Python3的这些新特性很方便

虽然上面这段代码也能达到重写print函数的目的，但是不推荐使用。

python2提供的字符串格式化系统还是不够好，太冗长麻烦，通常我们会写这样一段代码来输出日志信息：

# Python 2
print("{batch:3} {epoch:3} / {total_epochs:3}  accuracy: {acc_mean:0.4f}±{acc_std:0.4f} time: {avg_time:3.2f}".format(
    batch=batch, epoch=epoch, total_epochs=total_epochs, 
    acc_mean=numpy.mean(accuracies), acc_std=numpy.std(accuracies),
    avg_time=time / len(data_batch)
))

# Python 2 (too error-prone during fast modifications, please avoid):
print("{:3} {:3} / {:3}  accuracy: {:0.4f}±{:0.4f} time: {:3.2f}".format(
    batch, epoch, total_epochs, numpy.mean(accuracies), numpy.std(accuracies),
    time / len(data_batch)
))

输出的结果是：

120  12 / 300  accuracy: 0.8180±0.4649 time: 56.60

python3.6的f-strings功能实现起来就简单多了。

# Python 3.6+
print(f"{batch:3} {epoch:3} / {total_epochs:3}  accuracy: {numpy.mean(accuracies):0.4f}±{numpy.std(accuracies):0.4f} time: {time / len(data_batch):3.2f}")

而且，在编写查询或生成代码片段时非常方便：

query = f"INSERT INTO STATION VALUES (13, "{city}", "{state}", {latitude}, {longitude})"

下面这些比较操作在python3里是非法的

# All these comparisons are illegal in Python 3
3 < "3"
2 < None
(3, 4) < (3, None)
(4, 5) < [4, 5]

# False in both Python 2 and Python 3
(4, 5) == [4, 5]

不同类型的数据无法排序

sorted([2, "1", 3])  # invalid for Python 3, in Python 2 returns [2, 3, "1"]

s = "您好"
print(len(s))
print(s[:2])

Output:

Python 2: 6
��
Python 3: 2
您好.


x = u"со"
x += "co" # ok
x += "со" # fail

下面这段代码在Python2里运行失败但是Python3会成功运行，Python3的字符串都是Unicode编码，所以这样对NLP来说很方便，再比如：

"a" < type < u"a"  # Python 2: True
"a" < u"a"         # Python 2: False

from collections import Counter
Counter("Möbelstück")
Python 2: Counter({"xc3": 2, "b": 1, "e": 1, "c": 1, "k": 1, "M": 1, "l": 1, "s": 1, "t": 1, "xb6": 1, "xbc": 1})
Python 3: Counter({"M": 1, "ö": 1, "b": 1, "e": 1, "l": 1, "s": 1, "t": 1, "ü": 1, "c": 1, "k": 1})

CPython3.6+里的dict默认的行为和orderdict很类似

import json
x = {str(i):i for i in range(5)}
json.loads(json.dumps(x))
# Python 2
{u"1": 1, u"0": 0, u"3": 3, u"2": 2, u"4": 4}
# Python 3
{"0": 0, "1": 1, "2": 2, "3": 3, "4": 4}

同样的，**kwargs字典内容的数据和传入参数的顺序是一致的。

from torch import nn

# Python 2
model = nn.Sequential(OrderedDict([
          ("conv1", nn.Conv2d(1,20,5)),
          ("relu1", nn.ReLU()),
          ("conv2", nn.Conv2d(20,64,5)),
          ("relu2", nn.ReLU())
        ]))

# Python 3.6+, how it *can* be done, not supported right now in pytorch
model = nn.Sequential(
    conv1=nn.Conv2d(1,20,5),
    relu1=nn.ReLU(),
    conv2=nn.Conv2d(20,64,5),
    relu2=nn.ReLU())
)        

# handy when amount of additional stored info may vary between experiments, but the same code can be used in all cases
model_paramteres, optimizer_parameters, *other_params = load(checkpoint_name)

# picking two last values from a sequence
*prev, next_to_last, last = values_history

# This also works with any iterables, so if you have a function that yields e.g. qualities,
# below is a simple way to take only last two values from a list 
*prev, next_to_last, last = iter_train(args)

# Python 2
import cPickle as pickle
import numpy
print len(pickle.dumps(numpy.random.normal(size=[1000, 1000])))
# result: 23691675

# Python 3
import pickle
import numpy
len(pickle.dumps(numpy.random.normal(size=[1000, 1000])))
# result: 8000162

缩短到Python2时间的1/3

labels = 
predictions = [model.predict(data) for data, labels in dataset]

# labels are overwritten in Python 2
# labels are not affected by comprehension in Python 3

# Python 2
class MySubClass(MySuperClass):
    def __init__(self, name, **options):
        super(MySubClass, self).__init__(name="subclass", **options)
        
# Python 3
class MySubClass(MySuperClass):
    def __init__(self, name, **options):
        super().__init__(name="subclass", **options)

合并两个Dict

x = dict(a=1, b=2)
y = dict(b=3, d=4)
# Python 3.5+
z = {**x, **y} 
# z = {"a": 1, "b": 3, "d": 4}, note that value for `b` is taken from the latter dict.

Python3.5+不仅仅合并dict很方便，合并list等也很方便

[*a, *b, *c] # list, concatenating 
(*a, *b, *c) # tuple, concatenating 
{*a, *b, *c} # set, union 

Python 3.5+
do_something(**{**default_settings, **custom_settings})

# Also possible, this code also checks there is no intersection between keys of dictionaries
do_something(**first_args, **second_args)

python2提供了两个整数类型：int和long，python3只提供有个整数类型：int，如下的代码：

isinstance(x, numbers.Integral) # Python 2, the canonical way
isinstance(x, (long, int))      # Python 2
isinstance(x, int)              # Python 3, easier to remember

python3提供了很多新的特性，方便我们编码的同时，也带来了更好的安全性和较高的性能。而且官方也一直推荐尽快迁移到python3。当然，迁移的代价因系统而异，希望这篇文章能对你迁移python2到python3有些帮助。

英文原文

转自我的博客：捕蛇者说