Content Item class .__dict__ built-in method doesn't return 'size' attribute

jawhitacre@PA.LCL_PAGame

I am creating a table of all of my content items in ArcGIS Online and ArcGIS Enterprise. As a starting point, I am using the built-in vars() function to return all the Item attributes as a dictionary (.__dict__ can also be used with the same results). Here is my code (which is fast and works well):

# Log into ArcGIS Online
gis = GIS('home')

# Get all content items
qe = 'NOT owner:esri*'
items = gis.content.search(query=qe, max_items=-1, outside_org=False)

# Convert all Item class attributes to dictionary
items_dict = [vars(i) for i in items]

However, vars() does not return all the Item attributes listed in the ArcGIS REST API documentation for Item. Most notably, the 'size' attribute is not returned, and for my purposes, this attribute is important. Why is 'size' not returned using the vars() method?

I was able to use the following code to get it to work, but it went from less than three seconds to 6 minutes(!):

items_dict = [vars(i) for i in items if i.size >= 0]

By initiating the 'size' class attribute, it is then included, but only if it is initiated like this.

I also have other code (see below; sourced from Managing ArcGIS Online Content with ArcGIS Dashboards and ArcGIS Notebooks) that returns a smaller subset of Item property information, including 'size', but fewer attributes. But, it doesn't work on ArcGIS Enterprise...Any help would be appreciated.

#From the REST URL, request all items in the organization. 
#Create a dictionary of the results, and print out the output. 
url = f'{gis.url}/sharing/rest/content/portals/{gis.properties.id}'

params = {
    'f': 'csv',
    'token': gis._portal.con.token
}

#Get a string response from the request and construct a DataFrame
csv_out = requests.get(url, params=params).text
df = pd.read_csv(io.StringIO(str(csv_out)))

 

jawhitacre@PA.LCL_PAGame

Below is the final code for my best shot at a higher performing script to complete what I was looking for regarding this issue. Note that I have only tested this in ArcGIS Online Notebook and not on a Notebook in ArcGIS Pro. I did have issues with 'token' parameter working in ArcGIS Pro, but was able to get it to work when I used arcpy.GetSigninToken() function instead (not shown in script). This still does not fix the root issue, but it has far better performance than my earlier versions and other suggestions. for me, this code is taking less than 5 seconds to execute on about 1,300 AGO items. I also added some helper code to clean things up and add some other values.

# Imports
from arcgis.gis import GIS, Item, User
import csv
import datetime
import io
import os
import pandas as pd
import requests

# Connect to ArcGIS Online
gis = GIS("home")

# Create the '.../sharing/rest/content/portals/...' REST URL to request all items in the organization as a CSV 
gis_url = gis.url if gis.properties.isPortal else f'https://{gis.properties.urlKey.lower()}.maps.arcgis.com'
content_url = f'{gis_url}/sharing/rest/content/portals/{gis.properties.id}'
params = {'f': 'csv', 'token': gis._portal.con.token}

# Get the request string response
csv_out = requests.get(content_url, params=params).text

# Create Pandas DataFrame from the CSV data
df_csv = pd.read_csv(io.StringIO(str(csv_out)))

# Get all content items
qe = 'NOT owner:esri*'
items = gis.content.search(query=qe, max_items=-1, outside_org=False)

# Convert all Item class attributes to dictionary
items_dict = [vars(i) for i in items]

# Create a Pandas dataframe of all Items
df_items = pd.DataFrame.from_dict(items)

# Convert date fields from UNIX millisecond units to datetime
date_fields = ['created', 'modified', 'lastViewed']
for field in date_fields:
    if field in df_items.columns:
        df_items[field] = pd.to_datetime(df_items[field], unit='ms', origin='unix')

# Add and calculate the GIS URL field
df_items['gisUrl'] = gis_url

# Merge data frames
df_merge = pd.merge(df_items.drop(columns='size'), df_csv[['id', 'fullname', 'size']], on='id', how='left')

df_merge.fillna('None', inplace=True)

items_dict = df_merge.to_dict(orient='records')