I have the below two lists:
a = [{'a1': 1, 'a2': 2}, {'a3': 3}]
b = [{'b1': 1, 'b2': 2}, None]
I would like to merge them creating an output like the below, ignoring the None elements.
desired_output = [{'a1': 1, 'a2': 2, 'b1': 1, 'b2': 2}, {'a3': 3}]
You can use a list comprehension with zip.
res = [(x or {}) | (y or {}) for x, y in zip(a, b)]
You can use the zip function and a list comprehension to merge the two lists and ignore the None elements. Here is an example of how you can do it:
a = [{'a1': 1, 'a2': 2}, {'a3': 3}, None, {'a4': 4}]
b = [{'b1': 1, 'b2': 2}, None, {'b3': 4, 'b4': 5}, {'b5': 4, 'b6': 5}]
desired_output = [{**d1, **d2} for d1, d2 in zip(a, b) if None not in [d1,d2]]
print(desired_output)
# [{'a1': 1, 'a2': 2, 'b1': 1, 'b2': 2}, {'a4': 4, 'b5': 4, 'b6': 5}]
In this example, the zip function combines the elements of the two lists a and b into pairs (d1, d2). The list comprehension iterates over these pairs, and for each pair, it uses the ** operator to merge the two dictionaries and only considering the case when neither of d1 nor d2 is None.
Related
Let's say I'm performing some operation on an array as follows:
>>arr = np.array([1,2,34,567,433243,787,832])
>>h = np.where(arr < 100, {'hello' : 1}, {'hi' : 2 })
array([{'hello': 1}, {'hello': 1}, {'hello': 1}, {'hi': 2}, {'hi': 2},{'hi': 2}, {'hi': 2}], dtype=object)
When I try to add a key-value pair at some selective indices, It gets replicated across all the indices and gives me something like this:
>>h[0]['hola']=12
>>h[4]['heyy']=11
>>h
array([{'hello': 1, 'hola': 12}, {'hello': 1, 'hola': 12},{'hello': 1, 'hola': 12}, {'hi': 2, 'heyy': 11},{'hi': 2, 'heyy': 11}, {'hi': 2, 'heyy': 11},{'hi': 2, 'heyy': 11}], dtype=object)
While I expect the values to change only at those specific indices(0 & 4) and get something like this:
array([{'hello': 1, 'hola': 12}, {'hello': 1}, {'hello': 1}, {'hi': 2}, {'hi': 2, 'heyy': 11}, {'hi': 2,}, {'hi': 2}], dtype=object)
How do I obtain the desired output?.Thanks in advance
you are passing 3 args to np.where a condition an expression if its true and and expression if its false. These expressions are evaluated before being passed to the where method. Such that the dicts are created once for true and once for false then the same dict will be used whenever that Boolean value occurs in the expression.
Instaed you can just use a list comprehension to achieve what you want.
h = np.array([{'hello': 1} if i < 100 else {'hi': 2} for i in arr])
I just wanna sort these dictionaries with some values from an input file.
def sortdicts():
listofs=[]
listofs=splitndict()
print sorted(listofs)
The splitndict() function has this output:
[{'a': 1, 'b': 2}, {'c': 2, 'd': 4}, {'a': 7, 'c': 3}, {'y': 5, 'x': 0}]
While the input is from another file and it's:
a 1
b 2
c 2
d 4
a 7
c 3
x 0
y 5
I used this to split the dictionary:
def splitndict():
listofd=[]
variablesRead=readfromfile()
splitted=[i.split() for i in variablesRead]
d={}
for lines in splitted:
if lines:
d[lines[0]]=int(lines[1])
elif d=={}:
pass
else:
listofd.append(d)
d={}
print listofd
return listofd
The output file should look like this:
[{'y': 5, 'x': 0}, {'a': 1, 'b': 2}, {'c': 2, 'd': 4}, {'a': 7, 'c': 3}
This output because :
It needs to be sorted by the lowest value from each dictionary key.
array = [{'y': 5, 'x': 0}, {'a': 1, 'b': 2}, {'c': 2, 'd': 4}, {'a': 7, 'c': 3}]
for the above array:
array = sorted(array, lambda element: min(element.values()))
where "element.values()" returns all values from dictionary and "min" returns the minimum of those values.
"sorted" passes each dictionary (an element) inside the lambda function one by one. and sorts on the basis of the result from the lambda function.
x = [{'y': 5, 'x': 0}, {'a': 1, 'b': 2}, {'c': 2, 'd': 4}, {'a': 7, 'c': 3}]
sorted(x, key=lambda i: min(i.values()))
Output is
[{'y': 5, 'x': 0}, {'a': 1, 'b': 2}, {'c': 2, 'd': 4}, {'a': 7, 'c': 3}]
Say I have a list of dicts:
ls = [{'id': 1, 'a1': 2, 'a2': 3}, {'id':2, 'a2':4}, {'id':3, 'a2':5}]
where a1 doesn't exist in some dicts
I want to set a1 to 0 in these dicts where a1 is missing and change the key name a1 to b1 in all dicts. Here is the code I come up with
for l in ls:
l.setdefault('a1', 0)
l['b1'] = l.pop('a1')
I'm wondering is it possible to make it more efficient, as I need to run this piece of code millions of times. Any improvements would be appreciated.
Here is a slight improvement which exploits the fact that dict.pop() can take an argument to return as the default if the key is not in the dictionary:
ls = [{'id': 1, 'a1': 2, 'a2': 3}, {'id':2, 'a2':4}, {'id':3, 'a2':5}]
for d in ls:
d['b1'] = d.pop('a1', 0)
>>> ls
[{'a2': 3, 'id': 1, 'b1': 2}, {'a2': 4, 'id': 2, 'b1': 0}, {'a2': 5, 'id': 3, 'b1': 0}]
This may be a duplicate but the closest I could find was Comparing 2 lists consisting of dictionaries with unique keys in python which did not work for me.
So I have two lists of dictionaries.
y = [{'a': 3, 'b': 4, 'c': 5}, {'a': 1, 'b': 2, 'c': 3}]
y = [{'a': 4, 'b': 5, 'c': 6}, {'a': 1, 'b': 2, 'c': 3}]
How do I compare these two lists so my compare results in the intersection of the two lists. I can't convert it to set since it says unhashable type (dict)
Your question and it's title seem at odds with each other.
The intersection of the 2 lists would be the common elements of both list. The question title requests the elements that are not in both lists. Which is it that you want?
For the intersection, it is not very efficient (being O(n^2) in time), but this list comprehension will do it:
>>> a = [{'a': 3, 'b': 4, 'c': 5}, {'a': 1, 'b': 2, 'c': 3}]
>>> b = [{'a': 4, 'b': 5, 'c': 6}, {'a': 1, 'b': 2, 'c': 3}]
>>> [d for d in a if d in b]
[{'a': 1, 'b': 2, 'c': 3}]
y1 = [{'a': 3, 'b': 4, 'c': 5}, {'a': 1, 'b': 2, 'c': 3}]
y2 = [{'a': 4, 'b': 5, 'c': 6}, {'a': 1, 'b': 2, 'c': 3}]
print [x for x in y1 if x in y2] # prints [{'a': 1, 'c': 3, 'b': 2}]
A dict (or list) is not hashable, however, a tuple is. You can convert the list of dicts to a set of tuples. Perform the intersection and then convert back
the code to convert to a set-of-tuples
y_tupleset = set(tuple(sorted(d.items())) for d in y)
the code to convert back the intersected set-of-tuples to a list-of-dicts
y_dictlist = [dict(it) for it in list(y_tupleset)]
Thus, the full code would be:
y0 = [{'a': 3, 'b': 4, 'c': 5}, {'a': 1, 'b': 2, 'c': 3}]
y1 = [{'a': 4, 'b': 5, 'c': 6}, {'a': 1, 'b': 2, 'c': 3}]
y0_tupleset = set(tuple(sorted(d.items())) for d in y0)
y1_tupleset = set(tuple(sorted(d.items())) for d in y1)
y_inter = y0_tupleset.intersection(y1_tupleset)
y_inter_dictlist = [dict(it) for it in list(y_inter)]
print(y_inter_dictlist)
# prints the following line
[{'a': 1, 'c': 3, 'b': 2}]
edit: d.items() is valid on python3, for python2, it should be replaced with d.iteritems()
Pick your poison:
y1 = [{'a': 3, 'b': 4, 'c': 5}, {'a': 1, 'b': 2, 'c': 3}]
y2 = [{'a': 4, 'b': 5, 'c': 6}, {'a': 1, 'b': 2, 'c': 3}]
y3 = [{'a': 1, 'b': 2, 'c': 3}, {'a': 4, 'b': 2, 'c': 6}]
# Returns a list of keys that are in both dictionaries
def intersect_keys(d1, d2):
return [k for k in d1 if k in d2]
# Returns a list of values that are in both dictionaries
def intersect_vals(d1, d2):
return [v for v in d1.itervalues() if v in d2.itervalues()]
# Returns a list of (key,value) pairs that are in both dictionaries
def intersect_pairs(d1, d2):
return [(k,v) for (k,v) in d1.iteritems() if k in d2 and d2[k] == v]
print(intersect_keys(*y1)) # ['a', 'c', 'b']
print(intersect_vals(*y1)) # [3]
print(intersect_pairs(*y1)) # []
print(intersect_keys(*y2)) # ['a', 'c', 'b']
print(intersect_vals(*y2)) # []
print(intersect_pairs(*y2)) # []
print(intersect_keys(*y3)) # ['a', 'c', 'b']
print(intersect_vals(*y3)) # [2]
print(intersect_pairs(*y3)) # [('b', 2)]
Note: the examples compare the two elements of the y* list, which was how I interpreted your question. You could of course use something like:
print(intersect_pairs(y1[0], y2[0]))
To compute the intersection the first dictionary in the y1 and y2 lists.
I know that I can set a key-value pair by using
dict[key] = value
but I have a very long list of dicts of the type
dict = [{a:1, b:2, c:3, d:4},
{a:2, b:3, c:4, d:5},
{a:5, b:7, c:3, d:9}]
and I'd like to do something along the lines of
dict = map(lambda x: x['d'] <- x['d'] -1, dict)
how would I go about this? (This is a very simplified example so I'm not really trying to just subtract a number from all items by a particular key)
expected output would be in this case and not the general case I'm looking for
[{a:1, b:2, c:3, d:3},
{a:2, b:3, c:4, d:4},
{a:5, b:7, c:3, d:8}]
EDIT: 2
I believe the following does not work - so any similar solution would be helpful:
dict = map(lambda x: x.update(d, x[d] - 1), dict)
dicts = [{'a':1, 'b':2, 'c':3, 'd':4},
{'a':2, 'b':3, 'c':4, 'd':5},
{'a':5, 'b':7, 'c':3, 'd':9}]
for d in dicts:
d['d'] -= 1
Output:
In [94]: dicts
Out[94]:
[{'d': 3, 'b': 2, 'c': 3, 'a': 1},
{'d': 4, 'b': 3, 'c': 4, 'a': 2},
{'d': 8, 'b': 7, 'c': 3, 'a': 5}]
how about this: as exactly you said
>>> dicts = [{'a':1, 'b':2, 'c':3, 'd':4},
{'a':2, 'b':3, 'c':4, 'd':5},
{'a':5, 'b':7, 'c':3, 'd':9}]
>>> map(lambda x:x.update([('d',x['d']-1)]),dicts)
[None, None, None]
>>> dicts
[{'a': 1, 'c': 3, 'b': 2, 'd': 3}, {'a': 2, 'c': 4, 'b': 3, 'd': 4}, {'a': 5, 'c': 3, 'b': 7, 'd': 8}]
update will update the dictionary with (key,value) pair. Returns None
map is a way of transforming an iterable to a list by performing the same operation on every item from the iterable. I don't think that's what you want to do here, and it has confused you.
On the face of it (although you haven't mentioned what the real operation is that you want to perform) a simple for is all that is necessary:
dict_list = [
{'a': 1, 'b': 2, 'c': 3, 'd': 4},
{'a': 2, 'b': 3, 'c': 4, 'd': 5},
{'a': 5, 'b': 7, 'c': 3, 'd': 9},
]
for d in dict_list:
d['d'] -= 1
print(d)
output
{'a': 1, 'b': 2, 'c': 3, 'd': 3}
{'a': 2, 'b': 3, 'c': 4, 'd': 4}
{'a': 5, 'b': 7, 'c': 3, 'd': 8}
Using dict.__setitem__ and temporary list (or any other collection typer) trick:
>>> dicts = [{'a':1, 'b':2, 'c':3, 'd':4},
... {'a':2, 'b':3, 'c':4, 'd':5},
... {'a':5, 'b':7, 'c':3, 'd':9}]
>>> map(lambda d: [d.__setitem__('d', d['d'] - 1), d][1], dicts)
[{'a': 1, 'c': 3, 'b': 2, 'd': 3},
{'a': 2, 'c': 4, 'b': 3, 'd': 4},
{'a': 5, 'c': 3, 'b': 7, 'd': 8}]
Using simple for loop is moe recommended way. Especially there's a side effect in the function.
BTW, don't use dict as a variable name. It will shadows builtin function/type dict.
How about this:
my_dict = {k: f(v) for k, v in my_dict.iteritems()}
where f is whatever function you want.