I have three dictionaries:
X = {'a':2, 'b':3,'e':4}
Y = {'c':3, 'b':4,'a':5, 'd':7}
Z = {'c':8, 'b':7,'a':9, 'e':10,'f':10}
I want to add elements of X and Y if they are present in both dicts and then subtract them from z i.e. Z-X+Y
How can I do that ?
expected result:
res = {'a':2,'b':0,'c':5,'d':7,'e':6,'f':10}
What I tried:
from collections import Counter
xy = Counter(X) + Counter(Y)
res = Counter(Z) - xy
which return:
Counter({'c': 5, 'a': 2, 'e': 6, 'f': 10})
as you can see b and d are missing from my attempt
Your expected result is actually an operation of symmetric difference in terms of sets, but since collections.Counter doesn't support such an operation, you can emulate it with:
xy = Counter(X) + Counter(Y)
z = Counter(Z)
res = z - xy | xy - z
res becomes:
Counter({'f': 10, 'd': 7, 'e': 6, 'c': 5, 'a': 2})
But if you do want keys with value of 0, which Counter would hide from its output, you would have to iterate through a union of the keys of the 3 dicts:
{k: res.get(k, 0) for k in {*X, *Y, *Z}}
This returns:
{'a': 2, 'd': 7, 'e': 6, 'b': 0, 'f': 10, 'c': 5}
Related
Suppose that the range of parameters of interest are given a dictionary that contains the range for each parameter of interest:
G = {'a': [1,2], 'b': [3], 'c': [1, 2.5] }
The goal is to extract every parameter configuration on this grid. In the example above, there are 4 such, corresponding to 2 values of a, and two values of b:
G1 = {'a': 1, 'b': 3, 'c': 1 }
G2 = {'a': 2, 'b': 3, 'c': 1 }
G3 = {'a': 1, 'b': 3, 'c': 2.5 }
G4 = {'a': 2, 'b': 3, 'c': 2.5 }
It's straightforward to write two nested for loops to produce all such configurations, it becomes less trivial how to do it for a general case, when there are a variable number of lists in G.
The only solution that comes to my mind is to create a multi-index vector vec=[0,0] which is as long as the number of parameters, and increment to iterate over all possible configurations: [0,0] -> [1,0] -> [0,1] -> [1,1]:
G = {'a': [1,2], 'b': [3], 'c': [1, 2.5] }
def get_configs(G):
keys = list(G.keys())
lists = list(G.values())
sizes = [len(l) for l in lists]
num_confs = np.prod(sizes)
index = [0]*(len(G)+1)
configs = []
while len(configs)<num_confs:
configs.append( {keys[i]: lists[i][index[i]] for i in range(len(G))})
index[0] += 1
cur = 0
while len(configs)<num_confs and index[cur]>=sizes[cur]:
index[cur]=0
cur += 1
index[cur] += 1
return configs
configs = get_configs(G)
print(configs)
However, the solution seems a bit over-complicated and ugly. Is there a clean solution using python?
Here is a generalizable implementation using itertools.product:
from itertools import product
def dict_configs(d):
for vcomb in product(*d.values()):
yield dict(zip(d.keys(), vcomb))
Usage:
>>> G = {'a': [1,2], 'b': [3], 'c': [1, 2.5] }
>>> for config in dict_configs(G):
... print(config)
...
{'a': 1, 'b': 3, 'c': 1}
{'a': 1, 'b': 3, 'c': 2.5}
{'a': 2, 'b': 3, 'c': 1}
{'a': 2, 'b': 3, 'c': 2.5}
Here's some code to take a linear combination of two dictionaries:
def linearcombination(a1,d1,a2,d2):
return {k:a1*d1.get(k,0)+a2*d2.get(k,0) for k in {**d1,**d2}.keys()}
choosy1={"a":1,"b":2,"c":3}
choosy2={"a":1,"d":1}
choosy=linearcombination(1,choosy1,10,choosy2)
choosy is:
{'a': 11, 'c': 3, 'd': 10, 'b': 2}
How can I generalise it to allow linear combinations of arbitrary numbers of dictionaries?
Solution using sum in a dict comprehension over a set of keys:
from itertools import chain
def linear_combination_of_dicts(dicts, weights):
return {
k: sum( w * d.get(k, 0) for d, w in zip(dicts, weights) )
for k in set(chain.from_iterable(dicts))
}
Example:
>>> dicts = [{'a': 1, 'b': 2, 'c': 3}, {'a': 1, 'd': 1}]
>>> weights = [1, 10]
>>> linear_combination_of_dicts(dicts, weights)
{'c': 3, 'd': 10, 'a': 11, 'b': 2}
Here's an approach with pandas to handle dict key alignment:
def lc(coeffs, dicts):
return (pd.concat(pd.Series(d).fillna(0)*a for a,d in zip(coeffs,dicts))
.sum(level=0)
.to_dict()
)
lc([1,10], [choosy1, choosy2])
# {'a': 11, 'b': 2, 'c': 3, 'd': 10}
Im trying to multiple some values from dictionary
example
price_list = {'a': 3, 'b': 2, 'c': 5, 'd': 10}
when i type
total=sum(price_list.values())
print("Total sum is ",total)
it result 20
But now i want to multiple a with 3, b with 5, c with 2 and d with 3 and my desired output to be 59. What is easiest way to do that?
Assuming your numbers are stored in the list, iterate through the values, and multiply with your required number like so
price_dict = {'a': 3, 'b': 2, 'c': 5, 'd': 10}
numbers_dict = {'a': 3, 'b': 5, 'c': 2, 'd': 3}
result = 0
for key, value in price_dict.items():
result += numbers_dict[key] * value
print(result)
#59
You can just perform operations on the dictionary item like you would any other variable:
# multiply 'a' by 3
price_list['a'] *= 3
Try this:
price_list = {'a': 3, 'b': 2, 'c': 5, 'd': 10}
numbers = [3,5,2,3]
for k,n in list(zip(price_list, numbers)):
price_list[k] *= n
then the price list will change, you can use sum as you did to calculate the result.
How can I iterate over only X number of dictionary items? I can do it using the following bulky way, but I am sure Python allows a more elegant way.
d = {'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5}
x = 0
for key in d:
if x == 3:
break
print key
x += 1
If you want a random sample of X values from a dictionary you can use random.sample on the dictionary's keys:
from random import sample
d = {'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5}
X = 3
for key in sample(d, X):
print key, d[key]
And get output for example:
e 5
c 3
b 2
A = {0:{a:1, b:7}, 1:{a:5,b:5}, 2:{a:4,b:6}}
I want to attach an item guess to each sub dictionary based on the value b accounting of all b's in each sub dictionary.
Saying, in Dictionary A:
0-b-7 percentage of b: 7/(7+5+6)
1-b-5 percentage of b: 5/(7+5+6)
2-b-6 percentage of b: 1 - 7/(7+5+6) - 5/(7+5+6)
The desired Dictionary should be like
A = {0:{a:1, b:7, 'guess': 7/(7+5+6)},
1:{a:5,b:5, 'guess': 5/(7+5+6)},
2:{a:4,b:6, 'guess': 1 - 7/(7+5+6) - 5/(7+5+6)}}
I don't know how to incorporate the other two b's for a specific subdictionary.
One approach is to precompute the sum of all bs and then use it to add a new key-value pair to your dictionary.
b_total = float(sum(A[k]['b'] for k in A))
for k in A:
A[k]['guess'] = A[k]['b'] / b_total
#{0: {'a': 1, 'b': 7, 'guess': 0.3888888888888889},
# 1: {'a': 5, 'b': 5, 'guess': 0.2777777777777778},
# 2: {'a': 4, 'b': 6, 'guess': 0.3333333333333333}}
A = {0:{"a":1, "b":7}, 1:{"a":5,"b":5}, 2:{"a":4,"b":6}}
char = "b"
denominator = 0
# =========================
# First Calculate the sum
# =========================
for key in A:
inner_map = A[key]
denominator += inner_map[char]
# ========================================
# Now insert the new key to the inner_map
# ========================================
for key in A:
inner_map = A[key]
inner_map["guess"] = inner_map[char]/denominator
print(A)
Output:
{0: {'a': 1, 'b': 7, 'guess': 0.3888888888888889}, 1: {'a': 5, 'b': 5, 'guess': 0.2777777777777778}, 2: {'a': 4, 'b': 6, 'guess': 0.3333333333333333}}
Try this:
def add_calc(my_dict):
total_guesses = sum(map(lambda x: my_dict.get(x).get('b'), my_dict))
for item in my_dict.itervalues():
item.update({'guess': 1.0 * item.get('b') / total_guesses})
return my_dict
d = add_calc(A)
{0: {'a': 1, 'b': 7, 'guess': 0.3888888888888889},
1: {'a': 5, 'b': 5, 'guess': 0.2777777777777778},
2: {'a': 4, 'b': 6, 'guess': 0.3333333333333333}}
I'm on Python 2 btw, you didn't specify version
You can use dictionary unpacking:
A = {0:{'a':1, 'b':7}, 1:{'a':5, 'b':5}, 2:{'a':4, 'b':6}}
results = {a:{**b, **{'guess':b['b']/float(sum(c['b'] for _, c in A.items()))}} for a, b in A.items()}
Output:
{0: {'guess': 0.3888888888888889, 'b': 7, 'a': 1}, 1: {'guess': 0.2777777777777778, 'b': 5, 'a': 5}, 2: {'guess': 0.3333333333333333, 'b': 6, 'a': 4}}