# 来源：NumPy Cookbook 2e ch6

创建通用函数

from __future__ import print_function import numpy as np# 我们需要定义对单个元素操作的函数def double(a):    return 2 * a# frompyfunc（或者 vectorize）# 将其转换为对数组每个元素操作的函数ufunc = np.frompyfunc(double, 1, 1) print("Result", ufunc(np.arange(4)))# Result [0 2 4 6]

勾股数

from __future__ import print_function import numpy as np# 勾股数是指满足 a ** 2 + b ** 2 == c ** 2 的三个数# 我们使 a = m ** 2 - n ** 2，b = 2 * m * n# c = m ** 2 + n ** 2，来寻找 a + b + c == 1000 的勾股数# m 和 n 都取 0 ~ 32m = np.arange(33) n = np.arange(33) # 计算 a，b 和 c# outer 生成 a[i] op b[j] 为每个元素的矩阵# 相当于 meshgrid 之后再逐元素操作a = np.subtract.outer(m ** 2, n ** 2) b = 2 * np.multiply.outer(m, n) c = np.add.outer(m ** 2, n ** 2)# 取符合我们条件的下标# where 把布尔下标转换为位置下标idx =  np.where((a + b + c) == 1000) # 验证并打印结果np.testing.assert_equal(a[idx]**2 + b[idx]**2, c[idx]**2) print(a[idx], b[idx], c[idx]) # [375] [200] [425]

CharArray 字符串操作

# chararray 数组的元素只能是字符串# 并且拥有许多字符串专用的方法# 虽然我们可以为字符串创建通用函数# 但是直接使用这些方法更省事import urllib2 import numpy as np import re# 使用 urllib2 库下载网页# 更推荐 requests 库response = urllib2.urlopen('http://python.org/') html = response.read() # 替换掉所有标签html = re.sub(r'<.*?>', '', html) # 创建仅仅包含该 HTML 的一维数组# 并转为 chararraycarray = np.array(html).view(np.chararray) # expandtabs 将 TAB 转换为指定个数的空格carray = carray.expandtabs(1) # splitlines 按换行符分割，会多一个维度carray = carray.splitlines() print(carray)

创建屏蔽数组

from __future__ import print_function import numpy as np from scipy.misc import lena import matplotlib.pyplot as plt# 加载 Lena 图像lena = lena() # 掩码数组和图像形状一致，元素取 0 和 1 的随机数random_mask = np.random.randint(0, 2, size=lena.shape)# 绘制原始图像plt.subplot(221) plt.title("Original") plt.imshow(lena) plt.axis('off')# ma.array 创建屏蔽数组# 如果 random_mask 中某个元素是 0# masked_array 中就将其屏蔽# 访问会返回 masked# 但是转换回 np.array 时会恢复masked_array = np.ma.array(lena, mask=random_mask)print(masked_array) # 绘制掩码后的图像plt.subplot(222) plt.title("Masked") plt.imshow(masked_array) plt.axis('off')

忽略负数以及极值

from __future__ import print_function import numpy as np from matplotlib.finance import quotes_historical_yahoo from datetime import date import matplotlib.pyplot as pltdef get_close(ticker):    # 获取指定股票近一年的收盘价    today = date.today()    start = (today.year - 1, today.month, today.day)    quotes = quotes_historical_yahoo(ticker, start, today)    return np.array([q[4] for q in quotes])# 获取 AAPL 一年的收盘价close = get_close('AAPL')triples = np.arange(0, len(close), 3) print("Triples", triples[:10], "...")# Triples [ 0  3  6  9 12 15 18 21 24 27] ... # 创建等长的全 1 数组signs = np.ones(len(close)) print("Signs", signs[:10], "...")# Signs [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.] ... # sign 中每隔三个元素变为 -1signs[triples] = -1 print("Signs", signs[:10], "...")# Signs [-1.  1.  1. -1.  1.  1. -1.  1.  1. -1.] ...# ma.log 的作用是# 如果元素小于等于 0，将其屏蔽# 如果元素大于 0，取对数ma_log = np.ma.log(close * signs) print("Masked logs", ma_log[:10], "...")# Masked logs [-- 5.93655586575 5.95094223368 -- 5.97468290742 5.97510711452 -- 6.01674381162 5.97889061623 --] ...dev = close.std() avg = close.mean() # 屏蔽 avg - dev 到 avg + dev 之外的元素inside = np.ma.masked_outside(close, avg - dev, avg + dev) print("Inside", inside[:10], "...")# Inside [-- -- -- -- -- -- 409.429675172    410.240597855 -- --] ...# 绘制原始数据plt.subplot(311) plt.title("Original") plt.plot(close)# 绘制对数屏蔽后的数据plt.subplot(312) plt.title("Log Masked") plt.plot(np.exp(ma_log))# 绘制范围屏蔽后的数据plt.subplot(313) plt.title("Not Extreme") plt.plot(inside)plt.tight_layout() plt.show()

记录数组

# rec.array 是 array 的子类# 可以通过元素的属性来访问元素from __future__ import print_function import numpy as np from matplotlib.finance import quotes_historical_yahoo from datetime import datetickers = ['MRK', 'T', 'VZ']def get_close(ticker):    # 获取指定股票近一年的收盘价    today = date.today()    start = (today.year - 1, today.month, today.day)    quotes = quotes_historical_yahoo(ticker, start, today)    return np.array([q[4] for q in quotes])    # 创建记录数组，来统计每个股票的代码、# 标准分（标准差的倒数）、均值和得分weights = np.recarray((len(tickers),), dtype=[('symbol', np.str_, 16),    ('stdscore', float), ('mean', float), ('score', float)])for i, ticker in enumerate(tickers):    # 获取收盘价、计算对数收益    close = get_close(ticker)    logrets = np.diff(np.log(close))    # 保存符号、对数收益的均值和标准分    weights[i]['symbol'] = ticker    weights[i]['mean'] = logrets.mean()       weights[i]['stdscore'] = 1/logrets.std()    weights[i]['score'] = 0# 每个股票的均值和标准分需要除以相应的总数for key in ['mean', 'stdscore']:    wsum = weights[key].sum()    weights[key] = weights[key]/wsum# 得分是标准分和均值的均值weights['score'] = (weights['stdscore'] + weights['mean'])/2 weights['score'].sort()# 打印每个股票的信息for record in weights:    print("%s,mean=%.4f,stdscore=%.4f,score=%.4f" % (record['symbol'], record['mean'], record['stdscore'], record['score']))'''MRK,mean=0.8185,stdscore=0.2938,score=0.2177 T,mean=0.0927,stdscore=0.3427,score=0.2262 VZ,mean=0.0888,stdscore=0.3636,score=0.5561 '''