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# Python | Aggregate values by tuple keys

Sometimes, while working with records, we can have a problem in which we need to group the like keys and aggregate the values of like keys. This can have application in any kind of scoring. Let’s discuss certain ways in which this task can be performed.

Method #1 : Using Counter() + generator expression The combination of above functions can be used to perform this particular task. In this, we need to first combine the like key elements and task of aggregation is performed by Counter().

## Python3

 `# Python3 code to demonstrate working of``# Aggregate values by tuple keys``# using Counter() + generator expression``from` `collections ``import` `Counter` `# initialize list``test_list ``=` `[(``'gfg'``, ``50``), (``'is'``, ``30``), (``'best'``, ``100``),``                          ``(``'gfg'``, ``20``), (``'best'``, ``50``)]` `# printing original list``print``("The original ``list` `is` `: " ``+` `str``(test_list))` `# Aggregate values by tuple keys``# using Counter() + generator expression``res ``=` `list``(Counter(key ``for` `key, num ``in` `test_list``                  ``for` `idx ``in` `range``(num)).items())` `# printing result``print``("``List` `after grouping : " ``+` `str``(res))`

Output :

```The original list is : [('gfg', 50), ('is', 30), ('best', 100), ('gfg', 20), ('best', 50)]
List after grouping : [('best', 150), ('gfg', 70), ('is', 30)]```

Time Complexity: O(n*n) where n is the number of elements in the list “test_list”. Counter() + generator expression performs n*n number of operations.
Auxiliary Space: O(n), extra space is required where n is the number of elements in the list

Method #2 : Using groupby() + map() + itemgetter() + sum() The combination of above functions can also be used to perform this particular task. In this, we group the elements using groupby(), decision of key’s index is given by itemgetter. Task of addition(aggregation) is performed by sum() and extension of logic to all tuples is handled by map().

## Python3

 `# Python3 code to demonstrate working of``# Aggregate values by tuple keys``# using groupby() + map() + itemgetter() + sum()``from` `itertools ``import` `groupby``from` `operator ``import` `itemgetter` `# initialize list``test_list ``=` `[(``'gfg'``, ``50``), (``'is'``, ``30``), (``'best'``, ``100``),``                          ``(``'gfg'``, ``20``), (``'best'``, ``50``)]` `# printing original list``print``("The original ``list` `is` `: " ``+` `str``(test_list))` `# Aggregate values by tuple keys``# using groupby() + map() + itemgetter() + sum()``res ``=` `[(key, ``sum``(``map``(itemgetter(``1``), ele)))``       ``for` `key, ele ``in` `groupby(``sorted``(test_list, key ``=` `itemgetter(``0``)),``                                                ``key ``=` `itemgetter(``0``))]` `# printing result``print``("``List` `after grouping : " ``+` `str``(res))`

Output :

```The original list is : [('gfg', 50), ('is', 30), ('best', 100), ('gfg', 20), ('best', 50)]
List after grouping : [('best', 150), ('gfg', 70), ('is', 30)]```

Time Complexity: O(n*n), where n is the length of the list test_list
Auxiliary Space: O(n) additional space of size n is created where n is the number of elements in the res list

Method #3: Using reduce():

Algorithm:

• Import the required modules, functools, itertools and operator.
• Initialize the given list of tuples.
• Use the reduce function to iterate through the list of tuples, filtering the tuples with the same first element and summing their second element.
• Append the tuples obtained from step 3 to the accumulator list, if the first element of the tuple is not
• present in the accumulator list, otherwise return the accumulator list unchanged.
• Finally, print the list after grouping.

## Python3

 `from` `functools ``import` `reduce``from` `itertools ``import` `groupby``from` `operator ``import` `itemgetter` `# initialize list``test_list ``=` `[(``'gfg'``, ``50``), (``'is'``, ``30``), (``'best'``, ``100``),``             ``(``'gfg'``, ``20``), (``'best'``, ``50``)]` `# printing original list``print``(``"The original list is : "` `+` `str``(test_list))` `# use reduce() to aggregate values by tuple keys``res ``=` `reduce``(``lambda` `acc, x: acc ``+` `[(x[``0``],``        ``sum``(``map``(itemgetter(``1``), ``filter``(``lambda` `y: y[``0``] ``=``=``        ``x[``0``], test_list))))] ``if` `x[``0``] ``not` `in` `[elem[``0``] ``for` `elem ``in` `acc]``        ``else` `acc, test_list, [])` `# printing result``print``(``"List after grouping : "` `+` `str``(res))``# This code is contributed by Jyothi pinjala.`

Output

```The original list is : [('gfg', 50), ('is', 30), ('best', 100), ('gfg', 20), ('best', 50)]
List after grouping : [('gfg', 70), ('is', 30), ('best', 150)]```

Time Complexity: O(nlogn), where n is the length of the input list. This is due to the sorting operation performed by the groupby function.
Auxiliary Space: O(n), where n is the length of the input list. This is due to the list created by the reduce function to store the output tuples.

METHOD 4:Using dictionary.

APPROACH:

The program takes a list of tuples as input and aggregates the values by the tuple keys. In other words, it groups the values of tuples with the same key and sums their values.

ALGORITHM:

1.Initialize an empty dictionary d.
2.Loop through each tuple in the list:
a.Check if the key of the tuple is already present in the dictionary.
b.If the key is present, add the value of the tuple to the existing value of the key in the dictionary.
c.If the key is not present, add the key-value pair to the dictionary.
5.Convert the dictionary to a list of tuples using the items() method.
6.Print the list.

## Python3

 `# Input``lst ``=` `[(``'gfg'``, ``50``), (``'is'``, ``30``), (``'best'``, ``100``), (``'gfg'``, ``20``), (``'best'``, ``50``)]` `# Aggregate values using a dictionary``d ``=` `{}``for` `key, value ``in` `lst:``    ``if` `key ``in` `d:``        ``d[key] ``+``=` `value``    ``else``:``        ``d[key] ``=` `value` `# Convert dictionary to list of tuples``result ``=` `list``(d.items())` `# Output``print``(``"List after grouping :"``, result)`

Output

```List after grouping : [('gfg', 70), ('is', 30), ('best', 150)]
```

Time Complexity:

The time complexity of this program is O(n), where n is the length of the input list.

Space Complexity:

The space complexity of this program is O(m), where m is the number of unique keys in the input list. This is because the program creates a dictionary to store the keys and their corresponding values.