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Say I have
d = {"a":0,"b":0,"c":0}
is there a way to update the keys a and b at the same time, instead of looping over them, such like
update_keys = ["a","b"]
d.some_function(update_keys) +=[10,5]
print(d)
{"a":10,"b":5,"c":0}
Yes, you can use update like this:
d.update({'a':10, 'b':5})
Thus, your code would look this way:
d = {"a":0,"b":0,"c":0}
d.update({'a':10, 'b':5})
print(d)
and shows:
{"a":10,"b":5,"c":0}
If you mean a function that can add a new value to the existing value without an explict loop, you can definitely do it like this.
add_value = lambda d,k,v: d.update(zip(k,list(map(lambda _k,_v:d[_k]+_v,k,v)))) or d
and you can use it like this
>>> d = {"a":2,"b":3}
>>> add_value(d,["a","b"],[2,-3])
{'a': 4, 'b': 0}
There is nothing tricky here, I just replace the loop with a map and a lambda to do the update job and use list to wrap them up so Python will immediately evaluate the result of map. Then I use zip to create an updated key-value pair and use dict's update method the update the dictionary. However I really doubt if this has any practical usage since this is definitely more complex than a for loop and introduces extra complexity to the code.
Update values of multiple keys in dictionary
d = {"a":0,"b":0,"c":0}
d.update({'a': 40, 'b': 41, 'c': 89})
print(d)
{'a': 40, 'b': 41, 'c': 89}
If you are just storing integer values, then you can use the Counter class provided by the python module "collections":
from collections import Counter
d = Counter({"a":0,"b":0,"c":0})
result = d + Counter({"a":10, "b":5})
'result' will have the value of
Counter({'a': 10, 'b': 5})
And since Counter is subclassed from Dict, you have probably do not have to change anything else in your code.
>>> isinstance(result, dict)
True
You do not see the 'c' key in the result because 0-values are not stored in a Counter instance, which saves space.
You can check out more about the Counter instance here.
Storing other numeric types is supported, with some conditions:
"For in-place operations such as c[key] += 1, the value type need only support addition and subtraction. So fractions, floats, and decimals would work and negative values are supported. The same is also true for update() and subtract() which allow negative and zero values for both inputs and outputs."
Performing the inverse operation of "+" requires using the method "subtract", which is a note-worthy "gotcha".
>>> d = Counter({"a":10, "b":15})
>>> result.subtract(d)
>>> c
Counter({'a': 0, 'b': 0})
I have a dictionary
a={}
a['A']=1
a['B']=1
a['C']=2
I need to output the following
1 has occurred 2 times
2 has occurred 1 times
What is the best way to do this.
This is easily (and efficiently) done with collections.Counter(), which is designed to (unsurprisingly) count things:
>>> import collections
>>> a = {"A": 1, "B": 1, "C": 2}
>>> collections.Counter(a.values())
Counter({1: 2, 2: 1})
This gives you a dictionary-like object that can trivially be used to generate the output you want.
Use the Counter class:
from collections import Counter
a = {}
a["A"] = 1
a["B"] = 1
a["C"] = 2
c = Counter(a.values())
c
=> Counter({1: 2, 2: 1})
From the documentation:
A Counter is a dict subclass for counting hashable objects. It is an unordered collection where elements are stored as dictionary keys and their counts are stored as dictionary values. Counts are allowed to be any integer value including zero or negative counts. The Counter class is similar to bags or multisets in other languages.
Given a string s, I want to know how many times each character at the string occurs. Here is the code:
def main() :
while True :
try :
line=raw_input('Enter a string: ')
except EOFError :
break;
mp={};
for i in range(len(line)) :
if line[i] in mp :
mp[line[i]] += 1;
else :
mp[line[i]] = 1;
for i in range(len(line)) :
print line[i],': ',mp[line[i]];
if __name__ == '__main__' :
main();
When I run this code and I enter abbba, I get:
a : 2
b : 3
b : 3
b : 3
a : 2
I would like to get only:
a : 2
b : 3
I understand why this is happening, but as I'm new to python, I don't know any other ways to iterate over the elements of a map. Could anyone tell me how to do this? Thanks in advance.
You could try a Counter (Python 2.7 and above; see below for a pre-2.7 option):
>>> from collections import Counter
>>> Counter('abbba')
Counter({'b': 3, 'a': 2})
You can then access the elements just like a dictionary:
>>> counts = Counter('abbba')
>>> counts['a']
2
>>> counts['b']
3
And to iterate, you can use #BurhanKhalid's suggestion (the Counter behaves as a dictionary, where you can iterate over the key/value pairs):
>>> for k, v in Counter('abbba').iteritems():
... print k, v
...
a 2
b 3
If you're using a pre-2.7 version of Python, you can use a defaultdict to simplify your code a bit (process is still the same - only difference is that now you don't have to check for the key first - it will 'default' to 0 if a matching key isn't found). Counter has other features built into it, but if you simply want counts (and don't care about most_common, or being able to subtract, for instance), this should be fine and can be treated just as any other dictionary:
>>> from collections import defaultdict
>>> counts = defaultdict(int)
>>> for c in 'abbba':
... counts[c] += 1
...
>>> counts
defaultdict(<type 'int'>, {'a': 2, 'b': 3})
When you use iteritems() on a dictionary (or the Counter/defaultdict here), a key and a value are returned for each iteration (in this case, the key being the letter and the value being the number of occurrences). One thing to note about using dictionaries is that they are inherently unordered, so you won't necessarily get 'a', 'b', ... while iterating. One basic way to iterate through a dictionary in a sorted manner would be to iterate through a sorted list of the keys (here alphabetical, but sorted can be manipulated to handle a variety of options), and return the dictionary value for that key (there are other ways, but this will hopefully be somewhat informative):
>>> mapping = {'some': 2, 'example': 3, 'words': 5}
>>> mapping
{'some': 2, 'example': 3, 'words': 5}
>>> for key in sorted(mapping.keys()):
... print key, mapping[key]
...
example 3
some 2
words 5
Iterating over a mapping yields keys.
>>> d = {'foo': 42, 'bar': 'quux'}
>>> for k in d:
... print k, d[k]
...
foo 42
bar quux
You need to look up the help for dict(). It's all there -- 'for k in mp' iterates over keys, 'for v in mp.values()' iterates over values, 'for k,v in mp.items()' iterates over key, value pairs.
Also, you don't need those semicolons. While they are legal in Python, nobody uses them, there's pretty much no reason to.
Python 2.5 and above
dDIct = collections.defaultdict(int)
[(d[i]+=1) for i in line]
print dDict
In someone else's code I read the following two lines:
x = defaultdict(lambda: 0)
y = defaultdict(lambda: defaultdict(lambda: 0))
As the argument of defaultdict is a default factory, I think the first line means that when I call x[k] for a nonexistent key k (such as a statement like v=x[k]), the key-value pair (k,0) will be automatically added to the dictionary, as if the statement x[k]=0 is first executed. Am I correct?
And what about y? It seems that the default factory will create a defaultdict with default 0. But what does that mean concretely? I tried to play around with it in Python shell, but couldn't figure out what it is exactly.
I think the first line means that when I call x[k] for a nonexistent key k (such as a statement like v=x[k]), the key-value pair (k,0) will be automatically added to the dictionary, as if the statement x[k]=0 is first executed.
That's right. This is more idiomatically written
x = defaultdict(int)
In the case of y, when you do y["ham"]["spam"], the key "ham" is inserted in y if it does not exist. The value associated with it becomes a defaultdict in which "spam" is automatically inserted with a value of 0.
I.e., y is a kind of "two-tiered" defaultdict. If "ham" not in y, then evaluating y["ham"]["spam"] is like doing
y["ham"] = {}
y["ham"]["spam"] = 0
in terms of ordinary dict.
You are correct for what the first one does. As for y, it will create a defaultdict with default 0 when a key doesn't exist in y, so you can think of this as a nested dictionary. Consider the following example:
y = defaultdict(lambda: defaultdict(lambda: 0))
print y['k1']['k2'] # 0
print dict(y['k1']) # {'k2': 0}
To create an equivalent nested dictionary structure without defaultdict you would need to create an inner dict for y['k1'] and then set y['k1']['k2'] to 0, but defaultdict does all of this behind the scenes when it encounters keys it hasn't seen:
y = {}
y['k1'] = {}
y['k1']['k2'] = 0
The following function may help for playing around with this on an interpreter to better your understanding:
def to_dict(d):
if isinstance(d, defaultdict):
return dict((k, to_dict(v)) for k, v in d.items())
return d
This will return the dict equivalent of a nested defaultdict, which is a lot easier to read, for example:
>>> y = defaultdict(lambda: defaultdict(lambda: 0))
>>> y['a']['b'] = 5
>>> y
defaultdict(<function <lambda> at 0xb7ea93e4>, {'a': defaultdict(<function <lambda> at 0xb7ea9374>, {'b': 5})})
>>> to_dict(y)
{'a': {'b': 5}}
defaultdict takes a zero-argument callable to its constructor, which is called when the key is not found, as you correctly explained.
lambda: 0 will of course always return zero, but the preferred method to do that is defaultdict(int), which will do the same thing.
As for the second part, the author would like to create a new defaultdict(int), or a nested dictionary, whenever a key is not found in the top-level dictionary.
All answers are good enough still I am giving the answer to add more info:
"defaultdict requires an argument that is callable. That return result of that callable object is the default value that the dictionary returns when you try to access the dictionary with a key that does not exist."
Here's an example
SAMPLE= {'Age':28, 'Salary':2000}
SAMPLE = defaultdict(lambda:0,SAMPLE)
>>> SAMPLE
defaultdict(<function <lambda> at 0x0000000002BF7C88>, {'Salary': 2000, 'Age': 28})
>>> SAMPLE['Age']----> This will return 28
>>> SAMPLE['Phone']----> This will return 0 # you got 0 as output for a non existing key inside SAMPLE
y = defaultdict(lambda:defaultdict(lambda:0))
will be helpful if you try this y['a']['b'] += 1
I am trying to 'destructure' a dictionary and associate values with variables names after its keys. Something like
params = {'a':1,'b':2}
a,b = params.values()
But since dictionaries are not ordered, there is no guarantee that params.values() will return values in the order of (a, b). Is there a nice way to do this?
from operator import itemgetter
params = {'a': 1, 'b': 2}
a, b = itemgetter('a', 'b')(params)
Instead of elaborate lambda functions or dictionary comprehension, may as well use a built in library.
One way to do this with less repetition than Jochen's suggestion is with a helper function. This gives the flexibility to list your variable names in any order and only destructure a subset of what is in the dict:
pluck = lambda dict, *args: (dict[arg] for arg in args)
things = {'blah': 'bleh', 'foo': 'bar'}
foo, blah = pluck(things, 'foo', 'blah')
Also, instead of joaquin's OrderedDict you could sort the keys and get the values. The only catches are you need to specify your variable names in alphabetical order and destructure everything in the dict:
sorted_vals = lambda dict: (t[1] for t in sorted(dict.items()))
things = {'foo': 'bar', 'blah': 'bleh'}
blah, foo = sorted_vals(things)
How come nobody posted the simplest approach?
params = {'a':1,'b':2}
a, b = params['a'], params['b']
Python is only able to "destructure" sequences, not dictionaries. So, to write what you want, you will have to map the needed entries to a proper sequence. As of myself, the closest match I could find is the (not very sexy):
a,b = [d[k] for k in ('a','b')]
This works with generators too:
a,b = (d[k] for k in ('a','b'))
Here is a full example:
>>> d = dict(a=1,b=2,c=3)
>>> d
{'a': 1, 'c': 3, 'b': 2}
>>> a, b = [d[k] for k in ('a','b')]
>>> a
1
>>> b
2
>>> a, b = (d[k] for k in ('a','b'))
>>> a
1
>>> b
2
Here's another way to do it similarly to how a destructuring assignment works in JS:
params = {'b': 2, 'a': 1}
a, b, rest = (lambda a, b, **rest: (a, b, rest))(**params)
What we did was to unpack the params dictionary into key values (using **) (like in Jochen's answer), then we've taken those values in the lambda signature and assigned them according to the key name - and here's a bonus - we also get a dictionary of whatever is not in the lambda's signature so if you had:
params = {'b': 2, 'a': 1, 'c': 3}
a, b, rest = (lambda a, b, **rest: (a, b, rest))(**params)
After the lambda has been applied, the rest variable will now contain:
{'c': 3}
Useful for omitting unneeded keys from a dictionary.
Hope this helps.
Maybe you really want to do something like this?
def some_func(a, b):
print a,b
params = {'a':1,'b':2}
some_func(**params) # equiv to some_func(a=1, b=2)
If you are afraid of the issues involved in the use of the locals dictionary and you prefer to follow your original strategy, Ordered Dictionaries from python 2.7 and 3.1 collections.OrderedDicts allows you to recover you dictionary items in the order in which they were first inserted
(Ab)using the import system
The from ... import statement lets us desctructure and bind attribute names of an object. Of course, it only works for objects in the sys.modules dictionary, so one could use a hack like this:
import sys, types
mydict = {'a':1,'b':2}
sys.modules["mydict"] = types.SimpleNamespace(**mydict)
from mydict import a, b
A somewhat more serious hack would be to write a context manager to load and unload the module:
with obj_as_module(mydict, "mydict_module"):
from mydict_module import a, b
By pointing the __getattr__ method of the module directly to the __getitem__ method of the dict, the context manager can also avoid using SimpleNamespace(**mydict).
See this answer for an implementation and some extensions of the idea.
One can also temporarily replace the entire sys.modules dict with the dict of interest, and do import a, b without from.
Warning 1: as stated in the docs, this is not guaranteed to work on all Python implementations:
CPython implementation detail: This function relies on Python stack frame support
in the interpreter, which isn’t guaranteed to exist in all implementations
of Python. If running in an implementation without Python stack frame support
this function returns None.
Warning 2: this function does make the code shorter, but it probably contradicts the Python philosophy of being as explicit as you can. Moreover, it doesn't address the issues pointed out by John Christopher Jones in the comments, although you could make a similar function that works with attributes instead of keys. This is just a demonstration that you can do that if you really want to!
def destructure(dict_):
if not isinstance(dict_, dict):
raise TypeError(f"{dict_} is not a dict")
# the parent frame will contain the information about
# the current line
parent_frame = inspect.currentframe().f_back
# so we extract that line (by default the code context
# only contains the current line)
(line,) = inspect.getframeinfo(parent_frame).code_context
# "hello, key = destructure(my_dict)"
# -> ("hello, key ", "=", " destructure(my_dict)")
lvalues, _equals, _rvalue = line.strip().partition("=")
# -> ["hello", "key"]
keys = [s.strip() for s in lvalues.split(",") if s.strip()]
if missing := [key for key in keys if key not in dict_]:
raise KeyError(*missing)
for key in keys:
yield dict_[key]
In [5]: my_dict = {"hello": "world", "123": "456", "key": "value"}
In [6]: hello, key = destructure(my_dict)
In [7]: hello
Out[7]: 'world'
In [8]: key
Out[8]: 'value'
This solution allows you to pick some of the keys, not all, like in JavaScript. It's also safe for user-provided dictionaries
With Python 3.10, you can do:
d = {"a": 1, "b": 2}
match d:
case {"a": a, "b": b}:
print(f"A is {a} and b is {b}")
but it adds two extra levels of indentation, and you still have to repeat the key names.
Look for other answers as this won't cater to the unexpected order in the dictionary. will update this with a correct version sometime soon.
try this
data = {'a':'Apple', 'b':'Banana','c':'Carrot'}
keys = data.keys()
a,b,c = [data[k] for k in keys]
result:
a == 'Apple'
b == 'Banana'
c == 'Carrot'
Well, if you want these in a class you can always do this:
class AttributeDict(dict):
def __init__(self, *args, **kwargs):
super(AttributeDict, self).__init__(*args, **kwargs)
self.__dict__.update(self)
d = AttributeDict(a=1, b=2)
Based on #ShawnFumo answer I came up with this:
def destruct(dict): return (t[1] for t in sorted(dict.items()))
d = {'b': 'Banana', 'c': 'Carrot', 'a': 'Apple' }
a, b, c = destruct(d)
(Notice the order of items in dict)
An old topic, but I found this to be a useful method:
data = {'a':'Apple', 'b':'Banana','c':'Carrot'}
for key in data.keys():
locals()[key] = data[key]
This method loops over every key in your dictionary and sets a variable to that name and then assigns the value from the associated key to this new variable.
Testing:
print(a)
print(b)
print(c)
Output
Apple
Banana
Carrot
An easy and simple way to destruct dict in python:
params = {"a": 1, "b": 2}
a, b = [params[key] for key in ("a", "b")]
print(a, b)
# Output:
# 1 2
I don't know whether it's good style, but
locals().update(params)
will do the trick. You then have a, b and whatever was in your params dict available as corresponding local variables.
Since dictionaries are guaranteed to keep their insertion order in Python >= 3.7, that means that it's complete safe and idiomatic to just do this nowadays:
params = {'a': 1, 'b': 2}
a, b = params.values()
print(a)
print(b)
Output:
1
2