摘要：最大的優點之一就是語法簡潔，好的代碼就像偽代碼一樣，干凈整潔一目了然。程序必須先讓人讀懂，然后才能讓計算機執行。中間結果盡量使用代替不推薦推薦會帶來更高的內存使用效率，特別是當處理大數據操作的時候。

Python最大的優點之一就是語法簡潔，好的代碼就像偽代碼一樣，干凈、整潔、一目了然。

要寫出 Pythonic（優雅的、地道的、整潔的）代碼，需要多看多學大牛們寫的代碼，github 上有很多非常優秀的源代碼值得閱讀，比如：requests、flask、tornado，下面列舉一些常見的Pythonic寫法。

“Programs must be written for people to read, and only incidentally for machines to execute.”

##不推薦
temp = a
a = b
b = a  

##推薦
a, b = b, a  #  先生成一個元組(tuple)對象，然后unpack

##不推薦
l = ["David", "Pythonista", "+1-514-555-1234"]
first_name = l[0]
last_name = l[1]
phone_number = l[2]  

##推薦
l = ["David", "Pythonista", "+1-514-555-1234"]
first_name, last_name, phone_number = l

# Python 3 Only
first, *middle, last = another_list

##不推薦
if fruit == "apple" or fruit == "orange" or fruit == "berry":
    # 多次判斷  

##推薦
if fruit in ["apple", "orange", "berry"]:
    # 使用 in 更加簡潔

##不推薦
colors = ["red", "blue", "green", "yellow"]

result = ""
for s in colors:
    result += s  #  每次賦值都丟棄以前的字符串對象, 生成一個新對象  

##推薦
colors = ["red", "blue", "green", "yellow"]
result = "".join(colors)  #  沒有額外的內存分配

##不推薦
for key in my_dict.keys():
    #  my_dict[key] ...  

##推薦
for key in my_dict:
    #  my_dict[key] ...

# 只有當循環中需要更改key值的情況下，我們需要使用 my_dict.keys()
# 生成靜態的鍵值列表。

##不推薦
if my_dict.has_key(key):
    # ...do something with d[key]  

##推薦
if key in my_dict:
    # ...do something with d[key]

##不推薦
navs = {}
for (portfolio, equity, position) in data:
    if portfolio not in navs:
            navs[portfolio] = 0
    navs[portfolio] += position * prices[equity]
##推薦
navs = {}
for (portfolio, equity, position) in data:
    # 使用 get 方法
    navs[portfolio] = navs.get(portfolio, 0) + position * prices[equity]
    # 或者使用 setdefault 方法
    navs.setdefault(portfolio, 0)
    navs[portfolio] += position * prices[equity]

##不推薦
if x == True:
    # ....
if len(items) != 0:
    # ...
if items != []:
    # ...  

##推薦
if x:
    # ....
if items:
    # ...

##不推薦
items = "zero one two three".split()
# method 1
i = 0
for item in items:
    print i, item
    i += 1
# method 2
for i in range(len(items)):
    print i, items[i]

##推薦
items = "zero one two three".split()
for i, item in enumerate(items):
    print i, item

##不推薦
new_list = []
for item in a_list:
    if condition(item):
        new_list.append(fn(item))  

##推薦
new_list = [fn(item) for item in a_list if condition(item)]

##不推薦
for sub_list in nested_list:
    if list_condition(sub_list):
        for item in sub_list:
            if item_condition(item):
                # do something...  
##推薦
gen = (item for sl in nested_list if list_condition(sl) 
            for item in sl if item_condition(item))
for item in gen:
    # do something...

##不推薦
for x in x_list:
    for y in y_list:
        for z in z_list:
            # do something for x & y  

##推薦
from itertools import product
for x, y, z in product(x_list, y_list, z_list):
    # do something for x, y, z

##不推薦
def my_range(n):
    i = 0
    result = []
    while i < n:
        result.append(fn(i))
        i += 1
    return result  #  返回列表

##推薦
def my_range(n):
    i = 0
    result = []
    while i < n:
        yield fn(i)  #  使用生成器代替列表
        i += 1
# 盡量用生成器代替列表，除非必須用到列表特有的函數。

##不推薦
reduce(rf, filter(ff, map(mf, a_list)))

##推薦
from itertools import ifilter, imap
reduce(rf, ifilter(ff, imap(mf, a_list)))
# lazy evaluation 會帶來更高的內存使用效率，特別是當處理大數據操作的時候。

##不推薦
found = False
for item in a_list:
    if condition(item):
        found = True
        break
if found:
    # do something if found...  

##推薦
if any(condition(item) for item in a_list):
    # do something if found...

##不推薦
class Clock(object):
    def __init__(self):
        self.__hour = 1
    def setHour(self, hour):
        if 25 > hour > 0: self.__hour = hour
        else: raise BadHourException
    def getHour(self):
        return self.__hour

##推薦
class Clock(object):
    def __init__(self):
        self.__hour = 1
    def __setHour(self, hour):
        if 25 > hour > 0: self.__hour = hour
        else: raise BadHourException
    def __getHour(self):
        return self.__hour
    hour = property(__getHour, __setHour)

##不推薦
f = open("some_file.txt")
try:
    data = f.read()
    # 其他文件操作..
finally:
    f.close()

##推薦
with open("some_file.txt") as f:
    data = f.read()
    # 其他文件操作...

##不推薦
try:
    os.remove("somefile.txt")
except OSError:
    pass

##推薦
from contextlib import ignored  # Python 3 only

with ignored(OSError):
    os.remove("somefile.txt")

##不推薦
import threading
lock = threading.Lock()

lock.acquire()
try:
    # 互斥操作...
finally:
    lock.release()

##推薦
import threading
lock = threading.Lock()

with lock:
    # 互斥操作...

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