NumPy 数组与 Python 原生列表的性能对比

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import numpy as np
import time

# 创建 NumPy 数组
numpy_array_1 = np.arange(1000000)
numpy_array_2 = np.arange(1000000)

# 创建 Python 列表
python_list_1 = list(range(1000000))
python_list_2 = list(range(1000000))

# NumPy 数组相加
start_time = time.time()
numpy_sum = numpy_array_1 + numpy_array_2
numpy_time = time.time() - start_time

# Python 列表相加
start_time = time.time()
python_sum = [x + y for x, y in zip(python_list_1, python_list_2)]
python_time = time.time() - start_time

print(f"NumPy 时间: {numpy_time:.4f} 秒")
print(f"Python 列表时间: {python_time:.4f} 秒")

# 结果通常显示 NumPy 快得多

np.sum()

np.mean()

np.std()

int32

int64

float64

# 优化前的代码 (使用循环)
def sum_using_loop(arr):
    result = 0
    for i in range(len(arr)):
        result += arr[i]
    return result

# 优化后的代码 (使用 NumPy 内置函数)
def sum_using_numpy(arr):
    return np.sum(arr)

# 性能比较
arr = np.arange(1000000)

start_time = time.time()
loop_sum = sum_using_loop(arr)
loop_time = time.time() - start_time

start_time = time.time()
numpy_sum = sum_using_numpy(arr)
numpy_time = time.time() - start_time

print(f"循环时间: {loop_time:.4f} 秒")
print(f"NumPy 时间: {numpy_time:.4f} 秒")

# 通常 NumPy 版本更快

mixed_list = [1, "hello", 3.14]
print(mixed_list)