组:", reversed_arr)

11. Unique values and counts

unique_values = np.unique(arr)
print("唯一值:", unique_values)
counts = np.bincount(arr)
print("每个元素的出现次数:", counts)

12. Broadcasting

arr = np.array([1, 2, 3])
broadcasted_arr = arr * 2
print("广播机制后的数组:", broadcasted_arr)

13. Merging arrays

# Horizontal merge
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
merged_arr = np.concatenate((arr1, arr2), axis=0)
print("水平合并后的数组:", merged_arr)
# Vertical merge
arr1 = np.array([[1, 2, 3]])
arr2 = np.array([[4, 5, 6]])
merged_arr = np.concatenate((arr1, arr2), axis=0)
print("垂直合并后的数组:")
print(merged_arr)

14. Insert and delete

arr = np.array([1, 2, 3])
inserted_arr = np.insert(arr, 1, 99)
print("插入元素后的数组:", inserted_arr)
deleted_arr = np.delete(arr, 1)
print("删除元素后的数组:", deleted_arr)

15. Transpose

arr = np.array([[1, 2, 3], [4, 5, 6]])
transposed_arr = arr.T
print("转置后的数组:")
print(transposed_arr)

16. Flatten

arr = np.array([[1, 2, 3], [4, 5, 6]])
flattened_arr = arr.flatten()
print("展平后的数组:", flattened_arr)

17. Split by column

arr = np.array([[1, 2, 3], [4, 5, 6]])
split_by_column_arr = np.hsplit(arr, 3)
print("按列拆分后的数组:")
print(split_by_column_arr)

18. Multi‑dimensional merge

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
arr3 = np.array([7, 8, 9])
merged_multi_arr = np.array([arr1, arr2, arr3])
print("合并多维数组后的结果:")
print(merged_multi_arr)

19. Random choice (single element)

arr = np.array([1, 2, 3, 4, 5])
probabilities = np.array([0.1, 0.2, 0.3, 0.2, 0.2])
random_element = np.random.choice(arr, p=probabilities)
print("按照概率分布随机抽取的元素:", random_element)

20. Random choice (multiple elements)

n = 3
random_elements = np.random.choice(arr, size=n, p=probabilities)
print("按照概率分布随机抽取的多个元素:", random_elements)

21. Dot product

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
dot_product = np.dot(arr1, arr2)
print("点积:", dot_product)

22. Outer product

outer_product = np.outer(arr1, arr2)
print("外积:")
print(outer_product)

23. Cumulative sum

cumulative_sum = np.cumsum(arr)
print("累积和:", cumulative_sum)

24. Cumulative product

cumulative_product = np.cumprod(arr)
print("累积乘积:", cumulative_product)

25. Difference

difference = np.diff(arr)
print("差分:", difference)

By mastering these NumPy operations, you can dramatically improve the efficiency of data handling and scientific computation in Python.