How to Sum Dictionaries Values With Same Key Inside a List

Sum the values of dictionaries inside a list where the keys are the same

IIUC this is pretty straight forward. You just write a function to sum up all values per key. Then apply it to the transposed list of lists of dicts.

def sum_by_key(dicts):
    result = {}

    for d in dicts:
        for k, v in d.items():
            result[k] = result.get(k, 0) + v

    return result

lists_of_dicts = [[{1:2, 3:4}, {1:10, 2:9}], [{3:8, 2:4}, {3:1, 2:5}]]
result = [sum_by_key(dicts) for dicts in zip(*lists_of_dicts)]

print(result)

(lists_of_dicts would be [A, B, C, D] with your variables)

Output:

[{1: 2, 3: 12, 2: 4}, {1: 10, 2: 14, 3: 1}]

---

edit: with your new sample data

lists_of_dicts = [A, B, C, D]
result = [sum_by_key(dicts) for dicts in zip(*lists_of_dicts)]
print(result)

produces

[{'1': 1158, '2': 1450, '3': 542, '4': 2060}, {'1': 702, '2': 2600, '3': 1169, '4': 1670}, {'1': 880, '2': 2480, '3': 2000, '4': 3852}, {'1': 600, '2': 640, '3': 1142, '4': 6230}]

Python - Sum the value in the list of dictionary based on the same key

Your data is rather a DataFrame, a natural pandas solution is :

In [34]: pd.DataFrame.from_records(data).astype(int).groupby('player').sum().T.to_dict()

Out[34]: {1: {'stat2': 5, 'stat3': 13}, 3: {'stat2': 8, 'stat3': 9}}

Sum values of duplicate(common) keys in a list of dictionary

Following up on @python_user's comment

Here is how you implement the solution for your code

tax = [{'taxType': 'T1', 'amount': 140},{'taxType': 'T1', 'amount': 10}]

aggregated_data = {}

for dictionary in tax:
    key = (dictionary['taxType'])
   
    aggregated_data[key] = aggregated_data.get(key, 0) + dictionary['amount']
    
tax = [{'taxType': key, 'amount': value} for key, value in aggregated_data.items()]

print(tax)

output

[{'taxType': 'T1', 'amount': 150}]

How to sum elements in list of dictionaries if two key values are the same

This is possible, but non-trivial to implement in python. Might I suggest using pandas? This is simple with a groupby, sum, and to_dict.

import pandas as pd

(pd.DataFrame(dictionary)
   .groupby(['Location', 'Name'], as_index=False)
   .Flow.sum()
   .to_dict('r'))

[{'Flow': 160, 'Location': 'Europe', 'Name': 'B1'},
 {'Flow': 120, 'Location': 'USA', 'Name': 'A1'}]

To install, use pip install --user pandas.

---

Otherwise, you can apply a pseudo-generic group operation using itertools.groupby.

from itertools import groupby
from operator import itemgetter

grouper = ['Location', 'Name']
key = itemgetter(*grouper)
dictionary.sort(key=key)

[{**dict(zip(grouper, k)), 'Flow': sum(map(itemgetter('Flow'), g))} 
    for k, g in groupby(dictionary, key=key)]

[{'Flow': 160, 'Location': 'Europe', 'Name': 'B1'},
 {'Flow': 120, 'Location': 'USA', 'Name': 'A1'}]

Add up values of same key in a nested dictionary

If you wish to store your result in a dictionary, you can create one with the keys from your list and calculate the results there.

result = {i: 0 for i in mylist}

for k, v in d.items():
    result['age'] += v['age']
    result['answ1'] += v['answ1']
    result['answ2'] += v['answ2']
    result['answ3'] += v['answ3']

result
{'age': 71, 'answ1': 11, 'answ2': 8, 'answ3': 12}

However this does rely on the keys not changing, order should not matter.

EDIT

You can do this regardless of key names with the following update. Note it adds one extra iteration.

result = {i: 0 for i in mylist}
for k, v in d.items():
    for ke, va in v.items():
        result[ke] += v[ke]

How sum dict element by same key value

All your dictionaries have the same keys, you can first sum each of them using a default dict, then reconstructing a list from the results:

from collections import defaultdict
 
result = defaultdict(int)
for d in list_dict:
  result[d["id_sistema_productivo"]] += d["area"]

result = [{"id_sistema_productivo": id, "area": area} for id, area in result.items()]

If the dictionary keys are always the same, consider encapsulating it in a class, it will be clearer (example using dataclass which is really convenient):

from dataclasses import dataclass

@dataclass
class Element:
    id_sistema_productivo: int
    area: int

you can then express what you want more easily:

list_dict = [
  Element(48, 327),
  Element(51, 205.65),
  Element(48, 327),
  Element(51, 209.13)
]

result = defaultdict(int)
for element in list_dict:
    result[element.id_sistema_productivo] += element.area
result = [Element(i, a) for i, a in result.items()]

print(result)

Merge lists of dictionaries by summing values of the same key

Try this if you want to sum all values for unique 'name' in each dictionaries :

names = set([k['name'] for k in dict1+dict2])
dict3 = []
for name in names:
    temp_val = []
    for dict_ in dict1+dict2:
        if dict_['name'] == name:
            temp_val.append(dict_['value'])
    dict3.append({'name': name, 'value' : sum(temp_val)})

OUTPUT :

[{'name': 'A', 'value': 16}, {'name': 'B', 'value': 5}, {'name': 'C', 'value': 12}]

How to sum up values of duplicate keys in a dictionary?

You need to check for key existence first. If the key already exists, you add the value directly rather than call the Add() method.

    var mydic = new Dictionary<string, int>();
    for (int i = 0; i < keys.Count; i++)
    {
        if(mydic.ContainsKey(keys[i]))
            mydic[keys[i]] += values[i];
        else
            mydic.Add(keys[i], values[i]);
    }

Python3: get sum of values of specific key in list of dictionary

Pandas to the rescue!

First, create a dataframe from your data

df = pd.DataFrame(data)
print(df)

Output:
   id  value
0   1      3
1   2      1
2   3      5
3   1      1
4   1      2
5   3      2
6   1      3

Then, group by id and sum

result = df.groupby("id").sum()
print(result)

Output:
    value
id       
1       9
2       1
3       7

And then convert this to the list like you want:

new_list = [{"id": elemid, "value": val.value} for elemid, val in result.iterrows()]
print(new_list)
# Output: [{'id': 1, 'value': 9}, {'id': 2, 'value': 1}, {'id': 3, 'value': 7}]

Alternatively, you could just improve your existing way:

data = [
    {"id": 1, "value": 3},
    {"id": 2, "value": 1},
    {"id": 3, "value": 5},
    {"id": 1, "value": 1},
    {"id": 1, "value": 2},
    {"id": 3, "value": 2},
    {"id": 1, "value": 3}
]

new_data = {}
for elem in data:
    elemid = elem["id"]
    value = elem["value"]
    new_data[elemid] = new_data.get(elemid, 0) + value

print(new_data)
# Output: {1: 9, 2: 1, 3: 7}

Then, convert this to the list like you want:

new_list = [{"id": key, "value": value} for key, value in new_data.items()]
print(new_list)
# Output: [{'id': 1, 'value': 9}, {'id': 2, 'value': 1}, {'id': 3, 'value': 7}]

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