How to extract from the list of dictionary by checking latest timestamp and value

user_15299206 0
Viewed 191

There is a list of dictionaries below like

my_dictionary = [
  {
    'name': 'Tester',
    'id': '101',
    'status': 'Failed',
    'lastModifiedDate': '2022-02-14 12:25:32:013302'  },
  {
    'name': 'Developer',
    'id': '102',
    'status': 'Success',
    'lastModifiedDate': '2022-02-14 12:25:32:013302',
  },
  {
    'name': 'Tester',
    'id': '101',
    'status': 'Failed',
    'lastModifiedDate': '2022-02-21 12:25:32:013302'  }
]
  • I have two name values as Tester or Developer
  • By checking latest lastModifiedDate need to extract latest entry for Tester, Developer

Logic implemented in the below code is as follows:

  • First sort the on the basis of lastModifiedDate in descending order.
  • Extract the first occurrence in the list of dictionaries.
response = sorted(my_dictionary, key=lambda x: x['lastModifiedDate'], reverse=True)

response_latest = []
for item in response:
    extracted_response = {}
    for field in item:     
        if item['name'] == 'Tester':
            extracted_response[field] = item[field]
    response_latest.append(extracted_response)
    break
    for field in item:     
        if item['name'] == 'Developer':
            extracted_response[field] = item[field]
    response_latest.append(extracted_response)
    break
response_latest

The output of the above code on input as the above list of the dictionaries is

[{'name': 'Tester',
  'id': '101',
  'status': 'Failed',
  'lastModifiedDate': '2022-02-21 12:25:32:013302'}]

but the expected output is

[{'name': 'Tester',
  'id': '101',
  'status': 'Failed',
  'lastModifiedDate': '2022-02-21 12:25:32:013302'},
 {'name': 'Developer',
  'id': '102',
  'status': 'Success',
  'lastModifiedDate': '2022-02-14 12:25:32:013302'}]

Also, code should not fail in case of the absence of Developer or Tester dictionary name values.

my_dictionary = [{'name': 'Tester', 'id': '101', 'status': 'Failed', 'lastModifiedDate': '2022-02-14 12:25:32:013302'}, 
{'name': 'Tester', 'id': '101', 'status': 'Failed', 'lastModifiedDate': '2022-02-21 12:25:32:013302'}]
dictionary sorting python
3 Answers

If pandas is in play:

import pandas as pd
df = pd.DataFrame(myd)
maxes = df.groupby('name')['lastModifiedDate'].transform('max')
result = df[df['lastModifiedDate'] == maxes].to_dict(orient='records')

Output:

>>> result
[{'name': 'Developer', 'id': '102', 'status': 'Success', 'lastModifiedDate': '2022-02-14 12:25:32:013302'}, {'name': 'Tester', 'id': '101', 'status': 'Failed', 'lastModifiedDate': '2022-02-21 12:25:32:013302'}]
user_36200030

I tried filtering myd and then finding the latest of each one.

tester = list(filter(lambda x: x["name"] == "Tester", myd))
developer = list(filter(lambda x: x["name"] == "Developer", myd))

tester_latest = sorted(tester, key=lambda x: x["lastModifiedDate"], reverse=True)
developer_lastest = sorted(developer, key=lambda x: x["lastModifiedDate"], reverse=True)

response_latest = [
    tester_latest[0] if len(tester_latest) > 0 else None,
    developer_lastest[0] if len(developer_lastest) > 0 else None
]
response_latest = list(filter(None, response_latest)
response_latest
user_182710960

You can try the method of converting a list of dictionaries to a pandas data frame and perform a few actions on it.

This example includes two Tester dictionaries having the same lastModifiedDate but having different lastModifiedDate in the case of Developer

my_dictionary_list = [
  {
    'name': 'Tester',
    'id': '101',
    'status': 'Failed',
    'lastModifiedDate': '2022-02-14 12:25:32:013302'},
  {
    'name': 'Developer',
    'id': '102',
    'status': 'Success',
    'lastModifiedDate': '2022-02-14 12:25:32:129405'},
  {
    'name': 'Tester',
    'id': '103',
    'status': 'Failed',
    'lastModifiedDate': '2022-02-14 12:25:32:013302'},
  {
    'name': 'Developer',
    'id': '102',
    'status': 'Success',
    'lastModifiedDate': '2022-02-21 12:25:32:113215'},
  {
    'name': 'Tester',
    'id': '103',
    'status': 'Failed',
    'lastModifiedDate': '2022-02-12 12:25:32:013302'},
]

import pandas as pd
df = pd.DataFrame(my_dictionary_list)
maxes_by_group = df.groupby(['name'])['lastModifiedDate'].transform(max)
print("maxes_by_group output - \n{}".format(maxes_by_group))
result = df[df['lastModifiedDate'] == maxes_by_group].to_dict(orient='records')
print("--------------------")
print("Final result output - \n{}".format(result))

Output -

maxes_by_group output - 
0    2022-02-14 12:25:32:013302
1    2022-02-21 12:25:32:113215
2    2022-02-14 12:25:32:013302
3    2022-02-21 12:25:32:113215
4    2022-02-14 12:25:32:013302
Name: lastModifiedDate, dtype: object
--------------------
Final result output - 
[{'name': 'Tester', 'id': '101', 'status': 'Failed', 'lastModifiedDate': '2022-02-14 12:25:32:013302'}, {'name': 'Tester', 'id': '103', 'status': 'Failed', 'lastModifiedDate': '2022-02-14 12:25:32:013302'}, {'name': 'Developer', 'id': '102', 'status': 'Success', 'lastModifiedDate': '2022-02-21 12:25:32:113215'}]

maxes_by_group variable output represents descending order sorting of lastModifiedDate values.

You can see three dictionaries in the final result variable output. Two Tester name values are having the same maximum lastModifiedDate values and one Developer name value is having the maximum lastModifiedDate value.

user_91666730
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