ordereddictionary

I have a snippet of code which orders a dictionary alphabetically. Is there a way to select the ith key in the ordered dictionary and return its corresponding value? i.e. import collections initial = dict(a=1, b=2, c=2, d=1, e=3) ordered_dict = collections.OrderedDict(sorted(initial.items(), key=lambda t: t[0])) print(ordered_dict) OrderedDict([('a', 1), ('b', 2), ('c', 2), ('d', 1), ('e', 3)]) I want to have some function along the vein of... select = int(input("Input dictionary index")) #User inputs 2 #Program looks up the 2nd entry in ordered_dict (c in this case) #And then returns the

My first attempt to combine the features of two dictionaries in the collections module was to create a class that inherits them: from collections import OrderedDict, defaultdict class DefaultOrderedDict(defaultdict, OrderedDict): def __init__(self, default_factory=None, *a, **kw): super().__init__(default_factory, *a, **kw) However, I cannot assign an item to this dictionary: d = DefaultOrderedDict(lambda: 0) d['a'] = 1 Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/lib64/python3.3/collections/__init__.py", line 64, in __setitem__ self.__map[key] = link =

Say orig is an OrderedDict which contains normal string:string key value pairs, but sometimes the value could be another, nested OrderedDict . I want to sort orig by key, alphabetically (ascending), and do it recursively . Rules: Assume key strings are unpredictable Assume nesting can take place infinitely, e.g. level 1-50 all have both strings, OrderedDicts, etc as values. Need an assist with the sorted algorithm: import string from random import choice orig = OrderedDict(( ('a', choice(string.digits)), ('b', choice(string.digits)), ('c', choice(string.digits)), ('special', OrderedDict(( ('a'

Looking for a fast way to get a row in a pandas dataframe into a ordered dict with out using list. List are fine but with large data sets will take to long. I am using fiona GIS reader and the rows are ordereddicts with the schema giving the data type. I use pandas to join data. I many cases the rows will have different types so I was thinking turning into a numpy array with type string might do the trick. Unfortunately you can't just do an apply (since it fits it back to a DataFrame): In [1]: df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b']) In [2]: df Out[2]: a b 0 1 2 1 3 4 In [3]:

I'm looking for the most ideal data structure (for performance and ease of use) from which values can be retrieved by string key or index. Dictionary doesn't work because you can't really retrieve by index. Any ideas? You want the OrderedDictionary class. You will need to include the System.Collections.Specialized namespace: OrderedDictionary od = new OrderedDictionary(); od.Add("abc", 1); od.Add("def", 2); od.Add("ghi", 3); od.Add("jkl", 4); // Can access via index or key value: Console.WriteLine(od[1]); Console.WriteLine(od["def"]); There's System.Collections.ObjectModel. KeyedCollection<

I am trying to get a sorted dictionary. But the order of the items between mydict and orddict doesn't seem to change. from collections import OrderedDict mydict = {'a':1,'b':2,'c':3,'d':4} orddict = OrderedDict(mydict) print(mydict,orddict) # print items in mydict: print('mydict') for k,v in mydict.items(): print(k,v) print('ordereddict') # print items in ordered dictionary for k,v in orddict.items(): print(k,v) # print the dictionary keys # for key in mydict.keys(): # print(key) # print the dictionary values # for value in mydict.values(): # print(value) An OrderedDict preserves the order