You could say defaultdict is useful for settings defaults before filling the dict and setdefault is useful for setting defaults while or after filling the dict. Probably the most common use case: Grouping items (in unsorted data, else use itertools.groupby) # really verbose new = {} for (key, value) in data: if key in new: … Read more
You need to add 2 methods, note __hash__ and __eq__: class MyThing: def __init__(self,name,location,length): self.name = name self.location = location self.length = length def __hash__(self): return hash((self.name, self.location)) def __eq__(self, other): return (self.name, self.location) == (other.name, other.location) def __ne__(self, other): # Not strictly necessary, but to avoid having both x==y and x!=y # True at … Read more
An easy, correct way to implement __hash__() is to use a key tuple. It won’t be as fast as a specialized hash, but if you need that then you should probably implement the type in C. Here’s an example of using a key for hash and equality: class A: def __key(self): return (self.attr_a, self.attr_b, self.attr_c) … Read more
I think you have pointed out the most obvious difference. Apart from that, the first doesn’t need to lookup dict which should make it a tiny bit faster the second looks up dict in locals() and then globals() and the finds the builtin, so you can switch the behaviour by defining a local called dict … Read more
In [9]: pd.Series(df.Letter.values,index=df.Position).to_dict() Out[9]: {1: ‘a’, 2: ‘b’, 3: ‘c’, 4: ‘d’, 5: ‘e’} Speed comparion (using Wouter’s method) In [6]: df = pd.DataFrame(randint(0,10,10000).reshape(5000,2),columns=list(‘AB’)) In [7]: %timeit dict(zip(df.A,df.B)) 1000 loops, best of 3: 1.27 ms per loop In [8]: %timeit pd.Series(df.A.values,index=df.B).to_dict() 1000 loops, best of 3: 987 us per loop
Warning: this is a convenience method to convert a JSON string to a dictionary if, for some reason, you have to work from a JSON string. But if you have the JSON data available, you should instead work with the data, without using a string at all. Swift 3 func convertToDictionary(text: String) -> [String: Any]? … Read more
If you only need the dictionary keys 1, 2, and 3 use: your_dict.keys(). If you only need the dictionary values -0.3246, -0.9185, and -3985 use: your_dict.values(). If you want both keys and values use: your_dict.items() which returns a list of tuples [(key1, value1), (key2, value2), …].
So make a temporary dict with the key being the id. This filters out the duplicates. The values() of the dict will be the list In Python2.7 >>> L=[ … {‘id’:1,’name’:’john’, ‘age’:34}, … {‘id’:1,’name’:’john’, ‘age’:34}, … {‘id’:2,’name’:’hanna’, ‘age’:30}, … ] >>> {v[‘id’]:v for v in L}.values() [{‘age’: 34, ‘id’: 1, ‘name’: ‘john’}, {‘age’: 30, ‘id’: … Read more
On the one hand, throwing exceptions is inherently expensive, because the stack has to be unwound etc. On the other hand, accessing a value in a dictionary by its key is cheap, because it’s a fast, O(1) operation. BTW: The correct way to do this is to use TryGetValue obj item; if(!dict.TryGetValue(name, out item)) return … Read more
A list comprehension seems to be a good way to do this: >>> [mydict[x] for x in mykeys] [3, 1]