Python基本语法
1.基本容器类型
1.1 list
>>>classmates = ['Tom','Peter']
>>>classmates
['Tom','Peter']
classmates.append('AMS') #想尾插入
classmates.insert(1,'a')#向指定位置插入
classmates.pop()#删除末尾
classmates.pop(1)#删除指定位置
1.2 tuple
tuple使用时使用(),且不能改变元素
1.3 dic
dic与map基本一致
d = {'s":2,'a':3}
d.pop('s')#会删除's'以及对应的值
1.4 set
声明并定义一个set必须提供一个list
s=set(['a','b'])
s.add('c')
s.remove('c')
2.高级特性
2.1列表生成式
列表生成式即List Comprehensions,是Python内置的非常简单却强大的可以用来创建list的生成式。
>>> [x * x for x in range(1, 11) if x % 2 == 0]
[4, 16, 36, 64, 100]
还可以两层调用
>>> [m + n for m in 'ABC' for n in 'XYZ']
['AX', 'AY', 'AZ', 'BX', 'BY', 'BZ', 'CX', 'CY', 'CZ']
2.2 生成器generator
只要将一个列表生成式的[]换成(),就创建了一个生成器
生成器不耗用太多内存,仅仅在要使用时生成元素
杨辉三角:
def add(n):
L=[1]
while True:
yield L
L = [L[x]+L[x+1]for x in range(len(L)-1)]
L.insert(0,1)
L.append(1)
if len(L) > n:
break
斐波契利数列
def fib(max):
n, a, b = 0, 0, 1
while n < max:
yield b
a, b = b, a + b
n = n + 1
return 'done'
使用生成器时用Iterator即可
2.3迭代器
将一个非Iterator对象转换为Iterator对象,它们表示惰性计算对象
>>> isinstance(iter([]), Iterator)
True
>>> isinstance(iter('abc'), Iterator)
True
3.函数式编程
3.1 map
>>> def f(x):
... return x * x
...
>>> r = map(f, [1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> list(r)
[1, 4, 9, 16, 25, 36, 49, 64, 81]
map的第一个参数为函数对象,第二个参数为使用对象
3.2 reduce累积计算
>>> from functools import reduce
>>> def add(x, y):
... return x + y
...
>>> reduce(add, [1, 3, 5, 7, 9])
25
3.3 map与reduce运用实例
from functools import reduce
def str2int(s):
def fn(x,y):
return x * 10 + y
def char2num(s):
return {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9}[s]
return reduce(fn, map(char2num, s))
3.4 filter
>>> def is_odd(n):
... return n%2==1
...
>>> list(filter(is_odd,[1,2,3,4,5,6]))
[1, 3, 5]
查找素数
def odd_():
n = 1
while True:
n = n + 2
yield n
def lam_odd(n):
return lambda x : x % n >0
def result():
yield 2
it = odd_()
while True:
n = next(it)
yield n
it = filter(lam_odd(n),it)
for n in result():
if n < 50:
print(n)
else:
break
3.5 返回函数
def count():
def f(j):
def g():
return j*j
return g
fs = []
for i in range(1,4):
fs.append(f(i))
return fs
f1,f2,f3 = count()
n1,n2,n3 = f1(),f2(),f3()
print(n1,n2,n3)
4.面向对象高级特性
4.1 使用__slots__
class Student(object):
__slots__ = ('name', 'age') # 用tuple定义允许绑定的属性名称
4.2 使用@property
class Student(object):
@property
def score(self):
return self._score
@score.setter
def score(self, value):
if not isinstance(value, int):
raise ValueError('score must be an integer!')
if value < 0 or value > 100:
raise ValueError('score must between 0 ~ 100!')
self._score = value
s = Student()
s.score = 60
print('s.score =', s.score)
# ValueError: score must between 0 ~ 100!
s.score = 9999
4.3 str__与__repr
class Student(object):
def __init__(self, name):
self.name = name
def __str__(self):
return 'Student object (name=%s)' % self.name
__repr__ = __str__
这样使用print()方法或在交互环境中使用时,可以得到自己想要的结果
4.4 iter
>>> class Fib(object):
... def __init__(self):
... self.a,self.b = 0,1
... def __iter__(self):
... return self
... def __next__(self):
... temp = self.a
... self.a = self.a + self.b
... self.b = temp + self.b
... if self.a>50:
... raise StopIteration()
... return self.a
...
>>> for i in Fib():
... print(i)
...
1
2
4
8
16
32
5.错误异常处理
5.1处理异常
class TEST(object):
def __init__(self,s):
self.s = s
def foo(s):
return 10 / int(s)
def bar(s):
return foo(s) * 2
def main(self):
try:
bar(self.s)
except Exception as e:
print('Error:', e)
finally:
print('finally...')
5.2调试
def foo(s):
n = int(s)
assert n != 0, 'n is zero!'
return 10 / n
启动Python解释器时可以用-0参数来关闭assert
5.3单元测试
5.4文档测试
以上两个测试以后进行补充
6.IO编程
6.1 文件读写
with open('test.txt','r') as e :
print(e.read())
for i in e.readlines():
print(lines.strip())#进行行读写
如果要读取二进制文件如图片视频等,用rb方式打开即可
f = open('d.jpg','rb')
下面介绍写入方法
with open('test.txt','w') as f:
f.write('hello')
6.2 操作目录
>>> import os
>>> os.path.abspath('')
'E:\\test'
>>> os.path.join('E:\\test','new')
'E:\\test\\new'
>>> os.mkdir('E:\\test\\new')
>>> os.rmdir('E:\\test\\new')
重命名文件,删除文件
>>> os.rename('新建文本文档.txt','呵呵.txt')
>>> os.remove('呵呵.txt')
6.3 序列化
如果想另数据在网络或磁盘上传播,可以使用序列化
6.3.1 pickle
>>> import pickle
>>> d = dict(name = 'Bob',age = 20)
>>> f = open('py.txt','wb')
>>> pickle.dump(d,f)
>>> f.close()
>>> f = open('py.txt','rb')
>>> d = pickle.load(f)
>>> f.close()
>>> d
{'name': 'Bob', 'age': 20}
6.3.2 json
后续补充
7.多进程与多线程
7.1多进程
multiprocessing模块提供了一个Process类
from multiprocessing import Process
import os
def run_proc(name):
print('Run child process %s(%s)'%(name,os.getpid()))
if __name__ == '__main__':
print('Parent process %s.'%os.getpid())
p = Process(target = run_proc,args=('test',))
print('Child process will start')
p.start()
p.join()
print('end')
其他内容因为暂时用不到,以后进行补充,暂时不用