文章目录
- Python标准库
- 9.1 time库
- 9.1.1 获取现在的时间
- 9.1.2 时间戳与计时器
- 9.1.3 格式化
- 9.1.3 sleep
- 9.2 random库
- 9.2.1 随机种子——seed(a=None)
- 9.2.2 产生随机整数
- 9.2.3 产生随机浮点数
- 9.2.4 序列用函数
- 9.2.5 概率分布——以高斯分布为例
- 【例1】用random库实现简单的微信红包分配
- 【例2】生产4位由数字和英文字母构成的验证码
- 9.3 collections库——容器数据类型
- 9.3.1 namedtuple——具名元组
- 【例】模拟扑克牌
- 9.3.2 Counter——计数器工具
- 【例】从一副牌中抽取10张，大于10的比例有多少
- 9.3.3 deque——双向序列
- 9.4 itertools库——迭代器
- 9.4.1 排列组合迭代器
- 9.4.2 拉链
- 9.4.3 无穷迭代器
- 9.4.4 其他
Python标准库
Python自身提供了比较丰富的生态，拿来即用，可极大的提高开发效率
9.1 time库
Python处理时间的标准库
9.1.1 获取现在的时间
（1）time.localtime() 本地时间
（2）time.gmtime() UTC世界统一时间
北京时间比世界统一时间UTC早8个小时
import time
t_local = time.localtime()
t_UTC = time.gmtime()
print("t_local", t_local)
print("t_UTC", t_UTC)t_local time.struct_time(tm_year=2021, tm_mon=7, tm_mday=12, tm_hour=22, tm_min=7, tm_sec=14, tm_wday=0, tm_yday=193, tm_isdst=0)
t_UTC time.struct_time(tm_year=2021, tm_mon=7, tm_mday=12, tm_hour=14, tm_min=7, tm_sec=14, tm_wday=0, tm_yday=193, tm_isdst=0)
print(time.ctime()) # 返回本地时间字符串Mon Jul 12 22:08:48 2021
9.1.2 时间戳与计时器
（1）time.time() 返回自纪元以来的秒数，记录sleep
（2）time.perf_counter() 随意选取一个时间点，记录现在时间到该时间点的间隔秒数，记录sleep
（3）time.process_time() 随意选取一个时间点，记录现在时间到改时间点的间隔秒数，不记录sleep
perf_counter()精度较time()更高一些
import time
t_1_start = time.time()
t_2_start = time.perf_counter()
t_3_start = time.process_time()
print(t_1_start)
print(t_2_start)
print(t_3_start)
res = 0
for i in range(1000000):
res += 1
time.sleep(5)
t_1_end = time.time()
t_2_end = time.perf_counter()
t_3_end = time.process_time()
print("time方法：{:.3f}秒".format(t_1_end - t_1_start))
print("perf_counter方法：{:.3f}秒".format(t_2_end - t_2_start))
print("process_time方法：{:.3f}秒".format(t_3_end - t_3_start))1626099600.9892688
0.0351624
0.046875
time方法：5.074秒
perf_counter方法：5.074秒
process_time方法：0.062秒
9.1.3 格式化
（1）time.strftime 自定义格式化输出
lctime = time.localtime()
print(time.strftime("%Y-%m-%d %A %H:%M:%S", lctime))2021-07-12 Monday 22:23:00
9.1.3 sleep
（1）time.sleep()
9.2 random库
随机数在计算机应用中十分常见
Python通过random库提供各种伪随机数
基本可以用于除加密解密算法外的大多数工程应用
9.2.1 随机种子——seed(a=None)
（1）相同种子会产生相同的随机数
（2）如果不设置随机种子，以系统当前时间为默认值
from random import *
seed(10)
print(random())
seed(10)
print(random())0.5714025946899135
0.5714025946899135
种子一样，随机数相同
当我们不设置种子时，以系统当前时间为默认值
print(random())0.4288890546751146
9.2.2 产生随机整数
（1）randint(a, b)——产生[a, b]之间的随机整数
from random import *
numbers = [randint(1, 10) for i in range(10)]
print(numbers)[6, 2, 10, 9, 2, 5, 8, 4, 4, 7]
（2）randrange(a)——产生[0, a)之间的随机整数
numbers = [randrange(10) for i in range(10)]
print(numbers)[0, 1, 7, 8, 3, 9, 2, 9, 6, 1]
（3）randrange(a, b, step)——产生[a, b)之间以step为步长的随机整数
numbers = [randrange(0, 10, 2) for i in range(10)]
print(numbers)[4, 6, 4, 6, 4, 8, 6, 6, 6, 4]
9.2.3 产生随机浮点数
（1）random()——产生[0.0, 1.0)之间的随机浮点数
numbers = [random() for i in range(10)]
print(numbers)[0.48397409177180606, 0.7324418321781395, 0.49611232079609857, 0.7397374874919966, 0.36400551031311623, 0.7321555421809868, 0.5926034958320359, 0.038468497113128164, 0.279847091091285, 0.7987160402900167]
（2）uniform(a, b)——产生[a, b]之间的随机浮点数
numbers = [uniform(2.1, 3.5) for i in range(10)]
print(numbers)9.2.4 序列用函数
（1）choice(seq)——从序列类型中随机返回一个元素
print(choice(['win', 'lose', 'draw']))draw
print(choice("python"))h
（2）choices(seq, weights = None, k)——对序列类型进行k次重复采样，可设置权重
print(choices(['win', 'lose', 'draw'], k=5))[‘draw’, ‘draw’, ‘draw’, ‘draw’, ‘lose’]
print(choices(['win', 'lose', 'draw'], [4, 4, 2], k=10))[‘lose’, ‘win’, ‘lose’, ‘lose’, ‘lose’, ‘win’, ‘win’, ‘lose’, ‘win’, ‘win’]
（3）shuffle(seq)——将序列类型中元素随机排列，返回打乱后的序列
numbers = ["one", "two", "three", "four"]
shuffle(numbers)
print(numbers)[‘three’, ‘two’, ‘four’, ‘one’]
（4）sample(pop, k)——从序列类型随机选取k个元素，以列表类型返回
print(sample([10, 20, 30, 40, 50], k=3))[50, 10, 40]
此处k不能大于列表长度，否则会报错
9.2.5 概率分布——以高斯分布为例
gauss(mean, std)——生产一个符合高斯分布的随机数
number = gauss(0, 1)
print(number)0.5199081942767708
多生成几个
import matplotlib.pyplot as plt
res = [gauss(0, 1) for i in range(100000)]
plt.hist(res, bins=1000)
plt.show()
【例1】用random库实现简单的微信红包分配
import random
def red_packet(total, num):
for i in range(1, num):
per = random.uniform(0.01, total/(num-i+1)*2) # 保证每个人获得红包期望是total/num
total = total - per
print("第{}位红包金额：{:.2f}".format(i, per))
else:
print("第{}位红包金额：{:.2f}".format(num, total))
red_packet(10, 5)第1位红包金额：3.86
第2位红包金额：0.05
第3位红包金额：2.41
第4位红包金额：1.76
第5位红包金额：1.92
我们来多重复十万次，看看五个人期望是否一样
import random
import numpy as np
def red_packet(total, num):
ls = []
for i in range(1, num):
per = round(random.uniform(0.01, total / (num - i + 1) * 2), 2) # 保证每个人获得红包期望是total/num
ls.append(per)
total = total - per
else:
ls.append(total)
return ls
# 重复发十万次红包，统计每个位置的平均值（约等于期望）
res = []
for i in range(100000):
ls = red_packet(10, 5)
res.append(ls)
res = np.array(res)
print(res[:10]) # 取前十次结果
print(np.mean(res, axis=0)) # 求每人十万次的平均值[[0.9 3.81 2.08 2.97 0.24]
[0.89 2.38 3.76 1.3 1.67]
[1.21 3.95 0.67 2.23 1.94]
[2.45 3.18 2.55 1.48 0.34]
[2.99 1.6 3.23 0.05 2.13]
[0.37 3.73 0.44 1.91 3.55]
[3.1 3.07 2.06 1.06 0.71]
[2.82 1.47 2.68 2.35 0.68]
[3.12 2.79 2.48 1.39 0.22]
[3.96 0.04 2.82 2.6 0.58]]
[2.0070194 2.0003315 1.9930559 2.0067242 1.992869 ]
我们发现，每人获得红包的期望确实为2
最后一位偏小，小于2的原因在于因为随机起点从0.01开始，如果起点设为0，则不存在这种问题
【例2】生产4位由数字和英文字母构成的验证码
import random
import string
print(string.digits)
print(string.ascii_letters)
s = string.digits + string.ascii_letters
v = random.sample(s, 4)
print(v)
print(" ".join(v))0123456789
abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ
[‘9’, ‘2’, ‘Z’, ‘i’]
9 2 Z i
9.3 collections库——容器数据类型
import collections9.3.1 namedtuple——具名元组
- 点的坐标，仅看数据，很难知道表达的是一个点的坐标
p = (1, 2)- 构建一个新的元组子类
定义方法如下：typename是元组名字，field_names是域名
collections.namedtuple(typename, field_names, *, rename=False, defaults=None, module=None)Point = collections.namedtuple("Point", ["x", "y"])
p = Point(1, y=2) # 第一个是位置参数，第二个是关键字参数
print(p)Point(x=1, y=2)
- 可以调用属性
print(p.x)
print(p.y)1
2
- 有元组的性质
print(p[0])
print(p[1])
x, y = p
print(x)
print(y)1
2
1
2
- 确实是元组的子类
print(isinstance(p, tuple))True
【例】模拟扑克牌
Card = collections.namedtuple("Card", ["rank", "suit"])
ranks = [str(n) for n in range(2, 11)]+list("JQKA")
suits = "spades diamonds clubs hearts".split(" ")
print("ranks:", ranks)
print("suits:", suits)
cards = [Card(rank, suit) for rank in ranks for suit in suits]
print(cards)ranks: [‘2’, ‘3’, ‘4’, ‘5’, ‘6’, ‘7’, ‘8’, ‘9’, ‘10’, ‘J’, ‘Q’, ‘K’, ‘A’]
suits: [‘spades’, ‘diamonds’, ‘clubs’, ‘hearts’]
[Card(rank=‘2’, suit=‘spades’), Card(rank=‘2’, suit=‘diamonds’), Card(rank=‘2’, suit=‘clubs’), Card(rank=‘2’, suit=‘hearts’), Card(rank=‘3’, suit=‘spades’), Card(rank=‘3’, suit=‘diamonds’), Card(rank=‘3’, suit=‘clubs’), Card(rank=‘3’, suit=‘hearts’), Card(rank=‘4’, suit=‘spades’), Card(rank=‘4’, suit=‘diamonds’), Card(rank=‘4’, suit=‘clubs’), Card(rank=‘4’, suit=‘hearts’), Card(rank=‘5’, suit=‘spades’), Card(rank=‘5’, suit=‘diamonds’), Card(rank=‘5’, suit=‘clubs’), Card(rank=‘5’, suit=‘hearts’), Card(rank=‘6’, suit=‘spades’), Card(rank=‘6’, suit=‘diamonds’), Card(rank=‘6’, suit=‘clubs’), Card(rank=‘6’, suit=‘hearts’), Card(rank=‘7’, suit=‘spades’), Card(rank=‘7’, suit=‘diamonds’), Card(rank=‘7’, suit=‘clubs’), Card(rank=‘7’, suit=‘hearts’), Card(rank=‘8’, suit=‘spades’), Card(rank=‘8’, suit=‘diamonds’), Card(rank=‘8’, suit=‘clubs’), Card(rank=‘8’, suit=‘hearts’), Card(rank=‘9’, suit=‘spades’), Card(rank=‘9’, suit=‘diamonds’), Card(rank=‘9’, suit=‘clubs’), Card(rank=‘9’, suit=‘hearts’), Card(rank=‘10’, suit=‘spades’), Card(rank=‘10’, suit=‘diamonds’), Card(rank=‘10’, suit=‘clubs’), Card(rank=‘10’, suit=‘hearts’), Card(rank=‘J’, suit=‘spades’), Card(rank=‘J’, suit=‘diamonds’), Card(rank=‘J’, suit=‘clubs’), Card(rank=‘J’, suit=‘hearts’), Card(rank=‘Q’, suit=‘spades’), Card(rank=‘Q’, suit=‘diamonds’), Card(rank=‘Q’, suit=‘clubs’), Card(rank=‘Q’, suit=‘hearts’), Card(rank=‘K’, suit=‘spades’), Card(rank=‘K’, suit=‘diamonds’), Card(rank=‘K’, suit=‘clubs’), Card(rank=‘K’, suit=‘hearts’), Card(rank=‘A’, suit=‘spades’), Card(rank=‘A’, suit=‘diamonds’), Card(rank=‘A’, suit=‘clubs’), Card(rank=‘A’, suit=‘hearts’)]
from random import *# 洗牌
shuffle(cards)
print(cards)[Card(rank=‘Q’, suit=‘diamonds’), Card(rank=‘6’, suit=‘spades’), Card(rank=‘J’, suit=‘hearts’), Card(rank=‘10’, suit=‘spades’), Card(rank=‘A’, suit=‘clubs’), Card(rank=‘7’, suit=‘spades’), Card(rank=‘Q’, suit=‘clubs’), Card(rank=‘2’, suit=‘diamonds’), Card(rank=‘6’, suit=‘diamonds’), Card(rank=‘K’, suit=‘diamonds’), Card(rank=‘9’, suit=‘diamonds’), Card(rank=‘4’, suit=‘spades’), Card(rank=‘10’, suit=‘diamonds’), Card(rank=‘5’, suit=‘diamonds’), Card(rank=‘8’, suit=‘diamonds’), Card(rank=‘8’, suit=‘hearts’), Card(rank=‘J’, suit=‘spades’), Card(rank=‘5’, suit=‘spades’), Card(rank=‘6’, suit=‘clubs’), Card(rank=‘5’, suit=‘clubs’), Card(rank=‘9’, suit=‘hearts’), Card(rank=‘7’, suit=‘clubs’), Card(rank=‘K’, suit=‘clubs’), Card(rank=‘10’, suit=‘clubs’), Card(rank=‘A’, suit=‘spades’), Card(rank=‘8’, suit=‘spades’), Card(rank=‘J’, suit=‘clubs’), Card(rank=‘Q’, suit=‘hearts’), Card(rank=‘K’, suit=‘hearts’), Card(rank=‘10’, suit=‘hearts’), Card(rank=‘3’, suit=‘diamonds’), Card(rank=‘7’, suit=‘diamonds’), Card(rank=‘3’, suit=‘clubs’), Card(rank=‘8’, suit=‘clubs’), Card(rank=‘2’, suit=‘spades’), Card(rank=‘4’, suit=‘hearts’), Card(rank=‘4’, suit=‘clubs’), Card(rank=‘2’, suit=‘clubs’), Card(rank=‘9’, suit=‘clubs’), Card(rank=‘3’, suit=‘hearts’), Card(rank=‘6’, suit=‘hearts’), Card(rank=‘4’, suit=‘diamonds’), Card(rank=‘J’, suit=‘diamonds’), Card(rank=‘2’, suit=‘hearts’), Card(rank=‘7’, suit=‘hearts’), Card(rank=‘Q’, suit=‘spades’), Card(rank=‘3’, suit=‘spades’), Card(rank=‘A’, suit=‘diamonds’), Card(rank=‘A’, suit=‘hearts’), Card(rank=‘9’, suit=‘spades’), Card(rank=‘K’, suit=‘spades’), Card(rank=‘5’, suit=‘hearts’)]
# 随机抽一张牌
card = choice(cards)
print(card)Card(rank=‘J’, suit=‘hearts’)
# 随机抽多张牌
print(sample(cards, k=5))[Card(rank=‘Q’, suit=‘diamonds’), Card(rank=‘9’, suit=‘diamonds’), Card(rank=‘6’, suit=‘hearts’), Card(rank=‘2’, suit=‘diamonds’), Card(rank=‘3’, suit=‘spades’)]
9.3.2 Counter——计数器工具
from collections import Counters = "牛奶奶找刘奶奶买牛奶"
colors = ['red', 'blue', 'red', 'green', 'blue', 'blue']
cnt_str = Counter(s)
cnt_color = Counter(colors)
print(cnt_str)
print(cnt_color)Counter({‘奶’: 5, ‘牛’: 2, ‘找’: 1, ‘刘’: 1, ‘买’: 1})
Counter({‘blue’: 3, ‘red’: 2, ‘green’: 1})
- 是字典的一个子类
print(isinstance(Counter(), dict))True
- 最常见的统计——most_common(n)
提供n个频率最高的元素和计数
print(cnt_color.most_common(2))[(‘blue’, 3), (‘red’, 2)]
- 元素展开——elements()
print(list(cnt_str.elements()))[‘牛’, ‘牛’, ‘奶’, ‘奶’, ‘奶’, ‘奶’, ‘奶’, ‘找’, ‘刘’, ‘买’]
- 其他一些加减操作
c = Counter(a=4, b=1)
d = Counter(a=1, b=2)
print(c+d)Counter({‘a’: 5, ‘b’: 3})
【例】从一副牌中抽取10张，大于10的比例有多少
import collections
import random
cards = collections.Counter(tens=16, low_cards=36)
seen = random.sample(list(cards.elements()), k=10)
print(seen)
print(seen.count('tens') / 10)[‘low_cards’, ‘low_cards’, ‘low_cards’, ‘tens’, ‘tens’, ‘low_cards’, ‘low_cards’, ‘tens’, ‘tens’, ‘tens’]
0.5
9.3.3 deque——双向序列
列表访问数据非常快速
插入和删除操作非常慢——通过移动元素位置来实现
特别是insert(0, v)和pop(0)，在列表开始进行的插入和删除操作
双向队列可以方便的在队列两边高效、快速的增加和删除元素
from collections import deque
d = deque('cde')
print(d)deque([‘c’, ‘d’, ‘e’])
d.append('f') # 右端增加
d.append('g')
d.appendleft('b') # 左端增加
d.appendleft('a')
print(d)deque([‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’])
d.pop() # 右端删除
d.popleft() # 左端删除
print(d)deque([‘b’, ‘c’, ‘d’, ‘e’, ‘f’])
- deque 其他用法可参考官方文档
9.4 itertools库——迭代器
9.4.1 排列组合迭代器
（1）product——笛卡尔积
import itertools
for i in itertools.product('ABC', '01'):
print(i)(‘A’, ‘0’)
(‘A’, ‘1’)
(‘B’, ‘0’)
(‘B’, ‘1’)
(‘C’, ‘0’)
(‘C’, ‘1’)
for i in itertools.product('ABC', repeat=3):
print(i)(‘A’, ‘A’, ‘A’)
(‘A’, ‘A’, ‘B’)
(‘A’, ‘A’, ‘C’)
(‘A’, ‘B’, ‘A’)
(‘A’, ‘B’, ‘B’)
(‘A’, ‘B’, ‘C’)
(‘A’, ‘C’, ‘A’)
(‘A’, ‘C’, ‘B’)
(‘A’, ‘C’, ‘C’)
(‘B’, ‘A’, ‘A’)
(‘B’, ‘A’, ‘B’)
(‘B’, ‘A’, ‘C’)
(‘B’, ‘B’, ‘A’)
(‘B’, ‘B’, ‘B’)
(‘B’, ‘B’, ‘C’)
(‘B’, ‘C’, ‘A’)
(‘B’, ‘C’, ‘B’)
(‘B’, ‘C’, ‘C’)
(‘C’, ‘A’, ‘A’)
(‘C’, ‘A’, ‘B’)
(‘C’, ‘A’, ‘C’)
(‘C’, ‘B’, ‘A’)
(‘C’, ‘B’, ‘B’)
(‘C’, ‘B’, ‘C’)
(‘C’, ‘C’, ‘A’)
(‘C’, ‘C’, ‘B’)
(‘C’, ‘C’, ‘C’)
（2）permutations——排列
for i in itertools.permutations('ABCD', 3): # 3是排列的长度
print(i)(‘A’, ‘B’, ‘C’)
(‘A’, ‘B’, ‘D’)
(‘A’, ‘C’, ‘B’)
(‘A’, ‘C’, ‘D’)
(‘A’, ‘D’, ‘B’)
(‘A’, ‘D’, ‘C’)
(‘B’, ‘A’, ‘C’)
(‘B’, ‘A’, ‘D’)
(‘B’, ‘C’, ‘A’)
(‘B’, ‘C’, ‘D’)
(‘B’, ‘D’, ‘A’)
(‘B’, ‘D’, ‘C’)
(‘C’, ‘A’, ‘B’)
(‘C’, ‘A’, ‘D’)
(‘C’, ‘B’, ‘A’)
(‘C’, ‘B’, ‘D’)
(‘C’, ‘D’, ‘A’)
(‘C’, ‘D’, ‘B’)
(‘D’, ‘A’, ‘B’)
(‘D’, ‘A’, ‘C’)
(‘D’, ‘B’, ‘A’)
(‘D’, ‘B’, ‘C’)
(‘D’, ‘C’, ‘A’)
(‘D’, ‘C’, ‘B’)
for i in itertools.permutations(range(3)):
print(i)(0, 1, 2)
(0, 2, 1)
(1, 0, 2)
(1, 2, 0)
(2, 0, 1)
(2, 1, 0)
（3）combinations——组合
for i in itertools.combinations('ABCD', 2):
print(i)(‘A’, ‘B’)
(‘A’, ‘C’)
(‘A’, ‘D’)
(‘B’, ‘C’)
(‘B’, ‘D’)
(‘C’, ‘D’)
for i in itertools.combinations(range(4), 3):
print(i)(0, 1, 2)
(0, 1, 3)
(0, 2, 3)
(1, 2, 3)
（4）combinations_with_replacement——元素可重复组合
for i in itertools.combinations_with_replacement('ABC', 2):
print(i)(‘A’, ‘A’)
(‘A’, ‘B’)
(‘A’, ‘C’)
(‘B’, ‘B’)
(‘B’, ‘C’)
(‘C’, ‘C’)
9.4.2 拉链
（1）zip——短拉链
for i in zip('ABC', '012', 'xyz'):
print(i)(‘A’, ‘0’, ‘x’)
(‘B’, ‘1’, ‘y’)
(‘C’, ‘2’, ‘z’)
长度不一时，执行到最短的对象处，就停止
for i in zip('ABC', '012345'):
print(i)(‘A’, ‘0’)
(‘B’, ‘1’)
(‘C’, ‘2’)
（2）zip_longest——长拉链
长度不一时，执行到最长的对象处，就停止，缺省元素用None或指定字符替代
for i in itertools.zip_longest('ABC', '012345'):
print(i)(‘A’, ‘0’)
(‘B’, ‘1’)
(‘C’, ‘2’)
(None, ‘3’)
(None, ‘4’)
(None, ‘5’)
for i in itertools.zip_longest('ABC', '012345',fillvalue="?"):
print(i)(‘A’, ‘0’)
(‘B’, ‘1’)
(‘C’, ‘2’)
(’?’, ‘3’)
(’?’, ‘4’)
(’?’, ‘5’)
9.4.3 无穷迭代器
（1）count(start=0, step=1)——计数
创建一个迭代器，它从start值开始，返回均匀间隔的值
itertools.count(10)
11
12
.
.
.（2）cycle(iterable)——循环
创建一个迭代器，返回iterable中所有元素，无限重复
itertools.cycle("ABC")
A
B
C
A
B
C
.
.
.（3）repeat(object[, times])——重复
创建一个迭代器，不断重复object，除非设定参数times，否则将无限重复
for i in itertools.repeat(10, 3):
print(i)10
10
10
9.4.4 其他
（1）chain(iterables)——锁链
把一组迭代对象串联起来，形成一个更大的迭代器
for i in itertools.chain('ABC', [1, 2, 3]):
print(i)A
B
C
1
2
3
（2）enumerate(iterable, start=0)——枚举（Python内置）
产出由两个元素组成的元组，结构是(index, item)，其中index从start开始，item从iterable中取
for i in enumerate("Python", start=0):
print(i)(0, ‘P’)
(1, ‘y’)
(2, ‘t’)
(3, ‘h’)
(4, ‘o’)
(5, ‘n’)
（3）groupby(iterable, key=None)——分组
创建一个迭代器，按照key指定的方式，返回iterable中连续的键和组
一般来说，要预先对数据进行排序
key为None默认把连续重复元素分组
for key, group in itertools.groupby("AAAABBBCCDAABBB"): # 连续重复元素分组
print(key, list(group))A [‘A’, ‘A’, ‘A’, ‘A’]
B [‘B’, ‘B’, ‘B’]
C [‘C’, ‘C’]
D [‘D’]
A [‘A’, ‘A’]
B [‘B’, ‘B’, ‘B’]
我们可以按照长度分组
animals = ["duck", "eagle", "rat", "giraffe", "bear", "bat", "dolphin", "shark", "lion"]
animals.sort(key=len)
for key, group in itertools.groupby(animals, key=len):
print(key, list(group))3 [‘rat’, ‘bat’]
4 [‘duck’, ‘bear’, ‘lion’]
5 [‘eagle’, ‘shark’]
7 [‘giraffe’, ‘dolphin’]
我们也可以按照首字母分组
animals = ["duck", "eagle", "rat", "giraffe", "bear", "bat", "dolphin", "shark", "lion"]
animals.sort(key=lambda x: x[0])
for key, group in itertools.groupby(animals, key=lambda x: x[0]):
print(key, list(group))b [‘bear’, ‘bat’]
d [‘duck’, ‘dolphin’]
e [‘eagle’]
g [‘giraffe’]
l [‘lion’]
r [‘rat’]
s [‘shark’]
itertools库其他函数可参考官方文档
以上，便是第九节深入探索内容，包含time, random, collections, itertools四个库中的常用函数。
下一节将深入理解numpy库。
