Say I have a 2 dictionaries, each with around 100000 entries (each can be of different length):
dict1 = {"a": ["w", "x"], "b":["y"], "c":["z"] ...}
dict2 = {"x": ["a", "b"], "y":["b", "d"], "z":["d"] ...}
I need to perform an operation using these two dictionaries:
Treat each dict item as a set of mapping (i.e list of all mappings in dict1 would be"a"->"w", "a"->"x", "b"->"y" and "c"->"z")
Only keep mappings in dict1 if the reverse mapping exists in dict2.
The resulting dictionary would be:
{"a": ["x"], "b", ["y"]}
My current solution uses 2 m*n all zeros dataframes where m and n are the lengths of dict1 and dict2 respectively and the index labels are the keys in dict1 and the column labels are the keys in dict2.
For the first dataframe, I insert a 1 at each value where the index label -> column label represent a mapping in dict1. For the second dataframe, I insert a 1 at each value where the column label -> index label represent a mapping in dict2.
I then perform an element-size product between the two dataframes, which only leaves values that have a mapping "a1"->"x1" in dict1 and "x1"->"a1" in dict2.
However, this takes up way too much memory and is very expensive. Is there an alternative algorithm I can use?
How about to use the same idea, but replace a sparse matrix you're using with a set of key pairs? Something like:
import collections
def fn(dict1, dict2):
mapping_set = set()
for k, vv in dict2.items():
for v in vv:
mapping_set.add((k, v))
result_dict = collections.defaultdict(list)
for k, vv in dict1.items():
for v in vv:
if (v, k) in mapping_set: # Note reverse order of k and v
result_dict[k].append(v)
return result_dict
Update: It will use O(total number of values in dict2) of memory and O(total number of values in dict1) + O(total number of values in dict2) time - both a linear. It's not possible to solve the problem algorithmically faster as every value in every dict has to be visited at least once.
Given that you have python objects to begin with, you may want to stay in the python domain. If you need to iterate through the entire dict to create your matrix anyway, you may find that filtering in-place doesn't take much longer.
default = ()
result = {k: v for k, v in dict1.items() if k in dict2.get(v, default)}
If your list are short, this will be totally fine. If they contain many elements, linear search will start to compare to the overhead of set lookup. In that case, you may want to preprocess dict2 to contain sets rather than lists:
dict2 = {k: set(v) for k, v in dict2.items}
or in-place
for k, v in dict2.items():
dict2[k] = set(v)
Related
I have a dictionary like so
d = {"key_a":1, "anotherkey_a":2, "key_b":3, "anotherkey_b":4}
So the values and key names are not important here. The key (no pun intended) thing, is that related keys share the same suffix in my example above that is _a and _b.
These suffixes are not known before hand (they are not always _a and _b for example, and there are an unknown number of different suffixes.
What I would like to do, is to extract out related keys into their own dictionaries, and have all generated dictionaries in a list.
The output from above would be
output = [{"key_a":1, "anotherkey_a":2},{"key_b":3, "anotherkey_b":4}]
My current approach is to first get all the suffixes, and then generate the sub-dicts one at a time and append to the new list
output = list()
# Generate a set of suffixes
suffixes = set([k.split("_")[-1] for k in d.keys()])
# Create the subdict and append to output
for suffix in suffixes:
output.append({k:v for k,v in d.items() if k.endswith(suffix)})
This works (and is not prohibitively slow or anyhting) but I am simply wondering if there is a more elegant way to do it with a list or dict comprehension? Just out of interest...
Make your output a defaultdict rather than a list, with suffixes as keys:
from collections import defaultdict
output = defaultdict(lambda: {})
for k, v in d.items():
prefix, suffix = k.rsplit('_', 1)
output[suffix][k] = v
This will split your dict in a single pass and result in something like:
output = {"a" : {"key_a":1, "anotherkey_a":2}, "b": {"key_b":3, "anotherkey_b":4}}
and if you insist on converting it to a list, you can simply use:
output = list(output.values())
You could condense the lines
output = list()
for suffix in suffixes:
output.append({k:v for k,v in d.items() if k.endswith(suffix)})
to a list comprehension, like this
[{k:v for k,v in d.items() if k.endswith(suffix)} for suffix in suffixes]
Whether it is more elegant is probably in the eyes of the beholder.
The approach suggested by #Błotosmętek will probably be faster though, given a large dictionary, since it results in less looping.
def sub_dictionary_by_suffix(dictionary, suffix):
sub_dictionary = {k: v for k, v in dictionary.items() if k.endswith(suffix)}
return sub_dictionary
I hope it helps
Two dictionaries:
dict1 = {'firstvalue':1, 'secondvalue':2, 'fourthvalue':4}
dict2 = {'firstvalue':1, 'thirdvalue':3, 'fourthvalue':5}
I get set(['secondvalue']) as a result upon doing:
dict1.viewkeys() - dict2
I need {'secondvalue':2} as a result.
When I use set, and then do the - operation, it does not give the desired result as it consists of {'fourthvalue:4} as well.
How could I do it?
The problem with - is that (in this context) it is an operation of dict_keys and thus the results will have no values. Using - with viewitems() does not work, either, as those are tuples, i.e. will compare both keys and values.
Instead, you can use a conditional dictionary comprehension, keeping only those keys that do not appear in the second dictionary. Other than Counter, this also works in the more general case, where the values are not integers, and with integer values, it just checks whether a key is present irrespective of the value that is accociated with it.
>>> dict1 = {'firstvalue':1, 'secondvalue':2, 'fourthvalue':4}
>>> dict2 = {'firstvalue':1, 'thirdvalue':3, 'fourthvalue':5}
>>> {k: v for k, v in dict1.items() if k not in dict2}
{'secondvalue': 2}
IIUC and providing a solution to Finding a key-value pair present only in the first dictionary as specified, you could take a set from the key/value pairs as tuples, subtract both sets and construct a dictionary from the result:
dict(set(dict1.items()) - set(dict2.items()))
# {'fourthvalue': 4, 'secondvalue': 2}
Another simple variation with set difference:
res = {k: dict1[k] for k in dict1.keys() - dict2.keys()}
Python 2.x:
dict1 = {'firstvalue':1, 'secondvalue':2, 'fourthvalue':4}
dict2 = {'firstvalue':1, 'thirdvalue':3, 'fourthvalue':5}
keys = dict1.viewkeys() - dict2.viewkeys()
print ({key:dict1[key] for key in keys})
output:
{'secondvalue': 2}
I have looked at several threads similar but unable to get a workable solution. I am looping (2) dictionaries trying to create a single list from the values of one based on the keys of another. I have it done with for loops but looking to use a single line if possible. My code with for loops
for k, v in dict1.items():
for value in dict2[k]:
temp.append(value)
On the first loop thru the temp list would be and is from above code:
[16,18,20,22,24,26]
I then use min to get the min value of the list. Now I want to condense the for loop to a one liner. I have put together
temp=[dict2.values() for k in dict1.keys() if k in dict2.keys()]
When executed, instead of temp being a single list for the k that exist in the dict1, I get a list of list for all the values from all dict2.
[[16,18,20,22,24,26], [12,16,18,20,22,24], [16,18,22,26,30,32]]
It seems to be ignoring the if statement. I know my dict1 has only 1 key in this situation and I know the 1 key exist in the dict2. Is my one liner wrong?
Input Values for dictionaries:
dict1={'table1':[16,18,20,22,24,26]}
dict2={'table1':[16,18,20,22,24,26],'table2': [12,16,18,20,22,24], 'table3': [16,18,22,26,30,32]}
You can iterate through one dictionary checking for matching keys and create a list of lists. Use chain.from_iterable to flatten list and call min():
from itertools import chain
dict1 = {'table1': [16,18,20,22,24,26]}
dict2 = {'table1': [16,18,20,22,24,26], 'table2': [12,16,18,20,22,24], 'table3': [16,18,22,26,30,32]}
temp = [dict2[k] for k in dict1 if k in dict2]
print(min(chain.from_iterable(temp)))
# 16
The reason why your list comprehension does not work:
It looks like dict2 has 3 key-value pairs, and the values are [16,18,20,22,24,26], [12,16,18,20,22,24]and [16,18,22,26,30,32]. What you're doing in your list comprehension translates to
for k in dict1.keys():
if k in dict2.keys():
temp.append(dict2.values())
So if dict1has, let's say, 3 keys, this for loop will repeat 3 times. Because, as you said in a comment above, only one key is shared between dict1and dict2, the if statement only is True once, so all items of dict2.values() will be appended to temponce. What you want to do, if i got that right, is to append all items INSIDE one of the values of dict2, namely the one assigned to the one key that the two dicts share. Your idea was pretty close, you just have to add one little thing. As a one liner, it would look like this:
temp = [x for x in dict2[k] for k in dict1.keys() if k in dict2.keys()]
or, differently:
temp = [dict2[k] for k in set(dict1.keys()).intersection(set(dict2.keys()))]
You can use the operator itemgetter():
from operator import itemgetter
from itertools import chain
dict1 = {'table1': [16,18,20,22,24,26], 'table2': [12,16,18,20,22,24]}
dict2 = {'table1': [16,18,20,22,24,26], 'table2': [12,16,18,20,22,24], 'table3': [16,18,22,26,30,32]}
common_keys = set(dict1).intersection(dict2)
sublists = itemgetter(*common_keys)(dict2)
if len(common_keys) == 1:
max_val = max(sublists)
else:
max_val = max(chain.from_iterable(sublists))
print(max_val)
# 26
We have a dictionary d1 and a condition cond. We want d1 to contain only the values that satisfy the condition cond. One way to do it is:
d1 = {k:v for k,v in d1.items() if cond(v)}
But, this creates a new dictionary, which may be very memory-inefficient if d1 is large.
Another option is:
for k,v in d1.items():
if not cond(v):
d1.pop(k)
But, this modifies the dictionary while it is iterated upon, and generates an error: "RuntimeError: dictionary changed size during iteration".
What is the correct way in Python 3 to filter a dictionary in-place?
If there are not many keys the corresponding values of which satisfy the condition, then you might first aggregate the keys and then prune the dictionary:
for k in [k for k,v in d1.items() if cond(v)]:
del d1[k]
In case the list [k for k,v in d1.items() if cond(v)] would be too large, one might process the dictionary "in turns", i.e., to assemble the keys until their count does not exceed a threshold, prune the dictionary, and repeat until there are no more keys satisfying the condition:
from itertools import islice
def prune(d, cond, chunk_size = 1000):
change = True
while change:
change = False
keys = list(islice((k for k,v in d.items() if cond(v)), chunk_size))
for k in keys:
change = True
del d[k]
I have a dictionary like this :
d = {'v03':["elem_A","elem_B","elem_C"],'v02':["elem_A","elem_D","elem_C"],'v01':["elem_A","elem_E"]}
How would you return a new dictionary with the elements that are not contained in the key of the highest value ?
In this case :
d2 = {'v02':['elem_D'],'v01':["elem_E"]}
Thank you,
I prefer to do differences with the builtin data type designed for it: sets.
It is also preferable to write loops rather than elaborate comprehensions. One-liners are clever, but understandable code that you can return to and understand is even better.
d = {'v03':["elem_A","elem_B","elem_C"],'v02':["elem_A","elem_D","elem_C"],'v01':["elem_A","elem_E"]}
last = None
d2 = {}
for key in sorted(d.keys()):
if last:
if set(d[last]) - set(d[key]):
d2[last] = sorted(set(d[last]) - set(d[key]))
last = key
print d2
{'v01': ['elem_E'], 'v02': ['elem_D']}
from collections import defaultdict
myNewDict = defaultdict(list)
all_keys = d.keys()
all_keys.sort()
max_value = all_keys[-1]
for key in d:
if key != max_value:
for value in d[key]:
if value not in d[max_value]:
myNewDict[key].append(value)
You can get fancier with set operations by taking the set difference between the values in d[max_value] and each of the other keys but first I think you should get comfortable working with dictionaries and lists.
defaultdict(<type 'list'>, {'v01': ['elem_E'], 'v02': ['elem_D']})
one reason not to use sets is that the solution does not generalize enough because sets can only have hashable objects. If your values are lists of lists the members (sublists) are not hashable so you can't use a set operation
Depending on your python version, you may be able to get this done with only one line, using dict comprehension:
>>> d2 = {k:[v for v in values if not v in d.get(max(d.keys()))] for k, values in d.items()}
>>> d2
{'v01': ['elem_E'], 'v02': ['elem_D'], 'v03': []}
This puts together a copy of dict d with containing lists being stripped off all items stored at the max key. The resulting dict looks more or less like what you are going for.
If you don't want the empty list at key v03, wrap the result itself in another dict:
>>> {k:v for k,v in d2.items() if len(v) > 0}
{'v01': ['elem_E'], 'v02': ['elem_D']}
EDIT:
In case your original dict has a very large keyset [or said operation is required frequently], you might also want to substitute the expression d.get(max(d.keys())) by some previously assigned list variable for performance [but I ain't sure if it doesn't in fact get pre-computed anyway]. This speeds up the whole thing by almost 100%. The following runs 100,000 times in 1.5 secs on my machine, whereas the unsubstituted expression takes more than 3 seconds.
>>> bl = d.get(max(d.keys()))
>>> d2 = {k:v for k,v in {k:[v for v in values if not v in bl] for k, values in d.items()}.items() if len(v) > 0}