NumPy数据分析与图像处理实战
NumPy 数据分析基础
NumPy的核心是多维数组对象ndarray，支持高效的数值运算。创建数组可通过np.array()、np.arange()或np.zeros()等函数实现。例如，生成随机数组：
import numpy as np
data = np.random.randn(100, 3) # 100行3列的随机数组
数组的广播机制允许不同形状数组进行运算。例如标量与数组相乘会自动扩展：
arr = np.array([1, 2, 3])
result = arr * 5 # 结果为[5, 10, 15]
统计与聚合操作
NumPy提供np.sum()、np.mean()等聚合函数。沿特定轴计算时可指定axis参数：
matrix = np.array([[1, 2], [3, 4]])
col_sum = np.sum(matrix, axis=0) # 结果为[4, 6]
布尔索引能快速筛选数据。例如找出大于阈值的元素：
arr = np.array([5, 2, 8, 1])
mask = arr > 3
filtered = arr[mask] # 结果为[5, 8]
图像处理基础技术
图像可视为三维数组（高度×宽度×通道）。使用matplotlib加载并显示图像：
import matplotlib.pyplot as plt
image = plt.imread('photo.jpg')
plt.imshow(image)
颜色通道分离与合并示例：
red_channel = image[:, :, 0]
merged = np.stack([red_channel, green_channel, blue_channel], axis=2)
图像滤波与变换
高斯模糊可通过scipy.ndimage实现：
from scipy.ndimage import gaussian_filter
blurred = gaussian_filter(image, sigma=2)
矩阵运算实现图像旋转（需齐次坐标变换）：
theta = np.radians(30)
rotation_matrix = np.array([
[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), 0],
[0, 0, 1]
])
性能优化技巧
使用np.vectorize()加速Python函数：
def custom_func(x):
return x**2 + 1
vec_func = np.vectorize(custom_func)
内存布局优化可通过np.ascontiguousarray()实现：
contig_arr = np.ascontiguousarray(arr, dtype=np.float32)
实际应用案例
时间序列分析中滑动窗口计算：
def moving_average(data, window_size):
return np.convolve(data, np.ones(window_size)/window_size, mode='valid')
图像直方图均衡化增强对比度：
def hist_equalize(img):
hist, bins = np.histogram(img.flatten(), 256, [0,256])
cdf = hist.cumsum()
cdf_normalized = cdf * 255 / cdf[-1]
return np.interp(img.flatten(), bins[:-1], cdf_normalized).reshape(img.shape)
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