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How to map a nested list to a iterable dictionary in Python

I have a list of classes and their pre-requisites stored in the paired list:
classes = [
['Calculus', 'Geometry'],
['Trigonometry', 'Algebra'],
['Geometry', 'Trigonometry']
]
I want to map these classes out to specific indexes to build a graph for the courses using something like:
course_index = {
0: 'Calculus'
1: 'Geometry'
2: 'Trigonometry'
3: 'Algebra'
}
To obtain a new integer map mapped_classes = [[0,1],[2,3],[1,2]]
In order to create the course_index I created an empty indexed dict and added the items from classes that weren't present in course_index
course_index = {key: "" for key in range(4)}
index = 0
for x, y in classes:
if x not in course_index.values():
course_index[index] = x
index += 1
if y not in course_index.values():
course_index[index] = y
index += 1
What I can't figure out is how to map classes to their corresponding course_index to create mapped_classes.
Is there some mapping function that can simplify this process? My attempt to solve this is below, I'm not able to mutate the values in the current class list. It is also very inefficient with the nested loops.
for key, val in course_index:
for x, y in classes:
if x = val:
x = key
if y = val:
y = key

Invert the course_index dictionary.
Enumerate over classes.
invert = {v: k for k, v in course_index.items()}
for i, row in enumerate(classes):
classes[i][0] = invert[classes[i][0]]
classes[i][1] = invert[classes[i][1]]
print(classes)

Try using something like this:
mapped_classes = classes.copy()
for key, val in course_index.items():
for e, f in enumerate(classes):
for x in f:
if x == val:
mapped_classes[e][mapped_classes[e].index(x)] = key
print(mapped_classes)

Given a linear order completely represented by a list of tuples of strings, output the order as a list of strings

Given pairs of items of form [(a,b),...] where (a,b) means a > b, for example:
[('best','better'),('best','good'),('better','good')]
I would like to output a list of form:
['best','better','good']
This is very hard for some reason. Any thoughts?
======================== code =============================
I know why it doesn't work.
def to_rank(raw):
rank = []
for u,v in raw:
if u in rank and v in rank:
pass
elif u not in rank and v not in rank:
rank = insert_front (u,v,rank)
rank = insert_behind(v,u,rank)
elif u in rank and v not in rank:
rank = insert_behind(v,u,rank)
elif u not in rank and v in rank:
rank = insert_front(u,v,rank)
return [[r] for r in rank]
# #Use: insert word u infront of word v in list of words
def insert_front(u,v,words):
if words == []: return [u]
else:
head = words[0]
tail = words[1:]
if head == v: return [u] + words
else : return ([head] + insert_front(u,v,tail))
# #Use: insert word u behind word v in list of words
def insert_behind(u,v,words):
words.reverse()
words = insert_front(u,v,words)
words.reverse()
return words
=================== Update ===================
Per suggestion of many, this is a straight forward topological sort setting, I ultimately decided to use the code from this source: algocoding.wordpress.com/2015/04/05/topological-sorting-python/
which solved my problem.
def go_topsort(graph):
in_degree = { u : 0 for u in graph } # determine in-degree
for u in graph: # of each node
for v in graph[u]:
in_degree[v] += 1
Q = deque() # collect nodes with zero in-degree
for u in in_degree:
if in_degree[u] == 0:
Q.appendleft(u)
L = [] # list for order of nodes
while Q:
u = Q.pop() # choose node of zero in-degree
L.append(u) # and 'remove' it from graph
for v in graph[u]:
in_degree[v] -= 1
if in_degree[v] == 0:
Q.appendleft(v)
if len(L) == len(graph):
return L
else: # if there is a cycle,
return []
RockBilly's solution also work in my case, because in my setting, for every v < u, we are guaranteed to have a pair (u,v) in our list. So his answer is not very "computer-sciency", but it gets the job done in this case.

If you have a complete grammar specified then you can simply count up the items:
>>> import itertools as it
>>> from collections import Counter
>>> ranks = [('best','better'),('best','good'),('better','good')]
>>> c = Counter(x for x, y in ranks)
>>> sorted(set(it.chain(*ranks)), key=c.__getitem__, reverse=True)
['best', 'better', 'good']
If you have an incomplete grammar then you can build a graph and dfs all paths to find the longest. This isn't very inefficient, as I haven't thought about that yet :):
def dfs(graph, start, end):
stack = [[start]]
while stack:
path = stack.pop()
if path[-1] == end:
yield path
continue
for next_state in graph.get(path[-1], []):
if next_state in path:
continue
stack.append(path+[next_state])
def paths(ranks):
graph = {}
for n, m in ranks:
graph.setdefault(n,[]).append(m)
for start, end in it.product(set(it.chain(*ranks)), repeat=2):
yield from dfs(graph, start, end)
>>> ranks = [('black', 'dark'), ('black', 'dim'), ('black', 'gloomy'), ('dark', 'gloomy'), ('dim', 'dark'), ('dim', 'gloomy')]
>>> max(paths(ranks), key=len)
['black', 'dim', 'dark', 'gloomy']
>>> ranks = [('a','c'), ('b','a'),('b','c'), ('d','a'), ('d','b'), ('d','c')]
>>> max(paths(ranks), key=len)
['d', 'b', 'a', 'c']

What you're looking for is topological sort. You can do this in linear time using depth-first search (pseudocode included in the wiki I linked)

Here is one way. It is based on using the complete pairwise rankings to make an old-style (early Python 2) cmp function and then using functools.cmp_to_key to convert it to a key suitable for the Python 3 approach to sorting:
import functools
def sortByRankings(rankings):
def cmp(x,y):
if x == y:
return 0
elif (x,y) in rankings:
return -1
else:
return 1
items = list({x for y in rankings for x in y})
items.sort(key = functools.cmp_to_key(cmp))
return items
Tested like:
ranks = [('a','c'), ('b','a'),('b','c'), ('d','a'), ('d','b'), ('d','c')]
print(sortByRankings(ranks)) #prints ['d', 'b', 'a', 'c']
Note that to work correctly, the parameter rankings must contain an entry for each pair of distinct items. If it doesn't, you would first need to compute the transitive closure of the pairs that you do have before you feed it to this function.

You can take advantage of the fact that the lowest ranked item in the list will never appear at the start of any tuple. You can extract this lowest item, then remove all elements which contain this lowest item from your list, and repeat to get the next lowest.
This should work even if you have redundant elements, or have a sparser list than some of the examples here. I've broken it up into finding the lowest ranked item, and then the grunt work of using this to create a final ranking.
from copy import copy
def find_lowest_item(s):
#Iterate over set of all items
for item in set([item for sublist in s for item in sublist]):
#If an item does not appear at the start of any tuple, return it
if item not in [x[0] for x in s]:
return item
def sort_by_comparison(s):
final_list = []
#Make a copy so we don't mutate original list
new_s = copy(s)
#Get the set of all items
item_set = set([item for sublist in s for item in sublist])
for i in range(len(item_set)):
lowest = find_lowest_item(new_s)
if lowest is not None:
final_list.insert(0, lowest)
#For the highest ranked item, we just compare our current
#ranked list with the full set of items
else:
final_list.insert(0,set(item_set).difference(set(final_list)).pop())
#Update list of ranking tuples to remove processed items
new_s = [x for x in new_s if lowest not in x]
return final_list
list_to_compare = [('black', 'dark'), ('black', 'dim'), ('black', 'gloomy'), ('dark', 'gloomy'), ('dim', 'dark'), ('dim', 'gloomy')]
sort_by_comparison(list_to_compare)
['black', 'dim', 'dark', 'gloomy']
list2 = [('best','better'),('best','good'),('better','good')]
sort_by_comparison(list2)
['best', 'better', 'good']
list3 = [('best','better'),('better','good')]
sort_by_comparison(list3)
['best', 'better', 'good']

If you do sorting or create a dictionary from the list items, you are going to miss the order as #Rockybilly mentioned in his answer. I suggest you to create a list from the tuples of the original list and then remove duplicates.
def remove_duplicates(seq):
seen = set()
seen_add = seen.add
return [x for x in seq if not (x in seen or seen_add(x))]
i = [(5,2),(1,3),(1,4),(2,3),(2,4),(3,4)]
i = remove_duplicates(list(x for s in i for x in s))
print(i) # prints [5, 2, 1, 3, 4]
j = [('excellent','good'),('excellent','great'),('great','good')]
j = remove_duplicates(list(x for s in j for x in s))
print(j) # prints ['excellent', 'good', 'great']
See reference: How do you remove duplicates from a list in whilst preserving order?
For explanation on the remove_duplicates() function, see this stackoverflow post.

If the list is complete, meaning has enough information to do the ranking(Also no duplicate or redundant inputs), this will work.
from collections import defaultdict
lst = [('best','better'),('best','good'),('better','good')]
d = defaultdict(int)
for tup in lst:
d[tup[0]] += 1
d[tup[1]] += 0 # To create it in defaultdict
print sorted(d, key = lambda x: d[x], reverse=True)
# ['best', 'better', 'good']
Just give them points, increment the left one each time you encounter it in the list.
Edit: I do think the OP has a determined type of input. Always have tuple count of combination nCr(n, 2). Which makes this a correct solution. No need to complain about the edge cases, which I already knew posting the answer(and mentioned it).

Python Tulpe Key For Dict Partial Lookup

I have a dictionary with a tuple of 5 values as a key. For example:
D[i,j,k,g,h] = value.
Now i need to process all elements with a certain partial key pair (i1,g1):
I need now for each pair (i1,g1) all values that have i == i1 and g == g1 in the full key.
What is an pythonic and efficient way to retrieve this, knowing that i need the elements for all pairs and each full key belongs to exactly one partial key?
Is there a more appropriate data structure than dictionaries?
One reference implementation is this:
results = {}
for i in I:
for g in G:
results[i,g] = []
for i,j,k,g,h in D:
if i1 == i and g1 == g:
results[i,g].append(D[i,j,k,g,h])

Assuming you know all the valid values for the different indices you can get all possible keys using itertools.product:
import itertools
I = [3,6,9]
J = range(10)
K = "abcde"
G = ["first","second"]
H = range(10,20)
for tup in itertools.product(I,J,K,G,H):
my_dict[tup] = 0
To restrict the indices generated just put a limit on one / several of the indices that gets generated, for instance all of the keys where i = 6 would be:
itertools.product((6,), J,K,G,H)
A function to let you specify you want all the indices where i==6 and g =="first" would look like this:
def partial_indices(i_vals=I, j_vals=J, k_vals=K, g_vals = G, h_vals = H):
return itertools.product(i_vals, j_vals, k_vals, g_vals, h_vals)
partial_indices(i_vals=(6,), g_vals=("first",))
Or assuming that not all of these are present in the dictionary you can also pass the dictionary as an argument and check for membership before generating the keys:
def items_with_partial_indices(d, i_vals=I, j_vals=J, k_vals=K, g_vals = G, h_vals = H):
for tup in itertools.product(i_vals, j_vals, k_vals, g_vals, h_vals):
try:
yield tup, d[tup]
except KeyError:
pass

for k,v in D.iteritems():
if i in k and p in k:
print v

merge python dictionary of sets

I have a graph with 2 kinds of nodes- 'Letter nodes' (L) and 'Number nodes' (N). I have 2 dictionaries, one shows edges from L to N and the other shows edges from N to L.
A = {0:(b,), 1:(c,), 2:(c,), 3:(c,)}
B = {a:(3,), b:(0,), c:(1,2,3)}
A key,value pair c:(1,2,3) means there are edges from c to 1,2,3 (3 edges)
I want to merge these to one dictionary C so that the result is a new dictionary:
C = {(0,): (b,), (1, 2, 3): (a, c)}
or
C = {(b,):(0,), (a, c):(1, 2, 3)}
In the resulting dictionary I want the letter nodes and numerical nodes to be on separate sides of keys and values. I don't care which is the key or value just need them separated. How can I go about solving this efficiently?
CLARIFICATION: this of a graph with 2 types of nodes - number nodes, and letter nodes. the dictionary C says from letter nodes (a,c) you can reach the number nodes (1,2,3) i.e a->3->c->1, a->3->c->2 thus you can get to 1,2,3 from a. EVEN THOUGH THERE IS NO DIRECT EDGE FROM a to 2 or a to 1.

According to your statement, I guess you are trying to find a graph algorithms.
import itertools
def update_dict(A, result): #update vaules to the same set
for k in A:
result[k] = result.get(k, {k}).union(set(A[k]))
tmp = None
for i in result[k]:
tmp = result.get(k, {k}).union(result.get(i, {i}))
result[k] = tmp
for i in result[k]:
result[i] = result.get(i, {i}).union(result.get(k, {k}))
A = {0:('b',), 1:('c',), 2:('c',), 3:('c',)}
B = {'a':(3,), 'b':(0,), 'c':(1,2,3)}
result = dict()
update_dict(A, result)
update_dict(B, result)
update_dict(A, result) #update to fix bugs
update_dict(B, result)
k = sorted([sorted(list(v)) for v in result.values()])
k = list( k for k, _ in itertools.groupby(k)) #sort and remove dumplicated set
final_result = dict()
for v in k: #merge the result as expected
final_result.update({tuple([i for i in v if isinstance(i, int)]):tuple([i for i in v if not isinstance(i, int)])})
print final_result
#output
{(0,): ('b',), (1, 2, 3): ('a', 'c')}

So I'm not sure if this is the most efficient way of doing this at this point, but it works:
A = {0:('b',), 1:('c',), 2:('c',), 3:('c',)}
B = {'a':(3,), 'b':(0,), 'c':(1,2,3)}
# Put B in the same form as A
B_inv = {}
for k, v in B.items():
for i in v:
if B_inv.get(i) is not None:
B_inv[i] = B_inv[i].union(k)
else:
B_inv[i] = set(k)
B_inv = {k: tuple(v) for k, v in B_inv.items()}
AB = set(B_inv.items() + A.items()) # get AB as merged
This gets you the merged dictionaries. From here:
new_dict = {}
for a in AB:
for i in a[1]:
if new_dict.get(i) is not None:
new_dict[i] = new_dict[i].union([a[0]])
else:
new_dict[i] = set([a[0]])
# put in tuple form
new_dict = {tuple(k): tuple(v) for k,v in new_dict.items()}
This gives me:
{('a',): (3,), ('b',): (0,), ('c',): (1, 2, 3)}
Basically, I'm relying on the mutability of sets and their built-in functionality of eliminating duplicates to try to keep the number of loops through each dictionary to a minimum. Unless I missed something, this should be in linear time.
From here, I need to do comparison, and relying on sets again to prevent me from needing to do a worst-case pairwise comparison of every single element.
merge_list = []
for k, v in new_dict.items():
matched = False
nodeset = set([k[0]]).union(v)
for i in range(len(merge_list)):
if len(nodeset.intersection(merge_list[i])) != 0:
merge_list[i] = merge_list[i].union(nodeset)
matched = True
# did not find shared edges
if not matched:
merge_list.append(nodeset)
Finally, turn it into the form with a single "layer" and tuples.
C = {}
for item in merge_list:
temp_key = []
temp_val = []
for i in item:
if str(i).isalpha():
temp_key.append(i)
else:
temp_val.append(i)
C[tuple(temp_key)] = tuple(temp_val)
C gives me {('a', 'c'): (1, 3, 2), ('b',): (0,)}.

try this:
c = a.copy()
c.update(b)

In Python, How can I get the next and previous key:value of a particular key in a dictionary?

Okay, so this is a little hard to explain, but here goes:
I have a dictionary, which I'm adding content to. The content is a hashed username (key) with an IP address (value).
I was putting the hashes into an order by running them against base 16, and then using Collection.orderedDict.
So, the dictionary looked a little like this:
d = {'1234': '8.8.8.8', '2345':'0.0.0.0', '3213':'4.4.4.4', '4523':'1.1.1.1', '7654':'1.3.3.7', '9999':'127.0.0.1'}
What I needed was a mechanism that would allow me to pick one of those keys, and get the key/value item one higher and one lower. So, for example, If I were to pick 2345, the code would return the key:value combinations '1234:8.8.8.8' and '3213:4.4.4.4'
So, something like:
for i in d:
while i < len(d)
if i == '2345':
print i.nextItem
print i.previousItem
break()

Edit: OP now states that they are using OrderedDicts but the use case still requires this sort of approach.
Since dicts are not ordered you cannot directly do this. From your example, you are trying to reference the item like you would use a linked list.
A quick solution would be instead to extract the keys and sort them then iterate over that list:
keyList=sorted(d.keys())
for i,v in enumerate(keyList):
if v=='eeee':
print d[keyList[i+1]]
print d[keyList[i-1]]
The keyList holds the order of your items and you have to go back to it to find out what the next/previous key is to get the next/previous value. You also have to check for i+1 being greater than the list length and i-1 being less than 0.
You can use an OrderedDict similarly but I believe that you still have to do the above with a separate list as OrderedDict doesn't have next/prev methods.

As seen in the OrderedDict source code,
if you have a key and you want to find the next and prev in O(1) here's how you do that.
>>> from collections import OrderedDict
>>> d = OrderedDict([('aaaa', 'a',), ('bbbb', 'b'), ('cccc', 'c'), ('dddd', 'd'), ('eeee', 'e'), ('ffff', 'f')])
>>> i = 'eeee'
>>> link_prev, link_next, key = d._OrderedDict__map['eeee']
>>> print 'nextKey: ', link_next[2], 'prevKey: ', link_prev[2]
nextKey: ffff prevKey: dddd
This will give you next and prev by insertion order. If you add items in random order then just keep track of your items in sorted order.

You could also use the list.index() method.
This function is more generic (you can check positions +n and -n), it will catch attempts at searching a key that's not in the dict, and it will also return None if there's nothing before of after the key:
def keyshift(dictionary, key, diff):
if key in dictionary:
token = object()
keys = [token]*(diff*-1) + sorted(dictionary) + [token]*diff
newkey = keys[keys.index(key)+diff]
if newkey is token:
print None
else:
print {newkey: dictionary[newkey]}
else:
print 'Key not found'
keyshift(d, 'bbbb', -1)
keyshift(d, 'eeee', +1)

Try:
pos = 0
d = {'aaaa': 'a', 'bbbb':'b', 'cccc':'c', 'dddd':'d', 'eeee':'e', 'ffff':'f'}
for i in d:
pos+=1
if i == 'eeee':
listForm = list(d.values())
print(listForm[pos-1])
print(listForm[pos+1])
As in #AdamKerz's answer enumerate seems pythonic, but if you are a beginner this code might help you understand it in an easy way.
And I think its faster + smaller compared to sorting followed by building list & then enumerating

You could use a generic function, based on iterators, to get a moving window (taken from this question):
import itertools
def window(iterable, n=3):
it = iter(iterable)
result = tuple(itertools.islice(it, n))
if len(result) == n:
yield result
for element in it:
result = result[1:] + (element,)
yield result
l = range(8)
for i in window(l, 3):
print i
Using the above function with OrderedDict.items() will give you three (key, value) pairs, in order:
d = collections.OrderedDict(...)
for p_item, item, n_item in window(d.items()):
p_key, p_value = p_item
key, value = item
# Or, if you don't care about the next value:
n_key, _ = n_item
Of course using this function the first and last values will never be in the middle position (although this should not be difficult to do with some adaptation).
I think the biggest advantage is that it does not require table lookups in the previous and next keys, and also that it is generic and works with any iterable.

Maybe it is an overkill, but you can keep Track of the Keys inserted with a Helper Class and according to that list, you can retrieve the Key for Previous or Next. Just don't forget to check for border conditions, if the objects is already first or last element. This way, you will not need to always resort the ordered list or search for the element.
from collections import OrderedDict
class Helper(object):
"""Helper Class for Keeping track of Insert Order"""
def __init__(self, arg):
super(Helper, self).__init__()
dictContainer = dict()
ordering = list()
#staticmethod
def addItem(dictItem):
for key,value in dictItem.iteritems():
print key,value
Helper.ordering.append(key)
Helper.dictContainer[key] = value
#staticmethod
def getPrevious(key):
index = (Helper.ordering.index(key)-1)
return Helper.dictContainer[Helper.ordering[index]]
#Your unordered dictionary
d = {'aaaa': 'a', 'bbbb':'b', 'cccc':'c', 'dddd':'d', 'eeee':'e', 'ffff':'f'}
#Create Order over keys
ordered = OrderedDict(sorted(d.items(), key=lambda t: t[0]))
#Push your ordered list to your Helper class
Helper.addItem(ordered)
#Get Previous of
print Helper.getPrevious('eeee')
>>> d

You can store the keys and values in temp variable in prior, and can access previous and next key,value pair using index.
It is pretty dynamic, will work for any key you query. Please check this code :
d = {'1234': '8.8.8.8', '2345':'0.0.0.0', '3213':'4.4.4.4', '4523':'1.1.1.1', '7654':'1.3.3.7', '9999':'127.0.0.1'}
ch = raw_input('Pleasure Enter your choice : ')
keys = d.keys()
values = d.values()
#print keys, values
for k,v in d.iteritems():
if k == ch:
ind = d.keys().index(k)
print keys[ind-1], ':',values[ind-1]
print keys[ind+1], ':',values[ind+1]

I think this is a nice Pythonic way of resolving your problem using a lambda and list comprehension, although it may not be optimal in execution time:
import collections
x = collections.OrderedDict([('a','v1'),('b','v2'),('c','v3'),('d','v4')])
previousItem = lambda currentKey, thisOrderedDict : [
list( thisOrderedDict.items() )[ z - 1 ] if (z != 0) else None
for z in range( len( thisOrderedDict.items() ) )
if (list( thisOrderedDict.keys() )[ z ] == currentKey) ][ 0 ]
nextItem = lambda currentKey, thisOrderedDict : [
list( thisOrderedDict.items() )[ z + 1 ] if (z != (len( thisOrderedDict.items() ) - 1)) else None
for z in range( len( thisOrderedDict.items() ) )
if (list( thisOrderedDict.keys() )[ z ] == currentKey) ][ 0 ]
assert previousItem('c', x) == ('b', 'v2')
assert nextItem('c', x) == ('d', 'v4')
assert previousItem('a', x) is None
assert nextItem('d',x) is None

Another way that seems simple and straight forward: this function returns the key which is offset positions away from k
def get_shifted_key(d:dict, k:str, offset:int) -> str:
l = list(d.keys())
if k in l:
i = l.index(k) + offset
if 0 <= i < len(l):
return l[i]
return None

i know how to get next key:value of a particular key in a dictionary:
flag = 0
for k, v in dic.items():
if flag == 0:
code...
flag += 1
continue
code...{next key and value in for}

if correct :
d = { "a": 1, "b":2, "c":3 }
l = list( d.keys() ) # make a list of the keys
k = "b" # the actual key
i = l.index( k ) # get index of the actual key
for the next :
i = i+1 if i+1 < len( l ) else 0 # select next index or restart 0
n = l [ i ]
d [ n ]
for the previous :
i = i-1 if i-1 >= 0 else len( l ) -1 # select previous index or go end
p = l [ i ]
d [ p ]