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

I have list of dictionary below

I have two name values ['Tester', 'Developer']
By checking lastModifiedDate need to extract latest entry for 'Tester', 'Developer'

myd = [
  {
    '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'  }
]

My logic and code is below

First sort the lastModifiedDate
Extract the first occurence

response = sorted(myd,
                          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

My out is

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

Expected out 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 fails absence of Developer or Tester dictionary

myd = [ { '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' } ]

CodePudding user response：

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'}]

CodePudding user response：

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