本文是对于 现代 Python 开发：语法基础与工程实践的总结，更多 Python 相关资料参考 Python 学习与实践资料索引；本文参考了 Python Crash Course - Cheat Sheets，pysheeet 等。本文仅包含笔者在日常工作中经常使用的，并且认为较为关键的知识点与语法，如果想要进一步学习 Python 相关内容或者对于机器学习与数据挖掘方向感兴趣，可以参考程序猿的数据科学与机器学习实战手册。

基础语法

Python 是一门高阶、动态类型的多范式编程语言；定义 Python 文件的时候我们往往会先声明文件编码方式:

# 指定脚本调用方式

#!/usr/bin/env python

# 配置 utf-8 编码

# -*- coding: utf-8 -*-

# 配置其他编码

# -*- coding: -*-

# Vim 中还可以使用如下方式

# vim:fileencoding=

人生苦短，请用 Python，大量功能强大的语法糖的同时让很多时候 Python 代码看上去有点像伪代码。譬如我们用 Python 实现的简易的快排相较于 Java 会显得很短小精悍:

def quicksort(arr):

if len(arr) <= 1:

return arr

pivot = arr[len(arr) / 2]

left = [x for x in arr if x < pivot]

middle = [x for x in arr if x == pivot]

right = [x for x in arr if x > pivot]

return quicksort(left) + middle + quicksort(right)

print quicksort([3,6,8,10,1,2,1])

# Prints "[1, 1, 2, 3, 6, 8, 10]"

控制台交互

可以根据 __name__ 关键字来判断是否是直接使用 python 命令执行某个脚本，还是外部引用；Google 开源的 fire 也是不错的快速将某个类封装为命令行工具的框架：

import fire

class Calculator(object):

"""A simple calculator class."""

def double(self, number):

return 2 * number

if __name__ == '__main__':

fire.Fire(Calculator)

# python calculator.py double 10 # 20

# python calculator.py double --number=15 # 30

Python 2 中 print 是表达式，而 Python 3 中 print 是函数；如果希望在 Python 2 中将 print 以函数方式使用，则需要自定义引入:

from __future__ import print_function

我们也可以使用 pprint 来美化控制台输出内容：

import pprint

stuff = ['spam', 'eggs', 'lumberjack', 'knights', 'ni']

pprint.pprint(stuff)

# 自定义参数

pp = pprint.PrettyPrinter(depth=6)

tup = ('spam', ('eggs', ('lumberjack', ('knights', ('ni', ('dead',('parrot', ('fresh fruit',))))))))

pp.pprint(tup)

模块

Python 中的模块（Module）即是 Python 源码文件，其可以导出类、函数与全局变量；当我们从某个模块导入变量时，函数名往往就是命名空间（Namespace）。而 Python 中的包（Package）则是模块的文件夹，往往由 __init__.py 指明某个文件夹为包:

# 文件目录

someDir/

main.py

siblingModule.py

# siblingModule.py

def siblingModuleFun():

print('Hello from siblingModuleFun')

def siblingModuleFunTwo():

print('Hello from siblingModuleFunTwo')

import siblingModule

import siblingModule as sibMod

sibMod.siblingModuleFun()

from siblingModule import siblingModuleFun

siblingModuleFun()

try:

# Import 'someModuleA' that is only available in Windows

import someModuleA

except ImportError:

try:

# Import 'someModuleB' that is only available in Linux

import someModuleB

except ImportError:

Package 可以为某个目录下所有的文件设置统一入口:

someDir/

main.py

subModules/

__init__.py

subA.py

subSubModules/

__init__.py

subSubA.py

# subA.py

def subAFun():

print('Hello from subAFun')

def subAFunTwo():

print('Hello from subAFunTwo')

# subSubA.py

def subSubAFun():

print('Hello from subSubAFun')

def subSubAFunTwo():

print('Hello from subSubAFunTwo')

# __init__.py from subDir

# Adds 'subAFun()' and 'subAFunTwo()' to the 'subDir' namespace

from .subA import *

# The following two import statement do the same thing, they add 'subSubAFun()' and 'subSubAFunTwo()' to the 'subDir' namespace. The first one assumes '__init__.py' is empty in 'subSubDir', and the second one, assumes '__init__.py' in 'subSubDir' contains 'from .subSubA import *'.

# Assumes '__init__.py' is empty in 'subSubDir'

# Adds 'subSubAFun()' and 'subSubAFunTwo()' to the 'subDir' namespace

from .subSubDir.subSubA import *

# Assumes '__init__.py' in 'subSubDir' has 'from .subSubA import *'

# Adds 'subSubAFun()' and 'subSubAFunTwo()' to the 'subDir' namespace

from .subSubDir import *

# __init__.py from subSubDir

# Adds 'subSubAFun()' and 'subSubAFunTwo()' to the 'subSubDir' namespace

from .subSubA import *

# main.py

import subDir

subDir.subAFun() # Hello from subAFun

subDir.subAFunTwo() # Hello from subAFunTwo

subDir.subSubAFun() # Hello from subSubAFun

subDir.subSubAFunTwo() # Hello from subSubAFunTwo

表达式与控制流

条件选择

Python 中使用 if、elif、else 来进行基础的条件选择操作：

if x < 0:

x = 0

print('Negative changed to zero')

elif x == 0:

print('Zero')

else:

print('More')

Python 同样支持 ternary conditional operator:

a if condition else b

也可以使用 Tuple 来实现类似的效果：

# test 需要返回 True 或者 False

(falseValue, trueValue)[test]

# 更安全的做法是进行强制判断

(falseValue, trueValue)[test == True]

# 或者使用 bool 类型转换函数

(falseValue, trueValue)[bool()]

循环遍历

for-in 可以用来遍历数组与字典：

words = ['cat', 'window', 'defenestrate']

for w in words:

print(w, len(w))

# 使用数组访问操作符，能够迅速地生成数组的副本

for w in words[:]:

if len(w) > 6:

words.insert(0, w)

# words -> ['defenestrate', 'cat', 'window', 'defenestrate']

如果我们希望使用数字序列进行遍历，可以使用 Python 内置的 range 函数：

a = ['Mary', 'had', 'a', 'little', 'lamb']

for i in range(len(a)):

print(i, a[i])

基本数据类型

可以使用内建函数进行强制类型转换（Casting）:

int(str)

float(str)

str(int)

str(float)

Number: 数值类型

x = 3

print type(x) # Prints ""

print x # Prints "3"

print x + 1 # Addition; prints "4"

print x - 1 # Subtraction; prints "2"

print x * 2 # Multiplication; prints "6"

print x ** 2 # Exponentiation; prints "9"

x += 1

print x # Prints "4"

x *= 2

print x # Prints "8"

y = 2.5

print type(y) # Prints ""

print y, y + 1, y * 2, y ** 2 # Prints "2.5 3.5 5.0 6.25"

布尔类型

Python 提供了常见的逻辑操作符，不过需要注意的是 Python 中并没有使用 &&、|| 等，而是直接使用了英文单词。

t = True

f = False

print type(t) # Prints ""

print t and f # Logical AND; prints "False"

print t or f # Logical OR; prints "True"

print not t # Logical NOT; prints "False"

print t != f # Logical XOR; prints "True"

String: 字符串

Python 2 中支持 Ascii 码的 str() 类型，独立的 unicode() 类型，没有 byte 类型；而 Python 3 中默认的字符串为 utf-8 类型，并且包含了 byte 与 bytearray 两个字节类型：

type("Guido") # string type is str in python2

#

# 使用 __future__ 中提供的模块来降级使用 Unicode

from __future__ import unicode_literals

type("Guido") # string type become unicode

#

Python 字符串支持分片、模板字符串等常见操作:

var1 = 'Hello World!'

var2 = "Python Programming"

print "var1[0]: ", var1[0]

print "var2[1:5]: ", var2[1:5]

# var1[0]: H

# var2[1:5]: ytho

print "My name is %s and weight is %d kg!" % ('Zara', 21)

# My name is Zara and weight is 21 kg!

str[0:4]

len(str)

string.replace("-", " ")

",".join(list)

"hi {0}".format('j')

str.find(",")

str.index(",") # same, but raises IndexError

str.count(",")

str.split(",")

str.lower()

str.upper()

str.title()

str.lstrip()

str.rstrip()

str.strip()

str.islower()

# 移除所有的特殊字符

re.sub('[^A-Za-z0-9]+', '', mystring)

如果需要判断是否包含某个子字符串，或者搜索某个字符串的下标:

# in 操作符可以判断字符串

if "blah" not in somestring:

continue

# find 可以搜索下标

s = "This be a string"

if s.find("is") == -1:

print "No 'is' here!"

else:

print "Found 'is' in the string."

Regex: 正则表达式

import re

# 判断是否匹配

re.match(r'^[aeiou]', str)

# 以第二个参数指定的字符替换原字符串中内容

re.sub(r'^[aeiou]', '?', str)

re.sub(r'(xyz)', r'\1', str)

# 编译生成独立的正则表达式对象

expr = re.compile(r'^...$')

expr.match(...)

expr.sub(...)

下面列举了常见的表达式使用场景:

# 检测是否为 HTML 标签

re.search('<[^/>][^>]*>', '')

# 常见的用户名密码

re.match('^[a-zA-Z0-9-_]{3,16}$', 'Foo') is not None

re.match('^\w|[-_]{3,16}$', 'Foo') is not None

# Email

re.match('^([a-z0-9_\.-]+)@([\da-z\.-]+)\.([a-z\.]{2,6})$', 'hello.world@example.com')

# Url

exp = re.compile(r'''^(https?:\/\/)? # match http or https

([\da-z\.-]+) # match domain

\.([a-z\.]{2,6}) # match domain

([\/\w \.-]*)\/?$ # match api or file

''', re.X)

exp.match('www.google.com')

# IP 地址

exp = re.compile(r'''^(?:(?:25[0-5]

|2[0-4][0-9]

|[1]?[0-9][0-9]?)\.){3}

(?:25[0-5]

|2[0-4][0-9]

|[1]?[0-9][0-9]?)$''', re.X)

exp.match('192.168.1.1')

集合类型

List: 列表

Operation: 创建增删

list 是基础的序列类型：

l = []

l = list()

# 使用字符串的 split 方法，可以将字符串转化为列表

str.split(".")

# 如果需要将数组拼装为字符串，则可以使用 join

list1 = ['1', '2', '3']

str1 = ''.join(list1)

# 如果是数值数组，则需要先进行转换

list1 = [1, 2, 3]

str1 = ''.join(str(e) for e in list1)

可以使用 append 与 extend 向数组中插入元素或者进行数组连接

x = [1, 2, 3]

x.append([4, 5]) # [1, 2, 3, [4, 5]]

x.extend([4, 5]) # [1, 2, 3, 4, 5]，注意 extend 返回值为 None

可以使用 pop、slices、del、remove 等移除列表中元素：

myList = [10,20,30,40,50]

# 弹出第二个元素

myList.pop(1) # 20

# myList: myList.pop(1)

# 如果不加任何参数，则默认弹出最后一个元素

myList.pop()

# 使用 slices 来删除某个元素

a = [ 1, 2, 3, 4, 5, 6 ]

index = 3 # Only Positive index

a = a[:index] + a[index+1 :]

# 根据下标删除元素

myList = [10,20,30,40,50]

rmovIndxNo = 3

del myList[rmovIndxNo] # myList: [10, 20, 30, 50]

# 使用 remove 方法，直接根据元素删除

letters = ["a", "b", "c", "d", "e"]

numbers.remove(numbers[1])

print(*letters) # used a * to make it unpack you don't have to

Iteration: 索引遍历

你可以使用基本的 for 循环来遍历数组中的元素，就像下面介个样纸:

animals = ['cat', 'dog', 'monkey']

for animal in animals:

print animal

# Prints "cat", "dog", "monkey", each on its own line.

如果你在循环的同时也希望能够获取到当前元素下标，可以使用 enumerate 函数:

animals = ['cat', 'dog', 'monkey']

for idx, animal in enumerate(animals):

print '#%d: %s' % (idx + 1, animal)

# Prints "#1: cat", "#2: dog", "#3: monkey", each on its own line

Python 也支持切片（Slices）:

nums = range(5) # range is a built-in function that creates a list of integers

print nums # Prints "[0, 1, 2, 3, 4]"

print nums[2:4] # Get a slice from index 2 to 4 (exclusive); prints "[2, 3]"

print nums[2:] # Get a slice from index 2 to the end; prints "[2, 3, 4]"

print nums[:2] # Get a slice from the start to index 2 (exclusive); prints "[0, 1]"

print nums[:] # Get a slice of the whole list; prints ["0, 1, 2, 3, 4]"

print nums[:-1] # Slice indices can be negative; prints ["0, 1, 2, 3]"

nums[2:4] = [8, 9] # Assign a new sublist to a slice

print nums # Prints "[0, 1, 8, 9, 4]"

Comprehensions: 变换

Python 中同样可以使用 map、reduce、filter，map 用于变换数组:

# 使用 map 对数组中的每个元素计算平方

items = [1, 2, 3, 4, 5]

squared = list(map(lambda x: x**2, items))

# map 支持函数以数组方式连接使用

def multiply(x):

return (x*x)

def add(x):

return (x+x)

funcs = [multiply, add]

for i in range(5):

value = list(map(lambda x: x(i), funcs))

print(value)

reduce 用于进行归纳计算:

# reduce 将数组中的值进行归纳

from functools import reduce

product = reduce((lambda x, y: x * y), [1, 2, 3, 4])

# Output: 24

filter 则可以对数组进行过滤:

number_list = range(-5, 5)

less_than_zero = list(filter(lambda x: x < 0, number_list))

print(less_than_zero)

# Output: [-5, -4, -3, -2, -1]

字典类型

创建增删

d = {'cat': 'cute', 'dog': 'furry'} # 创建新的字典

print d['cat'] # 字典不支持点（Dot）运算符取值

如果需要合并两个或者多个字典类型：

# python 3.5

z = {**x, **y}

# python 2.7

def merge_dicts(*dict_args):

"""

Given any number of dicts, shallow copy and merge into a new dict,

precedence goes to key value pairs in latter dicts.

"""

result = {}

for dictionary in dict_args:

result.update(dictionary)

return result

索引遍历

可以根据键来直接进行元素访问:

# Python 中对于访问不存在的键会抛出 KeyError 异常，需要先行判断或者使用 get

print 'cat' in d # Check if a dictionary has a given key; prints "True"

# 如果直接使用 [] 来取值，需要先确定键的存在，否则会抛出异常

print d['monkey'] # KeyError: 'monkey' not a key of d

# 使用 get 函数则可以设置默认值

print d.get('monkey', 'N/A') # Get an element with a default; prints "N/A"

print d.get('fish', 'N/A') # Get an element with a default; prints "wet"

d.keys() # 使用 keys 方法可以获取所有的键

可以使用 for-in 来遍历数组:

# 遍历键

for key in d:

# 比前一种方式慢

for k in dict.keys(): ...

# 直接遍历值

for value in dict.itervalues(): ...

# Python 2.x 中遍历键值

for key, value in d.iteritems():

# Python 3.x 中遍历键值

for key, value in d.items():

其他序列类型

集合

# Same as {"a", "b","c"}

normal_set = set(["a", "b","c"])

# Adding an element to normal set is fine

normal_set.add("d")

print("Normal Set")

print(normal_set)

# A frozen set

frozen_set = frozenset(["e", "f", "g"])

print("Frozen Set")

print(frozen_set)

# Uncommenting below line would cause error as

# we are trying to add element to a frozen set

# frozen_set.add("h")

函数

函数定义

Python 中的函数使用 def 关键字进行定义，譬如:

def sign(x):

if x > 0:

return 'positive'

elif x < 0:

return 'negative'

else:

return 'zero'

for x in [-1, 0, 1]:

print sign(x)

# Prints "negative", "zero", "positive"

Python 支持运行时创建动态函数，也即是所谓的 lambda 函数：

def f(x): return x**2

# 等价于

g = lambda x: x**2

参数

Option Arguments: 不定参数

def example(a, b=None, *args, **kwargs):

print a, b

print args

print kwargs

example(1, "var", 2, 3, word="hello")

# 1 var

# (2, 3)

# {'word': 'hello'}

a_tuple = (1, 2, 3, 4, 5)

a_dict = {"1":1, "2":2, "3":3}

example(1, "var", *a_tuple, **a_dict)

# 1 var

# (1, 2, 3, 4, 5)

# {'1': 1, '2': 2, '3': 3}

生成器

def simple_generator_function():

yield 1

yield 2

yield 3

for value in simple_generator_function():

print(value)

# 输出结果

# 1

# 2

# 3

our_generator = simple_generator_function()

next(our_generator)

# 1

next(our_generator)

# 2

next(our_generator)

#3

# 生成器典型的使用场景譬如无限数组的迭代

def get_primes(number):

while True:

if is_prime(number):

yield number

number += 1

装饰器

装饰器是非常有用的设计模式:

# 简单装饰器

from functools import wraps

def decorator(func):

@wraps(func)

def wrapper(*args, **kwargs):

print('wrap function')

return func(*args, **kwargs)

return wrapper

@decorator

def example(*a, **kw):

pass

example.__name__ # attr of function preserve

# 'example'

# Decorator

# 带输入值的装饰器

from functools import wraps

def decorator_with_argument(val):

def decorator(func):

@wraps(func)

def wrapper(*args, **kwargs):

print "Val is {0}".format(val)

return func(*args, **kwargs)

return wrapper

return decorator

@decorator_with_argument(10)

def example():

print "This is example function."

example()

# Val is 10

# This is example function.

# 等价于

def example():

print "This is example function."

example = decorator_with_argument(10)(example)

example()

# Val is 10

# This is example function.

类与对象

类定义

Python 中对于类的定义也很直接:

class Greeter(object):

# Constructor

def __init__(self, name):

self.name = name # Create an instance variable

# Instance method

def greet(self, loud=False):

if loud:

print 'HELLO, %s!' % self.name.upper()

else:

print 'Hello, %s' % self.name

g = Greeter('Fred') # Construct an instance of the Greeter class

g.greet() # Call an instance method; prints "Hello, Fred"

g.greet(loud=True) # Call an instance method; prints "HELLO, FRED!"

# isinstance 方法用于判断某个对象是否源自某个类

ex = 10

isinstance(ex,int)

Managed Attributes: 受控属性

# property、setter、deleter 可以用于复写点方法

class Example(object):

def __init__(self, value):

self._val = value

@property

def val(self):

return self._val

@val.setter

def val(self, value):

if not isintance(value, int):

raise TypeError("Expected int")

self._val = value

@val.deleter

def val(self):

del self._val

@property

def square3(self):

return 2**3

ex = Example(123)

ex.val = "str"

# Traceback (most recent call last):

# File "", line 1, in

# File "test.py", line 12, in val

# raise TypeError("Expected int")

# TypeError: Expected int

类方法与静态方法

class example(object):

@classmethod

def clsmethod(cls):

print "I am classmethod"

@staticmethod

def stmethod():

print "I am staticmethod"

def instmethod(self):

print "I am instancemethod"

ex = example()

ex.clsmethod()

# I am classmethod

ex.stmethod()

# I am staticmethod

ex.instmethod()

# I am instancemethod

example.clsmethod()

# I am classmethod

example.stmethod()

# I am staticmethod

example.instmethod()

# Traceback (most recent call last):

# File "", line 1, in

# TypeError: unbound method instmethod() ...

对象

实例化

属性操作

Python 中对象的属性不同于字典键，可以使用点运算符取值，直接使用 in 判断会存在问题:

class A(object):

@property

def prop(self):

return 3

a = A()

print "'prop' in a.__dict__ =", 'prop' in a.__dict__

print "hasattr(a, 'prop') =", hasattr(a, 'prop')

print "a.prop =", a.prop

# 'prop' in a.__dict__ = False

# hasattr(a, 'prop') = True

# a.prop = 3

建议使用 hasattr、getattr、setattr 这种方式对于对象属性进行操作:

class Example(object):

def __init__(self):

self.name = "ex"

def printex(self):

print "This is an example"

# Check object has attributes

# hasattr(obj, 'attr')

ex = Example()

hasattr(ex,"name")

# True

hasattr(ex,"printex")

# True

hasattr(ex,"print")

# False

# Get object attribute

# getattr(obj, 'attr')

getattr(ex,'name')

# 'ex'

# Set object attribute

# setattr(obj, 'attr', value)

setattr(ex,'name','example')

ex.name

# 'example'

异常与测试

异常处理

Context Manager - with

with 常用于打开或者关闭某些资源:

host = 'localhost'

port = 5566

with Socket(host, port) as s:

while True:

conn, addr = s.accept()

msg = conn.recv(1024)

print msg

conn.send(msg)

conn.close()

单元测试

from __future__ import print_function

import unittest

def fib(n):

return 1 if n<=2 else fib(n-1)+fib(n-2)

def setUpModule():

print("setup module")

def tearDownModule():

print("teardown module")

class TestFib(unittest.TestCase):

def setUp(self):

print("setUp")

self.n = 10

def tearDown(self):

print("tearDown")

del self.n

@classmethod

def setUpClass(cls):

print("setUpClass")

@classmethod

def tearDownClass(cls):

print("tearDownClass")

def test_fib_assert_equal(self):

self.assertEqual(fib(self.n), 55)

def test_fib_assert_true(self):

self.assertTrue(fib(self.n) == 55)

if __name__ == "__main__":

unittest.main()

存储

文件读写

路径处理

Python 内置的 __file__ 关键字会指向当前文件的相对路径，可以根据它来构造绝对路径，或者索引其他文件:

# 获取当前文件的相对目录

dir = os.path.dirname(__file__) # src\app

## once you're at the directory level you want, with the desired directory as the final path node:

dirname1 = os.path.basename(dir)

dirname2 = os.path.split(dir)[1] ## if you look at the documentation, this is exactly what os.path.basename does.

# 获取当前代码文件的绝对路径，abspath 会自动根据相对路径与当前工作空间进行路径补全

os.path.abspath(os.path.dirname(__file__)) # D:\WorkSpace\OWS\tool\ui-tool-svn\python\src\app

# 获取当前文件的真实路径

os.path.dirname(os.path.realpath(__file__)) # D:\WorkSpace\OWS\tool\ui-tool-svn\python\src\app

# 获取当前执行路径

os.getcwd()

可以使用 listdir、walk、glob 模块来进行文件枚举与检索：

# 仅列举所有的文件

from os import listdir

from os.path import isfile, join

onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]

# 使用 walk 递归搜索

from os import walk

f = []

for (dirpath, dirnames, filenames) in walk(mypath):

f.extend(filenames)

break

# 使用 glob 进行复杂模式匹配

import glob

print(glob.glob("/home/adam/*.txt"))

# ['/home/adam/file1.txt', '/home/adam/file2.txt', .... ]

简单文件读写

# 可以根据文件是否存在选择写入模式

mode = 'a' if os.path.exists(writepath) else 'w'

# 使用 with 方法能够自动处理异常

with open("file.dat",mode) as f:

f.write(...)

...

# 操作完毕之后记得关闭文件

f.close()

# 读取文件内容

message = f.read()

复杂格式文件

JSON

import json

# Writing JSON data

with open('data.json', 'w') as f:

json.dump(data, f)

# Reading data back

with open('data.json', 'r') as f:

data = json.load(f)

XML

我们可以使用 lxml 来解析与处理 XML 文件，本部分即对其常用操作进行介绍。lxml 支持从字符串或者文件中创建 Element 对象：

from lxml import etree

# 可以从字符串开始构造

xml = ''

root = etree.fromstring(xml)

etree.tostring(root)

# b''

# 也可以从某个文件开始构造

tree = etree.parse("doc/test.xml")

# 或者指定某个 baseURL

root = etree.fromstring(xml, base_url="http://where.it/is/from.xml")

其提供了迭代器以对所有元素进行遍历：

# 遍历所有的节点

for tag in tree.iter():

if not len(tag):

print tag.keys() # 获取所有自定义属性

print (tag.tag, tag.text) # text 即文本子元素值

# 获取 XPath

for e in root.iter():

print tree.getpath(e)

lxml 支持以 XPath 查找元素，不过需要注意的是，XPath 查找的结果是数组，并且在包含命名空间的情况下，需要指定命名空间：

root.xpath('//page/text/text()',ns={prefix:url})

# 可以使用 getparent 递归查找父元素

el.getparent()

lxml 提供了 insert、append 等方法进行元素操作：

# append 方法默认追加到尾部

st = etree.Element("state", name="New Mexico")

co = etree.Element("county", name="Socorro")

st.append(co)

# insert 方法可以指定位置

node.insert(0, newKid)

Excel

可以使用 [xlrd]() 来读取 Excel 文件，使用 xlsxwriter 来写入与操作 Excel 文件。

# 读取某个 Cell 的原始值

sh.cell(rx, col).value

# 创建新的文件

workbook = xlsxwriter.Workbook(outputFile)

worksheet = workbook.add_worksheet()

# 设置从第 0 行开始写入

row = 0

# 遍历二维数组，并且将其写入到 Excel 中

for rowData in array:

for col, data in enumerate(rowData):

worksheet.write(row, col, data)

row = row + 1

workbook.close()

文件系统

对于高级的文件操作，我们可以使用 Python 内置的 shutil

# 递归删除 appName 下面的所有的文件夹

shutil.rmtree(appName)

网络交互

Requests

Requests 是优雅而易用的 Python 网络请求库:

import requests

r = requests.get('https://api.github.com/events')

r = requests.get('https://api.github.com/user', auth=('user', 'pass'))

r.status_code

# 200

r.headers['content-type']

# 'application/json; charset=utf8'

r.encoding

# 'utf-8'

r.text

# u'{"type":"User"...'

r.json()

# {u'private_gists': 419, u'total_private_repos': 77, ...}

r = requests.put('http://httpbin.org/put', data = {'key':'value'})

r = requests.delete('http://httpbin.org/delete')

r = requests.head('http://httpbin.org/get')

r = requests.options('http://httpbin.org/get')

数据存储

MySQL

import pymysql.cursors

# Connect to the database

connection = pymysql.connect(host='localhost',

user='user',

password='passwd',

db='db',

charset='utf8mb4',

cursorclass=pymysql.cursors.DictCursor)

try:

with connection.cursor() as cursor:

# Create a new record

sql = "INSERT INTO `users` (`email`, `password`) VALUES (%s, %s)"

cursor.execute(sql, ('webmaster@python.org', 'very-secret'))

# connection is not autocommit by default. So you must commit to save

# your changes.

connection.commit()

with connection.cursor() as cursor:

# Read a single record

sql = "SELECT `id`, `password` FROM `users` WHERE `email`=%s"

cursor.execute(sql, ('webmaster@python.org',))

result = cursor.fetchone()

print(result)

finally:

connection.close()