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collections

— Container datatypes

Source code: Lib/collections/__init__.py

This module implements specialized container datatypes providing alternatives to
Python’s general purpose built-in containers, dict, list,
set, and tuple.

namedtuple()

factory function for creating tuple subclasses with named fields

deque

list-like container with fast appends and pops on either end

ChainMap

dict-like class for creating a single view of multiple mappings

Counter

dict subclass for counting hashable objects

OrderedDict

dict subclass that remembers the order entries were added

defaultdict

dict subclass that calls a factory function to supply missing values

UserDict

wrapper around dictionary objects for easier dict subclassing

UserList

wrapper around list objects for easier list subclassing

UserString

wrapper around string objects for easier string subclassing

ChainMap

objects

New in version 3.3.

A ChainMap class is provided for quickly linking a number of mappings
so they can be treated as a single unit. It is often much faster than creating
a new dictionary and running multiple update() calls.

The class can be used to simulate nested scopes and is useful in templating.

class collections.ChainMap

(

*

maps

)

A ChainMap groups multiple dicts or other mappings together to
create a single, updateable view. If no maps are specified, a single empty
dictionary is provided so that a new chain always has at least one mapping.

The underlying mappings are stored in a list. That list is public and can
be accessed or updated using the maps attribute. There is no other state.

Lookups search the underlying mappings successively until a key is found. In
contrast, writes, updates, and deletions only operate on the first mapping.

A ChainMap incorporates the underlying mappings by reference. So, if
one of the underlying mappings gets updated, those changes will be reflected
in ChainMap.

All of the usual dictionary methods are supported. In addition, there is a
maps attribute, a method for creating new subcontexts, and a property for
accessing all but the first mapping:

maps

A user updateable list of mappings. The list is ordered from
first-searched to last-searched. It is the only stored state and can
be modified to change which mappings are searched. The list should
always contain at least one mapping.

new_child

(

m

=

None

,

**

kwargs

)

Returns a new ChainMap containing a new map followed by
all of the maps in the current instance. If m is specified,
it becomes the new map at the front of the list of mappings; if not
specified, an empty dict is used, so that a call to d.new_child()
is equivalent to: ChainMap({}, *d.maps). If any keyword arguments
are specified, they update passed map or new empty dict. This method
is used for creating subcontexts that can be updated without altering
values in any of the parent mappings.

Changed in version 3.4: The optional m parameter was added.

Changed in version 3.10: Keyword arguments support was added.

parents

Property returning a new ChainMap containing all of the maps in
the current instance except the first one. This is useful for skipping
the first map in the search. Use cases are similar to those for the
nonlocal keyword used in nested scopes. The use cases also parallel those for the built-in
super() function. A reference to d.parents is equivalent to:
ChainMap(*d.maps[1:]).

Note, the iteration order of a ChainMap() is determined by
scanning the mappings last to first:

>>>

baseline

=

{

'music'

:

'bach'

,

'art'

:

'rembrandt'

}

>>>

adjustments

=

{

'art'

:

'van gogh'

,

'opera'

:

'carmen'

}

>>>

list

(

ChainMap

(

adjustments

,

baseline

))

['music', 'art', 'opera']

This gives the same ordering as a series of dict.update() calls
starting with the last mapping:

>>>

combined

=

baseline

.

copy

()

>>>

combined

.

update

(

adjustments

)

>>>

list

(

combined

)

['music', 'art', 'opera']

Changed in version 3.9: Added support for | and |= operators, specified in PEP 584.

See also

  • The MultiContext class
    in the Enthought CodeTools package has options to support
    writing to any mapping in the chain.

  • Django’s Context class
    for templating is a read-only chain of mappings. It also features
    pushing and popping of contexts similar to the
    new_child() method and the
    parents property.

  • The Nested Contexts recipe has options to control
    whether writes and other mutations apply only to the first mapping or to
    any mapping in the chain.

  • A greatly simplified read-only version of Chainmap.

ChainMap

Examples and Recipes

This section shows various approaches to working with chained maps.

Example of simulating Python’s internal lookup chain:

import

builtins

pylookup

=

ChainMap

(

locals

(),

globals

(),

vars

(

builtins

))

Example of letting user specified command-line arguments take precedence over
environment variables which in turn take precedence over default values:

import

os

,

argparse

defaults

=

{

'color'

:

'red'

,

'user'

:

'guest'

}

parser

=

argparse

.

ArgumentParser

()

parser

.

add_argument

(

'-u'

,

'--user'

)

parser

.

add_argument

(

'-c'

,

'--color'

)

namespace

=

parser

.

parse_args

()

command_line_args

=

{

k

:

v

for

k

,

v

in

vars

(

namespace

)

.

items

()

if

v

is

not

None

}

combined

=

ChainMap

(

command_line_args

,

os

.

environ

,

defaults

)

print

(

combined

[

'color'

])

print

(

combined

[

'user'

])

Example patterns for using the ChainMap class to simulate nested
contexts:

c

=

ChainMap

()

# Create root context

d

=

c

.

new_child

()

# Create nested child context

e

=

c

.

new_child

()

# Child of c, independent from d

e

.

maps

[

]

# Current context dictionary -- like Python's locals()

e

.

maps

[

-

1

]

# Root context -- like Python's globals()

e

.

parents

# Enclosing context chain -- like Python's nonlocals

d

[

'x'

]

=

1

# Set value in current context

d

[

'x'

]

# Get first key in the chain of contexts

del

d

[

'x'

]

# Delete from current context

list

(

d

)

# All nested values

k

in

d

# Check all nested values

len

(

d

)

# Number of nested values

d

.

items

()

# All nested items

dict

(

d

)

# Flatten into a regular dictionary

The ChainMap class only makes updates (writes and deletions) to the
first mapping in the chain while lookups will search the full chain. However,
if deep writes and deletions are desired, it is easy to make a subclass that
updates keys found deeper in the chain:

class

DeepChainMap

(

ChainMap

):

'Variant of ChainMap that allows direct updates to inner scopes'

def

__setitem__

(

self

,

key

,

value

):

for

mapping

in

self

.

maps

:

if

key

in

mapping

:

mapping

[

key

]

=

value

return

self

.

maps

[

][

key

]

=

value

def

__delitem__

(

self

,

key

):

for

mapping

in

self

.

maps

:

if

key

in

mapping

:

del

mapping

[

key

]

return

raise

KeyError

(

key

)

>>>

d

=

DeepChainMap

({

'zebra'

:

'black'

},

{

'elephant'

:

'blue'

},

{

'lion'

:

'yellow'

})

>>>

d

[

'lion'

]

=

'orange'

# update an existing key two levels down

>>>

d

[

'snake'

]

=

'red'

# new keys get added to the topmost dict

>>>

del

d

[

'elephant'

]

# remove an existing key one level down

>>>

d

# display result

DeepChainMap

({

'zebra'

:

'black'

,

'snake'

:

'red'

},

{},

{

'lion'

:

'orange'

})

Counter

objects

A counter tool is provided to support convenient and rapid tallies.
For example:

>>>

# Tally occurrences of words in a list

>>>

cnt

=

Counter

()

>>>

for

word

in

[

'red'

,

'blue'

,

'red'

,

'green'

,

'blue'

,

'blue'

]:

...

cnt

[

word

]

+=

1

>>>

cnt

Counter({'blue': 3, 'red': 2, 'green': 1})

>>>

# Find the ten most common words in Hamlet

>>>

import

re

>>>

words

=

re

.

findall

(

r

'\w+'

,

open

(

'hamlet.txt'

)

.

read

()

.

lower

())

>>>

Counter

(

words

)

.

most_common

(

10

)

[('the', 1143), ('and', 966), ('to', 762), ('of', 669), ('i', 631),

('you', 554), ('a', 546), ('my', 514), ('hamlet', 471), ('in', 451)]

class collections.Counter

(

[

iterable-or-mapping

]

)

A Counter is a dict subclass for counting hashable objects.
It is a collection where elements are stored as dictionary keys
and their counts are stored as dictionary values. Counts are allowed to be
any integer value including zero or negative counts. The Counter
class is similar to bags or multisets in other languages.

Elements are counted from an iterable or initialized from another
mapping (or counter):

>>>

c

=

Counter

()

# a new, empty counter

>>>

c

=

Counter

(

'gallahad'

)

# a new counter from an iterable

>>>

c

=

Counter

({

'red'

:

4

,

'blue'

:

2

})

# a new counter from a mapping

>>>

c

=

Counter

(

cats

=

4

,

dogs

=

8

)

# a new counter from keyword args

Counter objects have a dictionary interface except that they return a zero
count for missing items instead of raising a KeyError:

>>>

c

=

Counter

([

'eggs'

,

'ham'

])

>>>

c

[

'bacon'

]

# count of a missing element is zero

Setting a count to zero does not remove an element from a counter.
Use del to remove it entirely:

>>>

c

[

'sausage'

]

=

# counter entry with a zero count

>>>

del

c

[

'sausage'

]

# del actually removes the entry

New in version 3.1.

Changed in version 3.7: As a dict subclass, Counter
Inherited the capability to remember insertion order. Math operations
on Counter objects also preserve order. Results are ordered
according to when an element is first encountered in the left operand
and then by the order encountered in the right operand.

Counter objects support three methods beyond those available for all
dictionaries:

elements

(

)

Return an iterator over elements repeating each as many times as its
count. Elements are returned in the order first encountered. If an
element’s count is less than one, elements() will ignore it.

>>>

c

=

Counter

(

a

=

4

,

b

=

2

,

c

=

,

d

=-

2

)

>>>

sorted

(

c

.

elements

())

['a', 'a', 'a', 'a', 'b', 'b']

most_common

(

[

n

]

)

Return a list of the n most common elements and their counts from the
most common to the least. If n is omitted or None,
most_common() returns all elements in the counter.
Elements with equal counts are ordered in the order first encountered:

>>>

Counter

(

'abracadabra'

)

.

most_common

(

3

)

[('a', 5), ('b', 2), ('r', 2)]

subtract

(

[

iterable-or-mapping

]

)

Elements are subtracted from an iterable or from another mapping
(or counter). Like dict.update() but subtracts counts instead
of replacing them. Both inputs and outputs may be zero or negative.

>>>

c

=

Counter

(

a

=

4

,

b

=

2

,

c

=

,

d

=-

2

)

>>>

d

=

Counter

(

a

=

1

,

b

=

2

,

c

=

3

,

d

=

4

)

>>>

c

.

subtract

(

d

)

>>>

c

Counter({'a': 3, 'b': 0, 'c': -3, 'd': -6})

New in version 3.2.

total

(

)

Compute the sum of the counts.

>>>

c

=

Counter

(

a

=

10

,

b

=

5

,

c

=

)

>>>

c

.

total

()

15

New in version 3.10.

The usual dictionary methods are available for Counter objects
except for two which work differently for counters.

fromkeys

(

iterable

)

This class method is not implemented for Counter objects.

Elements are counted from an iterable or added-in from another
mapping (or counter). Like dict.update() but adds counts
instead of replacing them. Also, the iterable is expected to be a
sequence of elements, not a sequence of (key, value) pairs.

Counters support rich comparison operators for equality, subset, and
superset relationships: ==, !=, <, <=, >, >=.
All of those tests treat missing elements as having zero counts so that
Counter(a=1) == Counter(a=1, b=0) returns true.

New in version 3.10: Rich comparison operations were added.

Changed in version 3.10: In equality tests, missing elements are treated as having zero counts.
Formerly, Counter(a=3) and Counter(a=3, b=0) were considered
distinct.

Common patterns for working with Counter objects:

c

.

total

()

# total of all counts

c

.

clear

()

# reset all counts

list

(

c

)

# list unique elements

set

(

c

)

# convert to a set

dict

(

c

)

# convert to a regular dictionary

c

.

items

()

# convert to a list of (elem, cnt) pairs

Counter

(

dict

(

list_of_pairs

))

# convert from a list of (elem, cnt) pairs

c

.

most_common

()[:

-

n

-

1

:

-

1

]

# n least common elements

+

c

# remove zero and negative counts

Several mathematical operations are provided for combining Counter
objects to produce multisets (counters that have counts greater than zero).
Addition and subtraction combine counters by adding or subtracting the counts
of corresponding elements. Intersection and union return the minimum and
maximum of corresponding counts. Each operation can accept inputs with signed
counts, but the output will exclude results with counts of zero or less.

>>>

c

=

Counter

(

a

=

3

,

b

=

1

)

>>>

d

=

Counter

(

a

=

1

,

b

=

2

)

>>>

c

+

d

# add two counters together: c[x] + d[x]

Counter({'a': 4, 'b': 3})

>>>

c

-

d

# subtract (keeping only positive counts)

Counter({'a': 2})

>>>

c

&

d

# intersection: min(c[x], d[x])

Counter({'a': 1, 'b': 1})

>>>

c

|

d

# union: max(c[x], d[x])

Counter({'a': 3, 'b': 2})

Unary addition and subtraction are shortcuts for adding an empty counter
or subtracting from an empty counter.

>>>

c

=

Counter

(

a

=

2

,

b

=-

4

)

>>>

+

c

Counter({'a': 2})

>>>

-

c

Counter({'b': 4})

New in version 3.3: Added support for unary plus, unary minus, and in-place multiset operations.

Note

Counters were primarily designed to work with positive integers to represent
running counts; however, care was taken to not unnecessarily preclude use
cases needing other types or negative values. To help with those use cases,
this section documents the minimum range and type restrictions.

  • The Counter class itself is a dictionary subclass with no
    restrictions on its keys and values. The values are intended to be numbers
    representing counts, but you could store anything in the value field.

  • The most_common() method requires only that the values be orderable.

  • For in-place operations such as c[key] += 1, the value type need only
    support addition and subtraction. So fractions, floats, and decimals would
    work and negative values are supported. The same is also true for
    update() and subtract() which allow negative and zero values
    for both inputs and outputs.

  • The multiset methods are designed only for use cases with positive values.
    The inputs may be negative or zero, but only outputs with positive values
    are created. There are no type restrictions, but the value type needs to
    support addition, subtraction, and comparison.

  • The elements() method requires integer counts. It ignores zero and
    negative counts.

See also

  • Bag class
    in Smalltalk.

  • Wikipedia entry for Multisets.

  • C++ multisets
    tutorial with examples.

  • For mathematical operations on multisets and their use cases, see
    Knuth, Donald. The Art of Computer Programming Volume II,
    Section 4.6.3, Exercise 19.

  • To enumerate all distinct multisets of a given size over a given set of
    elements, see itertools.combinations_with_replacement():

    map

    (

    Counter

    ,

    combinations_with_replacement

    (

    'ABC'

    ,

    2

    ))

    # --> AA AB AC BB BC CC

deque

objects

class collections.deque

(

[

iterable

[

, maxlen

]

]

)

Returns a new deque object initialized left-to-right (using append()) with
data from iterable. If iterable is not specified, the new deque is empty.

Deques are a generalization of stacks and queues (the name is pronounced “deck”
and is short for “double-ended queue”). Deques support thread-safe, memory
efficient appends and pops from either side of the deque with approximately the
same O(1) performance in either direction.

Though list objects support similar operations, they are optimized for
fast fixed-length operations and incur O(n) memory movement costs for
pop(0) and insert(0, v) operations which change both the size and
position of the underlying data representation.

If maxlen is not specified or is None, deques may grow to an
arbitrary length. Otherwise, the deque is bounded to the specified maximum
length. Once a bounded length deque is full, when new items are added, a
corresponding number of items are discarded from the opposite end. Bounded
length deques provide functionality similar to the tail filter in
Unix. They are also useful for tracking transactions and other pools of data
where only the most recent activity is of interest.

Deque objects support the following methods:

append

(

x

)

Add x to the right side of the deque.

appendleft

(

x

)

Add x to the left side of the deque.

clear

(

)

Remove all elements from the deque leaving it with length 0.

copy

(

)

Create a shallow copy of the deque.

New in version 3.5.

count

(

x

)

Count the number of deque elements equal to x.

New in version 3.2.

extend

(

iterable

)

Extend the right side of the deque by appending elements from the iterable
argument.

extendleft

(

iterable

)

Extend the left side of the deque by appending elements from iterable.
Note, the series of left appends results in reversing the order of
elements in the iterable argument.

index

(

x

[

, start

[

, stop

]

]

)

Return the position of x in the deque (at or after index start
and before index stop). Returns the first match or raises
ValueError if not found.

New in version 3.5.

insert

(

i

,

x

)

Insert x into the deque at position i.

If the insertion would cause a bounded deque to grow beyond maxlen,
an IndexError is raised.

New in version 3.5.

pop

(

)

Remove and return an element from the right side of the deque. If no
elements are present, raises an IndexError.

popleft

(

)

Remove and return an element from the left side of the deque. If no
elements are present, raises an IndexError.

remove

(

value

)

Remove the first occurrence of value. If not found, raises a
ValueError.

reverse

(

)

Reverse the elements of the deque in-place and then return None.

New in version 3.2.

rotate

(

n

=

1

)

Rotate the deque n steps to the right. If n is negative, rotate
to the left.

When the deque is not empty, rotating one step to the right is equivalent
to d.appendleft(d.pop()), and rotating one step to the left is
equivalent to d.append(d.popleft()).

Deque objects also provide one read-only attribute:

maxlen

Maximum size of a deque or None if unbounded.

New in version 3.1.

In addition to the above, deques support iteration, pickling, len(d),
reversed(d), copy.copy(d), copy.deepcopy(d), membership testing with
the in operator, and subscript references such as d[0] to access
the first element. Indexed access is O(1) at both ends but slows to O(n) in
the middle. For fast random access, use lists instead.

Starting in version 3.5, deques support __add__(), __mul__(),
and __imul__().

Example:

>>>

from

collections

import

deque

>>>

d

=

deque

(

'ghi'

)

# make a new deque with three items

>>>

for

elem

in

d

:

# iterate over the deque's elements

...

print

(

elem

.

upper

())

G

H

I

>>>

d

.

append

(

'j'

)

# add a new entry to the right side

>>>

d

.

appendleft

(

'f'

)

# add a new entry to the left side

>>>

d

# show the representation of the deque

deque(['f', 'g', 'h', 'i', 'j'])

>>>

d

.

pop

()

# return and remove the rightmost item

'j'

>>>

d

.

popleft

()

# return and remove the leftmost item

'f'

>>>

list

(

d

)

# list the contents of the deque

['g', 'h', 'i']

>>>

d

[

]

# peek at leftmost item

'g'

>>>

d

[

-

1

]

# peek at rightmost item

'i'

>>>

list

(

reversed

(

d

))

# list the contents of a deque in reverse

['i', 'h', 'g']

>>>

'h'

in

d

# search the deque

True

>>>

d

.

extend

(

'jkl'

)

# add multiple elements at once

>>>

d

deque(['g', 'h', 'i', 'j', 'k', 'l'])

>>>

d

.

rotate

(

1

)

# right rotation

>>>

d

deque(['l', 'g', 'h', 'i', 'j', 'k'])

>>>

d

.

rotate

(

-

1

)

# left rotation

>>>

d

deque(['g', 'h', 'i', 'j', 'k', 'l'])

>>>

deque

(

reversed

(

d

))

# make a new deque in reverse order

deque(['l', 'k', 'j', 'i', 'h', 'g'])

>>>

d

.

clear

()

# empty the deque

>>>

d

.

pop

()

# cannot pop from an empty deque

Traceback (most recent call last):

File

"<pyshell#6>"

,

line

1

,

in

-

toplevel

-

d

.

pop

()

IndexError

:

pop from an empty deque

>>>

d

.

extendleft

(

'abc'

)

# extendleft() reverses the input order

>>>

d

deque(['c', 'b', 'a'])

deque

Recipes

This section shows various approaches to working with deques.

Bounded length deques provide functionality similar to the tail filter
in Unix:

def

tail

(

filename

,

n

=

10

):

'Return the last n lines of a file'

with

open

(

filename

)

as

f

:

return

deque

(

f

,

n

)

Another approach to using deques is to maintain a sequence of recently
added elements by appending to the right and popping to the left:

def

moving_average

(

iterable

,

n

=

3

):

# moving_average([40, 30, 50, 46, 39, 44]) --> 40.0 42.0 45.0 43.0

# http://en.wikipedia.org/wiki/Moving_average

it

=

iter

(

iterable

)

d

=

deque

(

itertools

.

islice

(

it

,

n

-

1

))

d

.

appendleft

(

)

s

=

sum

(

d

)

for

elem

in

it

:

s

+=

elem

-

d

.

popleft

()

d

.

append

(

elem

)

yield

s

/

n

A round-robin scheduler can be implemented with
input iterators stored in a deque. Values are yielded from the active
iterator in position zero. If that iterator is exhausted, it can be removed
with popleft(); otherwise, it can be cycled back to the end with
the rotate() method:

def

roundrobin

(

*

iterables

):

"roundrobin('ABC', 'D', 'EF') --> A D E B F C"

iterators

=

deque

(

map

(

iter

,

iterables

))

while

iterators

:

try

:

while

True

:

yield

next

(

iterators

[

])

iterators

.

rotate

(

-

1

)

except

StopIteration

:

# Remove an exhausted iterator.

iterators

.

popleft

()

The rotate() method provides a way to implement deque slicing and
deletion. For example, a pure Python implementation of del d[n] relies on
the rotate() method to position elements to be popped:

def

delete_nth

(

d

,

n

):

d

.

rotate

(

-

n

)

d

.

popleft

()

d

.

rotate

(

n

)

To implement deque slicing, use a similar approach applying
rotate() to bring a target element to the left side of the deque. Remove
old entries with popleft(), add new entries with extend(), and then
reverse the rotation.
With minor variations on that approach, it is easy to implement Forth style
stack manipulations such as dup, drop, swap, over, pick,
rot, and roll.

defaultdict

objects

class collections.defaultdict

(

default_factory=None, /

[

, …

]

)

Return a new dictionary-like object. defaultdict is a subclass of the
built-in dict class. It overrides one method and adds one writable
instance variable. The remaining functionality is the same as for the
dict class and is not documented here.

The first argument provides the initial value for the default_factory
attribute; it defaults to None. All remaining arguments are treated the same
as if they were passed to the dict constructor, including keyword
arguments.

defaultdict objects support the following method in addition to the
standard dict operations:

__missing__

(

key

)

If the default_factory attribute is None, this raises a
KeyError exception with the key as argument.

If default_factory is not None, it is called without arguments
to provide a default value for the given key, this value is inserted in
the dictionary for the key, and returned.

If calling default_factory raises an exception this exception is
propagated unchanged.

This method is called by the __getitem__() method of the
dict class when the requested key is not found; whatever it
returns or raises is then returned or raised by __getitem__().

Note that __missing__() is not called for any operations besides
__getitem__(). This means that get() will, like normal
dictionaries, return None as a default rather than using
default_factory.

defaultdict objects support the following instance variable:

default_factory

This attribute is used by the __missing__() method; it is
initialized from the first argument to the constructor, if present, or to
None, if absent.

Changed in version 3.9: Added merge (|) and update (|=) operators, specified in
PEP 584.

defaultdict

Examples

Using list as the default_factory, it is easy to group a
sequence of key-value pairs into a dictionary of lists:

>>>

s

=

[(

'yellow'

,

1

),

(

'blue'

,

2

),

(

'yellow'

,

3

),

(

'blue'

,

4

),

(

'red'

,

1

)]

>>>

d

=

defaultdict

(

list

)

>>>

for

k

,

v

in

s

:

...

d

[

k

]

.

append

(

v

)

...

>>>

sorted

(

d

.

items

())

[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]

When each key is encountered for the first time, it is not already in the
mapping; so an entry is automatically created using the default_factory
function which returns an empty list. The list.append()
operation then attaches the value to the new list. When keys are encountered
again, the look-up proceeds normally (returning the list for that key) and the
list.append() operation adds another value to the list. This technique is
simpler and faster than an equivalent technique using dict.setdefault():

>>>

d

=

{}

>>>

for

k

,

v

in

s

:

...

d

.

setdefault

(

k

,

[])

.

append

(

v

)

...

>>>

sorted

(

d

.

items

())

[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]

Setting the default_factory to int makes the
defaultdict useful for counting (like a bag or multiset in other
languages):

>>>

s

=

'mississippi'

>>>

d

=

defaultdict

(

int

)

>>>

for

k

in

s

:

...

d

[

k

]

+=

1

...

>>>

sorted

(

d

.

items

())

[('i', 4), ('m', 1), ('p', 2), ('s', 4)]

When a letter is first encountered, it is missing from the mapping, so the
default_factory function calls int() to supply a default count of
zero. The increment operation then builds up the count for each letter.

The function int() which always returns zero is just a special case of
constant functions. A faster and more flexible way to create constant functions
is to use a lambda function which can supply any constant value (not just
zero):

>>>

def

constant_factory

(

value

):

...

return

lambda

:

value

>>>

d

=

defaultdict

(

constant_factory

(

'<missing>'

))

>>>

d

.

update

(

name

=

'John'

,

action

=

'ran'

)

>>>

'

%(name)s

%(action)s

to

%(object)s

'

%

d

'John ran to <missing>'

Setting the default_factory to set makes the
defaultdict useful for building a dictionary of sets:

>>>

s

=

[(

'red'

,

1

),

(

'blue'

,

2

),

(

'red'

,

3

),

(

'blue'

,

4

),

(

'red'

,

1

),

(

'blue'

,

4

)]

>>>

d

=

defaultdict

(

set

)

>>>

for

k

,

v

in

s

:

...

d

[

k

]

.

add

(

v

)

...

>>>

sorted

(

d

.

items

())

[('blue', {2, 4}), ('red', {1, 3})]

namedtuple()

Factory Function for Tuples with Named Fields

Named tuples assign meaning to each position in a tuple and allow for more readable,
self-documenting code. They can be used wherever regular tuples are used, and
they add the ability to access fields by name instead of position index.

collections.namedtuple

(

typename

,

field_names

,

*

,

rename

=

False

,

defaults

=

None

,

module

=

None

)

Returns a new tuple subclass named typename. The new subclass is used to
create tuple-like objects that have fields accessible by attribute lookup as
well as being indexable and iterable. Instances of the subclass also have a
helpful docstring (with typename and field_names) and a helpful __repr__()
method which lists the tuple contents in a name=value format.

The field_names are a sequence of strings such as ['x', 'y'].
Alternatively, field_names can be a single string with each fieldname
separated by whitespace and/or commas, for example 'x y' or 'x, y'.

Any valid Python identifier may be used for a fieldname except for names
starting with an underscore. Valid identifiers consist of letters, digits,
and underscores but do not start with a digit or underscore and cannot be
a keyword such as class, for, return, global, pass,
or raise.

If rename is true, invalid fieldnames are automatically replaced
with positional names. For example, ['abc', 'def', 'ghi', 'abc'] is
converted to ['abc', '_1', 'ghi', '_3'], eliminating the keyword
def and the duplicate fieldname abc.

defaults can be None or an iterable of default values.
Since fields with a default value must come after any fields without a
default, the defaults are applied to the rightmost parameters. For
example, if the fieldnames are ['x', 'y', 'z'] and the defaults are
(1, 2), then x will be a required argument, y will default to
1, and z will default to 2.

If module is defined, the __module__ attribute of the named tuple is
set to that value.

Named tuple instances do not have per-instance dictionaries, so they are
lightweight and require no more memory than regular tuples.

To support pickling, the named tuple class should be assigned to a variable
that matches typename.

Changed in version 3.1: Added support for rename.

Changed in version 3.6: The verbose and rename parameters became
keyword-only arguments.

Changed in version 3.6: Added the module parameter.

Changed in version 3.7: Removed the verbose parameter and the _source attribute.

Changed in version 3.7: Added the defaults parameter and the _field_defaults
attribute.

>>>

# Basic example

>>>

Point

=

namedtuple

(

'Point'

,

[

'x'

,

'y'

])

>>>

p

=

Point

(

11

,

y

=

22

)

# instantiate with positional or keyword arguments

>>>

p

[

]

+

p

[

1

]

# indexable like the plain tuple (11, 22)

33

>>>

x

,

y

=

p

# unpack like a regular tuple

>>>

x

,

y

(11, 22)

>>>

p

.

x

+

p

.

y

# fields also accessible by name

33

>>>

p

# readable __repr__ with a name=value style

Point(x=11, y=22)

Named tuples are especially useful for assigning field names to result tuples returned
by the csv or sqlite3 modules:

EmployeeRecord

=

namedtuple

(

'EmployeeRecord'

,

'name, age, title, department, paygrade'

)

import

csv

for

emp

in

map

(

EmployeeRecord

.

_make

,

csv

.

reader

(

open

(

"employees.csv"

,

"rb"

))):

print

(

emp

.

name

,

emp

.

title

)

import

sqlite3

conn

=

sqlite3

.

connect

(

'/companydata'

)

cursor

=

conn

.

cursor

()

cursor

.

execute

(

'SELECT name, age, title, department, paygrade FROM employees'

)

for

emp

in

map

(

EmployeeRecord

.

_make

,

cursor

.

fetchall

()):

print

(

emp

.

name

,

emp

.

title

)

In addition to the methods inherited from tuples, named tuples support
three additional methods and two attributes. To prevent conflicts with
field names, the method and attribute names start with an underscore.

classmethod somenamedtuple._make

(

iterable

)

Class method that makes a new instance from an existing sequence or iterable.

>>>

t

=

[

11

,

22

]

>>>

Point

.

_make

(

t

)

Point(x=11, y=22)

somenamedtuple._asdict

(

)

Return a new dict which maps field names to their corresponding
values:

>>>

p

=

Point

(

x

=

11

,

y

=

22

)

>>>

p

.

_asdict

()

{'x': 11, 'y': 22}

Changed in version 3.1: Returns an OrderedDict instead of a regular dict.

Changed in version 3.8: Returns a regular dict instead of an OrderedDict.
As of Python 3.7, regular dicts are guaranteed to be ordered. If the
extra features of OrderedDict are required, the suggested
remediation is to cast the result to the desired type:
OrderedDict(nt._asdict()).

somenamedtuple._replace

(

**

kwargs

)

Return a new instance of the named tuple replacing specified fields with new
values:

>>>

p

=

Point

(

x

=

11

,

y

=

22

)

>>>

p

.

_replace

(

x

=

33

)

Point(x=33, y=22)

>>>

for

partnum

,

record

in

inventory

.

items

():

...

inventory

[

partnum

]

=

record

.

_replace

(

price

=

newprices

[

partnum

],

timestamp

=

time

.

now

())

somenamedtuple._fields

Tuple of strings listing the field names. Useful for introspection
and for creating new named tuple types from existing named tuples.

>>>

p

.

_fields

# view the field names

('x', 'y')

>>>

Color

=

namedtuple

(

'Color'

,

'red green blue'

)

>>>

Pixel

=

namedtuple

(

'Pixel'

,

Point

.

_fields

+

Color

.

_fields

)

>>>

Pixel

(

11

,

22

,

128

,

255

,

)

Pixel(x=11, y=22, red=128, green=255, blue=0)

somenamedtuple._field_defaults

Dictionary mapping field names to default values.

>>>

Account

=

namedtuple

(

'Account'

,

[

'type'

,

'balance'

],

defaults

=

[

])

>>>

Account

.

_field_defaults

{'balance': 0}

>>>

Account

(

'premium'

)

Account(type='premium', balance=0)

To retrieve a field whose name is stored in a string, use the getattr()
function:

>>>

getattr

(

p

,

'x'

)

11

To convert a dictionary to a named tuple, use the double-star-operator
(as described in Unpacking Argument Lists):

>>>

d

=

{

'x'

:

11

,

'y'

:

22

}

>>>

Point

(

**

d

)

Point(x=11, y=22)

Since a named tuple is a regular Python class, it is easy to add or change
functionality with a subclass. Here is how to add a calculated field and
a fixed-width print format:

>>>

class

Point

(

namedtuple

(

'Point'

,

[

'x'

,

'y'

])):

...

__slots__

=

()

...

@property

...

def

hypot

(

self

):

...

return

(

self

.

x

**

2

+

self

.

y

**

2

)

**

0.5

...

def

__str__

(

self

):

...

return

'Point: x=

%6.3f

y=

%6.3f

hypot=

%6.3f

'

%

(

self

.

x

,

self

.

y

,

self

.

hypot

)

>>>

for

p

in

Point

(

3

,

4

),

Point

(

14

,

5

/

7

):

...

print

(

p

)

Point: x= 3.000 y= 4.000 hypot= 5.000

Point: x=14.000 y= 0.714 hypot=14.018

The subclass shown above sets __slots__ to an empty tuple. This helps
keep memory requirements low by preventing the creation of instance dictionaries.

Subclassing is not useful for adding new, stored fields. Instead, simply
create a new named tuple type from the _fields attribute:

>>>

Point3D

=

namedtuple

(

'Point3D'

,

Point

.

_fields

+

(

'z'

,))

Docstrings can be customized by making direct assignments to the __doc__
fields:

>>>

Book

=

namedtuple

(

'Book'

,

[

'id'

,

'title'

,

'authors'

])

>>>

Book

.

__doc__

+=

': Hardcover book in active collection'

>>>

Book

.

id

.

__doc__

=

'13-digit ISBN'

>>>

Book

.

title

.

__doc__

=

'Title of first printing'

>>>

Book

.

authors

.

__doc__

=

'List of authors sorted by last name'

Changed in version 3.5: Property docstrings became writeable.

See also

  • See typing.NamedTuple for a way to add type hints for named
    tuples. It also provides an elegant notation using the class
    keyword:

    class

    Component

    (

    NamedTuple

    ):

    part_number

    :

    int

    weight

    :

    float

    description

    :

    Optional

    [

    str

    ]

    =

    None

  • See types.SimpleNamespace() for a mutable namespace based on an
    underlying dictionary instead of a tuple.

  • The dataclasses module provides a decorator and functions for
    automatically adding generated special methods to user-defined classes.

OrderedDict

objects

Ordered dictionaries are just like regular dictionaries but have some extra
capabilities relating to ordering operations. They have become less
important now that the built-in dict class gained the ability
to remember insertion order (this new behavior became guaranteed in
Python 3.7).

Some differences from dict still remain:

  • The regular dict was designed to be very good at mapping
    operations. Tracking insertion order was secondary.

  • The OrderedDict was designed to be good at reordering operations.
    Space efficiency, iteration speed, and the performance of update
    operations were secondary.

  • Algorithmically, OrderedDict can handle frequent reordering
    operations better than dict. This makes it suitable for tracking
    recent accesses (for example in an LRU cache).

  • The equality operation for OrderedDict checks for matching order.

  • The popitem() method of OrderedDict has a different
    signature. It accepts an optional argument to specify which item is popped.

  • OrderedDict has a move_to_end() method to
    efficiently reposition an element to an endpoint.

  • Until Python 3.8, dict lacked a __reversed__() method.

class collections.OrderedDict

(

[

items

]

)

Return an instance of a dict subclass that has methods
specialized for rearranging dictionary order.

New in version 3.1.

popitem

(

last

=

True

)

The popitem() method for ordered dictionaries returns and removes a
(key, value) pair. The pairs are returned in
LIFO order if last is true
or FIFO order if false.

move_to_end

(

key

,

last

=

True

)

Move an existing key to either end of an ordered dictionary. The item
is moved to the right end if last is true (the default) or to the
beginning if last is false. Raises KeyError if the key does
not exist:

>>>

d

=

OrderedDict

.

fromkeys

(

'abcde'

)

>>>

d

.

move_to_end

(

'b'

)

>>>

''

.

join

(

d

.

keys

())

'acdeb'

>>>

d

.

move_to_end

(

'b'

,

last

=

False

)

>>>

''

.

join

(

d

.

keys

())

'bacde'

New in version 3.2.

In addition to the usual mapping methods, ordered dictionaries also support
reverse iteration using reversed().

Equality tests between OrderedDict objects are order-sensitive
and are implemented as list(od1.items())==list(od2.items()).
Equality tests between OrderedDict objects and other
Mapping objects are order-insensitive like regular
dictionaries. This allows OrderedDict objects to be substituted
anywhere a regular dictionary is used.

Changed in version 3.5: The items, keys, and values views
of OrderedDict now support reverse iteration using reversed().

Changed in version 3.6: With the acceptance of PEP 468, order is retained for keyword arguments
passed to the OrderedDict constructor and its update()
method.

Changed in version 3.9: Added merge (|) and update (|=) operators, specified in PEP 584.

OrderedDict

Examples and Recipes

It is straightforward to create an ordered dictionary variant
that remembers the order the keys were last inserted.
If a new entry overwrites an existing entry, the
original insertion position is changed and moved to the end:

class

LastUpdatedOrderedDict

(

OrderedDict

):

'Store items in the order the keys were last added'

def

__setitem__

(

self

,

key

,

value

):

super

()

.

__setitem__

(

key

,

value

)

self

.

move_to_end

(

key

)

An OrderedDict would also be useful for implementing
variants of functools.lru_cache():

from

time

import

time

class

TimeBoundedLRU

:

"LRU Cache that invalidates and refreshes old entries."

def

__init__

(

self

,

func

,

maxsize

=

128

,

maxage

=

30

):

self

.

cache

=

OrderedDict

()

# { args : (timestamp, result)}

self

.

func

=

func

self

.

maxsize

=

maxsize

self

.

maxage

=

maxage

def

__call__

(

self

,

*

args

):

if

args

in

self

.

cache

:

self

.

cache

.

move_to_end

(

args

)

timestamp

,

result

=

self

.

cache

[

args

]

if

time

()

-

timestamp

<=

self

.

maxage

:

return

result

result

=

self

.

func

(

*

args

)

self

.

cache

[

args

]

=

time

(),

result

if

len

(

self

.

cache

)

>

self

.

maxsize

:

self

.

cache

.

popitem

(

)

return

result

class

MultiHitLRUCache

:

""" LRU cache that defers caching a result until

it has been requested multiple times.

To avoid flushing the LRU cache with one-time requests,

we don't cache until a request has been made more than once.

"""

def

__init__

(

self

,

func

,

maxsize

=

128

,

maxrequests

=

4096

,

cache_after

=

1

):

self

.

requests

=

OrderedDict

()

# { uncached_key : request_count }

self

.

cache

=

OrderedDict

()

# { cached_key : function_result }

self

.

func

=

func

self

.

maxrequests

=

maxrequests

# max number of uncached requests

self

.

maxsize

=

maxsize

# max number of stored return values

self

.

cache_after

=

cache_after

def

__call__

(

self

,

*

args

):

if

args

in

self

.

cache

:

self

.

cache

.

move_to_end

(

args

)

return

self

.

cache

[

args

]

result

=

self

.

func

(

*

args

)

self

.

requests

[

args

]

=

self

.

requests

.

get

(

args

,

)

+

1

if

self

.

requests

[

args

]

<=

self

.

cache_after

:

self

.

requests

.

move_to_end

(

args

)

if

len

(

self

.

requests

)

>

self

.

maxrequests

:

self

.

requests

.

popitem

(

)

else

:

self

.

requests

.

pop

(

args

,

None

)

self

.

cache

[

args

]

=

result

if

len

(

self

.

cache

)

>

self

.

maxsize

:

self

.

cache

.

popitem

(

)

return

result

UserDict

objects

The class, UserDict acts as a wrapper around dictionary objects.
The need for this class has been partially supplanted by the ability to
subclass directly from dict; however, this class can be easier
to work with because the underlying dictionary is accessible as an
attribute.

class collections.UserDict

(

[

initialdata

]

)

Class that simulates a dictionary. The instance’s contents are kept in a
regular dictionary, which is accessible via the data attribute of
UserDict instances. If initialdata is provided, data is
initialized with its contents; note that a reference to initialdata will not
be kept, allowing it to be used for other purposes.

In addition to supporting the methods and operations of mappings,
UserDict instances provide the following attribute:

data

A real dictionary used to store the contents of the UserDict
class.

UserList

objects

This class acts as a wrapper around list objects. It is a useful base class
for your own list-like classes which can inherit from them and override
existing methods or add new ones. In this way, one can add new behaviors to
lists.

The need for this class has been partially supplanted by the ability to
subclass directly from list; however, this class can be easier
to work with because the underlying list is accessible as an attribute.

class collections.UserList

(

[

list

]

)

Class that simulates a list. The instance’s contents are kept in a regular
list, which is accessible via the data attribute of UserList
instances. The instance’s contents are initially set to a copy of list,
defaulting to the empty list []. list can be any iterable, for
example a real Python list or a UserList object.

In addition to supporting the methods and operations of mutable sequences,
UserList instances provide the following attribute:

data

A real list object used to store the contents of the
UserList class.

Subclassing requirements: Subclasses of UserList are expected to
offer a constructor which can be called with either no arguments or one
argument. List operations which return a new sequence attempt to create an
instance of the actual implementation class. To do so, it assumes that the
constructor can be called with a single parameter, which is a sequence object
used as a data source.

If a derived class does not wish to comply with this requirement, all of the
special methods supported by this class will need to be overridden; please
consult the sources for information about the methods which need to be provided
in that case.

UserString

objects

The class, UserString acts as a wrapper around string objects.
The need for this class has been partially supplanted by the ability to
subclass directly from str; however, this class can be easier
to work with because the underlying string is accessible as an
attribute.

class collections.UserString

(

seq

)

Class that simulates a string object. The instance’s
content is kept in a regular string object, which is accessible via the
data attribute of UserString instances. The instance’s
contents are initially set to a copy of seq. The seq argument can
be any object which can be converted into a string using the built-in
str() function.

In addition to supporting the methods and operations of strings,
UserString instances provide the following attribute:

data

A real str object used to store the contents of the
UserString class.

Changed in version 3.5: New methods __getnewargs__, __rmod__, casefold,
format_map, isprintable, and maketrans.

[NEW] Count set bits in an integer | counter zero – Sambeauty

 

Write an efficient program to count the number of 1s in the binary representation of an integer.

Examples : 

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Input : n = 6
Output : 2
Binary representation of 6 is 110 and has 2 set bits

Input : n = 13
Output : 3
Binary representation of 13 is 1101 and has 3 set bits

 

setbit

 

 

1. Simple Method Loop through all bits in an integer, check if a bit is set and if it is, then increment the set bit count. See the program below. 

C++

#include <bits/stdc++.h>

using namespace std;

 

unsigned int countSetBits(unsigned int n)

{

    unsigned int count = 0;

    while (n) {

        count += n & 1;

        n >>= 1;

    }

    return count;

}

 

int main()

{

    int i = 9;

    cout << countSetBits(i);

    return 0;

}

 

 
 

C

#include <stdio.h>

 

   

unsigned int countSetBits(unsigned int n)

{

    unsigned int count = 0;

    while (n) {

        count += n & 1;

        n >>= 1;

    }

    return count;

}

 

int main()

{

    int i = 9;

    printf("%d", countSetBits(i));

    return 0;

}

 
 

Java

import java.io.*;

 

class countSetBits {

    

    

    

    static int countSetBits(int n)

    {

        int count = ;

        while (n > ) {

            count += n & 1;

            n >>= 1;

        }

        return count;

    }

 

    

    public static void main(String args[])

    {

        int i = 9;

        System.out.println(countSetBits(i));

    }

}

 

 
 

Python3

 

def  countSetBits(n):

    count =

    while (n):

        count += n & 1

        n >>= 1

    return count

 

 

i = 9

print(countSetBits(i))

 

 
 

C#

using System;

 

class GFG {

    

    

    

    static int countSetBits(int n)

    {

        int count = 0;

        while (n > 0) {

            count += n & 1;

            n >>= 1;

        }

        return count;

    }

 

    

    public static void Main()

    {

        int i = 9;

        Console.Write(countSetBits(i));

    }

}

 

 
 

PHP

<?php

 

function countSetBits($n)

{

    $count = 0;

    while ($n)

    {

        $count += $n & 1;

        $n >>= 1;

    }

    return $count;

}

 

$i = 9;

echo countSetBits($i);

 

?>

 
 

Javascript

<script>

   

   

 

   

   

   function countSetBits(n)

   {

     var count = 0;

     while (n)

     {

       count += n & 1;

       n >>= 1;

     }

     return count;

   }

 

   

   var i = 9;

   document.write(countSetBits(i));

 

     

 </script>

 
 

Output : 

2

Time Complexity: Θ(logn) (Theta of logn)

Auxiliary Space: O(1)

Recursive Approach:  

C++

#include <bits/stdc++.h>

using namespace std;

 

int countSetBits(int n)

{

    

    if (n == 0)

        return 0;

 

    else

 

        

        return (n & 1) + countSetBits(n >> 1);

}

 

int main()

{

    

    int n = 9;

 

    

    cout << countSetBits(n);

 

    return 0;

}

 

 
 

Java

import java.io.*;

 

class GFG {

 

    

    public static int countSetBits(int n)

    {

 

        

        if (n == )

            return ;

 

        else

 

            

            return (n & 1) + countSetBits(n >> 1);

    }

 

    

    public static void main(String[] args)

    {

 

        

        int n = 9;

 

        

        System.out.println(countSetBits(n));

    }

}

 

 
 

Python3

 

def countSetBits( n):

     

    

    if (n == ):

        return

 

    else:

 

        

        

        return (n & 1) + countSetBits(n >> 1)

         

n = 9

 

print( countSetBits(n))    

         

 
 

C#

using System;

 

class GFG {

 

    

    

    public static int countSetBits(int n)

    {

 

        

        if (n == 0)

            return 0;

 

        else

 

            

            

            return (n & 1) + countSetBits(n >> 1);

    }

 

    

    static public void Main()

    {

 

        

        

        int n = 9;

 

        

        Console.WriteLine(countSetBits(n));

    }

}

 

 
 

PHP

<?php

 

function countSetBits($n)

{

    

    if ($n == 0)

        return 0;

 

    else

 

        

        

        return ($n & 1) +

                countSetBits($n >> 1);

}

 

 

$n = 9;

 

echo countSetBits($n);

 

?>

 
 

Javascript

<script>

 

 

function countSetBits(n)

{

 

    

    if (n == 0)

        return 0;

 

    else

 

        

        return (n & 1) + countSetBits(n >> 1);

}

 

 

    

    let n = 9;

 

    

    document.write(countSetBits(n));

 

 

</script>

 
 

Output : 

2

2. Brian Kernighan’s Algorithm: 
Subtracting 1 from a decimal number flips all the bits after the rightmost set bit(which is 1) including the rightmost set bit. 
for example : 
10 in binary is 00001010 
9 in binary is 00001001 
8 in binary is 00001000 
7 in binary is 00000111 
So if we subtract a number by 1 and do it bitwise & with itself (n & (n-1)), we unset the rightmost set bit. If we do n & (n-1) in a loop and count the number of times the loop executes, we get the set bit count. 
The beauty of this solution is the number of times it loops is equal to the number of set bits in a given integer. 

   1  Initialize count: = 0
   2  If integer n is not zero
      (a) Do bitwise & with (n-1) and assign the value back to n
          n: = n&(n-1)
      (b) Increment count by 1
      (c) go to step 2
   3  Else return count

Implementation of Brian Kernighan’s Algorithm:  

C++

#include <iostream>

using namespace std;

class gfg {

    

public:

    unsigned int countSetBits(int n)

    {

        unsigned int count = 0;

        while (n) {

            n &= (n - 1);

            count++;

        }

        return count;

    }

};

int main()

{

    gfg g;

    int i = 9;

    cout << g.countSetBits(i);

    return 0;

}

 
 

C

#include <stdio.h>

 

   

unsigned int countSetBits(int n)

{

    unsigned int count = 0;

    while (n) {

        n &= (n - 1);

        count++;

    }

    return count;

}

 

int main()

{

    int i = 9;

    printf("%d", countSetBits(i));

    getchar();

    return 0;

}

 
 

Java

import java.io.*;

 

class countSetBits {

    

    

    

    static int countSetBits(int n)

    {

        int count = ;

        while (n > ) {

            n &= (n - 1);

            count++;

        }

        return count;

    }

 

    

    public static void main(String args[])

    {

        int i = 9;

        System.out.println(countSetBits(i));

    }

}

 

 
 

Python3

def countSetBits(n):

 

    count =

    while (n):

        n &= (n-1)

        count+= 1

     

    return count

 

 

i = 9

print(countSetBits(i))

  

 
 

C#

using System;

 

class GFG {

 

    

    

    

    static int countSetBits(int n)

    {

        int count = 0;

        while (n > 0) {

            n &= (n - 1);

            count++;

        }

        return count;

    }

 

    

    static public void Main()

    {

        int i = 9;

        Console.WriteLine(countSetBits(i));

    }

}

 

 
 

PHP

<?php

 

function countSetBits($n)

{

    $count = 0;

    while ($n)

    {

    $n &= ($n - 1) ;

    $count++;

    }

    return $count;

}

 

$i = 9;

echo countSetBits($i);

 

?>

 
 

Javascript

<script>

 

 

 

function countSetBits(n)

{

    var count = 0;

    while (n > 0)

    {

        n &= (n - 1);

        count++;

    }

    return count;

}

 

var i = 9;

document.write(countSetBits(i));

 

 

</script>

 
 

Output : 

2

Example for Brian Kernighan’s Algorithm:  

   n =  9 (1001)
   count = 0

   Since 9 > 0, subtract by 1 and do bitwise & with (9-1)
   n = 9&8  (1001 & 1000)
   n = 8
   count  = 1

   Since 8 > 0, subtract by 1 and do bitwise & with (8-1)
   n = 8&7  (1000 & 0111)
   n = 0
   count = 2

   Since n = 0, return count which is 2 now.

Time Complexity: O(logn)

Recursive Approach:  

C++

#include <bits/stdc++.h>

using namespace std;

 

int countSetBits(int n)

{

    

    if (n == 0)

        return 0;

    else

        return 1 + countSetBits(n & (n - 1));

}

 

int main()

{

    

    int n = 9;

 

    

    cout << countSetBits(n);

 

    return 0;

}

 

 
 

Java

import java.io.*;

 

class GFG {

 

    

    public static int countSetBits(int n)

    {

 

        

        if (n == )

            return ;

        else

            return 1 + countSetBits(n & (n - 1));

    }

 

    

    public static void main(String[] args)

    {

 

        

        int n = 9;

 

        

        System.out.println(countSetBits(n));

    }

}

 

 
 

Python3

 

def countSetBits(n):

 

    

    if (n == ):

        return

    else:

        return 1 + countSetBits(n & (n - 1))

             

             

n = 9

     

print(countSetBits(n))

 

 
 

C#

using System;

 

class GFG {

 

    

    

    public static int countSetBits(int n)

    {

 

        

        if (n == 0)

            return 0;

        else

            return 1 + countSetBits(n & (n - 1));

    }

 

    

    static public void Main()

    {

 

        

        int n = 9;

 

        

        Console.WriteLine(countSetBits(n));

    }

}

 

 
 

PHP

<?php

 

function countSetBits($n)

{

    

    if ($n == 0)

        return 0;

    else

        return 1 +

          countSetBits($n &

                      ($n - 1));

}

 

 

$n = 9;

 

echo countSetBits($n);

     

?>

 
 

Javascript

<script>

 

 

function countSetBits(n)

{

    

    if (n == 0)

        return 0;

    else

        return 1 + countSetBits(n & (n - 1));

}

 

 

var n = 9;

 

document.write(countSetBits(n));

 

</script>

 
 

Output : 

2

3. Using Lookup table: We can count bits in O(1) time using the lookup table.
Below is the implementation of the above approach:

C++

#include <bits/stdc++.h>

using namespace std;

 

int BitsSetTable256[256];

 

void initialize()

{

 

    

    

    BitsSetTable256[0] = 0;

    for (int i = 0; i < 256; i++)

    {

        BitsSetTable256[i] = (i & 1) +

        BitsSetTable256[i / 2];

    }

}

 

int countSetBits(int n)

{

    return (BitsSetTable256[n & 0xff] +

            BitsSetTable256[(n >> 8) & 0xff] +

            BitsSetTable256[(n >> 16) & 0xff] +

            BitsSetTable256[n >> 24]);

}

 

int main()

{

    

    initialize();

    int n = 9;

    cout << countSetBits(n);

}

 

 
 

Java

class GFG {

 

    

    static int[] BitsSetTable256 = new int[256];

 

    

    public static void initialize()

    {

 

        

        

        BitsSetTable256[] = ;

        for (int i = ; i < 256; i++) {

            BitsSetTable256[i] = (i & 1) + BitsSetTable256[i / 2];

        }

    }

 

    

    

    public static int countSetBits(int n)

    {

        return (BitsSetTable256[n & 0xff]

                + BitsSetTable256[(n >> 8) & 0xff]

                + BitsSetTable256[(n >> 16) & 0xff]

                + BitsSetTable256[n >> 24]);

    }

 

    

    public static void main(String[] args)

    {

 

        

        initialize();

        int n = 9;

        System.out.print(countSetBits(n));

    }

}

 
 

Python

BitsSetTable256 = [] * 256

 

def initialize():

     

    

    

    BitsSetTable256[] =

    for i in range(256):

        BitsSetTable256[i] = (i & 1) + BitsSetTable256[i // 2]

 

def countSetBits(n):

    return (BitsSetTable256[n & 0xff] +

            BitsSetTable256[(n >> 8) & 0xff] +

            BitsSetTable256[(n >> 16) & 0xff] +

            BitsSetTable256[n >> 24])

 

 

initialize()

n = 9

print(countSetBits(n))

 

 
 

C#

using System;

using System.Collections.Generic;

 

class GFG

{

 

    

    static int[] BitsSetTable256 = new int[256];

 

    

    public static void initialize()

    {

 

        

        

        BitsSetTable256[0] = 0;

        for (int i = 0; i < 256; i++)

        {

            BitsSetTable256[i] = (i & 1) + BitsSetTable256[i / 2];

        }

    }

 

    

    

    public static int countSetBits(int n)

    {

        return (BitsSetTable256[n & 0xff]

                + BitsSetTable256[(n >> 8) & 0xff]

                + BitsSetTable256[(n >> 16) & 0xff]

                + BitsSetTable256[n >> 24]);

    }

 

    

    public static void Main(String[] args)

    {

 

        

        initialize();

        int n = 9;

        Console.Write(countSetBits(n));

    }

}

 

 
 

Javascript

<script>

 

 

var BitsSetTable256 = Array.from({length: 256}, (_, i) => 0);

 

function initialize()

{

 

    

    

    BitsSetTable256[0] = 0;

    for (var i = 0; i < 256; i++) {

        BitsSetTable256[i] = (i & 1) +

        BitsSetTable256[parseInt(i / 2)];

    }

}

 

function countSetBits(n)

{

    return (BitsSetTable256[n & 0xff]

            + BitsSetTable256[(n >> 8) & 0xff]

            + BitsSetTable256[(n >> 16) & 0xff]

            + BitsSetTable256[n >> 24]);

}

 

 

initialize();

var n = 9;

document.write(countSetBits(n));

 

 

</script>

 
 

Output: 

2

 

We can find one use of counting set bits at Count number of bits to be flipped to convert A to B
Note: In GCC, we can directly count set bits using __builtin_popcount(). So we can avoid a separate function for counting set bits. 

C++

#include <iostream>

using namespace std;

 

int main()

{

    cout << __builtin_popcount(4) << endl;

    cout << __builtin_popcount(15);

 

    return 0;

}

 
 

Java

 

import java.io.*;

 

class GFG {

 

    

    public static void main(String[] args)

    {

 

        System.out.println(Integer.bitCount(4));

        System.out.println(Integer.bitCount(15));

    }

}

 

 
 

Python3

 

print(bin(4).count('1'));

print(bin(15).count('1'));

 

 
 

C#

using System;

using System.Linq;

 

class GFG {

 

    

    public static void Main()

    {

 

        Console.WriteLine(Convert.ToString(4, 2).Count(c = > c == '1'));

        Console.WriteLine(Convert.ToString(15, 2).Count(c = > c == '1'));

    }

}

 

 
 

PHP

<?php

 

$t = log10(4);

$x = log(15, 2);

$tt = ceil($t);

$xx = ceil($x);

 

echo ($tt), "\n";

echo ($xx), "\n";

 

?>

 
 

Javascript

<script>

 

 

document.write((4).toString(2).split('').

  filter(x => x == '1').length + "<br>");

document.write((15).toString(2).split('').

  filter(x => x == '1').length);

 

</script>

 
 

Output : 

1
4

4. Mapping numbers with the bit. It simply maintains a map(or array) of numbers to bits for a nibble. A Nibble contains 4 bits. So we need an array of up to 15. 
int num_to_bits[16] = {0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4}; 
Now we just need to get nibbles of a given long/int/word etc recursively.

C++

#include <bits/stdc++.h>

using namespace std;

 

int num_to_bits[16] = { 0, 1, 1, 2, 1, 2, 2, 3,

                        1, 2, 2, 3, 2, 3, 3, 4 };

 

unsigned int countSetBitsRec(unsigned int num)

{

    int nibble = 0;

    if (0 == num)

        return num_to_bits[0];

 

    

    nibble = num & 0xf;

 

    

    

    

    return num_to_bits[nibble] + countSetBitsRec(num >> 4);

}

 

int main()

{

    int num = 31;

    cout << countSetBitsRec(num);

    return 0;

}

 

 
 

C

#include <stdio.h>

 

int num_to_bits[16] = { 0, 1, 1, 2, 1, 2, 2, 3,

                        1, 2, 2, 3, 2, 3, 3, 4 };

 

  

unsigned int countSetBitsRec(unsigned int num)

{

    int nibble = 0;

    if (0 == num)

        return num_to_bits[0];

 

    

    nibble = num & 0xf;

 

    

    

    

    return num_to_bits[nibble] + countSetBitsRec(num >> 4);

}

 

int main()

{

    int num = 31;

    printf("%d\n", countSetBitsRec(num));

}

 
 

Java

 

class GFG {

    static int[] num_to_bits = new int[] { , 1, 1, 2, 1, 2, 2,

                                           3, 1, 2, 2, 3, 2, 3, 3, 4 };

 

    

    static int countSetBitsRec(int num)

    {

        int nibble = ;

        if ( == num)

            return num_to_bits[];

 

        

        nibble = num & 0xf;

 

        

        

        

        return num_to_bits[nibble] + countSetBitsRec(num >> 4);

    }

 

    

    public static void main(String[] args)

    {

        int num = 31;

        System.out.println(countSetBitsRec(num));

    }

}

 
 

Python3

 

num_to_bits =[, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4];

 

def countSetBitsRec(num):

    nibble = ;

    if( == num):

        return num_to_bits[];

     

    

    nibble = num & 0xf;

     

    

    

    

     

    return num_to_bits[nibble] + countSetBitsRec(num >> 4);

  

 

  

num = 31;

print(countSetBitsRec(num));

 

 

 
 

C#

 

class GFG {

    static int[] num_to_bits = new int[16] { 0, 1, 1, 2, 1, 2, 2,

                                             3, 1, 2, 2, 3, 2, 3, 3, 4 };

 

    

    static int countSetBitsRec(int num)

    {

        int nibble = 0;

        if (0 == num)

            return num_to_bits[0];

 

        

        nibble = num & 0xf;

 

        

        

        

        return num_to_bits[nibble] + countSetBitsRec(num >> 4);

    }

 

    

    static void Main()

    {

        int num = 31;

        System.Console.WriteLine(countSetBitsRec(num));

    }

}

 
 

PHP

<?php

 

$num_to_bits = array(0, 1, 1, 2, 1, 2, 2, 3,

                     1, 2, 2, 3, 2, 3, 3, 4);

 

function countSetBitsRec( $num)

{

    global $num_to_bits;

    $nibble = 0;

    if (0 == $num)

        return $num_to_bits[0];

     

    

    $nibble = $num & 0xf;

     

    

    

    

    return $num_to_bits[$nibble] +

           countSetBitsRec($num >> 4);

}

 

$num = 31;

echo (countSetBitsRec($num));

 

?>

 
 

Javascript

<script>

 

var num_to_bits =[ 0, 1, 1, 2, 1, 2, 2,

                   3, 1, 2, 2, 3, 2, 3, 3, 4 ];

 

function countSetBitsRec(num)

{

    var nibble = 0;

    if (0 == num)

        return num_to_bits[0];

 

    

    nibble = num & 0xf;

 

    

    

    

    return num_to_bits[nibble] + countSetBitsRec(num >> 4);

}

 

var num = 31;

document.write(countSetBitsRec(num));

 

 

</script>

 
 

Output : 

5

Time Complexity: O(log n), because we have log(16, n) levels of recursion.
Storage Complexity: O(1) Whether the given number is short, int, long, or long long we require an array of 16 sizes only, which is constant.

5. Checking each bit in a number: 

Each bit in the number is checked for whether it is set or not. The number is bitwise AND with powers of 2, so if the result is not equal to zero, we come to know that the particular bit in the position is set.

C

#include <stdio.h>

 

int countSetBits(int N)

{

    int count = 0;

   

    

    for (int i = 0; i < sizeof(int) * 8; i++) {

        if (N & (1 << i))

            count++;

    }

    return count;

}

 

int main()

{

    int N = 15;

 

    printf("%d", countSetBits(N));

    return 0;

}

 
 

C++

#include <iostream>

using namespace std;

 

int countSetBits(int N)

{

    int count = 0;

    

    for (int i = 0; i < sizeof(int) * 8; i++) {

        if (N & (1 << i))

            count++;

    }

    return count;

}

 

int main()

{

 

    int N = 15;

 

    cout << countSetBits(N) << endl;

    return 0;

}

 
 

Java

public class GFG

{

   

  

  

  static int countSetBits(int N)

  {

    int count = ;

    

    for (int i = ; i < 4 * 8; i++)

    {

      if ((N & (1 << i)) != )

        count++;

    }

    return count;

  }

 

  

  public static void main(String[] args)

  {

    int N = 15;

    System.out.println(countSetBits(N));

  }

}

 

 
 

Python3

def countSetBits(N):

  count =

 

  

  for i in range(4*8):

    if(N & (1 << i)):

      count += 1

      return count

 

    

    N = 15

    print(countSetBits(N))

 

    

 
 

C#

using System;

class GFG

{

 

  

  

  static int countSetBits(int N)

  {

    int count = 0;

 

    

    for (int i = 0; i < 4 * 8; i++)

    {

      if ((N & (1 << i)) != 0)

        count++;

    }

    return count;

  }

 

  

  static void Main()

  {

    int N = 15;

    Console.WriteLine(countSetBits(N));

  }

}

 

 
 

Javascript

<script>

   

  

  

  function countSetBits(N)

  {

      var count = 0;

    

    for (i = 0; i < 4 * 8; i++)

    {

        if ((N & (1 << i)) != 0)

        count++;

    }

    return count;

  }

 

  

  var N = 15;

  document.write(countSetBits(N));

 

</script>

 
 

Output

4

 

Count set bits in an integer Using Lookup Table

 

My Personal Notes


ME PASO EL CS:CONDITION ZERO EN EXPERTO #1 (de_dust)


Instagram: https://www.instagram.com/lucassmunir/
Twitch: https://www.twitch.tv/munirrrr_
QUE ONDA PERRIS VAMO A VER QUE ONDA CON ESTO PORQUE PERRI VOS TENES QUE ENTEDER QUE LOS PERRIS DE HOY EN DIA ANDAN EN ESTA MOVIDA PERRI DE LOS CUALES LOS PERRIS QUE MIRAN LOS PERRIS DE AHORA QUEDAN ATONITOS DE TAL LVL DE LOS PERRIS, PERO BUENO PERRIS, ESPERO SIGAN MIRANDO ESTO PERRIS SALUDOS PERRIS.

นอกจากการดูบทความนี้แล้ว คุณยังสามารถดูข้อมูลที่เป็นประโยชน์อื่นๆ อีกมากมายที่เราให้ไว้ที่นี่: ดูความรู้เพิ่มเติมที่นี่

ME PASO EL CS:CONDITION ZERO EN EXPERTO #1 (de_dust)

Counter-Strike CZ – Zombie Mod [CSO] – zm_tankard_cso – SISA Server


CounterStrike: Condition Zero Zombie CSO mod multiplayer gameplay on the custom zm tankard cso map
Previous Video ( CS:GO ) https://youtu.be/UMHkSdcPHJw
MAPPER
Creator of the map: ?
SERVER
Name: SISA 3 CZ
IP: 83.222.116.228:27016
Weapons Used
0:00 SPAS12 Deluxe
3:48 Flamethrower
More Zombie Mode
CS GO videos: https://www.youtube.com/playlist?list=PLwKF962A9VUhvh3mrO_qy9nve9WMzi4ga
CS Source videos: https://www.youtube.com/playlist?list=PLwKF962A9VUg43EQCpVxxLmgmH9yh5BM
Popular videos: https://www.youtube.com/playlist?list=PLwKF962A9VUhpzRRZ1EXwZOU1ixz7ouIF
How to play / join zombies servers you can find in axonek3 steam group:
http://steamcommunity.com/groups/axonek3
Video 1080p 60fps // PC Specs: Intel i73770K | Geforce GTX 750ti 2GB | 16GB Ram

Counter-Strike CZ - Zombie Mod [CSO] - zm_tankard_cso - SISA Server

Counter Strike Condition Zero | Hướng dẫn cài đặt game


Download :http://www.mediafire.com/file/acpchqleil36av6/Counter+Strike+Condition+Zero.rar
CD key : 58v2ecckcjb8vsemew9yacb2k
CHÚC CÁC BẠN THÀNH CÔNG
LIKE AND SUBSCRIBE
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Counter Strike Condition Zero | Hướng dẫn cài đặt game

Counter-Strike: Condition Zero (Assault) [CT]


CounterStrike: Condition Zero gameplay with bots

Counter-Strike: Condition Zero (Assault) [CT]

Counter-Strike: Condition Zero Deleted Scenes – Walkthrough Bonus 1 – Fastline


You are a Japnese Kidotai in the Shinkane Subway Station in Tokyo, Japan when the Yakuza terrorists bomb it.
Overview:
An explosion has occurred in the Shinkane Subway Station.
Objectives:
Locate terrorists.
Neutralize any opposition.
Bonus missions are only of the Steam version I believe.
My walkthrough for CounterStrike: Condition Zero Deleted Scenes, a singleplayer expansion to CounterStrike: Condition Zero. Condition Zero and the Deleted Scenes comes bundled with the original GoldSrc CounterStrike on Steam for $9.00. I don’t think you can have one without the other. BTW this is still GoldSrc, the HalfLife 1 engine.

Counter-Strike: Condition Zero Deleted Scenes - Walkthrough Bonus 1 - Fastline

นอกจากการดูบทความนี้แล้ว คุณยังสามารถดูข้อมูลที่เป็นประโยชน์อื่นๆ อีกมากมายที่เราให้ไว้ที่นี่: ดูวิธีอื่นๆWiki

ขอบคุณมากสำหรับการดูหัวข้อโพสต์ counter zero

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Nguyễn Huệ

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