doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
|---|---|
iterator.__next__()
Return the next item from the container. If there are no further items, raise the StopIteration exception. This method corresponds to the tp_iternext slot of the type structure for Python objects in the Python/C API. | python.library.stdtypes#iterator.__next__ |
itertools — Functions creating iterators for efficient looping This module implements a number of iterator building blocks inspired by constructs from APL, Haskell, and SML. Each has been recast in a form suitable for Python. The module standardizes a core set of fast, memory efficient tools that are useful by themselves or in combination. Together, they form an “iterator algebra” making it possible to construct specialized tools succinctly and efficiently in pure Python. For instance, SML provides a tabulation tool: tabulate(f) which produces a sequence f(0), f(1), .... The same effect can be achieved in Python by combining map() and count() to form map(f, count()). These tools and their built-in counterparts also work well with the high-speed functions in the operator module. For example, the multiplication operator can be mapped across two vectors to form an efficient dot-product: sum(map(operator.mul, vector1, vector2)). Infinite iterators:
Iterator Arguments Results Example
count() start, [step] start, start+step, start+2*step, … count(10) --> 10 11 12 13 14 ...
cycle() p p0, p1, … plast, p0, p1, … cycle('ABCD') --> A B C D A B C D ...
repeat() elem [,n] elem, elem, elem, … endlessly or up to n times repeat(10, 3) --> 10 10 10 Iterators terminating on the shortest input sequence:
Iterator Arguments Results Example
accumulate() p [,func] p0, p0+p1, p0+p1+p2, … accumulate([1,2,3,4,5]) --> 1 3 6 10 15
chain() p, q, … p0, p1, … plast, q0, q1, … chain('ABC', 'DEF') --> A B C D E F
chain.from_iterable() iterable p0, p1, … plast, q0, q1, … chain.from_iterable(['ABC', 'DEF']) --> A B C D E F
compress() data, selectors (d[0] if s[0]), (d[1] if s[1]), … compress('ABCDEF', [1,0,1,0,1,1]) --> A C E F
dropwhile() pred, seq seq[n], seq[n+1], starting when pred fails dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1
filterfalse() pred, seq elements of seq where pred(elem) is false filterfalse(lambda x: x%2, range(10)) --> 0 2 4 6 8
groupby() iterable[, key] sub-iterators grouped by value of key(v)
islice() seq, [start,] stop [, step] elements from seq[start:stop:step] islice('ABCDEFG', 2, None) --> C D E F G
starmap() func, seq func(*seq[0]), func(*seq[1]), … starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000
takewhile() pred, seq seq[0], seq[1], until pred fails takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4
tee() it, n it1, it2, … itn splits one iterator into n
zip_longest() p, q, … (p[0], q[0]), (p[1], q[1]), … zip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D- Combinatoric iterators:
Iterator Arguments Results
product() p, q, … [repeat=1] cartesian product, equivalent to a nested for-loop
permutations() p[, r] r-length tuples, all possible orderings, no repeated elements
combinations() p, r r-length tuples, in sorted order, no repeated elements
combinations_with_replacement() p, r r-length tuples, in sorted order, with repeated elements
Examples Results
product('ABCD', repeat=2) AA AB AC AD BA BB BC BD CA CB CC CD DA DB DC DD
permutations('ABCD', 2) AB AC AD BA BC BD CA CB CD DA DB DC
combinations('ABCD', 2) AB AC AD BC BD CD
combinations_with_replacement('ABCD', 2) AA AB AC AD BB BC BD CC CD DD Itertool functions The following module functions all construct and return iterators. Some provide streams of infinite length, so they should only be accessed by functions or loops that truncate the stream.
itertools.accumulate(iterable[, func, *, initial=None])
Make an iterator that returns accumulated sums, or accumulated results of other binary functions (specified via the optional func argument). If func is supplied, it should be a function of two arguments. Elements of the input iterable may be any type that can be accepted as arguments to func. (For example, with the default operation of addition, elements may be any addable type including Decimal or Fraction.) Usually, the number of elements output matches the input iterable. However, if the keyword argument initial is provided, the accumulation leads off with the initial value so that the output has one more element than the input iterable. Roughly equivalent to: def accumulate(iterable, func=operator.add, *, initial=None):
'Return running totals'
# accumulate([1,2,3,4,5]) --> 1 3 6 10 15
# accumulate([1,2,3,4,5], initial=100) --> 100 101 103 106 110 115
# accumulate([1,2,3,4,5], operator.mul) --> 1 2 6 24 120
it = iter(iterable)
total = initial
if initial is None:
try:
total = next(it)
except StopIteration:
return
yield total
for element in it:
total = func(total, element)
yield total
There are a number of uses for the func argument. It can be set to min() for a running minimum, max() for a running maximum, or operator.mul() for a running product. Amortization tables can be built by accumulating interest and applying payments. First-order recurrence relations can be modeled by supplying the initial value in the iterable and using only the accumulated total in func argument: >>> data = [3, 4, 6, 2, 1, 9, 0, 7, 5, 8]
>>> list(accumulate(data, operator.mul)) # running product
[3, 12, 72, 144, 144, 1296, 0, 0, 0, 0]
>>> list(accumulate(data, max)) # running maximum
[3, 4, 6, 6, 6, 9, 9, 9, 9, 9]
# Amortize a 5% loan of 1000 with 4 annual payments of 90
>>> cashflows = [1000, -90, -90, -90, -90]
>>> list(accumulate(cashflows, lambda bal, pmt: bal*1.05 + pmt))
[1000, 960.0, 918.0, 873.9000000000001, 827.5950000000001]
# Chaotic recurrence relation https://en.wikipedia.org/wiki/Logistic_map
>>> logistic_map = lambda x, _: r * x * (1 - x)
>>> r = 3.8
>>> x0 = 0.4
>>> inputs = repeat(x0, 36) # only the initial value is used
>>> [format(x, '.2f') for x in accumulate(inputs, logistic_map)]
['0.40', '0.91', '0.30', '0.81', '0.60', '0.92', '0.29', '0.79', '0.63',
'0.88', '0.39', '0.90', '0.33', '0.84', '0.52', '0.95', '0.18', '0.57',
'0.93', '0.25', '0.71', '0.79', '0.63', '0.88', '0.39', '0.91', '0.32',
'0.83', '0.54', '0.95', '0.20', '0.60', '0.91', '0.30', '0.80', '0.60']
See functools.reduce() for a similar function that returns only the final accumulated value. New in version 3.2. Changed in version 3.3: Added the optional func parameter. Changed in version 3.8: Added the optional initial parameter.
itertools.chain(*iterables)
Make an iterator that returns elements from the first iterable until it is exhausted, then proceeds to the next iterable, until all of the iterables are exhausted. Used for treating consecutive sequences as a single sequence. Roughly equivalent to: def chain(*iterables):
# chain('ABC', 'DEF') --> A B C D E F
for it in iterables:
for element in it:
yield element
classmethod chain.from_iterable(iterable)
Alternate constructor for chain(). Gets chained inputs from a single iterable argument that is evaluated lazily. Roughly equivalent to: def from_iterable(iterables):
# chain.from_iterable(['ABC', 'DEF']) --> A B C D E F
for it in iterables:
for element in it:
yield element
itertools.combinations(iterable, r)
Return r length subsequences of elements from the input iterable. The combination tuples are emitted in lexicographic ordering according to the order of the input iterable. So, if the input iterable is sorted, the combination tuples will be produced in sorted order. Elements are treated as unique based on their position, not on their value. So if the input elements are unique, there will be no repeat values in each combination. Roughly equivalent to: def combinations(iterable, r):
# combinations('ABCD', 2) --> AB AC AD BC BD CD
# combinations(range(4), 3) --> 012 013 023 123
pool = tuple(iterable)
n = len(pool)
if r > n:
return
indices = list(range(r))
yield tuple(pool[i] for i in indices)
while True:
for i in reversed(range(r)):
if indices[i] != i + n - r:
break
else:
return
indices[i] += 1
for j in range(i+1, r):
indices[j] = indices[j-1] + 1
yield tuple(pool[i] for i in indices)
The code for combinations() can be also expressed as a subsequence of permutations() after filtering entries where the elements are not in sorted order (according to their position in the input pool): def combinations(iterable, r):
pool = tuple(iterable)
n = len(pool)
for indices in permutations(range(n), r):
if sorted(indices) == list(indices):
yield tuple(pool[i] for i in indices)
The number of items returned is n! / r! / (n-r)! when 0 <= r <= n or zero when r > n.
itertools.combinations_with_replacement(iterable, r)
Return r length subsequences of elements from the input iterable allowing individual elements to be repeated more than once. The combination tuples are emitted in lexicographic ordering according to the order of the input iterable. So, if the input iterable is sorted, the combination tuples will be produced in sorted order. Elements are treated as unique based on their position, not on their value. So if the input elements are unique, the generated combinations will also be unique. Roughly equivalent to: def combinations_with_replacement(iterable, r):
# combinations_with_replacement('ABC', 2) --> AA AB AC BB BC CC
pool = tuple(iterable)
n = len(pool)
if not n and r:
return
indices = [0] * r
yield tuple(pool[i] for i in indices)
while True:
for i in reversed(range(r)):
if indices[i] != n - 1:
break
else:
return
indices[i:] = [indices[i] + 1] * (r - i)
yield tuple(pool[i] for i in indices)
The code for combinations_with_replacement() can be also expressed as a subsequence of product() after filtering entries where the elements are not in sorted order (according to their position in the input pool): def combinations_with_replacement(iterable, r):
pool = tuple(iterable)
n = len(pool)
for indices in product(range(n), repeat=r):
if sorted(indices) == list(indices):
yield tuple(pool[i] for i in indices)
The number of items returned is (n+r-1)! / r! / (n-1)! when n > 0. New in version 3.1.
itertools.compress(data, selectors)
Make an iterator that filters elements from data returning only those that have a corresponding element in selectors that evaluates to True. Stops when either the data or selectors iterables has been exhausted. Roughly equivalent to: def compress(data, selectors):
# compress('ABCDEF', [1,0,1,0,1,1]) --> A C E F
return (d for d, s in zip(data, selectors) if s)
New in version 3.1.
itertools.count(start=0, step=1)
Make an iterator that returns evenly spaced values starting with number start. Often used as an argument to map() to generate consecutive data points. Also, used with zip() to add sequence numbers. Roughly equivalent to: def count(start=0, step=1):
# count(10) --> 10 11 12 13 14 ...
# count(2.5, 0.5) -> 2.5 3.0 3.5 ...
n = start
while True:
yield n
n += step
When counting with floating point numbers, better accuracy can sometimes be achieved by substituting multiplicative code such as: (start + step * i
for i in count()). Changed in version 3.1: Added step argument and allowed non-integer arguments.
itertools.cycle(iterable)
Make an iterator returning elements from the iterable and saving a copy of each. When the iterable is exhausted, return elements from the saved copy. Repeats indefinitely. Roughly equivalent to: def cycle(iterable):
# cycle('ABCD') --> A B C D A B C D A B C D ...
saved = []
for element in iterable:
yield element
saved.append(element)
while saved:
for element in saved:
yield element
Note, this member of the toolkit may require significant auxiliary storage (depending on the length of the iterable).
itertools.dropwhile(predicate, iterable)
Make an iterator that drops elements from the iterable as long as the predicate is true; afterwards, returns every element. Note, the iterator does not produce any output until the predicate first becomes false, so it may have a lengthy start-up time. Roughly equivalent to: def dropwhile(predicate, iterable):
# dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1
iterable = iter(iterable)
for x in iterable:
if not predicate(x):
yield x
break
for x in iterable:
yield x
itertools.filterfalse(predicate, iterable)
Make an iterator that filters elements from iterable returning only those for which the predicate is False. If predicate is None, return the items that are false. Roughly equivalent to: def filterfalse(predicate, iterable):
# filterfalse(lambda x: x%2, range(10)) --> 0 2 4 6 8
if predicate is None:
predicate = bool
for x in iterable:
if not predicate(x):
yield x
itertools.groupby(iterable, key=None)
Make an iterator that returns consecutive keys and groups from the iterable. The key is a function computing a key value for each element. If not specified or is None, key defaults to an identity function and returns the element unchanged. Generally, the iterable needs to already be sorted on the same key function. The operation of groupby() is similar to the uniq filter in Unix. It generates a break or new group every time the value of the key function changes (which is why it is usually necessary to have sorted the data using the same key function). That behavior differs from SQL’s GROUP BY which aggregates common elements regardless of their input order. The returned group is itself an iterator that shares the underlying iterable with groupby(). Because the source is shared, when the groupby() object is advanced, the previous group is no longer visible. So, if that data is needed later, it should be stored as a list: groups = []
uniquekeys = []
data = sorted(data, key=keyfunc)
for k, g in groupby(data, keyfunc):
groups.append(list(g)) # Store group iterator as a list
uniquekeys.append(k)
groupby() is roughly equivalent to: class groupby:
# [k for k, g in groupby('AAAABBBCCDAABBB')] --> A B C D A B
# [list(g) for k, g in groupby('AAAABBBCCD')] --> AAAA BBB CC D
def __init__(self, iterable, key=None):
if key is None:
key = lambda x: x
self.keyfunc = key
self.it = iter(iterable)
self.tgtkey = self.currkey = self.currvalue = object()
def __iter__(self):
return self
def __next__(self):
self.id = object()
while self.currkey == self.tgtkey:
self.currvalue = next(self.it) # Exit on StopIteration
self.currkey = self.keyfunc(self.currvalue)
self.tgtkey = self.currkey
return (self.currkey, self._grouper(self.tgtkey, self.id))
def _grouper(self, tgtkey, id):
while self.id is id and self.currkey == tgtkey:
yield self.currvalue
try:
self.currvalue = next(self.it)
except StopIteration:
return
self.currkey = self.keyfunc(self.currvalue)
itertools.islice(iterable, stop)
itertools.islice(iterable, start, stop[, step])
Make an iterator that returns selected elements from the iterable. If start is non-zero, then elements from the iterable are skipped until start is reached. Afterward, elements are returned consecutively unless step is set higher than one which results in items being skipped. If stop is None, then iteration continues until the iterator is exhausted, if at all; otherwise, it stops at the specified position. Unlike regular slicing, islice() does not support negative values for start, stop, or step. Can be used to extract related fields from data where the internal structure has been flattened (for example, a multi-line report may list a name field on every third line). Roughly equivalent to: def islice(iterable, *args):
# islice('ABCDEFG', 2) --> A B
# islice('ABCDEFG', 2, 4) --> C D
# islice('ABCDEFG', 2, None) --> C D E F G
# islice('ABCDEFG', 0, None, 2) --> A C E G
s = slice(*args)
start, stop, step = s.start or 0, s.stop or sys.maxsize, s.step or 1
it = iter(range(start, stop, step))
try:
nexti = next(it)
except StopIteration:
# Consume *iterable* up to the *start* position.
for i, element in zip(range(start), iterable):
pass
return
try:
for i, element in enumerate(iterable):
if i == nexti:
yield element
nexti = next(it)
except StopIteration:
# Consume to *stop*.
for i, element in zip(range(i + 1, stop), iterable):
pass
If start is None, then iteration starts at zero. If step is None, then the step defaults to one.
itertools.permutations(iterable, r=None)
Return successive r length permutations of elements in the iterable. If r is not specified or is None, then r defaults to the length of the iterable and all possible full-length permutations are generated. The permutation tuples are emitted in lexicographic ordering according to the order of the input iterable. So, if the input iterable is sorted, the combination tuples will be produced in sorted order. Elements are treated as unique based on their position, not on their value. So if the input elements are unique, there will be no repeat values in each permutation. Roughly equivalent to: def permutations(iterable, r=None):
# permutations('ABCD', 2) --> AB AC AD BA BC BD CA CB CD DA DB DC
# permutations(range(3)) --> 012 021 102 120 201 210
pool = tuple(iterable)
n = len(pool)
r = n if r is None else r
if r > n:
return
indices = list(range(n))
cycles = list(range(n, n-r, -1))
yield tuple(pool[i] for i in indices[:r])
while n:
for i in reversed(range(r)):
cycles[i] -= 1
if cycles[i] == 0:
indices[i:] = indices[i+1:] + indices[i:i+1]
cycles[i] = n - i
else:
j = cycles[i]
indices[i], indices[-j] = indices[-j], indices[i]
yield tuple(pool[i] for i in indices[:r])
break
else:
return
The code for permutations() can be also expressed as a subsequence of product(), filtered to exclude entries with repeated elements (those from the same position in the input pool): def permutations(iterable, r=None):
pool = tuple(iterable)
n = len(pool)
r = n if r is None else r
for indices in product(range(n), repeat=r):
if len(set(indices)) == r:
yield tuple(pool[i] for i in indices)
The number of items returned is n! / (n-r)! when 0 <= r <= n or zero when r > n.
itertools.product(*iterables, repeat=1)
Cartesian product of input iterables. Roughly equivalent to nested for-loops in a generator expression. For example, product(A, B) returns the same as ((x,y) for x in A for y in B). The nested loops cycle like an odometer with the rightmost element advancing on every iteration. This pattern creates a lexicographic ordering so that if the input’s iterables are sorted, the product tuples are emitted in sorted order. To compute the product of an iterable with itself, specify the number of repetitions with the optional repeat keyword argument. For example, product(A, repeat=4) means the same as product(A, A, A, A). This function is roughly equivalent to the following code, except that the actual implementation does not build up intermediate results in memory: def product(*args, repeat=1):
# product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy
# product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111
pools = [tuple(pool) for pool in args] * repeat
result = [[]]
for pool in pools:
result = [x+[y] for x in result for y in pool]
for prod in result:
yield tuple(prod)
Before product() runs, it completely consumes the input iterables, keeping pools of values in memory to generate the products. Accordingly, it is only useful with finite inputs.
itertools.repeat(object[, times])
Make an iterator that returns object over and over again. Runs indefinitely unless the times argument is specified. Used as argument to map() for invariant parameters to the called function. Also used with zip() to create an invariant part of a tuple record. Roughly equivalent to: def repeat(object, times=None):
# repeat(10, 3) --> 10 10 10
if times is None:
while True:
yield object
else:
for i in range(times):
yield object
A common use for repeat is to supply a stream of constant values to map or zip: >>> list(map(pow, range(10), repeat(2)))
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
itertools.starmap(function, iterable)
Make an iterator that computes the function using arguments obtained from the iterable. Used instead of map() when argument parameters are already grouped in tuples from a single iterable (the data has been “pre-zipped”). The difference between map() and starmap() parallels the distinction between function(a,b) and function(*c). Roughly equivalent to: def starmap(function, iterable):
# starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000
for args in iterable:
yield function(*args)
itertools.takewhile(predicate, iterable)
Make an iterator that returns elements from the iterable as long as the predicate is true. Roughly equivalent to: def takewhile(predicate, iterable):
# takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4
for x in iterable:
if predicate(x):
yield x
else:
break
itertools.tee(iterable, n=2)
Return n independent iterators from a single iterable. The following Python code helps explain what tee does (although the actual implementation is more complex and uses only a single underlying FIFO queue). Roughly equivalent to: def tee(iterable, n=2):
it = iter(iterable)
deques = [collections.deque() for i in range(n)]
def gen(mydeque):
while True:
if not mydeque: # when the local deque is empty
try:
newval = next(it) # fetch a new value and
except StopIteration:
return
for d in deques: # load it to all the deques
d.append(newval)
yield mydeque.popleft()
return tuple(gen(d) for d in deques)
Once tee() has made a split, the original iterable should not be used anywhere else; otherwise, the iterable could get advanced without the tee objects being informed. tee iterators are not threadsafe. A RuntimeError may be raised when using simultaneously iterators returned by the same tee() call, even if the original iterable is threadsafe. This itertool may require significant auxiliary storage (depending on how much temporary data needs to be stored). In general, if one iterator uses most or all of the data before another iterator starts, it is faster to use list() instead of tee().
itertools.zip_longest(*iterables, fillvalue=None)
Make an iterator that aggregates elements from each of the iterables. If the iterables are of uneven length, missing values are filled-in with fillvalue. Iteration continues until the longest iterable is exhausted. Roughly equivalent to: def zip_longest(*args, fillvalue=None):
# zip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D-
iterators = [iter(it) for it in args]
num_active = len(iterators)
if not num_active:
return
while True:
values = []
for i, it in enumerate(iterators):
try:
value = next(it)
except StopIteration:
num_active -= 1
if not num_active:
return
iterators[i] = repeat(fillvalue)
value = fillvalue
values.append(value)
yield tuple(values)
If one of the iterables is potentially infinite, then the zip_longest() function should be wrapped with something that limits the number of calls (for example islice() or takewhile()). If not specified, fillvalue defaults to None.
Itertools Recipes This section shows recipes for creating an extended toolset using the existing itertools as building blocks. Substantially all of these recipes and many, many others can be installed from the more-itertools project found on the Python Package Index: pip install more-itertools
The extended tools offer the same high performance as the underlying toolset. The superior memory performance is kept by processing elements one at a time rather than bringing the whole iterable into memory all at once. Code volume is kept small by linking the tools together in a functional style which helps eliminate temporary variables. High speed is retained by preferring “vectorized” building blocks over the use of for-loops and generators which incur interpreter overhead. def take(n, iterable):
"Return first n items of the iterable as a list"
return list(islice(iterable, n))
def prepend(value, iterator):
"Prepend a single value in front of an iterator"
# prepend(1, [2, 3, 4]) -> 1 2 3 4
return chain([value], iterator)
def tabulate(function, start=0):
"Return function(0), function(1), ..."
return map(function, count(start))
def tail(n, iterable):
"Return an iterator over the last n items"
# tail(3, 'ABCDEFG') --> E F G
return iter(collections.deque(iterable, maxlen=n))
def consume(iterator, n=None):
"Advance the iterator n-steps ahead. If n is None, consume entirely."
# Use functions that consume iterators at C speed.
if n is None:
# feed the entire iterator into a zero-length deque
collections.deque(iterator, maxlen=0)
else:
# advance to the empty slice starting at position n
next(islice(iterator, n, n), None)
def nth(iterable, n, default=None):
"Returns the nth item or a default value"
return next(islice(iterable, n, None), default)
def all_equal(iterable):
"Returns True if all the elements are equal to each other"
g = groupby(iterable)
return next(g, True) and not next(g, False)
def quantify(iterable, pred=bool):
"Count how many times the predicate is true"
return sum(map(pred, iterable))
def pad_none(iterable):
"""Returns the sequence elements and then returns None indefinitely.
Useful for emulating the behavior of the built-in map() function.
"""
return chain(iterable, repeat(None))
def ncycles(iterable, n):
"Returns the sequence elements n times"
return chain.from_iterable(repeat(tuple(iterable), n))
def dotproduct(vec1, vec2):
return sum(map(operator.mul, vec1, vec2))
def convolve(signal, kernel):
# See: https://betterexplained.com/articles/intuitive-convolution/
# convolve(data, [0.25, 0.25, 0.25, 0.25]) --> Moving average (blur)
# convolve(data, [1, -1]) --> 1st finite difference (1st derivative)
# convolve(data, [1, -2, 1]) --> 2nd finite difference (2nd derivative)
kernel = tuple(kernel)[::-1]
n = len(kernel)
window = collections.deque([0], maxlen=n) * n
for x in chain(signal, repeat(0, n-1)):
window.append(x)
yield sum(map(operator.mul, kernel, window))
def flatten(list_of_lists):
"Flatten one level of nesting"
return chain.from_iterable(list_of_lists)
def repeatfunc(func, times=None, *args):
"""Repeat calls to func with specified arguments.
Example: repeatfunc(random.random)
"""
if times is None:
return starmap(func, repeat(args))
return starmap(func, repeat(args, times))
def pairwise(iterable):
"s -> (s0,s1), (s1,s2), (s2, s3), ..."
a, b = tee(iterable)
next(b, None)
return zip(a, b)
def grouper(iterable, n, fillvalue=None):
"Collect data into fixed-length chunks or blocks"
# grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
args = [iter(iterable)] * n
return zip_longest(*args, fillvalue=fillvalue)
def roundrobin(*iterables):
"roundrobin('ABC', 'D', 'EF') --> A D E B F C"
# Recipe credited to George Sakkis
num_active = len(iterables)
nexts = cycle(iter(it).__next__ for it in iterables)
while num_active:
try:
for next in nexts:
yield next()
except StopIteration:
# Remove the iterator we just exhausted from the cycle.
num_active -= 1
nexts = cycle(islice(nexts, num_active))
def partition(pred, iterable):
"Use a predicate to partition entries into false entries and true entries"
# partition(is_odd, range(10)) --> 0 2 4 6 8 and 1 3 5 7 9
t1, t2 = tee(iterable)
return filterfalse(pred, t1), filter(pred, t2)
def powerset(iterable):
"powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
s = list(iterable)
return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))
def unique_everseen(iterable, key=None):
"List unique elements, preserving order. Remember all elements ever seen."
# unique_everseen('AAAABBBCCDAABBB') --> A B C D
# unique_everseen('ABBCcAD', str.lower) --> A B C D
seen = set()
seen_add = seen.add
if key is None:
for element in filterfalse(seen.__contains__, iterable):
seen_add(element)
yield element
else:
for element in iterable:
k = key(element)
if k not in seen:
seen_add(k)
yield element
def unique_justseen(iterable, key=None):
"List unique elements, preserving order. Remember only the element just seen."
# unique_justseen('AAAABBBCCDAABBB') --> A B C D A B
# unique_justseen('ABBCcAD', str.lower) --> A B C A D
return map(next, map(operator.itemgetter(1), groupby(iterable, key)))
def iter_except(func, exception, first=None):
""" Call a function repeatedly until an exception is raised.
Converts a call-until-exception interface to an iterator interface.
Like builtins.iter(func, sentinel) but uses an exception instead
of a sentinel to end the loop.
Examples:
iter_except(functools.partial(heappop, h), IndexError) # priority queue iterator
iter_except(d.popitem, KeyError) # non-blocking dict iterator
iter_except(d.popleft, IndexError) # non-blocking deque iterator
iter_except(q.get_nowait, Queue.Empty) # loop over a producer Queue
iter_except(s.pop, KeyError) # non-blocking set iterator
"""
try:
if first is not None:
yield first() # For database APIs needing an initial cast to db.first()
while True:
yield func()
except exception:
pass
def first_true(iterable, default=False, pred=None):
"""Returns the first true value in the iterable.
If no true value is found, returns *default*
If *pred* is not None, returns the first item
for which pred(item) is true.
"""
# first_true([a,b,c], x) --> a or b or c or x
# first_true([a,b], x, f) --> a if f(a) else b if f(b) else x
return next(filter(pred, iterable), default)
def random_product(*args, repeat=1):
"Random selection from itertools.product(*args, **kwds)"
pools = [tuple(pool) for pool in args] * repeat
return tuple(map(random.choice, pools))
def random_permutation(iterable, r=None):
"Random selection from itertools.permutations(iterable, r)"
pool = tuple(iterable)
r = len(pool) if r is None else r
return tuple(random.sample(pool, r))
def random_combination(iterable, r):
"Random selection from itertools.combinations(iterable, r)"
pool = tuple(iterable)
n = len(pool)
indices = sorted(random.sample(range(n), r))
return tuple(pool[i] for i in indices)
def random_combination_with_replacement(iterable, r):
"Random selection from itertools.combinations_with_replacement(iterable, r)"
pool = tuple(iterable)
n = len(pool)
indices = sorted(random.choices(range(n), k=r))
return tuple(pool[i] for i in indices)
def nth_combination(iterable, r, index):
"Equivalent to list(combinations(iterable, r))[index]"
pool = tuple(iterable)
n = len(pool)
if r < 0 or r > n:
raise ValueError
c = 1
k = min(r, n-r)
for i in range(1, k+1):
c = c * (n - k + i) // i
if index < 0:
index += c
if index < 0 or index >= c:
raise IndexError
result = []
while r:
c, n, r = c*r//n, n-1, r-1
while index >= c:
index -= c
c, n = c*(n-r)//n, n-1
result.append(pool[-1-n])
return tuple(result) | python.library.itertools |
itertools.accumulate(iterable[, func, *, initial=None])
Make an iterator that returns accumulated sums, or accumulated results of other binary functions (specified via the optional func argument). If func is supplied, it should be a function of two arguments. Elements of the input iterable may be any type that can be accepted as arguments to func. (For example, with the default operation of addition, elements may be any addable type including Decimal or Fraction.) Usually, the number of elements output matches the input iterable. However, if the keyword argument initial is provided, the accumulation leads off with the initial value so that the output has one more element than the input iterable. Roughly equivalent to: def accumulate(iterable, func=operator.add, *, initial=None):
'Return running totals'
# accumulate([1,2,3,4,5]) --> 1 3 6 10 15
# accumulate([1,2,3,4,5], initial=100) --> 100 101 103 106 110 115
# accumulate([1,2,3,4,5], operator.mul) --> 1 2 6 24 120
it = iter(iterable)
total = initial
if initial is None:
try:
total = next(it)
except StopIteration:
return
yield total
for element in it:
total = func(total, element)
yield total
There are a number of uses for the func argument. It can be set to min() for a running minimum, max() for a running maximum, or operator.mul() for a running product. Amortization tables can be built by accumulating interest and applying payments. First-order recurrence relations can be modeled by supplying the initial value in the iterable and using only the accumulated total in func argument: >>> data = [3, 4, 6, 2, 1, 9, 0, 7, 5, 8]
>>> list(accumulate(data, operator.mul)) # running product
[3, 12, 72, 144, 144, 1296, 0, 0, 0, 0]
>>> list(accumulate(data, max)) # running maximum
[3, 4, 6, 6, 6, 9, 9, 9, 9, 9]
# Amortize a 5% loan of 1000 with 4 annual payments of 90
>>> cashflows = [1000, -90, -90, -90, -90]
>>> list(accumulate(cashflows, lambda bal, pmt: bal*1.05 + pmt))
[1000, 960.0, 918.0, 873.9000000000001, 827.5950000000001]
# Chaotic recurrence relation https://en.wikipedia.org/wiki/Logistic_map
>>> logistic_map = lambda x, _: r * x * (1 - x)
>>> r = 3.8
>>> x0 = 0.4
>>> inputs = repeat(x0, 36) # only the initial value is used
>>> [format(x, '.2f') for x in accumulate(inputs, logistic_map)]
['0.40', '0.91', '0.30', '0.81', '0.60', '0.92', '0.29', '0.79', '0.63',
'0.88', '0.39', '0.90', '0.33', '0.84', '0.52', '0.95', '0.18', '0.57',
'0.93', '0.25', '0.71', '0.79', '0.63', '0.88', '0.39', '0.91', '0.32',
'0.83', '0.54', '0.95', '0.20', '0.60', '0.91', '0.30', '0.80', '0.60']
See functools.reduce() for a similar function that returns only the final accumulated value. New in version 3.2. Changed in version 3.3: Added the optional func parameter. Changed in version 3.8: Added the optional initial parameter. | python.library.itertools#itertools.accumulate |
itertools.chain(*iterables)
Make an iterator that returns elements from the first iterable until it is exhausted, then proceeds to the next iterable, until all of the iterables are exhausted. Used for treating consecutive sequences as a single sequence. Roughly equivalent to: def chain(*iterables):
# chain('ABC', 'DEF') --> A B C D E F
for it in iterables:
for element in it:
yield element | python.library.itertools#itertools.chain |
classmethod chain.from_iterable(iterable)
Alternate constructor for chain(). Gets chained inputs from a single iterable argument that is evaluated lazily. Roughly equivalent to: def from_iterable(iterables):
# chain.from_iterable(['ABC', 'DEF']) --> A B C D E F
for it in iterables:
for element in it:
yield element | python.library.itertools#itertools.chain.from_iterable |
itertools.combinations(iterable, r)
Return r length subsequences of elements from the input iterable. The combination tuples are emitted in lexicographic ordering according to the order of the input iterable. So, if the input iterable is sorted, the combination tuples will be produced in sorted order. Elements are treated as unique based on their position, not on their value. So if the input elements are unique, there will be no repeat values in each combination. Roughly equivalent to: def combinations(iterable, r):
# combinations('ABCD', 2) --> AB AC AD BC BD CD
# combinations(range(4), 3) --> 012 013 023 123
pool = tuple(iterable)
n = len(pool)
if r > n:
return
indices = list(range(r))
yield tuple(pool[i] for i in indices)
while True:
for i in reversed(range(r)):
if indices[i] != i + n - r:
break
else:
return
indices[i] += 1
for j in range(i+1, r):
indices[j] = indices[j-1] + 1
yield tuple(pool[i] for i in indices)
The code for combinations() can be also expressed as a subsequence of permutations() after filtering entries where the elements are not in sorted order (according to their position in the input pool): def combinations(iterable, r):
pool = tuple(iterable)
n = len(pool)
for indices in permutations(range(n), r):
if sorted(indices) == list(indices):
yield tuple(pool[i] for i in indices)
The number of items returned is n! / r! / (n-r)! when 0 <= r <= n or zero when r > n. | python.library.itertools#itertools.combinations |
itertools.combinations_with_replacement(iterable, r)
Return r length subsequences of elements from the input iterable allowing individual elements to be repeated more than once. The combination tuples are emitted in lexicographic ordering according to the order of the input iterable. So, if the input iterable is sorted, the combination tuples will be produced in sorted order. Elements are treated as unique based on their position, not on their value. So if the input elements are unique, the generated combinations will also be unique. Roughly equivalent to: def combinations_with_replacement(iterable, r):
# combinations_with_replacement('ABC', 2) --> AA AB AC BB BC CC
pool = tuple(iterable)
n = len(pool)
if not n and r:
return
indices = [0] * r
yield tuple(pool[i] for i in indices)
while True:
for i in reversed(range(r)):
if indices[i] != n - 1:
break
else:
return
indices[i:] = [indices[i] + 1] * (r - i)
yield tuple(pool[i] for i in indices)
The code for combinations_with_replacement() can be also expressed as a subsequence of product() after filtering entries where the elements are not in sorted order (according to their position in the input pool): def combinations_with_replacement(iterable, r):
pool = tuple(iterable)
n = len(pool)
for indices in product(range(n), repeat=r):
if sorted(indices) == list(indices):
yield tuple(pool[i] for i in indices)
The number of items returned is (n+r-1)! / r! / (n-1)! when n > 0. New in version 3.1. | python.library.itertools#itertools.combinations_with_replacement |
itertools.compress(data, selectors)
Make an iterator that filters elements from data returning only those that have a corresponding element in selectors that evaluates to True. Stops when either the data or selectors iterables has been exhausted. Roughly equivalent to: def compress(data, selectors):
# compress('ABCDEF', [1,0,1,0,1,1]) --> A C E F
return (d for d, s in zip(data, selectors) if s)
New in version 3.1. | python.library.itertools#itertools.compress |
itertools.count(start=0, step=1)
Make an iterator that returns evenly spaced values starting with number start. Often used as an argument to map() to generate consecutive data points. Also, used with zip() to add sequence numbers. Roughly equivalent to: def count(start=0, step=1):
# count(10) --> 10 11 12 13 14 ...
# count(2.5, 0.5) -> 2.5 3.0 3.5 ...
n = start
while True:
yield n
n += step
When counting with floating point numbers, better accuracy can sometimes be achieved by substituting multiplicative code such as: (start + step * i
for i in count()). Changed in version 3.1: Added step argument and allowed non-integer arguments. | python.library.itertools#itertools.count |
itertools.cycle(iterable)
Make an iterator returning elements from the iterable and saving a copy of each. When the iterable is exhausted, return elements from the saved copy. Repeats indefinitely. Roughly equivalent to: def cycle(iterable):
# cycle('ABCD') --> A B C D A B C D A B C D ...
saved = []
for element in iterable:
yield element
saved.append(element)
while saved:
for element in saved:
yield element
Note, this member of the toolkit may require significant auxiliary storage (depending on the length of the iterable). | python.library.itertools#itertools.cycle |
itertools.dropwhile(predicate, iterable)
Make an iterator that drops elements from the iterable as long as the predicate is true; afterwards, returns every element. Note, the iterator does not produce any output until the predicate first becomes false, so it may have a lengthy start-up time. Roughly equivalent to: def dropwhile(predicate, iterable):
# dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1
iterable = iter(iterable)
for x in iterable:
if not predicate(x):
yield x
break
for x in iterable:
yield x | python.library.itertools#itertools.dropwhile |
itertools.filterfalse(predicate, iterable)
Make an iterator that filters elements from iterable returning only those for which the predicate is False. If predicate is None, return the items that are false. Roughly equivalent to: def filterfalse(predicate, iterable):
# filterfalse(lambda x: x%2, range(10)) --> 0 2 4 6 8
if predicate is None:
predicate = bool
for x in iterable:
if not predicate(x):
yield x | python.library.itertools#itertools.filterfalse |
itertools.groupby(iterable, key=None)
Make an iterator that returns consecutive keys and groups from the iterable. The key is a function computing a key value for each element. If not specified or is None, key defaults to an identity function and returns the element unchanged. Generally, the iterable needs to already be sorted on the same key function. The operation of groupby() is similar to the uniq filter in Unix. It generates a break or new group every time the value of the key function changes (which is why it is usually necessary to have sorted the data using the same key function). That behavior differs from SQL’s GROUP BY which aggregates common elements regardless of their input order. The returned group is itself an iterator that shares the underlying iterable with groupby(). Because the source is shared, when the groupby() object is advanced, the previous group is no longer visible. So, if that data is needed later, it should be stored as a list: groups = []
uniquekeys = []
data = sorted(data, key=keyfunc)
for k, g in groupby(data, keyfunc):
groups.append(list(g)) # Store group iterator as a list
uniquekeys.append(k)
groupby() is roughly equivalent to: class groupby:
# [k for k, g in groupby('AAAABBBCCDAABBB')] --> A B C D A B
# [list(g) for k, g in groupby('AAAABBBCCD')] --> AAAA BBB CC D
def __init__(self, iterable, key=None):
if key is None:
key = lambda x: x
self.keyfunc = key
self.it = iter(iterable)
self.tgtkey = self.currkey = self.currvalue = object()
def __iter__(self):
return self
def __next__(self):
self.id = object()
while self.currkey == self.tgtkey:
self.currvalue = next(self.it) # Exit on StopIteration
self.currkey = self.keyfunc(self.currvalue)
self.tgtkey = self.currkey
return (self.currkey, self._grouper(self.tgtkey, self.id))
def _grouper(self, tgtkey, id):
while self.id is id and self.currkey == tgtkey:
yield self.currvalue
try:
self.currvalue = next(self.it)
except StopIteration:
return
self.currkey = self.keyfunc(self.currvalue) | python.library.itertools#itertools.groupby |
itertools.islice(iterable, stop)
itertools.islice(iterable, start, stop[, step])
Make an iterator that returns selected elements from the iterable. If start is non-zero, then elements from the iterable are skipped until start is reached. Afterward, elements are returned consecutively unless step is set higher than one which results in items being skipped. If stop is None, then iteration continues until the iterator is exhausted, if at all; otherwise, it stops at the specified position. Unlike regular slicing, islice() does not support negative values for start, stop, or step. Can be used to extract related fields from data where the internal structure has been flattened (for example, a multi-line report may list a name field on every third line). Roughly equivalent to: def islice(iterable, *args):
# islice('ABCDEFG', 2) --> A B
# islice('ABCDEFG', 2, 4) --> C D
# islice('ABCDEFG', 2, None) --> C D E F G
# islice('ABCDEFG', 0, None, 2) --> A C E G
s = slice(*args)
start, stop, step = s.start or 0, s.stop or sys.maxsize, s.step or 1
it = iter(range(start, stop, step))
try:
nexti = next(it)
except StopIteration:
# Consume *iterable* up to the *start* position.
for i, element in zip(range(start), iterable):
pass
return
try:
for i, element in enumerate(iterable):
if i == nexti:
yield element
nexti = next(it)
except StopIteration:
# Consume to *stop*.
for i, element in zip(range(i + 1, stop), iterable):
pass
If start is None, then iteration starts at zero. If step is None, then the step defaults to one. | python.library.itertools#itertools.islice |
itertools.permutations(iterable, r=None)
Return successive r length permutations of elements in the iterable. If r is not specified or is None, then r defaults to the length of the iterable and all possible full-length permutations are generated. The permutation tuples are emitted in lexicographic ordering according to the order of the input iterable. So, if the input iterable is sorted, the combination tuples will be produced in sorted order. Elements are treated as unique based on their position, not on their value. So if the input elements are unique, there will be no repeat values in each permutation. Roughly equivalent to: def permutations(iterable, r=None):
# permutations('ABCD', 2) --> AB AC AD BA BC BD CA CB CD DA DB DC
# permutations(range(3)) --> 012 021 102 120 201 210
pool = tuple(iterable)
n = len(pool)
r = n if r is None else r
if r > n:
return
indices = list(range(n))
cycles = list(range(n, n-r, -1))
yield tuple(pool[i] for i in indices[:r])
while n:
for i in reversed(range(r)):
cycles[i] -= 1
if cycles[i] == 0:
indices[i:] = indices[i+1:] + indices[i:i+1]
cycles[i] = n - i
else:
j = cycles[i]
indices[i], indices[-j] = indices[-j], indices[i]
yield tuple(pool[i] for i in indices[:r])
break
else:
return
The code for permutations() can be also expressed as a subsequence of product(), filtered to exclude entries with repeated elements (those from the same position in the input pool): def permutations(iterable, r=None):
pool = tuple(iterable)
n = len(pool)
r = n if r is None else r
for indices in product(range(n), repeat=r):
if len(set(indices)) == r:
yield tuple(pool[i] for i in indices)
The number of items returned is n! / (n-r)! when 0 <= r <= n or zero when r > n. | python.library.itertools#itertools.permutations |
itertools.product(*iterables, repeat=1)
Cartesian product of input iterables. Roughly equivalent to nested for-loops in a generator expression. For example, product(A, B) returns the same as ((x,y) for x in A for y in B). The nested loops cycle like an odometer with the rightmost element advancing on every iteration. This pattern creates a lexicographic ordering so that if the input’s iterables are sorted, the product tuples are emitted in sorted order. To compute the product of an iterable with itself, specify the number of repetitions with the optional repeat keyword argument. For example, product(A, repeat=4) means the same as product(A, A, A, A). This function is roughly equivalent to the following code, except that the actual implementation does not build up intermediate results in memory: def product(*args, repeat=1):
# product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy
# product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111
pools = [tuple(pool) for pool in args] * repeat
result = [[]]
for pool in pools:
result = [x+[y] for x in result for y in pool]
for prod in result:
yield tuple(prod)
Before product() runs, it completely consumes the input iterables, keeping pools of values in memory to generate the products. Accordingly, it is only useful with finite inputs. | python.library.itertools#itertools.product |
itertools.repeat(object[, times])
Make an iterator that returns object over and over again. Runs indefinitely unless the times argument is specified. Used as argument to map() for invariant parameters to the called function. Also used with zip() to create an invariant part of a tuple record. Roughly equivalent to: def repeat(object, times=None):
# repeat(10, 3) --> 10 10 10
if times is None:
while True:
yield object
else:
for i in range(times):
yield object
A common use for repeat is to supply a stream of constant values to map or zip: >>> list(map(pow, range(10), repeat(2)))
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81] | python.library.itertools#itertools.repeat |
itertools.starmap(function, iterable)
Make an iterator that computes the function using arguments obtained from the iterable. Used instead of map() when argument parameters are already grouped in tuples from a single iterable (the data has been “pre-zipped”). The difference between map() and starmap() parallels the distinction between function(a,b) and function(*c). Roughly equivalent to: def starmap(function, iterable):
# starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000
for args in iterable:
yield function(*args) | python.library.itertools#itertools.starmap |
itertools.takewhile(predicate, iterable)
Make an iterator that returns elements from the iterable as long as the predicate is true. Roughly equivalent to: def takewhile(predicate, iterable):
# takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4
for x in iterable:
if predicate(x):
yield x
else:
break | python.library.itertools#itertools.takewhile |
itertools.tee(iterable, n=2)
Return n independent iterators from a single iterable. The following Python code helps explain what tee does (although the actual implementation is more complex and uses only a single underlying FIFO queue). Roughly equivalent to: def tee(iterable, n=2):
it = iter(iterable)
deques = [collections.deque() for i in range(n)]
def gen(mydeque):
while True:
if not mydeque: # when the local deque is empty
try:
newval = next(it) # fetch a new value and
except StopIteration:
return
for d in deques: # load it to all the deques
d.append(newval)
yield mydeque.popleft()
return tuple(gen(d) for d in deques)
Once tee() has made a split, the original iterable should not be used anywhere else; otherwise, the iterable could get advanced without the tee objects being informed. tee iterators are not threadsafe. A RuntimeError may be raised when using simultaneously iterators returned by the same tee() call, even if the original iterable is threadsafe. This itertool may require significant auxiliary storage (depending on how much temporary data needs to be stored). In general, if one iterator uses most or all of the data before another iterator starts, it is faster to use list() instead of tee(). | python.library.itertools#itertools.tee |
itertools.zip_longest(*iterables, fillvalue=None)
Make an iterator that aggregates elements from each of the iterables. If the iterables are of uneven length, missing values are filled-in with fillvalue. Iteration continues until the longest iterable is exhausted. Roughly equivalent to: def zip_longest(*args, fillvalue=None):
# zip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D-
iterators = [iter(it) for it in args]
num_active = len(iterators)
if not num_active:
return
while True:
values = []
for i, it in enumerate(iterators):
try:
value = next(it)
except StopIteration:
num_active -= 1
if not num_active:
return
iterators[i] = repeat(fillvalue)
value = fillvalue
values.append(value)
yield tuple(values)
If one of the iterables is potentially infinite, then the zip_longest() function should be wrapped with something that limits the number of calls (for example islice() or takewhile()). If not specified, fillvalue defaults to None. | python.library.itertools#itertools.zip_longest |
json — JSON encoder and decoder Source code: Lib/json/__init__.py JSON (JavaScript Object Notation), specified by RFC 7159 (which obsoletes RFC 4627) and by ECMA-404, is a lightweight data interchange format inspired by JavaScript object literal syntax (although it is not a strict subset of JavaScript 1 ). json exposes an API familiar to users of the standard library marshal and pickle modules. Encoding basic Python object hierarchies: >>> import json
>>> json.dumps(['foo', {'bar': ('baz', None, 1.0, 2)}])
'["foo", {"bar": ["baz", null, 1.0, 2]}]'
>>> print(json.dumps("\"foo\bar"))
"\"foo\bar"
>>> print(json.dumps('\u1234'))
"\u1234"
>>> print(json.dumps('\\'))
"\\"
>>> print(json.dumps({"c": 0, "b": 0, "a": 0}, sort_keys=True))
{"a": 0, "b": 0, "c": 0}
>>> from io import StringIO
>>> io = StringIO()
>>> json.dump(['streaming API'], io)
>>> io.getvalue()
'["streaming API"]'
Compact encoding: >>> import json
>>> json.dumps([1, 2, 3, {'4': 5, '6': 7}], separators=(',', ':'))
'[1,2,3,{"4":5,"6":7}]'
Pretty printing: >>> import json
>>> print(json.dumps({'4': 5, '6': 7}, sort_keys=True, indent=4))
{
"4": 5,
"6": 7
}
Decoding JSON: >>> import json
>>> json.loads('["foo", {"bar":["baz", null, 1.0, 2]}]')
['foo', {'bar': ['baz', None, 1.0, 2]}]
>>> json.loads('"\\"foo\\bar"')
'"foo\x08ar'
>>> from io import StringIO
>>> io = StringIO('["streaming API"]')
>>> json.load(io)
['streaming API']
Specializing JSON object decoding: >>> import json
>>> def as_complex(dct):
... if '__complex__' in dct:
... return complex(dct['real'], dct['imag'])
... return dct
...
>>> json.loads('{"__complex__": true, "real": 1, "imag": 2}',
... object_hook=as_complex)
(1+2j)
>>> import decimal
>>> json.loads('1.1', parse_float=decimal.Decimal)
Decimal('1.1')
Extending JSONEncoder: >>> import json
>>> class ComplexEncoder(json.JSONEncoder):
... def default(self, obj):
... if isinstance(obj, complex):
... return [obj.real, obj.imag]
... # Let the base class default method raise the TypeError
... return json.JSONEncoder.default(self, obj)
...
>>> json.dumps(2 + 1j, cls=ComplexEncoder)
'[2.0, 1.0]'
>>> ComplexEncoder().encode(2 + 1j)
'[2.0, 1.0]'
>>> list(ComplexEncoder().iterencode(2 + 1j))
['[2.0', ', 1.0', ']']
Using json.tool from the shell to validate and pretty-print: $ echo '{"json":"obj"}' | python -m json.tool
{
"json": "obj"
}
$ echo '{1.2:3.4}' | python -m json.tool
Expecting property name enclosed in double quotes: line 1 column 2 (char 1)
See Command Line Interface for detailed documentation. Note JSON is a subset of YAML 1.2. The JSON produced by this module’s default settings (in particular, the default separators value) is also a subset of YAML 1.0 and 1.1. This module can thus also be used as a YAML serializer. Note This module’s encoders and decoders preserve input and output order by default. Order is only lost if the underlying containers are unordered. Prior to Python 3.7, dict was not guaranteed to be ordered, so inputs and outputs were typically scrambled unless collections.OrderedDict was specifically requested. Starting with Python 3.7, the regular dict became order preserving, so it is no longer necessary to specify collections.OrderedDict for JSON generation and parsing. Basic Usage
json.dump(obj, fp, *, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw)
Serialize obj as a JSON formatted stream to fp (a .write()-supporting file-like object) using this conversion table. If skipkeys is true (default: False), then dict keys that are not of a basic type (str, int, float, bool, None) will be skipped instead of raising a TypeError. The json module always produces str objects, not bytes objects. Therefore, fp.write() must support str input. If ensure_ascii is true (the default), the output is guaranteed to have all incoming non-ASCII characters escaped. If ensure_ascii is false, these characters will be output as-is. If check_circular is false (default: True), then the circular reference check for container types will be skipped and a circular reference will result in an OverflowError (or worse). If allow_nan is false (default: True), then it will be a ValueError to serialize out of range float values (nan, inf, -inf) in strict compliance of the JSON specification. If allow_nan is true, their JavaScript equivalents (NaN, Infinity, -Infinity) will be used. If indent is a non-negative integer or string, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0, negative, or "" will only insert newlines. None (the default) selects the most compact representation. Using a positive integer indent indents that many spaces per level. If indent is a string (such as "\t"), that string is used to indent each level. Changed in version 3.2: Allow strings for indent in addition to integers. If specified, separators should be an (item_separator, key_separator) tuple. The default is (', ', ': ') if indent is None and (',', ': ') otherwise. To get the most compact JSON representation, you should specify (',', ':') to eliminate whitespace. Changed in version 3.4: Use (',', ': ') as default if indent is not None. If specified, default should be a function that gets called for objects that can’t otherwise be serialized. It should return a JSON encodable version of the object or raise a TypeError. If not specified, TypeError is raised. If sort_keys is true (default: False), then the output of dictionaries will be sorted by key. To use a custom JSONEncoder subclass (e.g. one that overrides the default() method to serialize additional types), specify it with the cls kwarg; otherwise JSONEncoder is used. Changed in version 3.6: All optional parameters are now keyword-only. Note Unlike pickle and marshal, JSON is not a framed protocol, so trying to serialize multiple objects with repeated calls to dump() using the same fp will result in an invalid JSON file.
json.dumps(obj, *, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw)
Serialize obj to a JSON formatted str using this conversion table. The arguments have the same meaning as in dump(). Note Keys in key/value pairs of JSON are always of the type str. When a dictionary is converted into JSON, all the keys of the dictionary are coerced to strings. As a result of this, if a dictionary is converted into JSON and then back into a dictionary, the dictionary may not equal the original one. That is, loads(dumps(x)) != x if x has non-string keys.
json.load(fp, *, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw)
Deserialize fp (a .read()-supporting text file or binary file containing a JSON document) to a Python object using this conversion table. object_hook is an optional function that will be called with the result of any object literal decoded (a dict). The return value of object_hook will be used instead of the dict. This feature can be used to implement custom decoders (e.g. JSON-RPC class hinting). object_pairs_hook is an optional function that will be called with the result of any object literal decoded with an ordered list of pairs. The return value of object_pairs_hook will be used instead of the dict. This feature can be used to implement custom decoders. If object_hook is also defined, the object_pairs_hook takes priority. Changed in version 3.1: Added support for object_pairs_hook. parse_float, if specified, will be called with the string of every JSON float to be decoded. By default, this is equivalent to float(num_str). This can be used to use another datatype or parser for JSON floats (e.g. decimal.Decimal). parse_int, if specified, will be called with the string of every JSON int to be decoded. By default, this is equivalent to int(num_str). This can be used to use another datatype or parser for JSON integers (e.g. float). parse_constant, if specified, will be called with one of the following strings: '-Infinity', 'Infinity', 'NaN'. This can be used to raise an exception if invalid JSON numbers are encountered. Changed in version 3.1: parse_constant doesn’t get called on ‘null’, ‘true’, ‘false’ anymore. To use a custom JSONDecoder subclass, specify it with the cls kwarg; otherwise JSONDecoder is used. Additional keyword arguments will be passed to the constructor of the class. If the data being deserialized is not a valid JSON document, a JSONDecodeError will be raised. Changed in version 3.6: All optional parameters are now keyword-only. Changed in version 3.6: fp can now be a binary file. The input encoding should be UTF-8, UTF-16 or UTF-32.
json.loads(s, *, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw)
Deserialize s (a str, bytes or bytearray instance containing a JSON document) to a Python object using this conversion table. The other arguments have the same meaning as in load(). If the data being deserialized is not a valid JSON document, a JSONDecodeError will be raised. Changed in version 3.6: s can now be of type bytes or bytearray. The input encoding should be UTF-8, UTF-16 or UTF-32. Changed in version 3.9: The keyword argument encoding has been removed.
Encoders and Decoders
class json.JSONDecoder(*, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, strict=True, object_pairs_hook=None)
Simple JSON decoder. Performs the following translations in decoding by default:
JSON Python
object dict
array list
string str
number (int) int
number (real) float
true True
false False
null None It also understands NaN, Infinity, and -Infinity as their corresponding float values, which is outside the JSON spec. object_hook, if specified, will be called with the result of every JSON object decoded and its return value will be used in place of the given dict. This can be used to provide custom deserializations (e.g. to support JSON-RPC class hinting). object_pairs_hook, if specified will be called with the result of every JSON object decoded with an ordered list of pairs. The return value of object_pairs_hook will be used instead of the dict. This feature can be used to implement custom decoders. If object_hook is also defined, the object_pairs_hook takes priority. Changed in version 3.1: Added support for object_pairs_hook. parse_float, if specified, will be called with the string of every JSON float to be decoded. By default, this is equivalent to float(num_str). This can be used to use another datatype or parser for JSON floats (e.g. decimal.Decimal). parse_int, if specified, will be called with the string of every JSON int to be decoded. By default, this is equivalent to int(num_str). This can be used to use another datatype or parser for JSON integers (e.g. float). parse_constant, if specified, will be called with one of the following strings: '-Infinity', 'Infinity', 'NaN'. This can be used to raise an exception if invalid JSON numbers are encountered. If strict is false (True is the default), then control characters will be allowed inside strings. Control characters in this context are those with character codes in the 0–31 range, including '\t' (tab), '\n', '\r' and '\0'. If the data being deserialized is not a valid JSON document, a JSONDecodeError will be raised. Changed in version 3.6: All parameters are now keyword-only.
decode(s)
Return the Python representation of s (a str instance containing a JSON document). JSONDecodeError will be raised if the given JSON document is not valid.
raw_decode(s)
Decode a JSON document from s (a str beginning with a JSON document) and return a 2-tuple of the Python representation and the index in s where the document ended. This can be used to decode a JSON document from a string that may have extraneous data at the end.
class json.JSONEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)
Extensible JSON encoder for Python data structures. Supports the following objects and types by default:
Python JSON
dict object
list, tuple array
str string
int, float, int- & float-derived Enums number
True true
False false
None null Changed in version 3.4: Added support for int- and float-derived Enum classes. To extend this to recognize other objects, subclass and implement a default() method with another method that returns a serializable object for o if possible, otherwise it should call the superclass implementation (to raise TypeError). If skipkeys is false (the default), a TypeError will be raised when trying to encode keys that are not str, int, float or None. If skipkeys is true, such items are simply skipped. If ensure_ascii is true (the default), the output is guaranteed to have all incoming non-ASCII characters escaped. If ensure_ascii is false, these characters will be output as-is. If check_circular is true (the default), then lists, dicts, and custom encoded objects will be checked for circular references during encoding to prevent an infinite recursion (which would cause an OverflowError). Otherwise, no such check takes place. If allow_nan is true (the default), then NaN, Infinity, and -Infinity will be encoded as such. This behavior is not JSON specification compliant, but is consistent with most JavaScript based encoders and decoders. Otherwise, it will be a ValueError to encode such floats. If sort_keys is true (default: False), then the output of dictionaries will be sorted by key; this is useful for regression tests to ensure that JSON serializations can be compared on a day-to-day basis. If indent is a non-negative integer or string, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0, negative, or "" will only insert newlines. None (the default) selects the most compact representation. Using a positive integer indent indents that many spaces per level. If indent is a string (such as "\t"), that string is used to indent each level. Changed in version 3.2: Allow strings for indent in addition to integers. If specified, separators should be an (item_separator, key_separator) tuple. The default is (', ', ': ') if indent is None and (',', ': ') otherwise. To get the most compact JSON representation, you should specify (',', ':') to eliminate whitespace. Changed in version 3.4: Use (',', ': ') as default if indent is not None. If specified, default should be a function that gets called for objects that can’t otherwise be serialized. It should return a JSON encodable version of the object or raise a TypeError. If not specified, TypeError is raised. Changed in version 3.6: All parameters are now keyword-only.
default(o)
Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError). For example, to support arbitrary iterators, you could implement default() like this: def default(self, o):
try:
iterable = iter(o)
except TypeError:
pass
else:
return list(iterable)
# Let the base class default method raise the TypeError
return json.JSONEncoder.default(self, o)
encode(o)
Return a JSON string representation of a Python data structure, o. For example: >>> json.JSONEncoder().encode({"foo": ["bar", "baz"]})
'{"foo": ["bar", "baz"]}'
iterencode(o)
Encode the given object, o, and yield each string representation as available. For example: for chunk in json.JSONEncoder().iterencode(bigobject):
mysocket.write(chunk)
Exceptions
exception json.JSONDecodeError(msg, doc, pos)
Subclass of ValueError with the following additional attributes:
msg
The unformatted error message.
doc
The JSON document being parsed.
pos
The start index of doc where parsing failed.
lineno
The line corresponding to pos.
colno
The column corresponding to pos.
New in version 3.5.
Standard Compliance and Interoperability The JSON format is specified by RFC 7159 and by ECMA-404. This section details this module’s level of compliance with the RFC. For simplicity, JSONEncoder and JSONDecoder subclasses, and parameters other than those explicitly mentioned, are not considered. This module does not comply with the RFC in a strict fashion, implementing some extensions that are valid JavaScript but not valid JSON. In particular: Infinite and NaN number values are accepted and output; Repeated names within an object are accepted, and only the value of the last name-value pair is used. Since the RFC permits RFC-compliant parsers to accept input texts that are not RFC-compliant, this module’s deserializer is technically RFC-compliant under default settings. Character Encodings The RFC requires that JSON be represented using either UTF-8, UTF-16, or UTF-32, with UTF-8 being the recommended default for maximum interoperability. As permitted, though not required, by the RFC, this module’s serializer sets ensure_ascii=True by default, thus escaping the output so that the resulting strings only contain ASCII characters. Other than the ensure_ascii parameter, this module is defined strictly in terms of conversion between Python objects and Unicode strings, and thus does not otherwise directly address the issue of character encodings. The RFC prohibits adding a byte order mark (BOM) to the start of a JSON text, and this module’s serializer does not add a BOM to its output. The RFC permits, but does not require, JSON deserializers to ignore an initial BOM in their input. This module’s deserializer raises a ValueError when an initial BOM is present. The RFC does not explicitly forbid JSON strings which contain byte sequences that don’t correspond to valid Unicode characters (e.g. unpaired UTF-16 surrogates), but it does note that they may cause interoperability problems. By default, this module accepts and outputs (when present in the original str) code points for such sequences. Infinite and NaN Number Values The RFC does not permit the representation of infinite or NaN number values. Despite that, by default, this module accepts and outputs Infinity, -Infinity, and NaN as if they were valid JSON number literal values: >>> # Neither of these calls raises an exception, but the results are not valid JSON
>>> json.dumps(float('-inf'))
'-Infinity'
>>> json.dumps(float('nan'))
'NaN'
>>> # Same when deserializing
>>> json.loads('-Infinity')
-inf
>>> json.loads('NaN')
nan
In the serializer, the allow_nan parameter can be used to alter this behavior. In the deserializer, the parse_constant parameter can be used to alter this behavior. Repeated Names Within an Object The RFC specifies that the names within a JSON object should be unique, but does not mandate how repeated names in JSON objects should be handled. By default, this module does not raise an exception; instead, it ignores all but the last name-value pair for a given name: >>> weird_json = '{"x": 1, "x": 2, "x": 3}'
>>> json.loads(weird_json)
{'x': 3}
The object_pairs_hook parameter can be used to alter this behavior. Top-level Non-Object, Non-Array Values The old version of JSON specified by the obsolete RFC 4627 required that the top-level value of a JSON text must be either a JSON object or array (Python dict or list), and could not be a JSON null, boolean, number, or string value. RFC 7159 removed that restriction, and this module does not and has never implemented that restriction in either its serializer or its deserializer. Regardless, for maximum interoperability, you may wish to voluntarily adhere to the restriction yourself. Implementation Limitations Some JSON deserializer implementations may set limits on: the size of accepted JSON texts the maximum level of nesting of JSON objects and arrays the range and precision of JSON numbers the content and maximum length of JSON strings This module does not impose any such limits beyond those of the relevant Python datatypes themselves or the Python interpreter itself. When serializing to JSON, beware any such limitations in applications that may consume your JSON. In particular, it is common for JSON numbers to be deserialized into IEEE 754 double precision numbers and thus subject to that representation’s range and precision limitations. This is especially relevant when serializing Python int values of extremely large magnitude, or when serializing instances of “exotic” numerical types such as decimal.Decimal. Command Line Interface Source code: Lib/json/tool.py The json.tool module provides a simple command line interface to validate and pretty-print JSON objects. If the optional infile and outfile arguments are not specified, sys.stdin and sys.stdout will be used respectively: $ echo '{"json": "obj"}' | python -m json.tool
{
"json": "obj"
}
$ echo '{1.2:3.4}' | python -m json.tool
Expecting property name enclosed in double quotes: line 1 column 2 (char 1)
Changed in version 3.5: The output is now in the same order as the input. Use the --sort-keys option to sort the output of dictionaries alphabetically by key. Command line options
infile
The JSON file to be validated or pretty-printed: $ python -m json.tool mp_films.json
[
{
"title": "And Now for Something Completely Different",
"year": 1971
},
{
"title": "Monty Python and the Holy Grail",
"year": 1975
}
]
If infile is not specified, read from sys.stdin.
outfile
Write the output of the infile to the given outfile. Otherwise, write it to sys.stdout.
--sort-keys
Sort the output of dictionaries alphabetically by key. New in version 3.5.
--no-ensure-ascii
Disable escaping of non-ascii characters, see json.dumps() for more information. New in version 3.9.
--json-lines
Parse every input line as separate JSON object. New in version 3.8.
--indent, --tab, --no-indent, --compact
Mutually exclusive options for whitespace control. New in version 3.9.
-h, --help
Show the help message.
Footnotes
1
As noted in the errata for RFC 7159, JSON permits literal U+2028 (LINE SEPARATOR) and U+2029 (PARAGRAPH SEPARATOR) characters in strings, whereas JavaScript (as of ECMAScript Edition 5.1) does not. | python.library.json |
json.dump(obj, fp, *, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw)
Serialize obj as a JSON formatted stream to fp (a .write()-supporting file-like object) using this conversion table. If skipkeys is true (default: False), then dict keys that are not of a basic type (str, int, float, bool, None) will be skipped instead of raising a TypeError. The json module always produces str objects, not bytes objects. Therefore, fp.write() must support str input. If ensure_ascii is true (the default), the output is guaranteed to have all incoming non-ASCII characters escaped. If ensure_ascii is false, these characters will be output as-is. If check_circular is false (default: True), then the circular reference check for container types will be skipped and a circular reference will result in an OverflowError (or worse). If allow_nan is false (default: True), then it will be a ValueError to serialize out of range float values (nan, inf, -inf) in strict compliance of the JSON specification. If allow_nan is true, their JavaScript equivalents (NaN, Infinity, -Infinity) will be used. If indent is a non-negative integer or string, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0, negative, or "" will only insert newlines. None (the default) selects the most compact representation. Using a positive integer indent indents that many spaces per level. If indent is a string (such as "\t"), that string is used to indent each level. Changed in version 3.2: Allow strings for indent in addition to integers. If specified, separators should be an (item_separator, key_separator) tuple. The default is (', ', ': ') if indent is None and (',', ': ') otherwise. To get the most compact JSON representation, you should specify (',', ':') to eliminate whitespace. Changed in version 3.4: Use (',', ': ') as default if indent is not None. If specified, default should be a function that gets called for objects that can’t otherwise be serialized. It should return a JSON encodable version of the object or raise a TypeError. If not specified, TypeError is raised. If sort_keys is true (default: False), then the output of dictionaries will be sorted by key. To use a custom JSONEncoder subclass (e.g. one that overrides the default() method to serialize additional types), specify it with the cls kwarg; otherwise JSONEncoder is used. Changed in version 3.6: All optional parameters are now keyword-only. Note Unlike pickle and marshal, JSON is not a framed protocol, so trying to serialize multiple objects with repeated calls to dump() using the same fp will result in an invalid JSON file. | python.library.json#json.dump |
json.dumps(obj, *, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw)
Serialize obj to a JSON formatted str using this conversion table. The arguments have the same meaning as in dump(). Note Keys in key/value pairs of JSON are always of the type str. When a dictionary is converted into JSON, all the keys of the dictionary are coerced to strings. As a result of this, if a dictionary is converted into JSON and then back into a dictionary, the dictionary may not equal the original one. That is, loads(dumps(x)) != x if x has non-string keys. | python.library.json#json.dumps |
exception json.JSONDecodeError(msg, doc, pos)
Subclass of ValueError with the following additional attributes:
msg
The unformatted error message.
doc
The JSON document being parsed.
pos
The start index of doc where parsing failed.
lineno
The line corresponding to pos.
colno
The column corresponding to pos.
New in version 3.5. | python.library.json#json.JSONDecodeError |
colno
The column corresponding to pos. | python.library.json#json.JSONDecodeError.colno |
doc
The JSON document being parsed. | python.library.json#json.JSONDecodeError.doc |
lineno
The line corresponding to pos. | python.library.json#json.JSONDecodeError.lineno |
msg
The unformatted error message. | python.library.json#json.JSONDecodeError.msg |
pos
The start index of doc where parsing failed. | python.library.json#json.JSONDecodeError.pos |
class json.JSONDecoder(*, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, strict=True, object_pairs_hook=None)
Simple JSON decoder. Performs the following translations in decoding by default:
JSON Python
object dict
array list
string str
number (int) int
number (real) float
true True
false False
null None It also understands NaN, Infinity, and -Infinity as their corresponding float values, which is outside the JSON spec. object_hook, if specified, will be called with the result of every JSON object decoded and its return value will be used in place of the given dict. This can be used to provide custom deserializations (e.g. to support JSON-RPC class hinting). object_pairs_hook, if specified will be called with the result of every JSON object decoded with an ordered list of pairs. The return value of object_pairs_hook will be used instead of the dict. This feature can be used to implement custom decoders. If object_hook is also defined, the object_pairs_hook takes priority. Changed in version 3.1: Added support for object_pairs_hook. parse_float, if specified, will be called with the string of every JSON float to be decoded. By default, this is equivalent to float(num_str). This can be used to use another datatype or parser for JSON floats (e.g. decimal.Decimal). parse_int, if specified, will be called with the string of every JSON int to be decoded. By default, this is equivalent to int(num_str). This can be used to use another datatype or parser for JSON integers (e.g. float). parse_constant, if specified, will be called with one of the following strings: '-Infinity', 'Infinity', 'NaN'. This can be used to raise an exception if invalid JSON numbers are encountered. If strict is false (True is the default), then control characters will be allowed inside strings. Control characters in this context are those with character codes in the 0–31 range, including '\t' (tab), '\n', '\r' and '\0'. If the data being deserialized is not a valid JSON document, a JSONDecodeError will be raised. Changed in version 3.6: All parameters are now keyword-only.
decode(s)
Return the Python representation of s (a str instance containing a JSON document). JSONDecodeError will be raised if the given JSON document is not valid.
raw_decode(s)
Decode a JSON document from s (a str beginning with a JSON document) and return a 2-tuple of the Python representation and the index in s where the document ended. This can be used to decode a JSON document from a string that may have extraneous data at the end. | python.library.json#json.JSONDecoder |
decode(s)
Return the Python representation of s (a str instance containing a JSON document). JSONDecodeError will be raised if the given JSON document is not valid. | python.library.json#json.JSONDecoder.decode |
raw_decode(s)
Decode a JSON document from s (a str beginning with a JSON document) and return a 2-tuple of the Python representation and the index in s where the document ended. This can be used to decode a JSON document from a string that may have extraneous data at the end. | python.library.json#json.JSONDecoder.raw_decode |
class json.JSONEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)
Extensible JSON encoder for Python data structures. Supports the following objects and types by default:
Python JSON
dict object
list, tuple array
str string
int, float, int- & float-derived Enums number
True true
False false
None null Changed in version 3.4: Added support for int- and float-derived Enum classes. To extend this to recognize other objects, subclass and implement a default() method with another method that returns a serializable object for o if possible, otherwise it should call the superclass implementation (to raise TypeError). If skipkeys is false (the default), a TypeError will be raised when trying to encode keys that are not str, int, float or None. If skipkeys is true, such items are simply skipped. If ensure_ascii is true (the default), the output is guaranteed to have all incoming non-ASCII characters escaped. If ensure_ascii is false, these characters will be output as-is. If check_circular is true (the default), then lists, dicts, and custom encoded objects will be checked for circular references during encoding to prevent an infinite recursion (which would cause an OverflowError). Otherwise, no such check takes place. If allow_nan is true (the default), then NaN, Infinity, and -Infinity will be encoded as such. This behavior is not JSON specification compliant, but is consistent with most JavaScript based encoders and decoders. Otherwise, it will be a ValueError to encode such floats. If sort_keys is true (default: False), then the output of dictionaries will be sorted by key; this is useful for regression tests to ensure that JSON serializations can be compared on a day-to-day basis. If indent is a non-negative integer or string, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0, negative, or "" will only insert newlines. None (the default) selects the most compact representation. Using a positive integer indent indents that many spaces per level. If indent is a string (such as "\t"), that string is used to indent each level. Changed in version 3.2: Allow strings for indent in addition to integers. If specified, separators should be an (item_separator, key_separator) tuple. The default is (', ', ': ') if indent is None and (',', ': ') otherwise. To get the most compact JSON representation, you should specify (',', ':') to eliminate whitespace. Changed in version 3.4: Use (',', ': ') as default if indent is not None. If specified, default should be a function that gets called for objects that can’t otherwise be serialized. It should return a JSON encodable version of the object or raise a TypeError. If not specified, TypeError is raised. Changed in version 3.6: All parameters are now keyword-only.
default(o)
Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError). For example, to support arbitrary iterators, you could implement default() like this: def default(self, o):
try:
iterable = iter(o)
except TypeError:
pass
else:
return list(iterable)
# Let the base class default method raise the TypeError
return json.JSONEncoder.default(self, o)
encode(o)
Return a JSON string representation of a Python data structure, o. For example: >>> json.JSONEncoder().encode({"foo": ["bar", "baz"]})
'{"foo": ["bar", "baz"]}'
iterencode(o)
Encode the given object, o, and yield each string representation as available. For example: for chunk in json.JSONEncoder().iterencode(bigobject):
mysocket.write(chunk) | python.library.json#json.JSONEncoder |
default(o)
Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError). For example, to support arbitrary iterators, you could implement default() like this: def default(self, o):
try:
iterable = iter(o)
except TypeError:
pass
else:
return list(iterable)
# Let the base class default method raise the TypeError
return json.JSONEncoder.default(self, o) | python.library.json#json.JSONEncoder.default |
encode(o)
Return a JSON string representation of a Python data structure, o. For example: >>> json.JSONEncoder().encode({"foo": ["bar", "baz"]})
'{"foo": ["bar", "baz"]}' | python.library.json#json.JSONEncoder.encode |
iterencode(o)
Encode the given object, o, and yield each string representation as available. For example: for chunk in json.JSONEncoder().iterencode(bigobject):
mysocket.write(chunk) | python.library.json#json.JSONEncoder.iterencode |
json.load(fp, *, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw)
Deserialize fp (a .read()-supporting text file or binary file containing a JSON document) to a Python object using this conversion table. object_hook is an optional function that will be called with the result of any object literal decoded (a dict). The return value of object_hook will be used instead of the dict. This feature can be used to implement custom decoders (e.g. JSON-RPC class hinting). object_pairs_hook is an optional function that will be called with the result of any object literal decoded with an ordered list of pairs. The return value of object_pairs_hook will be used instead of the dict. This feature can be used to implement custom decoders. If object_hook is also defined, the object_pairs_hook takes priority. Changed in version 3.1: Added support for object_pairs_hook. parse_float, if specified, will be called with the string of every JSON float to be decoded. By default, this is equivalent to float(num_str). This can be used to use another datatype or parser for JSON floats (e.g. decimal.Decimal). parse_int, if specified, will be called with the string of every JSON int to be decoded. By default, this is equivalent to int(num_str). This can be used to use another datatype or parser for JSON integers (e.g. float). parse_constant, if specified, will be called with one of the following strings: '-Infinity', 'Infinity', 'NaN'. This can be used to raise an exception if invalid JSON numbers are encountered. Changed in version 3.1: parse_constant doesn’t get called on ‘null’, ‘true’, ‘false’ anymore. To use a custom JSONDecoder subclass, specify it with the cls kwarg; otherwise JSONDecoder is used. Additional keyword arguments will be passed to the constructor of the class. If the data being deserialized is not a valid JSON document, a JSONDecodeError will be raised. Changed in version 3.6: All optional parameters are now keyword-only. Changed in version 3.6: fp can now be a binary file. The input encoding should be UTF-8, UTF-16 or UTF-32. | python.library.json#json.load |
json.loads(s, *, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw)
Deserialize s (a str, bytes or bytearray instance containing a JSON document) to a Python object using this conversion table. The other arguments have the same meaning as in load(). If the data being deserialized is not a valid JSON document, a JSONDecodeError will be raised. Changed in version 3.6: s can now be of type bytes or bytearray. The input encoding should be UTF-8, UTF-16 or UTF-32. Changed in version 3.9: The keyword argument encoding has been removed. | python.library.json#json.loads |
exception KeyboardInterrupt
Raised when the user hits the interrupt key (normally Control-C or Delete). During execution, a check for interrupts is made regularly. The exception inherits from BaseException so as to not be accidentally caught by code that catches Exception and thus prevent the interpreter from exiting. | python.library.exceptions#KeyboardInterrupt |
exception KeyError
Raised when a mapping (dictionary) key is not found in the set of existing keys. | python.library.exceptions#KeyError |
keyword — Testing for Python keywords Source code: Lib/keyword.py This module allows a Python program to determine if a string is a keyword.
keyword.iskeyword(s)
Return True if s is a Python keyword.
keyword.kwlist
Sequence containing all the keywords defined for the interpreter. If any keywords are defined to only be active when particular __future__ statements are in effect, these will be included as well.
keyword.issoftkeyword(s)
Return True if s is a Python soft keyword. New in version 3.9.
keyword.softkwlist
Sequence containing all the soft keywords defined for the interpreter. If any soft keywords are defined to only be active when particular __future__ statements are in effect, these will be included as well. New in version 3.9. | python.library.keyword |
keyword.iskeyword(s)
Return True if s is a Python keyword. | python.library.keyword#keyword.iskeyword |
keyword.issoftkeyword(s)
Return True if s is a Python soft keyword. New in version 3.9. | python.library.keyword#keyword.issoftkeyword |
keyword.kwlist
Sequence containing all the keywords defined for the interpreter. If any keywords are defined to only be active when particular __future__ statements are in effect, these will be included as well. | python.library.keyword#keyword.kwlist |
keyword.softkwlist
Sequence containing all the soft keywords defined for the interpreter. If any soft keywords are defined to only be active when particular __future__ statements are in effect, these will be included as well. New in version 3.9. | python.library.keyword#keyword.softkwlist |
len(s)
Return the length (the number of items) of an object. The argument may be a sequence (such as a string, bytes, tuple, list, or range) or a collection (such as a dictionary, set, or frozen set). CPython implementation detail: len raises OverflowError on lengths larger than sys.maxsize, such as range(2 ** 100). | python.library.functions#len |
license
Object that when printed, prints the message “Type license() to see the full license text”, and when called, displays the full license text in a pager-like fashion (one screen at a time). | python.library.constants#license |
linecache — Random access to text lines Source code: Lib/linecache.py The linecache module allows one to get any line from a Python source file, while attempting to optimize internally, using a cache, the common case where many lines are read from a single file. This is used by the traceback module to retrieve source lines for inclusion in the formatted traceback. The tokenize.open() function is used to open files. This function uses tokenize.detect_encoding() to get the encoding of the file; in the absence of an encoding token, the file encoding defaults to UTF-8. The linecache module defines the following functions:
linecache.getline(filename, lineno, module_globals=None)
Get line lineno from file named filename. This function will never raise an exception — it will return '' on errors (the terminating newline character will be included for lines that are found). If a file named filename is not found, the function first checks for a PEP 302 __loader__ in module_globals. If there is such a loader and it defines a get_source method, then that determines the source lines (if get_source() returns None, then '' is returned). Finally, if filename is a relative filename, it is looked up relative to the entries in the module search path, sys.path.
linecache.clearcache()
Clear the cache. Use this function if you no longer need lines from files previously read using getline().
linecache.checkcache(filename=None)
Check the cache for validity. Use this function if files in the cache may have changed on disk, and you require the updated version. If filename is omitted, it will check all the entries in the cache.
linecache.lazycache(filename, module_globals)
Capture enough detail about a non-file-based module to permit getting its lines later via getline() even if module_globals is None in the later call. This avoids doing I/O until a line is actually needed, without having to carry the module globals around indefinitely. New in version 3.5.
Example: >>> import linecache
>>> linecache.getline(linecache.__file__, 8)
'import sys\n' | python.library.linecache |
linecache.checkcache(filename=None)
Check the cache for validity. Use this function if files in the cache may have changed on disk, and you require the updated version. If filename is omitted, it will check all the entries in the cache. | python.library.linecache#linecache.checkcache |
linecache.clearcache()
Clear the cache. Use this function if you no longer need lines from files previously read using getline(). | python.library.linecache#linecache.clearcache |
linecache.getline(filename, lineno, module_globals=None)
Get line lineno from file named filename. This function will never raise an exception — it will return '' on errors (the terminating newline character will be included for lines that are found). If a file named filename is not found, the function first checks for a PEP 302 __loader__ in module_globals. If there is such a loader and it defines a get_source method, then that determines the source lines (if get_source() returns None, then '' is returned). Finally, if filename is a relative filename, it is looked up relative to the entries in the module search path, sys.path. | python.library.linecache#linecache.getline |
linecache.lazycache(filename, module_globals)
Capture enough detail about a non-file-based module to permit getting its lines later via getline() even if module_globals is None in the later call. This avoids doing I/O until a line is actually needed, without having to carry the module globals around indefinitely. New in version 3.5. | python.library.linecache#linecache.lazycache |
class list([iterable])
Lists may be constructed in several ways: Using a pair of square brackets to denote the empty list: []
Using square brackets, separating items with commas: [a], [a, b, c]
Using a list comprehension: [x for x in iterable]
Using the type constructor: list() or list(iterable)
The constructor builds a list whose items are the same and in the same order as iterable’s items. iterable may be either a sequence, a container that supports iteration, or an iterator object. If iterable is already a list, a copy is made and returned, similar to iterable[:]. For example, list('abc') returns ['a', 'b', 'c'] and list( (1, 2, 3) ) returns [1, 2, 3]. If no argument is given, the constructor creates a new empty list, []. Many other operations also produce lists, including the sorted() built-in. Lists implement all of the common and mutable sequence operations. Lists also provide the following additional method:
sort(*, key=None, reverse=False)
This method sorts the list in place, using only < comparisons between items. Exceptions are not suppressed - if any comparison operations fail, the entire sort operation will fail (and the list will likely be left in a partially modified state). sort() accepts two arguments that can only be passed by keyword (keyword-only arguments): key specifies a function of one argument that is used to extract a comparison key from each list element (for example, key=str.lower). The key corresponding to each item in the list is calculated once and then used for the entire sorting process. The default value of None means that list items are sorted directly without calculating a separate key value. The functools.cmp_to_key() utility is available to convert a 2.x style cmp function to a key function. reverse is a boolean value. If set to True, then the list elements are sorted as if each comparison were reversed. This method modifies the sequence in place for economy of space when sorting a large sequence. To remind users that it operates by side effect, it does not return the sorted sequence (use sorted() to explicitly request a new sorted list instance). The sort() method is guaranteed to be stable. A sort is stable if it guarantees not to change the relative order of elements that compare equal — this is helpful for sorting in multiple passes (for example, sort by department, then by salary grade). For sorting examples and a brief sorting tutorial, see Sorting HOW TO. CPython implementation detail: While a list is being sorted, the effect of attempting to mutate, or even inspect, the list is undefined. The C implementation of Python makes the list appear empty for the duration, and raises ValueError if it can detect that the list has been mutated during a sort. | python.library.stdtypes#list |
class list([iterable])
Rather than being a function, list is actually a mutable sequence type, as documented in Lists and Sequence Types — list, tuple, range. | python.library.functions#list |
sort(*, key=None, reverse=False)
This method sorts the list in place, using only < comparisons between items. Exceptions are not suppressed - if any comparison operations fail, the entire sort operation will fail (and the list will likely be left in a partially modified state). sort() accepts two arguments that can only be passed by keyword (keyword-only arguments): key specifies a function of one argument that is used to extract a comparison key from each list element (for example, key=str.lower). The key corresponding to each item in the list is calculated once and then used for the entire sorting process. The default value of None means that list items are sorted directly without calculating a separate key value. The functools.cmp_to_key() utility is available to convert a 2.x style cmp function to a key function. reverse is a boolean value. If set to True, then the list elements are sorted as if each comparison were reversed. This method modifies the sequence in place for economy of space when sorting a large sequence. To remind users that it operates by side effect, it does not return the sorted sequence (use sorted() to explicitly request a new sorted list instance). The sort() method is guaranteed to be stable. A sort is stable if it guarantees not to change the relative order of elements that compare equal — this is helpful for sorting in multiple passes (for example, sort by department, then by salary grade). For sorting examples and a brief sorting tutorial, see Sorting HOW TO. CPython implementation detail: While a list is being sorted, the effect of attempting to mutate, or even inspect, the list is undefined. The C implementation of Python makes the list appear empty for the duration, and raises ValueError if it can detect that the list has been mutated during a sort. | python.library.stdtypes#list.sort |
locale — Internationalization services Source code: Lib/locale.py The locale module opens access to the POSIX locale database and functionality. The POSIX locale mechanism allows programmers to deal with certain cultural issues in an application, without requiring the programmer to know all the specifics of each country where the software is executed. The locale module is implemented on top of the _locale module, which in turn uses an ANSI C locale implementation if available. The locale module defines the following exception and functions:
exception locale.Error
Exception raised when the locale passed to setlocale() is not recognized.
locale.setlocale(category, locale=None)
If locale is given and not None, setlocale() modifies the locale setting for the category. The available categories are listed in the data description below. locale may be a string, or an iterable of two strings (language code and encoding). If it’s an iterable, it’s converted to a locale name using the locale aliasing engine. An empty string specifies the user’s default settings. If the modification of the locale fails, the exception Error is raised. If successful, the new locale setting is returned. If locale is omitted or None, the current setting for category is returned. setlocale() is not thread-safe on most systems. Applications typically start with a call of import locale
locale.setlocale(locale.LC_ALL, '')
This sets the locale for all categories to the user’s default setting (typically specified in the LANG environment variable). If the locale is not changed thereafter, using multithreading should not cause problems.
locale.localeconv()
Returns the database of the local conventions as a dictionary. This dictionary has the following strings as keys:
Category Key Meaning
LC_NUMERIC 'decimal_point' Decimal point character.
'grouping' Sequence of numbers specifying which relative positions the 'thousands_sep' is expected. If the sequence is terminated with CHAR_MAX, no further grouping is performed. If the sequence terminates with a 0, the last group size is repeatedly used.
'thousands_sep' Character used between groups.
LC_MONETARY 'int_curr_symbol' International currency symbol.
'currency_symbol' Local currency symbol.
'p_cs_precedes/n_cs_precedes' Whether the currency symbol precedes the value (for positive resp. negative values).
'p_sep_by_space/n_sep_by_space' Whether the currency symbol is separated from the value by a space (for positive resp. negative values).
'mon_decimal_point' Decimal point used for monetary values.
'frac_digits' Number of fractional digits used in local formatting of monetary values.
'int_frac_digits' Number of fractional digits used in international formatting of monetary values.
'mon_thousands_sep' Group separator used for monetary values.
'mon_grouping' Equivalent to 'grouping', used for monetary values.
'positive_sign' Symbol used to annotate a positive monetary value.
'negative_sign' Symbol used to annotate a negative monetary value.
'p_sign_posn/n_sign_posn' The position of the sign (for positive resp. negative values), see below. All numeric values can be set to CHAR_MAX to indicate that there is no value specified in this locale. The possible values for 'p_sign_posn' and 'n_sign_posn' are given below.
Value Explanation
0 Currency and value are surrounded by parentheses.
1 The sign should precede the value and currency symbol.
2 The sign should follow the value and currency symbol.
3 The sign should immediately precede the value.
4 The sign should immediately follow the value.
CHAR_MAX Nothing is specified in this locale. The function sets temporarily the LC_CTYPE locale to the LC_NUMERIC locale or the LC_MONETARY locale if locales are different and numeric or monetary strings are non-ASCII. This temporary change affects other threads. Changed in version 3.7: The function now sets temporarily the LC_CTYPE locale to the LC_NUMERIC locale in some cases.
locale.nl_langinfo(option)
Return some locale-specific information as a string. This function is not available on all systems, and the set of possible options might also vary across platforms. The possible argument values are numbers, for which symbolic constants are available in the locale module. The nl_langinfo() function accepts one of the following keys. Most descriptions are taken from the corresponding description in the GNU C library.
locale.CODESET
Get a string with the name of the character encoding used in the selected locale.
locale.D_T_FMT
Get a string that can be used as a format string for time.strftime() to represent date and time in a locale-specific way.
locale.D_FMT
Get a string that can be used as a format string for time.strftime() to represent a date in a locale-specific way.
locale.T_FMT
Get a string that can be used as a format string for time.strftime() to represent a time in a locale-specific way.
locale.T_FMT_AMPM
Get a format string for time.strftime() to represent time in the am/pm format.
DAY_1 ... DAY_7
Get the name of the n-th day of the week. Note This follows the US convention of DAY_1 being Sunday, not the international convention (ISO 8601) that Monday is the first day of the week.
ABDAY_1 ... ABDAY_7
Get the abbreviated name of the n-th day of the week.
MON_1 ... MON_12
Get the name of the n-th month.
ABMON_1 ... ABMON_12
Get the abbreviated name of the n-th month.
locale.RADIXCHAR
Get the radix character (decimal dot, decimal comma, etc.).
locale.THOUSEP
Get the separator character for thousands (groups of three digits).
locale.YESEXPR
Get a regular expression that can be used with the regex function to recognize a positive response to a yes/no question. Note The expression is in the syntax suitable for the regex() function from the C library, which might differ from the syntax used in re.
locale.NOEXPR
Get a regular expression that can be used with the regex(3) function to recognize a negative response to a yes/no question.
locale.CRNCYSTR
Get the currency symbol, preceded by “-” if the symbol should appear before the value, “+” if the symbol should appear after the value, or “.” if the symbol should replace the radix character.
locale.ERA
Get a string that represents the era used in the current locale. Most locales do not define this value. An example of a locale which does define this value is the Japanese one. In Japan, the traditional representation of dates includes the name of the era corresponding to the then-emperor’s reign. Normally it should not be necessary to use this value directly. Specifying the E modifier in their format strings causes the time.strftime() function to use this information. The format of the returned string is not specified, and therefore you should not assume knowledge of it on different systems.
locale.ERA_D_T_FMT
Get a format string for time.strftime() to represent date and time in a locale-specific era-based way.
locale.ERA_D_FMT
Get a format string for time.strftime() to represent a date in a locale-specific era-based way.
locale.ERA_T_FMT
Get a format string for time.strftime() to represent a time in a locale-specific era-based way.
locale.ALT_DIGITS
Get a representation of up to 100 values used to represent the values 0 to 99.
locale.getdefaultlocale([envvars])
Tries to determine the default locale settings and returns them as a tuple of the form (language code, encoding). According to POSIX, a program which has not called setlocale(LC_ALL, '') runs using the portable 'C' locale. Calling setlocale(LC_ALL, '') lets it use the default locale as defined by the LANG variable. Since we do not want to interfere with the current locale setting we thus emulate the behavior in the way described above. To maintain compatibility with other platforms, not only the LANG variable is tested, but a list of variables given as envvars parameter. The first found to be defined will be used. envvars defaults to the search path used in GNU gettext; it must always contain the variable name 'LANG'. The GNU gettext search path contains 'LC_ALL', 'LC_CTYPE', 'LANG' and 'LANGUAGE', in that order. Except for the code 'C', the language code corresponds to RFC 1766. language code and encoding may be None if their values cannot be determined.
locale.getlocale(category=LC_CTYPE)
Returns the current setting for the given locale category as sequence containing language code, encoding. category may be one of the LC_* values except LC_ALL. It defaults to LC_CTYPE. Except for the code 'C', the language code corresponds to RFC 1766. language code and encoding may be None if their values cannot be determined.
locale.getpreferredencoding(do_setlocale=True)
Return the encoding used for text data, according to user preferences. User preferences are expressed differently on different systems, and might not be available programmatically on some systems, so this function only returns a guess. On some systems, it is necessary to invoke setlocale() to obtain the user preferences, so this function is not thread-safe. If invoking setlocale is not necessary or desired, do_setlocale should be set to False. On Android or in the UTF-8 mode (-X utf8 option), always return 'UTF-8', the locale and the do_setlocale argument are ignored. Changed in version 3.7: The function now always returns UTF-8 on Android or if the UTF-8 mode is enabled.
locale.normalize(localename)
Returns a normalized locale code for the given locale name. The returned locale code is formatted for use with setlocale(). If normalization fails, the original name is returned unchanged. If the given encoding is not known, the function defaults to the default encoding for the locale code just like setlocale().
locale.resetlocale(category=LC_ALL)
Sets the locale for category to the default setting. The default setting is determined by calling getdefaultlocale(). category defaults to LC_ALL.
locale.strcoll(string1, string2)
Compares two strings according to the current LC_COLLATE setting. As any other compare function, returns a negative, or a positive value, or 0, depending on whether string1 collates before or after string2 or is equal to it.
locale.strxfrm(string)
Transforms a string to one that can be used in locale-aware comparisons. For example, strxfrm(s1) < strxfrm(s2) is equivalent to strcoll(s1, s2) < 0. This function can be used when the same string is compared repeatedly, e.g. when collating a sequence of strings.
locale.format_string(format, val, grouping=False, monetary=False)
Formats a number val according to the current LC_NUMERIC setting. The format follows the conventions of the % operator. For floating point values, the decimal point is modified if appropriate. If grouping is true, also takes the grouping into account. If monetary is true, the conversion uses monetary thousands separator and grouping strings. Processes formatting specifiers as in format % val, but takes the current locale settings into account. Changed in version 3.7: The monetary keyword parameter was added.
locale.format(format, val, grouping=False, monetary=False)
Please note that this function works like format_string() but will only work for exactly one %char specifier. For example, '%f' and '%.0f' are both valid specifiers, but '%f KiB' is not. For whole format strings, use format_string(). Deprecated since version 3.7: Use format_string() instead.
locale.currency(val, symbol=True, grouping=False, international=False)
Formats a number val according to the current LC_MONETARY settings. The returned string includes the currency symbol if symbol is true, which is the default. If grouping is true (which is not the default), grouping is done with the value. If international is true (which is not the default), the international currency symbol is used. Note that this function will not work with the ‘C’ locale, so you have to set a locale via setlocale() first.
locale.str(float)
Formats a floating point number using the same format as the built-in function str(float), but takes the decimal point into account.
locale.delocalize(string)
Converts a string into a normalized number string, following the LC_NUMERIC settings. New in version 3.5.
locale.atof(string)
Converts a string to a floating point number, following the LC_NUMERIC settings.
locale.atoi(string)
Converts a string to an integer, following the LC_NUMERIC conventions.
locale.LC_CTYPE
Locale category for the character type functions. Depending on the settings of this category, the functions of module string dealing with case change their behaviour.
locale.LC_COLLATE
Locale category for sorting strings. The functions strcoll() and strxfrm() of the locale module are affected.
locale.LC_TIME
Locale category for the formatting of time. The function time.strftime() follows these conventions.
locale.LC_MONETARY
Locale category for formatting of monetary values. The available options are available from the localeconv() function.
locale.LC_MESSAGES
Locale category for message display. Python currently does not support application specific locale-aware messages. Messages displayed by the operating system, like those returned by os.strerror() might be affected by this category.
locale.LC_NUMERIC
Locale category for formatting numbers. The functions format(), atoi(), atof() and str() of the locale module are affected by that category. All other numeric formatting operations are not affected.
locale.LC_ALL
Combination of all locale settings. If this flag is used when the locale is changed, setting the locale for all categories is attempted. If that fails for any category, no category is changed at all. When the locale is retrieved using this flag, a string indicating the setting for all categories is returned. This string can be later used to restore the settings.
locale.CHAR_MAX
This is a symbolic constant used for different values returned by localeconv().
Example: >>> import locale
>>> loc = locale.getlocale() # get current locale
# use German locale; name might vary with platform
>>> locale.setlocale(locale.LC_ALL, 'de_DE')
>>> locale.strcoll('f\xe4n', 'foo') # compare a string containing an umlaut
>>> locale.setlocale(locale.LC_ALL, '') # use user's preferred locale
>>> locale.setlocale(locale.LC_ALL, 'C') # use default (C) locale
>>> locale.setlocale(locale.LC_ALL, loc) # restore saved locale
Background, details, hints, tips and caveats The C standard defines the locale as a program-wide property that may be relatively expensive to change. On top of that, some implementation are broken in such a way that frequent locale changes may cause core dumps. This makes the locale somewhat painful to use correctly. Initially, when a program is started, the locale is the C locale, no matter what the user’s preferred locale is. There is one exception: the LC_CTYPE category is changed at startup to set the current locale encoding to the user’s preferred locale encoding. The program must explicitly say that it wants the user’s preferred locale settings for other categories by calling setlocale(LC_ALL, ''). It is generally a bad idea to call setlocale() in some library routine, since as a side effect it affects the entire program. Saving and restoring it is almost as bad: it is expensive and affects other threads that happen to run before the settings have been restored. If, when coding a module for general use, you need a locale independent version of an operation that is affected by the locale (such as certain formats used with time.strftime()), you will have to find a way to do it without using the standard library routine. Even better is convincing yourself that using locale settings is okay. Only as a last resort should you document that your module is not compatible with non-C locale settings. The only way to perform numeric operations according to the locale is to use the special functions defined by this module: atof(), atoi(), format(), str(). There is no way to perform case conversions and character classifications according to the locale. For (Unicode) text strings these are done according to the character value only, while for byte strings, the conversions and classifications are done according to the ASCII value of the byte, and bytes whose high bit is set (i.e., non-ASCII bytes) are never converted or considered part of a character class such as letter or whitespace. For extension writers and programs that embed Python Extension modules should never call setlocale(), except to find out what the current locale is. But since the return value can only be used portably to restore it, that is not very useful (except perhaps to find out whether or not the locale is C). When Python code uses the locale module to change the locale, this also affects the embedding application. If the embedding application doesn’t want this to happen, it should remove the _locale extension module (which does all the work) from the table of built-in modules in the config.c file, and make sure that the _locale module is not accessible as a shared library. Access to message catalogs
locale.gettext(msg)
locale.dgettext(domain, msg)
locale.dcgettext(domain, msg, category)
locale.textdomain(domain)
locale.bindtextdomain(domain, dir)
The locale module exposes the C library’s gettext interface on systems that provide this interface. It consists of the functions gettext(), dgettext(), dcgettext(), textdomain(), bindtextdomain(), and bind_textdomain_codeset(). These are similar to the same functions in the gettext module, but use the C library’s binary format for message catalogs, and the C library’s search algorithms for locating message catalogs. Python applications should normally find no need to invoke these functions, and should use gettext instead. A known exception to this rule are applications that link with additional C libraries which internally invoke gettext() or dcgettext(). For these applications, it may be necessary to bind the text domain, so that the libraries can properly locate their message catalogs. | python.library.locale |
locale.ALT_DIGITS
Get a representation of up to 100 values used to represent the values 0 to 99. | python.library.locale#locale.ALT_DIGITS |
locale.atof(string)
Converts a string to a floating point number, following the LC_NUMERIC settings. | python.library.locale#locale.atof |
locale.atoi(string)
Converts a string to an integer, following the LC_NUMERIC conventions. | python.library.locale#locale.atoi |
locale.bindtextdomain(domain, dir) | python.library.locale#locale.bindtextdomain |
locale.CHAR_MAX
This is a symbolic constant used for different values returned by localeconv(). | python.library.locale#locale.CHAR_MAX |
locale.CODESET
Get a string with the name of the character encoding used in the selected locale. | python.library.locale#locale.CODESET |
locale.CRNCYSTR
Get the currency symbol, preceded by “-” if the symbol should appear before the value, “+” if the symbol should appear after the value, or “.” if the symbol should replace the radix character. | python.library.locale#locale.CRNCYSTR |
locale.currency(val, symbol=True, grouping=False, international=False)
Formats a number val according to the current LC_MONETARY settings. The returned string includes the currency symbol if symbol is true, which is the default. If grouping is true (which is not the default), grouping is done with the value. If international is true (which is not the default), the international currency symbol is used. Note that this function will not work with the ‘C’ locale, so you have to set a locale via setlocale() first. | python.library.locale#locale.currency |
locale.dcgettext(domain, msg, category) | python.library.locale#locale.dcgettext |
locale.delocalize(string)
Converts a string into a normalized number string, following the LC_NUMERIC settings. New in version 3.5. | python.library.locale#locale.delocalize |
locale.dgettext(domain, msg) | python.library.locale#locale.dgettext |
locale.D_FMT
Get a string that can be used as a format string for time.strftime() to represent a date in a locale-specific way. | python.library.locale#locale.D_FMT |
locale.D_T_FMT
Get a string that can be used as a format string for time.strftime() to represent date and time in a locale-specific way. | python.library.locale#locale.D_T_FMT |
locale.ERA
Get a string that represents the era used in the current locale. Most locales do not define this value. An example of a locale which does define this value is the Japanese one. In Japan, the traditional representation of dates includes the name of the era corresponding to the then-emperor’s reign. Normally it should not be necessary to use this value directly. Specifying the E modifier in their format strings causes the time.strftime() function to use this information. The format of the returned string is not specified, and therefore you should not assume knowledge of it on different systems. | python.library.locale#locale.ERA |
locale.ERA_D_FMT
Get a format string for time.strftime() to represent a date in a locale-specific era-based way. | python.library.locale#locale.ERA_D_FMT |
locale.ERA_D_T_FMT
Get a format string for time.strftime() to represent date and time in a locale-specific era-based way. | python.library.locale#locale.ERA_D_T_FMT |
locale.ERA_T_FMT
Get a format string for time.strftime() to represent a time in a locale-specific era-based way. | python.library.locale#locale.ERA_T_FMT |
exception locale.Error
Exception raised when the locale passed to setlocale() is not recognized. | python.library.locale#locale.Error |
locale.format(format, val, grouping=False, monetary=False)
Please note that this function works like format_string() but will only work for exactly one %char specifier. For example, '%f' and '%.0f' are both valid specifiers, but '%f KiB' is not. For whole format strings, use format_string(). Deprecated since version 3.7: Use format_string() instead. | python.library.locale#locale.format |
locale.format_string(format, val, grouping=False, monetary=False)
Formats a number val according to the current LC_NUMERIC setting. The format follows the conventions of the % operator. For floating point values, the decimal point is modified if appropriate. If grouping is true, also takes the grouping into account. If monetary is true, the conversion uses monetary thousands separator and grouping strings. Processes formatting specifiers as in format % val, but takes the current locale settings into account. Changed in version 3.7: The monetary keyword parameter was added. | python.library.locale#locale.format_string |
locale.getdefaultlocale([envvars])
Tries to determine the default locale settings and returns them as a tuple of the form (language code, encoding). According to POSIX, a program which has not called setlocale(LC_ALL, '') runs using the portable 'C' locale. Calling setlocale(LC_ALL, '') lets it use the default locale as defined by the LANG variable. Since we do not want to interfere with the current locale setting we thus emulate the behavior in the way described above. To maintain compatibility with other platforms, not only the LANG variable is tested, but a list of variables given as envvars parameter. The first found to be defined will be used. envvars defaults to the search path used in GNU gettext; it must always contain the variable name 'LANG'. The GNU gettext search path contains 'LC_ALL', 'LC_CTYPE', 'LANG' and 'LANGUAGE', in that order. Except for the code 'C', the language code corresponds to RFC 1766. language code and encoding may be None if their values cannot be determined. | python.library.locale#locale.getdefaultlocale |
locale.getlocale(category=LC_CTYPE)
Returns the current setting for the given locale category as sequence containing language code, encoding. category may be one of the LC_* values except LC_ALL. It defaults to LC_CTYPE. Except for the code 'C', the language code corresponds to RFC 1766. language code and encoding may be None if their values cannot be determined. | python.library.locale#locale.getlocale |
locale.getpreferredencoding(do_setlocale=True)
Return the encoding used for text data, according to user preferences. User preferences are expressed differently on different systems, and might not be available programmatically on some systems, so this function only returns a guess. On some systems, it is necessary to invoke setlocale() to obtain the user preferences, so this function is not thread-safe. If invoking setlocale is not necessary or desired, do_setlocale should be set to False. On Android or in the UTF-8 mode (-X utf8 option), always return 'UTF-8', the locale and the do_setlocale argument are ignored. Changed in version 3.7: The function now always returns UTF-8 on Android or if the UTF-8 mode is enabled. | python.library.locale#locale.getpreferredencoding |
locale.gettext(msg) | python.library.locale#locale.gettext |
locale.LC_ALL
Combination of all locale settings. If this flag is used when the locale is changed, setting the locale for all categories is attempted. If that fails for any category, no category is changed at all. When the locale is retrieved using this flag, a string indicating the setting for all categories is returned. This string can be later used to restore the settings. | python.library.locale#locale.LC_ALL |
locale.LC_COLLATE
Locale category for sorting strings. The functions strcoll() and strxfrm() of the locale module are affected. | python.library.locale#locale.LC_COLLATE |
locale.LC_CTYPE
Locale category for the character type functions. Depending on the settings of this category, the functions of module string dealing with case change their behaviour. | python.library.locale#locale.LC_CTYPE |
locale.LC_MESSAGES
Locale category for message display. Python currently does not support application specific locale-aware messages. Messages displayed by the operating system, like those returned by os.strerror() might be affected by this category. | python.library.locale#locale.LC_MESSAGES |
locale.LC_MONETARY
Locale category for formatting of monetary values. The available options are available from the localeconv() function. | python.library.locale#locale.LC_MONETARY |
locale.LC_NUMERIC
Locale category for formatting numbers. The functions format(), atoi(), atof() and str() of the locale module are affected by that category. All other numeric formatting operations are not affected. | python.library.locale#locale.LC_NUMERIC |
locale.LC_TIME
Locale category for the formatting of time. The function time.strftime() follows these conventions. | python.library.locale#locale.LC_TIME |
locale.localeconv()
Returns the database of the local conventions as a dictionary. This dictionary has the following strings as keys:
Category Key Meaning
LC_NUMERIC 'decimal_point' Decimal point character.
'grouping' Sequence of numbers specifying which relative positions the 'thousands_sep' is expected. If the sequence is terminated with CHAR_MAX, no further grouping is performed. If the sequence terminates with a 0, the last group size is repeatedly used.
'thousands_sep' Character used between groups.
LC_MONETARY 'int_curr_symbol' International currency symbol.
'currency_symbol' Local currency symbol.
'p_cs_precedes/n_cs_precedes' Whether the currency symbol precedes the value (for positive resp. negative values).
'p_sep_by_space/n_sep_by_space' Whether the currency symbol is separated from the value by a space (for positive resp. negative values).
'mon_decimal_point' Decimal point used for monetary values.
'frac_digits' Number of fractional digits used in local formatting of monetary values.
'int_frac_digits' Number of fractional digits used in international formatting of monetary values.
'mon_thousands_sep' Group separator used for monetary values.
'mon_grouping' Equivalent to 'grouping', used for monetary values.
'positive_sign' Symbol used to annotate a positive monetary value.
'negative_sign' Symbol used to annotate a negative monetary value.
'p_sign_posn/n_sign_posn' The position of the sign (for positive resp. negative values), see below. All numeric values can be set to CHAR_MAX to indicate that there is no value specified in this locale. The possible values for 'p_sign_posn' and 'n_sign_posn' are given below.
Value Explanation
0 Currency and value are surrounded by parentheses.
1 The sign should precede the value and currency symbol.
2 The sign should follow the value and currency symbol.
3 The sign should immediately precede the value.
4 The sign should immediately follow the value.
CHAR_MAX Nothing is specified in this locale. The function sets temporarily the LC_CTYPE locale to the LC_NUMERIC locale or the LC_MONETARY locale if locales are different and numeric or monetary strings are non-ASCII. This temporary change affects other threads. Changed in version 3.7: The function now sets temporarily the LC_CTYPE locale to the LC_NUMERIC locale in some cases. | python.library.locale#locale.localeconv |
locale.nl_langinfo(option)
Return some locale-specific information as a string. This function is not available on all systems, and the set of possible options might also vary across platforms. The possible argument values are numbers, for which symbolic constants are available in the locale module. The nl_langinfo() function accepts one of the following keys. Most descriptions are taken from the corresponding description in the GNU C library.
locale.CODESET
Get a string with the name of the character encoding used in the selected locale.
locale.D_T_FMT
Get a string that can be used as a format string for time.strftime() to represent date and time in a locale-specific way.
locale.D_FMT
Get a string that can be used as a format string for time.strftime() to represent a date in a locale-specific way.
locale.T_FMT
Get a string that can be used as a format string for time.strftime() to represent a time in a locale-specific way.
locale.T_FMT_AMPM
Get a format string for time.strftime() to represent time in the am/pm format.
DAY_1 ... DAY_7
Get the name of the n-th day of the week. Note This follows the US convention of DAY_1 being Sunday, not the international convention (ISO 8601) that Monday is the first day of the week.
ABDAY_1 ... ABDAY_7
Get the abbreviated name of the n-th day of the week.
MON_1 ... MON_12
Get the name of the n-th month.
ABMON_1 ... ABMON_12
Get the abbreviated name of the n-th month.
locale.RADIXCHAR
Get the radix character (decimal dot, decimal comma, etc.).
locale.THOUSEP
Get the separator character for thousands (groups of three digits).
locale.YESEXPR
Get a regular expression that can be used with the regex function to recognize a positive response to a yes/no question. Note The expression is in the syntax suitable for the regex() function from the C library, which might differ from the syntax used in re.
locale.NOEXPR
Get a regular expression that can be used with the regex(3) function to recognize a negative response to a yes/no question.
locale.CRNCYSTR
Get the currency symbol, preceded by “-” if the symbol should appear before the value, “+” if the symbol should appear after the value, or “.” if the symbol should replace the radix character.
locale.ERA
Get a string that represents the era used in the current locale. Most locales do not define this value. An example of a locale which does define this value is the Japanese one. In Japan, the traditional representation of dates includes the name of the era corresponding to the then-emperor’s reign. Normally it should not be necessary to use this value directly. Specifying the E modifier in their format strings causes the time.strftime() function to use this information. The format of the returned string is not specified, and therefore you should not assume knowledge of it on different systems.
locale.ERA_D_T_FMT
Get a format string for time.strftime() to represent date and time in a locale-specific era-based way.
locale.ERA_D_FMT
Get a format string for time.strftime() to represent a date in a locale-specific era-based way.
locale.ERA_T_FMT
Get a format string for time.strftime() to represent a time in a locale-specific era-based way.
locale.ALT_DIGITS
Get a representation of up to 100 values used to represent the values 0 to 99. | python.library.locale#locale.nl_langinfo |
locale.NOEXPR
Get a regular expression that can be used with the regex(3) function to recognize a negative response to a yes/no question. | python.library.locale#locale.NOEXPR |
locale.normalize(localename)
Returns a normalized locale code for the given locale name. The returned locale code is formatted for use with setlocale(). If normalization fails, the original name is returned unchanged. If the given encoding is not known, the function defaults to the default encoding for the locale code just like setlocale(). | python.library.locale#locale.normalize |
locale.RADIXCHAR
Get the radix character (decimal dot, decimal comma, etc.). | python.library.locale#locale.RADIXCHAR |
locale.resetlocale(category=LC_ALL)
Sets the locale for category to the default setting. The default setting is determined by calling getdefaultlocale(). category defaults to LC_ALL. | python.library.locale#locale.resetlocale |
locale.setlocale(category, locale=None)
If locale is given and not None, setlocale() modifies the locale setting for the category. The available categories are listed in the data description below. locale may be a string, or an iterable of two strings (language code and encoding). If it’s an iterable, it’s converted to a locale name using the locale aliasing engine. An empty string specifies the user’s default settings. If the modification of the locale fails, the exception Error is raised. If successful, the new locale setting is returned. If locale is omitted or None, the current setting for category is returned. setlocale() is not thread-safe on most systems. Applications typically start with a call of import locale
locale.setlocale(locale.LC_ALL, '')
This sets the locale for all categories to the user’s default setting (typically specified in the LANG environment variable). If the locale is not changed thereafter, using multithreading should not cause problems. | python.library.locale#locale.setlocale |
locale.str(float)
Formats a floating point number using the same format as the built-in function str(float), but takes the decimal point into account. | python.library.locale#locale.str |
locale.strcoll(string1, string2)
Compares two strings according to the current LC_COLLATE setting. As any other compare function, returns a negative, or a positive value, or 0, depending on whether string1 collates before or after string2 or is equal to it. | python.library.locale#locale.strcoll |
locale.strxfrm(string)
Transforms a string to one that can be used in locale-aware comparisons. For example, strxfrm(s1) < strxfrm(s2) is equivalent to strcoll(s1, s2) < 0. This function can be used when the same string is compared repeatedly, e.g. when collating a sequence of strings. | python.library.locale#locale.strxfrm |
locale.textdomain(domain) | python.library.locale#locale.textdomain |
locale.THOUSEP
Get the separator character for thousands (groups of three digits). | python.library.locale#locale.THOUSEP |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.