What is the total amount within a specified date range when retrieved as JSON?

Consider the following JSON structure:

{
    "timesheets": [
        {
            "user": {
                "username": "erik",
                "first_name": "Erik",
            },
            "project_id": 4,
            "calc_full_week": {
                "2020-06-22": 5,
                "2020-06-23": 10,
                "2020-06-24": 8,
                "2020-06-25": 13,
                "2020-06-26": null,
                "2020-06-27": null,
                "2020-06-28": null
            }
        },
        {
            "user": {
                "username": "erik",
                "first_name": "Erik",
            },
            "project_id": 4,
            "calc_full_week": {
                "2020-06-29": 15,
                "2020-06-30": 10,
                "2020-07-01": null,
                "2020-07-02": null,
                "2020-07-03": null,
                "2020-07-04": null,
                "2020-07-05": null
            }
        },
        {
            "user": {
                "username": "rawwe",
                "first_name": "joey",
            },
            "project_id": 4,
            "calc_full_week": {
                "2020-06-22": 3,
                "2020-06-23": 10.4,
                "2020-06-24": 8,
                "2020-06-25": 8,
                "2020-06-26": 8,
                "2020-06-27": 8,
                "2020-06-28": 5
            }
        }
    ]
}

I am seeking an efficient way to extract the sum of values from calc_full_week within a specified range. For example, if I provide the range 2020-06-25 - 2020-07-03, the total sum of values within that range should be calculated (13+15+10+8+8+8+5 for the given JSON).

Now, the dilemma is whether to perform these calculations on the backend (django) or using JavaScript on the client side.

In my Django model, the structure is as shown below:


class Timesheet(Model):
    year = PositiveIntegerField(validators=[MaxValueValidator(2500), MinValueValidator(1900)])
    week = PositiveIntegerField()
    project = ForeignKey("projects.Project", on_delete=CASCADE)
    user = ForeignKey("accounts.User", on_delete=CASCADE)
    day_1 = DecimalField("monday", blank=True, null=True, max_digits=4, decimal_places=1)
    day_2 = DecimalField("tuesday", blank=True, null=True, max_digits=4, decimal_places=1)
    day_3 = DecimalField("wednesday", blank=True, null=True, max_digits=4, decimal_places=1)
    day_4 = DecimalField("thursday", blank=True, null=True, max_digits=4, decimal_places=1)
    day_5 = DecimalField("friday", blank=True, null=True, max_digits=4, decimal_places=1)
    day_6 = DecimalField("saturday", blank=True, null=True, max_digits=4, decimal_places=1)
    day_7 = DecimalField("sunday", blank=True, null=True, max_digits=4, decimal_places=1)

I serialize this data and include a 

 calc_full_week = serializers.SerializerMethodField()
attribute, with the calculation logic detailed in the method below:

 def get_calc_full_week(self, obj):
        current_week = obj.week
        current_year = obj.year
        # Since week 1 corresponds to week 0
        week = current_week - 1
        # List containing each date of a given week
        startdate = time.asctime(time.strptime('%d %d 1' % (current_year, week), '%Y %W %w'))
        startdate = datetime.datetime.strptime(startdate, '%a %b %d %H:%M:%S %Y')

        date_dict = {}
        days_in_week = [obj.day_1, obj.day_2, obj.day_3, obj.day_4, obj.day_5, obj.day_6, obj.day_7]
        for i in range(0, 7):
            day = startdate + datetime.timedelta(days=i)
            date_dict[day.strftime('%Y-%m-%d')] = days_in_week[i]

        return date_dict
javascriptdjangovue.jsdjango-rest-framework

Answer №1

const data = {
    "statistics": [
        {
            "user": {
                "username": "erik",
                "first_name": "Erik",
            },
            "project_id": 4,
            "weekly_hours": {
                "2020-06-22": 5,
                "2020-06-23": 10,
                "2020-06-24": 8,
                "2020-06-25": 13,
                "2020-06-26": null,
                "2020-06-27": null,
                "2020-06-28": null
            }
        },
        {
            "user": {
                "username": "erik",
                "first_name": "Erik",
            },
            "project_id": 4,
            "weekly_hours": {
                "2020-06-29": 15,
                "2020-06-30": 10,
                "2020-07-01": null,
                "2020-07-02": null,
                "2020-07-03": null,
                "2020-07-04": null,
                "2020-07-05": null
            }
        },
        {
            "user": {
                "username": "rawwe",
                "first_name": "joey",
            },
            "project_id": 4,
            "weekly_hours": {
                "2020-06-22": 3,
                "2020-06-23": 10.4,
                "2020-06-24": 8,
                "2020-06-25": 8,
                "2020-06-26": 8,
                "2020-06-27": 8,
                "2020-06-28": 5
            }
        }
    ]
}

const calculateTotalHours = (startDate, endDate) => {
  const startRange = +startDate.split('-').join('')
  const endRange = +endDate.split('-').join('')
  let totalHours = 0
  data.statistics.reduce((acc, val) => {
    Object.keys(val.weekly_hours).map((date, index) => {
      const dateSplit = +date.split('-').join('')
      if (dateSplit >= startRange && dateSplit <= endRange) {
        totalHours += val.weekly_hours[date]
      }
    })
  }, 0)
  return totalHours
}

console.log(calculateTotalHours('2020-06-25', '2020-07-03'))

Answer №2

If you're interested in achieving this task using Python, consider the following approach:

Start by creating a list of allowed dates:

my_date_range = ['2020-06-25', '2020-06-26', ]

It would be beneficial to have a separate method that generates this list based on a specified start and end date, like the one suggested in this Stack Overflow answer.

To simplify the extraction of required sub-nodes from nested dictionaries or JSON objects, you can leverage a library like dpath:

import json
import dpath

data = json.loads(json_string)  # convert JSON string to dictionary format

# filter the dictionary for all date values within a certain range
# utilizing wildcard (*) for flexibility in key name filtering
# setting yielded=True returns a generator for tuples instead of a dictionary
result = dpath.util.search(data, "timesheets/*/calc_full_week/2020-*", yielded=True)

# Calculate the sum of the required dates while filtering out None values
my_sum = sum(filter(None, [v for k, v in result if k in my_date_range]))

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