Python – Cumulative Mean of Dictionary keys

Given the dictionary list, our task is to write a Python Program to extract the mean of all keys. 

Input : test_list = [{‘gfg’ : 34, ‘is’ : 8, ‘best’ : 10},

             {‘gfg’ : 1, ‘for’ : 10, ‘neveropen’ : 9, ‘and’ : 5, ‘best’ : 12},

             {‘neveropen’ : 8, ‘find’ : 3, ‘gfg’ : 3, ‘best’ : 8}]

Output : {‘gfg’: 12.666666666666666, ‘is’: 8, ‘best’: 10, ‘for’: 10, ‘neveropen’: 8.5, ‘and’: 5, ‘find’: 3}

Explanation : best has 3 values, 10, 8 and 12, their mean computed to 10, hence in result.

Input : test_list = [{‘gfg’ : 34, ‘is’ : 8, ‘best’ : 10},

             {‘gfg’ : 1, ‘for’ : 10, ‘and’ : 5, ‘best’ : 12},

             { ‘find’ : 3, ‘gfg’ : 3, ‘best’ : 8}]

Output : {‘gfg’: 12.666666666666666, ‘is’: 8, ‘best’: 10, ‘for’: 10, ‘and’: 5, ‘find’: 3}

Explanation : best has 3 values, 10, 8 and 12, their mean computed to 10, hence in result.

Method #1 : Using mean() + loop 

In this, for extracting each list loop is used and all the values are summed and memorized using a dictionary. Mean is extracted later by dividing by the occurrence of each key. 

Output:

The original list is : [{‘gfg’: 34, ‘is’: 8, ‘best’: 10}, {‘gfg’: 1, ‘for’: 10, ‘neveropen’: 9, ‘and’: 5, ‘best’: 12}, {‘neveropen’: 8, ‘find’: 3, ‘gfg’: 3, ‘best’: 8}]

The Extracted average : {‘gfg’: 12.666666666666666, ‘is’: 8, ‘best’: 10, ‘for’: 10, ‘neveropen’: 8.5, ‘and’: 5, ‘find’: 3}

Time Complexity: O(n)
Auxiliary Space: O(n)

Method #2 : Using defaultdict() + mean()

In this, the task of memorizing is done using defaultdict(). This reduces one conditional check and makes the code more concise.

Output:

The original list is : [{‘gfg’: 34, ‘is’: 8, ‘best’: 10}, {‘gfg’: 1, ‘for’: 10, ‘neveropen’: 9, ‘and’: 5, ‘best’: 12}, {‘neveropen’: 8, ‘find’: 3, ‘gfg’: 3, ‘best’: 8}]

The Extracted average : {‘gfg’: 12.666666666666666, ‘is’: 8, ‘best’: 10, ‘for’: 10, ‘neveropen’: 8.5, ‘and’: 5, ‘find’: 3}

Time Complexity: O(n²)
Auxiliary Space: O(n)

Method #3: Using pandas library

• Import the pandas library.
• Create a pandas DataFrame from the test_list.
• Use the melt function to transform the DataFrame from wide to long format, with one row for each key-value pair.
• Use the groupby function to group the DataFrame by the keys and calculate the mean of the values for each key.Convert the resulting pandas Series to a dictionary.
•  

Output:

The Extracted average : {'and': 5.0, 'best': 10.0, 'find': 3.0, 'for': 10.0, 'neveropen': 8.5, 'gfg': 12.666666666666666, 'is': 8.0}

Time complexity: O(n*logn), where n is the total number of key-value pairs in the test_list.
Auxiliary space: O(n), where n is the total number of key-value pairs in the test_list.

Method #4:  using a list comprehension and the setdefault() method 

• Create a list of dictionaries test_list.
• Create an empty dictionary res.
• Loop over each dictionary d in test_list.
• Loop over each key-value pair (key, val) in d.
• If the key key is not in res, set its value to an empty list. Append the value val to the list associated with the key key in the res dictionary.
• Create a new dictionary res_mean.
• Loop over each key-value pair (key, val) in the res dictionary.
• Compute the mean of the values val associated with the key key using the mean function from the statistics module.
• Add a new key-value pair to the res_mean dictionary with the key key and the value equal to the mean value computed in step 8.
• Print the res_mean dictionary as a string, with a message indicating that it contains the extracted average values.

The Extracted average : {'gfg': 12.666666666666666, 'is': 8, 'best': 10, 'for': 10, 'neveropen': 8.5, 'and': 5, 'find': 3}

Time complexity: O(nk), where n is the number of dictionaries in test_list and k is the average number of keys in each dictionary. 
Auxiliary space: O(mk), where m is the number of unique keys in all the dictionaries in test_list and k is the average number of values associated with each key.