由于Python的字符串不能更改，我想知道如何更有效地连接字符串?
我可以这样写:
s += stringfromelsewhere
或者像这样:
s = []
s.append(somestring)
# later
s = ''.join(s)
在写这个问题的时候，我发现了一篇关于这个话题的好文章。
http://www.skymind.com/~ocrow/python_string/
但它在Python 2.x中。，所以问题是Python 3中有什么变化吗?
当前回答
你可以用不同的方法来做。
str1 = "Hello"
str2 = "World"
str_list = ['Hello', 'World']
str_dict = {'str1': 'Hello', 'str2': 'World'}
# Concatenating With the + Operator
print(str1 + ' ' + str2) # Hello World
# String Formatting with the % Operator
print("%s %s" % (str1, str2)) # Hello World
# String Formatting with the { } Operators with str.format()
print("{}{}".format(str1, str2)) # Hello World
print("{0}{1}".format(str1, str2)) # Hello World
print("{str1} {str2}".format(str1=str_dict['str1'], str2=str_dict['str2'])) # Hello World
print("{str1} {str2}".format(**str_dict)) # Hello World
# Going From a List to a String in Python With .join()
print(' '.join(str_list)) # Hello World
# Python f'strings --> 3.6 onwards
print(f"{str1} {str2}") # Hello World
我通过以下文章创建了这个小摘要。
Python 3的f-Strings:改进的字符串格式化语法(指南)(还包括速度测试) 格式化字符串字面量 字符串连接和格式化 Python中的分割、连接和连接字符串
其他回答
写出这个函数
def str_join(*args):
return ''.join(map(str, args))
这样你就可以随时随地打电话了
str_join('Pine') # Returns : Pine
str_join('Pine', 'apple') # Returns : Pineapple
str_join('Pine', 'apple', 3) # Returns : Pineapple3
你可以用不同的方法来做。
str1 = "Hello"
str2 = "World"
str_list = ['Hello', 'World']
str_dict = {'str1': 'Hello', 'str2': 'World'}
# Concatenating With the + Operator
print(str1 + ' ' + str2) # Hello World
# String Formatting with the % Operator
print("%s %s" % (str1, str2)) # Hello World
# String Formatting with the { } Operators with str.format()
print("{}{}".format(str1, str2)) # Hello World
print("{0}{1}".format(str1, str2)) # Hello World
print("{str1} {str2}".format(str1=str_dict['str1'], str2=str_dict['str2'])) # Hello World
print("{str1} {str2}".format(**str_dict)) # Hello World
# Going From a List to a String in Python With .join()
print(' '.join(str_list)) # Hello World
# Python f'strings --> 3.6 onwards
print(f"{str1} {str2}") # Hello World
我通过以下文章创建了这个小摘要。
Python 3的f-Strings:改进的字符串格式化语法(指南)(还包括速度测试) 格式化字符串字面量 字符串连接和格式化 Python中的分割、连接和连接字符串
你也可以使用这个(更有效)。(https://softwareengineering.stackexchange.com/questions/304445/why-is-s-better-than-for-concatenation)
s += "%s" %(stringfromelsewhere)
我的用例略有不同。我必须构造一个查询，其中超过20个字段是动态的。 我遵循了这种使用格式方法的方法
query = "insert into {0}({1},{2},{3}) values({4}, {5}, {6})"
query.format('users','name','age','dna','suzan',1010,'nda')
这对我来说相对简单，而不是使用+或其他方法
如果要连接很多值，则两者都不使用。附加列表的开销很大。你可以使用StringIO。特别是当你通过大量的操作建立它的时候。
from cStringIO import StringIO
# python3: from io import StringIO
buf = StringIO()
buf.write('foo')
buf.write('foo')
buf.write('foo')
buf.getvalue()
# 'foofoofoo'
如果您已经从其他操作返回了一个完整的列表，那么只需使用“.join(aList)”
来自python常见问题:将多个字符串连接在一起的最有效方法是什么?
str and bytes objects are immutable, therefore concatenating many strings together is inefficient as each concatenation creates a new object. In the general case, the total runtime cost is quadratic in the total string length. To accumulate many str objects, the recommended idiom is to place them into a list and call str.join() at the end: chunks = [] for s in my_strings: chunks.append(s) result = ''.join(chunks) (another reasonably efficient idiom is to use io.StringIO) To accumulate many bytes objects, the recommended idiom is to extend a bytearray object using in-place concatenation (the += operator): result = bytearray() for b in my_bytes_objects: result += b
编辑:我很愚蠢，把结果向后粘贴，使它看起来像添加到列表中比cStringIO更快。我还添加了对bytearray/str concat的测试，以及使用更大字符串的更大列表的第二轮测试。(python 2.7.3)
大型字符串列表的Ipython测试示例
try:
from cStringIO import StringIO
except:
from io import StringIO
source = ['foo']*1000
%%timeit buf = StringIO()
for i in source:
buf.write(i)
final = buf.getvalue()
# 1000 loops, best of 3: 1.27 ms per loop
%%timeit out = []
for i in source:
out.append(i)
final = ''.join(out)
# 1000 loops, best of 3: 9.89 ms per loop
%%timeit out = bytearray()
for i in source:
out += i
# 10000 loops, best of 3: 98.5 µs per loop
%%timeit out = ""
for i in source:
out += i
# 10000 loops, best of 3: 161 µs per loop
## Repeat the tests with a larger list, containing
## strings that are bigger than the small string caching
## done by the Python
source = ['foo']*1000
# cStringIO
# 10 loops, best of 3: 19.2 ms per loop
# list append and join
# 100 loops, best of 3: 144 ms per loop
# bytearray() +=
# 100 loops, best of 3: 3.8 ms per loop
# str() +=
# 100 loops, best of 3: 5.11 ms per loop
