雖然上面這段代碼也能達到重寫print函數的目的，但是不推薦使用。

python2提供的字符串格式化系統還是不夠好，太冗長麻煩，通常我們會寫這樣一段代碼來輸出日志信息：

# 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)
))

輸出的結果是：

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

python3.6的f-strings功能實現起來就簡單多了。

# 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，在編寫查詢或生成代碼片段時非常方便：

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

下面這些比較操作在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]

不同類型的數據無法排序

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

下面這段代碼在Python2里運行失敗但是Python3會成功運行，Python3的字符串都是Unicode編碼，所以這樣對NLP來說很方便，再比如：

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

from collections import Counter
Counter("M?belstück")
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, "?": 1, "b": 1, "e": 1, "l": 1, "s": 1, "t": 1, "ü": 1, "c": 1, "k": 1})

CPython3.6+里的dict默認的行為和orderdict很類似

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}

同樣的，**kwargs字典內容的數據和傳入參數的順序是一致的。

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

縮短到Python2時間的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)

合并兩個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+不僅僅合并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提供了兩個整數類型：int和long，python3只提供有個整數類型：int，如下的代碼：

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，方便我們編碼的同時，也帶來了更好的安全性和較高的性能。而且官方也一直推薦盡快遷移到python3。當然，遷移的代價因系統而異，希望這篇文章能對你遷移python2到python3有些幫助。

英文原文

轉自我的博客：捕蛇者說