Send email to people who I have shared the App with

Hey @verytiredgirl!

As @ColemenWilson mentioned, I was able to write some Python code that pulls the data you are looking for and pushes it to a Domo dataset for use within a workflow (and anywhere else you'd like it). I have this configured to work within a Jupyter Workspace within our Domo instance. Here's the code I have:

import requests

import json

import pandas as pd

import numpy as np

import domojupyter as domo

# You'll need to create a dev token, store it in an Account, and then add that Account to this Jupyter Workspace, naming it "Domo Dev Token"
account_properties = domo.get_account_property_keys('Domo Dev Token')

devToken = domo.get_account_property_value('Domo Dev Token', account_properties[0])

def app_user_access(app_id):

  """Retrieves user access data related to a particular App Studio app
  Args:

      app_id (str): The id of the app to get a user access list from


  Returns:

      df_combined (Pandas dataframe): A dataframe of all users (and groups with users) that have access to an app

  """

  # You'll

  baseURL = "https://[your-instance-name].domo.com/api/content/v1/dataapps/" + str(app_id) + "/adminsummary"

  headers = {

      "x-domo-developer-token": devToken,

      "accept": "application/json"

  }


  response = requests.request("GET", baseURL, headers=headers)


  # Pull direct access list

  users = json.loads(response.text)['access']['users']

  user_df = pd.json_normalize(users)[['id', 'displayName', 'emailAddress', 'title']]

  user_df.rename(columns={'id': 'User ID', 'displayName': 'Display Name', 'title': 'User Title', 'emailAddress': 'Email'}, inplace=True)


  # Pull users with access via a group

  if 'groups' in json.loads(response.text)['access']:

      groups = json.loads(response.text)['access']['groups']

      group_users_df = pd.json_normalize(groups, 'userIds', ['id', 'name'], record_prefix="userID")

      group_users_df.rename(columns={'userID0': 'User ID', 'id': 'Group ID', 'name': 'Group Name'}, inplace=True)


      # Join in user data
      # This is just the "People" Domo Governance dataset

      user_data = domo.read_dataframe('Domo Governance People dataset', query='SELECT * FROM table')

      user_data['User ID'] = user_data['User ID'].astype('int64')

      group_users_df = group_users_df.merge(user_data[['User ID', 'Display Name', 'Title', 'Email']], on='User ID', how='left')

      group_users_df.rename(columns={'Title': 'User Title'}, inplace=True)


      df_combined = pd.concat([user_df, group_users_df], ignore_index=True, sort=False)

  else:

      df_combined = user_df


  df_combined['App ID'] = app_id

  return df_combined

def all_app_user_access():

    """Retrieves user access data related to all App Studio apps


    Returns:

        all_app_access_df (Pandas dataframe): A dataframe of all users (and groups with users) that have access to all apps in Domo

    """


    # Get a list of all app ids 
    # NOTE: This dataflow was custom made, so you'll need to create it and add it to the Jupyter Workspace


    pages_df = domo.read_dataframe('Apps and Pages Dataset', query='SELECT * FROM table')

    apps_df = pages_df[pages_df['Is an App'] == 1]

    apps_df = apps_df[['App ID', 'App Name']].drop_duplicates()

    list_of_app_ids = apps_df['App ID'].tolist()

    list_of_app_names = apps_df['App Name'].tolist()


    # Loop through all apps

    all_app_access_df = pd.DataFrame()


    for i in range(0,len(list_of_app_ids)):

        app_data = app_user_access(list_of_app_ids[i])

        app_data['App Name'] = list_of_app_names[i]

        all_app_access_df = pd.concat([all_app_access_df, app_data], ignore_index=True, sort=False)


    return all_app_access_df


domo.write_dataframe(all_app_user_access(), 'Apps with Users Dataset')

However, this code only works if you create a dataset called "Apps and Pages Dataset" via a dataflow that looks like this:

I'm basically just getting a list of all Apps and their associated pages, with owner info as well.

Kinda complicated, but it works for us, so I hope this helps!