http://sahandsaba.com/thirty-python-language-features-and-tricks-you-may-not-know.html  感谢原作者

30 Python Language Features and Tricks You May Not Know About

Posted on Mar 05, 2014 , last modified on Mar 16, 2014

By Sahand Saba
 

1.1   Unpacking

>>> a, b, c = 1, 2, 3

>>> a, b, c

(1, 2, 3)

>>> a, b, c = [1, 2, 3]

>>> a, b, c

(1, 2, 3)

>>> a, b, c = (2 * i + 1 for i in range(3))

>>> a, b, c

(1, 3, 5)

>>> a, (b, c), d = [1, (2, 3), 4]

>>> a

1

>>> b

2

>>> c

3

>>> d

4

1.2   Unpacking for swapping variables

>>> a, b = 1, 2

>>> a, b = b, a

>>> a, b

(2, 1)

1.3   Extended unpacking (Python 3 only)

>>> a, *b, c = [1, 2, 3, 4, 5]

>>> a

1

>>> b

[2, 3, 4]

>>> c

5

1.4   Negative indexing

>>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

>>> a[-1]

10

>>> a[-3]

8

1.5   List slices (a[start:end])

>>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

>>> a[2:8]

[2, 3, 4, 5, 6, 7]

1.6   List slices with negative indexing

>>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

>>> a[-4:-2]

[7, 8]

1.7   List slices with step (a[start:end:step])

>>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

>>> a[::2]

[0, 2, 4, 6, 8, 10]

>>> a[::3]

[0, 3, 6, 9]

>>> a[2:8:2]

[2, 4, 6]

1.8   List slices with negative step

>>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

>>> a[::-1]

[10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]

>>> a[::-2]

[10, 8, 6, 4, 2, 0]

1.9   List slice assignment

>>> a = [1, 2, 3, 4, 5]

>>> a[2:3] = [0, 0]

>>> a

[1, 2, 0, 0, 4, 5]

>>> a[1:1] = [8, 9]

>>> a

[1, 8, 9, 2, 0, 0, 4, 5]

>>> a[1:-1] = []

>>> a

[1, 5]

1.10   Naming slices (slice(start, end, step))

>>> a = [0, 1, 2, 3, 4, 5]

>>> LASTTHREE = slice(-3, None)

>>> LASTTHREE

slice(-3, None, None)

>>> a[LASTTHREE]

[3, 4, 5]

1.11   Zipping and unzipping lists and iterables

>>> a = [1, 2, 3]

>>> b = ['a', 'b', 'c']

>>> z = zip(a, b)

>>> z

[(1, 'a'), (2, 'b'), (3, 'c')]

>>> zip(*z)

[(1, 2, 3), ('a', 'b', 'c')]

1.12   Grouping adjacent list items using zip

>>> a = [1, 2, 3, 4, 5, 6]

>>> zip(*([iter(a)] * 2))

[(1, 2), (3, 4), (5, 6)]

>>> group_adjacent = lambda a, k: zip(*([iter(a)] * k))

>>> group_adjacent(a, 3)

[(1, 2, 3), (4, 5, 6)]

>>> group_adjacent(a, 2)

[(1, 2), (3, 4), (5, 6)]

>>> group_adjacent(a, 1)

[(1,), (2,), (3,), (4,), (5,), (6,)]

>>> zip(a[::2], a[1::2])

[(1, 2), (3, 4), (5, 6)]

>>> zip(a[::3], a[1::3], a[2::3])

[(1, 2, 3), (4, 5, 6)]

>>> group_adjacent = lambda a, k: zip(*(a[i::k] for i in range(k)))

>>> group_adjacent(a, 3)

[(1, 2, 3), (4, 5, 6)]

>>> group_adjacent(a, 2)

[(1, 2), (3, 4), (5, 6)]

>>> group_adjacent(a, 1)

[(1,), (2,), (3,), (4,), (5,), (6,)]

1.13   Inverting a dictionary using zip

>>> m = {'a': 1, 'b': 2, 'c': 3, 'd': 4}

>>> m.items()

[('a', 1), ('c', 3), ('b', 2), ('d', 4)]

>>> zip(m.values(), m.keys())

[(1, 'a'), (3, 'c'), (2, 'b'), (4, 'd')]

>>> mi = dict(zip(m.values(), m.keys()))

>>> mi

{1: 'a', 2: 'b', 3: 'c', 4: 'd'}

1.14   Flattening lists:

>>> a = [[1, 2], [3, 4], [5, 6]]

>>> list(itertools.chain.from_iterable(a))

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

>>> sum(a, [])

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

>>> [x for l in a for x in l]

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

>>> a = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]

>>> [x for l1 in a for l2 in l1 for x in l2]

[1, 2, 3, 4, 5, 6, 7, 8]

>>> a = [1, 2, [3, 4], [[5, 6], [7, 8]]]

>>> flatten = lambda x: [y for l in x for y in flatten(l)] if type(x) is list else [x]

>>> flatten(a)

[1, 2, 3, 4, 5, 6, 7, 8]

Note: according to Python's documentation on sum, itertools.chain.from_iterable is the preferred method for this.

1.15   Generator expressions

>>> g = (x ** 2 for x in xrange(10))

>>> next(g)

0

>>> next(g)

1

>>> next(g)

4

>>> next(g)

9

>>> sum(x ** 3 for x in xrange(10))

2025

>>> sum(x ** 3 for x in xrange(10) if x % 3 == 1)

408

1.16   Dictionary comprehensions

>>> m = {x: x ** 2 for x in range(5)}

>>> m

{0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

>>> m = {x: 'A' + str(x) for x in range(10)}

>>> m

{0: 'A0', 1: 'A1', 2: 'A2', 3: 'A3', 4: 'A4', 5: 'A5', 6: 'A6', 7: 'A7', 8: 'A8', 9: 'A9'}

1.17   Inverting a dictionary using a dictionary comprehension

>>> m = {'a': 1, 'b': 2, 'c': 3, 'd': 4}

>>> m

{'d': 4, 'a': 1, 'b': 2, 'c': 3}

>>> {v: k for k, v in m.items()}

{1: 'a', 2: 'b', 3: 'c', 4: 'd'}

1.18   Named tuples (collections.namedtuple)

>>> Point = collections.namedtuple('Point', ['x', 'y'])

>>> p = Point(x=1.0, y=2.0)

>>> p

Point(x=1.0, y=2.0)

>>> p.x

1.0

>>> p.y

2.0

1.19   Inheriting from named tuples:

>>> class Point(collections.namedtuple('PointBase', ['x', 'y'])):

...     __slots__ = ()

...     def __add__(self, other):

...             return Point(x=self.x + other.x, y=self.y + other.y)

...

>>> p = Point(x=1.0, y=2.0)

>>> q = Point(x=2.0, y=3.0)

>>> p + q

Point(x=3.0, y=5.0)

1.20   Sets and set operations

>>> A = {1, 2, 3, 3}

>>> A

set([1, 2, 3])

>>> B = {3, 4, 5, 6, 7}

>>> B

set([3, 4, 5, 6, 7])

>>> A | B

set([1, 2, 3, 4, 5, 6, 7])

>>> A & B

set([3])

>>> A - B

set([1, 2])

>>> B - A

set([4, 5, 6, 7])

>>> A ^ B

set([1, 2, 4, 5, 6, 7])

>>> (A ^ B) == ((A - B) | (B - A))

True

1.21   Multisets and multiset operations (collections.Counter)

>>> A = collections.Counter([1, 2, 2])

>>> B = collections.Counter([2, 2, 3])

>>> A

Counter({2: 2, 1: 1})

>>> B

Counter({2: 2, 3: 1})

>>> A | B

Counter({2: 2, 1: 1, 3: 1})

>>> A & B

Counter({2: 2})

>>> A + B

Counter({2: 4, 1: 1, 3: 1})

>>> A - B

Counter({1: 1})

>>> B - A

Counter({3: 1})

1.22   Most common elements in an iterable (collections.Counter)

>>> A = collections.Counter([1, 1, 2, 2, 3, 3, 3, 3, 4, 5, 6, 7])

>>> A

Counter({3: 4, 1: 2, 2: 2, 4: 1, 5: 1, 6: 1, 7: 1})

>>> A.most_common(1)

[(3, 4)]

>>> A.most_common(3)

[(3, 4), (1, 2), (2, 2)]

1.23   Double-ended queue (collections.deque)

>>> Q = collections.deque()

>>> Q.append(1)

>>> Q.appendleft(2)

>>> Q.extend([3, 4])

>>> Q.extendleft([5, 6])

>>> Q

deque([6, 5, 2, 1, 3, 4])

>>> Q.pop()

4

>>> Q.popleft()

6

>>> Q

deque([5, 2, 1, 3])

>>> Q.rotate(3)

>>> Q

deque([2, 1, 3, 5])

>>> Q.rotate(-3)

>>> Q

deque([5, 2, 1, 3])

1.24   Double-ended queue with maximum length (collections.deque)

>>> last_three = collections.deque(maxlen=3)

>>> for i in xrange(10):

...     last_three.append(i)

...     print ', '.join(str(x) for x in last_three)

...

0

0, 1

0, 1, 2

1, 2, 3

2, 3, 4

3, 4, 5

4, 5, 6

5, 6, 7

6, 7, 8

7, 8, 9

1.25   Ordered dictionaries (collections.OrderedDict)

>>> m = dict((str(x), x) for x in range(10))

>>> print ', '.join(m.keys())

1, 0, 3, 2, 5, 4, 7, 6, 9, 8

>>> m = collections.OrderedDict((str(x), x) for x in range(10))

>>> print ', '.join(m.keys())

0, 1, 2, 3, 4, 5, 6, 7, 8, 9

>>> m = collections.OrderedDict((str(x), x) for x in range(10, 0, -1))

>>> print ', '.join(m.keys())

10, 9, 8, 7, 6, 5, 4, 3, 2, 1

1.26   Default dictionaries (collections.defaultdict)

>>> m = dict()

>>> m['a']

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

KeyError: 'a'

>>>

>>> m = collections.defaultdict(int)

>>> m['a']

0

>>> m['b']

0

>>> m = collections.defaultdict(str)

>>> m['a']

''

>>> m['b'] += 'a'

>>> m['b']

'a'

>>> m = collections.defaultdict(lambda: '[default value]')

>>> m['a']

'[default value]'

>>> m['b']

'[default value]'

1.27   Using default dictionaries to represent simple trees

>>> import json

>>> tree = lambda: collections.defaultdict(tree)

>>> root = tree()

>>> root['menu']['id'] = 'file'

>>> root['menu']['value'] = 'File'

>>> root['menu']['menuitems']['new']['value'] = 'New'

>>> root['menu']['menuitems']['new']['onclick'] = 'new();'

>>> root['menu']['menuitems']['open']['value'] = 'Open'

>>> root['menu']['menuitems']['open']['onclick'] = 'open();'

>>> root['menu']['menuitems']['close']['value'] = 'Close'

>>> root['menu']['menuitems']['close']['onclick'] = 'close();'

>>> print json.dumps(root, sort_keys=True, indent=4, separators=(',', ': '))

{

    "menu": {

        "id": "file",

        "menuitems": {

            "close": {

                "onclick": "close();",

                "value": "Close"

            },

            "new": {

                "onclick": "new();",

                "value": "New"

            },

            "open": {

                "onclick": "open();",

                "value": "Open"

            }

        },

        "value": "File"

    }

}

(See https://gist.github.com/hrldcpr/2012250 for more on this.)

1.28   Mapping objects to unique counting numbers (collections.defaultdict)

>>> import itertools, collections

>>> value_to_numeric_map = collections.defaultdict(itertools.count().next)

>>> value_to_numeric_map['a']

0

>>> value_to_numeric_map['b']

1

>>> value_to_numeric_map['c']

2

>>> value_to_numeric_map['a']

0

>>> value_to_numeric_map['b']

1

1.29   Largest and smallest elements (heapq.nlargest and heapq.nsmallest)

>>> a = [random.randint(0, 100) for __ in xrange(100)]

>>> heapq.nsmallest(5, a)

[3, 3, 5, 6, 8]

>>> heapq.nlargest(5, a)

[100, 100, 99, 98, 98]

1.30   Cartesian products (itertools.product)

>>> for p in itertools.product([1, 2, 3], [4, 5]):

(1, 4)

(1, 5)

(2, 4)

(2, 5)

(3, 4)

(3, 5)

>>> for p in itertools.product([0, 1], repeat=4):

...     print ''.join(str(x) for x in p)

...

0000

0001

0010

0011

0100

0101

0110

0111

1000

1001

1010

1011

1100

1101

1110

1111

1.31   Combinations and combinations with replacement (itertools.combinations anditertools.combinations_with_replacement)

>>> for c in itertools.combinations([1, 2, 3, 4, 5], 3):

...     print ''.join(str(x) for x in c)

...

123

124

125

134

135

145

234

235

245

345

>>> for c in itertools.combinations_with_replacement([1, 2, 3], 2):

...     print ''.join(str(x) for x in c)

...

11

12

13

22

23

33

1.32   Permutations (itertools.permutations)

>>> for p in itertools.permutations([1, 2, 3, 4]):

...     print ''.join(str(x) for x in p)

...

1234

1243

1324

1342

1423

1432

2134

2143

2314

2341

2413

2431

3124

3142

3214

3241

3412

3421

4123

4132

4213

4231

4312

4321

1.33   Chaining iterables (itertools.chain)

>>> a = [1, 2, 3, 4]

>>> for p in itertools.chain(itertools.combinations(a, 2), itertools.combinations(a, 3)):

...     print p

...

(1, 2)

(1, 3)

(1, 4)

(2, 3)

(2, 4)

(3, 4)

(1, 2, 3)

(1, 2, 4)

(1, 3, 4)

(2, 3, 4)

>>> for subset in itertools.chain.from_iterable(itertools.combinations(a, n) for n in range(len(a) + 1))

...     print subset

...

()

(1,)

(2,)

(3,)

(4,)

(1, 2)

(1, 3)

(1, 4)

(2, 3)

(2, 4)

(3, 4)

(1, 2, 3)

(1, 2, 4)

(1, 3, 4)

(2, 3, 4)

(1, 2, 3, 4)

1.34   Grouping rows by a given key (itertools.groupby)

>>> import itertools

>>> with open('contactlenses.csv', 'r') as infile:

...     data = [line.strip().split(',') for line in infile]

...

>>> data = data[1:]

>>> def print_data(rows):

...     print '\n'.join('\t'.join('{: <16}'.format(s) for s in row) for row in rows)

...

>>> print_data(data)

young               myope                   no                      reduced                 none

young               myope                   no                      normal                  soft

young               myope                   yes                     reduced                 none

young               myope                   yes                     normal                  hard

young               hypermetrope            no                      reduced                 none

young               hypermetrope            no                      normal                  soft

young               hypermetrope            yes                     reduced                 none

young               hypermetrope            yes                     normal                  hard

pre-presbyopic      myope                   no                      reduced                 none

pre-presbyopic      myope                   no                      normal                  soft

pre-presbyopic      myope                   yes                     reduced                 none

pre-presbyopic      myope                   yes                     normal                  hard

pre-presbyopic      hypermetrope            no                      reduced                 none

pre-presbyopic      hypermetrope            no                      normal                  soft

pre-presbyopic      hypermetrope            yes                     reduced                 none

pre-presbyopic      hypermetrope            yes                     normal                  none

presbyopic          myope                   no                      reduced                 none

presbyopic          myope                   no                      normal                  none

presbyopic          myope                   yes                     reduced                 none

presbyopic          myope                   yes                     normal                  hard

presbyopic          hypermetrope            no                      reduced                 none

presbyopic          hypermetrope            no                      normal                  soft

presbyopic          hypermetrope            yes                     reduced                 none

presbyopic          hypermetrope            yes                     normal                  none

>>> data.sort(key=lambda r: r[-1])

>>> for value, group in itertools.groupby(data, lambda r: r[-1]):

...     print '-----------'

...     print 'Group: ' + value

...     print_data(group)

...

-----------

Group: hard

young               myope                   yes                     normal                  hard

young               hypermetrope            yes                     normal                  hard

pre-presbyopic      myope                   yes                     normal                  hard

presbyopic          myope                   yes                     normal                  hard

-----------

Group: none

young               myope                   no                      reduced                 none

young               myope                   yes                     reduced                 none

young               hypermetrope            no                      reduced                 none

young               hypermetrope            yes                     reduced                 none

pre-presbyopic      myope                   no                      reduced                 none

pre-presbyopic      myope                   yes                     reduced                 none

pre-presbyopic      hypermetrope            no                      reduced                 none

pre-presbyopic      hypermetrope            yes                     reduced                 none

pre-presbyopic      hypermetrope            yes                     normal                  none

presbyopic          myope                   no                      reduced                 none

presbyopic          myope                   no                      normal                  none

presbyopic          myope                   yes                     reduced                 none

presbyopic          hypermetrope            no                      reduced                 none

presbyopic          hypermetrope            yes                     reduced                 none

presbyopic          hypermetrope            yes                     normal                  none

-----------

Group: soft

young               myope                   no                      normal                  soft

young               hypermetrope            no                      normal                  soft

pre-presbyopic      myope                   no                      normal                  soft

pre-presbyopic      hypermetrope            no                      normal                  soft

presbyopic          hypermetrope            no                      normal                  soft
 

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