[Github高赞文章]python2愉快地迁移到Python3_code changing from python2 to python3(2)

# Python 3
_print = print # store the original print function
def print(*args, **kargs):
    pass  # do something useful, e.g. store output to some file

@contextlib.contextmanager
def replace_print():
    import builtins
    _print = print # saving old print function
    # or use some other function here
    builtins.print = lambda *args, **kwargs: _print(‘new printing’, *args, **kwargs)
    yield
    builtins.print = _print
with replace_print():

# Python 3
result = process(x) if is_valid(x) else print('invalid item: ', x)

# grouping decimal numbers by thousands
one_million = 1_000_000
# grouping hexadecimal addresses by words
addr = 0xCAFE_F00D
# grouping bits into nibbles in a binary literal
flags = 0b_0011_1111_0100_1110
# same, for string conversions
flags = int(‘0b_1111_0000’, 2)

# 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)
)

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}')

data = pandas.read_csv(‘timing.csv’)
velocity = data[‘distance’] / data[‘time’]

3 < ‘3’
2 < None
(3, 4) < (3, None)
(4, 5) < [4, 5]

if a is not None:
  pass
if a: # WRONG check for None
  pass

s = ‘您好’
print(len(s))
print(s[:2])

Python 2: 6
��
Python 3: 2
您好.

x = u’со’
x += ‘co’ # ok
x += ‘со’ # fail

‘a’ < type < u’a’  # Python 2: True
‘a’ < u’a’         # Python 2: False

from collections import Counter
Counter(‘Möbelstück’)

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}

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)

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

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

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)

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.

[*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)

@dataclass
class Person:
    name: str
    age: int
@dataclass
class Coder(Person):
    preferred_language: str = ‘Python 3’