Python 使用和高性能技巧操作大全

import randomrandom.choices(seq, k=1)  # 长度为k的list，有放回采样random.sample(seq, k)     # 长度为k的list，无放回采样

func = lambda y: x + y          # x的值在函数运行时被绑定func = lambda y, x=x: x + y     # x的值在函数定义时被绑定

import copyy = copy.copy(x)      # 只复制最顶层y = copy.deepcopy(x)  # 复制所有嵌套部分

a = [1, 2, [3, 4]]# Alias.b_alias = a  assert b_alias == a and b_alias is a# Shallow copy.b_shallow_copy = a[:] 
assert b_shallow_copy == a and b_shallow_copy is not a and b_shallow_copy[2] is a[2]# Deep copy.import copyb_deep_copy = copy.deepcopy(a) 
assert b_deep_copy == a and b_deep_copy is not a and b_deep_copy[2] is not a[2]

x == y  # 两引用对象是否有相同值x is y  # 两引用是否指向同一对象

type(a) == int      # 忽略面向对象设计中的多态特征isinstance(a, int)  # 考虑了面向对象设计中的多态特征

str.find(sub, start=None, end=None); str.rfind(...)     # 如果找不到返回-1str.index(sub, start=None, end=None); 
str.rindex(...)   # 如果找不到抛出ValueError异常

print(a[-1], a[-2], a[-3])print(a[~0], a[~1], a[~2])

a = float('inf')b = float('-inf')

a = Trueb = False

if x is None:    pass

a, b = b, a

if 0 < a < 5:    pass

with open(file_path, 'rt', encoding='utf-8') as f:   for line in f:       
    print(line)       # 末尾的\n会保留

import osos.path.join('usr', 'lib', 'local')

import csv# 无header的读写with open(name, 'rt', encoding='utf-8', newline='') as f:  # newline=''让Python不将换行统一处理   
for row in csv.reader(f):        
print(row[0], row[1])  # CSV读到的数据都是str类型with open(name, mode='wt') as f:   
f_csv = csv.writer(f)   
f_csv.writerow(['symbol', 'change'])# 有header的读写with open(name, mode='rt', newline='') as f:   
for row in csv.DictReader(f):        
print(row['symbol'], row['change'])with open(name, mode='wt') as f:    
header = ['symbol', 'change']    
f_csv = csv.DictWriter(f, header)    
f_csv.writeheader()    
f_csv.writerow({'symbol': xx, 'change': xx})

import syscsv.field_size_limit(sys.maxsize)

f = csv.reader(f, delimiter='\t')

import itertoolsitertools.islice(iterable, start=None, stop, step=None)# islice('ABCDEF', 2, None) -> C, D, E, Fitertools.filterfalse(predicate, iterable)          
# 过滤掉predicate为False的元素# filterfalse(lambda x: x < 5, [1, 4, 6, 4, 1]) -> 6itertools.takewhile(predicate, iterable)         
# 当predicate为False时停止迭代# takewhile(lambda x: x < 5, [1, 4, 6, 4, 1]) -> 1, 4itertools.dropwhile(predicate, iterable)           
# 当predicate为False时开始迭代# dropwhile(lambda x: x < 5, [1, 4, 6, 4, 1]) -> 6, 4, 1itertools.compress(iterable, selectors)            
# 根据selectors每个元素是True或False进行选择# compress('ABCDEF', [1, 0, 1, 0, 1, 1]) -> A, C, E, F

sorted(iterable,key=None,reverse=False)

itertools.groupby(iterable,key=None)#按值分组，iterable需要先被排序
#groupby(sorted([1,4,6,4,1]))->(1,iter1),(4,iter4),(6,iter6)

itertools.permutations(iterable,r=None)#排列，返回值是Tuple
#permutations('ABCD',2)->AB,AC,AD,BA,BC,BD,CA,CB,CD,DA,DB,DC

itertools.combinations(iterable,r=None)#组合，返回值是Tuple
itertools.combinations_with_replacement(...)
#combinations('ABCD',2)->AB,AC,AD,BC,BD,CD

itertools.chain(*iterables)#多个序列直接拼接
#chain('ABC','DEF')->A,B,C,D,E,F
importheapq
heapq.merge(*iterables,key=None,reverse=False)#多个序列按顺序拼接
#merge('ABF','CDE')->A,B,C,D,E,F
zip(*iterables)#当最短的序列耗尽时停止，结果只能被消耗一次
itertools.zip_longest(*iterables,fillvalue=None)#当最长的序列耗尽时停止，结果只能被消耗一次

importcollections
#创建
collections.Counter(iterable)
#频次
collections.Counter[key]#key出现频次
#返回n个出现频次最高的元素和其对应出现频次，如果n为None，返回所有元素
collections.Counter.most_common(n=None)
#插入/更新
collections.Counter.update(iterable)
counter1+counter2;counter1-counter2#counter加减
#检查两个字符串的组成元素是否相同
collections.Counter(list1)==collections.Counter(list2)

importcollections
collections.defaultdict(type)#当第一次访问dict[key]时，会无参数调用type，给dict[key]提供一个初始值

importcollections
collections.OrderedDict(items=None)#迭代时保留原始插入顺序

importsys
sys.stderr.write('')

#在代码中的debug部分
if__debug__:
pass

$python-0main.py

pylintmain.py

$python-mcProfilemain.py

#代码块耗时定义
fromcontextlibimportcontextmanager
fromtimeimportperf_counter
@contextmanager
deftimeblock(label):
tic=perf_counter()
try:
yield
finally:
toc=perf_counter()
print('%s:%s'%(label,toc-tic))
#代码块耗时测试
withtimeblock('counting'):
pass

items=[2,1,3,4]
argmin=min(range(len(items)),key=items.__getitem__)

A=[['a11','a12'],['a21','a22'],['a31','a32']]
A_transpose=list(zip(*A))#listoftuple
A_transpose=list(list(col)forcolinzip(*A))#listoflist

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

#Preferred.
list(zip(*[iter(A)]*2))