foreninglet-data

Python module for generalising access to the ForeningLet (www.foreninglet.dk) member system API

Current functionality

The project is work in progress. As of now, calling the ForeningLet API has been generalised, and the memberlist can be retrieved.

A memberlist object can be genereated, and so far has functionality to count the members, and genders.

Installation

For now this code can be pip installed by referring to the get repo:

python -m pip install git+https://github.com/simcax/foreninglet-data

Compulsory Settings

Export settings as environment variables:

API_USERNAME=username
API_PASSWORD=password
API_BASE_URL=https://foreninglet.dk/api/
API_VERSION=version=1
API_MEMBERS_API=members
API_ACTIVITIES_API=activities
API_RESIGNED_MEMBERS_API=members/status/resigned
MEMBERSHIP_KEYWORDS=keyword1,keyword2 

The MEMBERSHIP_KEYWORDS environment variable is needed to correctly match a membership to a member. I.e. consider having the following memberships:

• 3 måneders medlemskab
• 6 måneders medlemskab
• 9 måneders medlemskab

Then giving an environment variable this way: MEMBERSHIP_KEYWORDS=medlemskab will get the membership activity IDs from the activity API, and assign the correct membership to each member in the membership list object:

>>> memberlist_obj.memberlist[0].get('Membership')
>>> '3 måneders medlemskab'

Usage

Then import:

from foreninglet_data.api import ForeningLet
from foreninglet_data.memberlist import Memberlist

And get the memberlist:

fl_obj = ForeningLet()
memberlist = fl_obj.get_memberlist()
memberlist_obj = Memberlist(memberlist)

This will give you a Python memberlist object. The memberlist object contains all the information from the members API call, now accessible as an object:

memberlist_obj.members 

gives you a dictionary with all the members in it. In addition the object also holds the data as a Pandas Dataframe:

memberlist_obj.memberlist_dataframe

This will enable further data manipulation and investigation into the data returned.

This has not been tested with HUGE organisations, only up till about 1200 members. There could be some limitations on memory consumption. (Hence the sub 1.0 release ;-) )

Retrieve the membercount:

# All members
memberlist.member_count
# All the GenuineMembers
memberlist.genuine_member_count
# The number of new members in the current month
memberlist.new_members_current_month
memberlist.new_members_current_month_percentage
# The number of new members in the previous month
memberlist.new_members_previous_month
memberlist.new_members_previous_month_percentage
# Number of members joined last year
memberlist.members_last_year
# Number of members joined current year
memberlist.members_current_year
# Members enrolled per year
memberlist.members_per_year 
- Returns a dictionary with year as key and number of enrolled members as values:
  {'2020': 14, '2021': 15, '2022': 15, '2023': 10}

Retrieve the count of genders:

memberlist.count_men
memberlist.count_women

Getting a list of member ages

memberlist.member_age_list

This is a dict with a full range of each age from the youngest to the eldest member. In the case no members are of a given age, the dict will contain the age but a count of 0.

The dict is sorted by age.

i.e:

{23: 1, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 1, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, ...}