App Authentication using API Key Credentials added

.github/workflows/DockerBuild.NotebookImage.yaml

@@ -11,15 +11,27 @@ on:
       version:
         description: "Version of ArcGIS API for Python to install in the image"
         type: string
-        default: "2.4.1"
+        default: "2.4.2"
+      arcgis_mapping_version:
+        description: "Version of arcgis-mapping to install in the image (only used if version >= 2.4.0)"
+        type: string
+        default: "4.33.0"
       python_version:
         description: "Python version to base image on"
         type: string
-        default: "3.11"
+        default: "3.13"
       is_latest_release:
         description: "Version of ArcGIS API for Python is Latest current release"
         type: boolean
         default: false
+      arcgis_wheel_url:
+        description: "Use a custom version of ArcGIS API for Python from this wheel URL, if empty, use pypi.org; if provided, `-prerelease` will be appended to the image tag"
+        type: string
+        default: ""
+      arcgis_mapping_wheel_url:
+        description: "Use a custom version of arcgis-mapping from this wheel URL, if empty, install from pypi.org; if provided, `-prerelease` will be appended to the image tag"
+        type: string
+        default: ""
       is_default_supported_python:
         description: "Python version is default supported version (i.e. python used by Pro and Enterprise)"
         type: boolean
@@ -52,10 +64,10 @@ jobs:
           images: |
             ghcr.io/esri/arcgis-python-api-notebook
           tags: |
-            type=raw,value=${{ inputs.version }}-python${{ inputs.python_version }}
-            type=raw,value=${{ inputs.version }},enable=${{ inputs.is_default_supported_python && github.ref_name == github.event.repository.default_branch }}
-            type=raw,value=latest,enable=${{ inputs.is_latest_release && inputs.is_default_supported_python && github.ref_name == github.event.repository.default_branch }}
-            type=schedule,pattern={{date 'YY.MM'}},enable=${{ inputs.is_latest_release && inputs.is_default_supported_python && github.ref_name == github.event.repository.default_branch }}
+            type=raw,value=${{ inputs.version }}-python${{ inputs.python_version }}${{ inputs.arcgis_wheel_url != '' && '-prerelease' || '' }}
+            type=raw,value=${{ inputs.version }},enable=${{ inputs.is_default_supported_python && inputs.arcgis_wheel_url == '' && github.ref_name == github.event.repository.default_branch }}
+            type=raw,value=latest,enable=${{ inputs.arcgis_wheel_url == '' && inputs.is_latest_release && inputs.is_default_supported_python && github.ref_name == github.event.repository.default_branch }}
+            type=schedule,pattern={{date 'YY.MM'}},enable=${{ inputs.arcgis_wheel_url == '' && inputs.is_latest_release && inputs.is_default_supported_python && github.ref_name == github.event.repository.default_branch }}
 
       - id: docker_build
         name: Build image and push to GitHub Container Registry
@@ -67,6 +79,9 @@ jobs:
           build-args: |
             python_version=${{ inputs.python_version }}
             arcgis_version=${{ inputs.version }}
+            arcgis_mapping_version=${{ inputs.arcgis_mapping_version }}
+            arcgis_wheel_url=${{ inputs.arcgis_wheel_url }}
+            arcgis_mapping_wheel_url=${{ inputs.arcgis_mapping_wheel_url }}
           tags: ${{ steps.meta.outputs.tags }}
           provenance: false
           platforms: linux/amd64

docker/NotebookImage.Dockerfile

@@ -1,9 +1,14 @@
-ARG python_version="3.11"
+ARG python_version="3.13"
 FROM quay.io/jupyter/minimal-notebook:python-${python_version}
 
 ARG python_version
-ARG arcgis_version="2.4.1"
-ARG gdal_version="3.10.2"
+ARG arcgis_version="2.4.2"
+ARG arcgis_mapping_version="4.33.0"
+ARG gdal_version="3.11.4"
+# If arcgis_wheel_url is provided, the Dockerfile will attempt to install the ArcGIS API for Python from that URL
+ARG arcgis_wheel_url=""
+ARG arcgis_mapping_wheel_url=""
+
 ARG sampleslink="https://github.com/Esri/arcgis-python-api/releases/download/v${arcgis_version}/samples.zip"
 ARG githubfolder="arcgis-python-api"
 ARG env_name=arcgis
@@ -25,17 +30,20 @@ RUN conda install -n ${env_name} -c conda-forge gdal=${gdal_version} -y --quiet
     && conda clean --all -f -y \
     && find /opt/conda -name __pycache__ -type d -exec rm -rf {} +
 
+RUN echo "${arcgis_wheel_url:-arcgis==${arcgis_version}.*}" > /tmp/arcgis_source.txt
+
 # Install ArcGIS API for Python from pypi
 RUN . activate ${env_name} \
-    # adding .* ensures the latest patch version is installed
-    && python -m pip install "arcgis==${arcgis_version}.*" \
+    && python -m pip install "$(cat /tmp/arcgis_source.txt)" \
     && conda clean --all -f -y \
     && find /opt/conda -name __pycache__ -type d -exec rm -rf {} +
 
+RUN echo "${arcgis_mapping_wheel_url:-arcgis-mapping==${arcgis_mapping_version}.*}" > /tmp/arcgis_mapping_source.txt
+
 # Install arcgis-mapping if arcgis_version >= 2.4.0
 RUN (dpkg --compare-versions $arcgis_version ge 2.4.0 \
     && . activate ${env_name} \
-    && python -m pip install arcgis-mapping \
+    && python -m pip install "$(cat /tmp/arcgis_mapping_source.txt)" \
     && conda clean --all -f -y \
     && find /opt/conda -name __pycache__ -type d -exec rm -rf {} +;) \
     || echo "[INFO] Skipped installing arcgis-mapping for version $arcgis_version (>= 2.4.0 required for arcgis-mapping)"

guide/01-getting-started/install-and-set-up.ipynb

@@ -259,7 +259,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "source": [
     "## Install as a Docker image\n",
@@ -411,13 +414,11 @@
    "source": [
     "If you already have an environment with the `arcgis` package installed, you can further install its deep learning dependencies to take advantage of the `arcgis.learn` module. Some of the deep learning samples available [here](https://developers.arcgis.com/python/sample-notebooks/) can be referenced to understand the workflow.\n",
     "\n",
-    "**Note:** To use the deep learning capabilities in ArcGIS Pro 2.9 / ArcGIS API for Python 1.9.0 onwards, the minimum required version of the Nvidia GPU driver is 456.38.\n",
-    "\n",
     "### Using the Deep Learning Frameworks Installer\n",
     "\n",
     "To work with the deep learning tools in ArcGIS Pro 2.6 and later, you need to install supported deep learning frameworks packages.\n",
     "\n",
-    "`Deep Learning Libraries Installer for ArcGIS` is now available at https://github.com/Esri/deep-learning-frameworks. This installer includes a broad collection of components, such as PyTorch, TensorFlow, Fast.ai and scikit-learn, for performing deep learning and machine learning tasks. Note that this will install the deep learning frameworks into your currently active Pro Python environment; if you wish to install in an environment aside from the default arcgispro-py3 environment, switch to that environment before running the MSI using either ArcGIS Pro's Python manager UI or on the command line with proswap. Any subsequent clones of that environment will also include this full collection of packages. \n",
+    "`Deep Learning Libraries Installer for ArcGIS` is now available at https://github.com/Esri/deep-learning-frameworks. This installer includes a broad collection of components, such as PyTorch, Fast.ai and scikit-learn, for performing deep learning and machine learning tasks. Note that this will install the deep learning frameworks into your currently active Pro Python environment; if you wish to install in an environment aside from the default arcgispro-py3 environment, switch to that environment before running the MSI using either ArcGIS Pro's Python manager UI or on the command line with proswap. Any subsequent clones of that environment will also include this full collection of packages. \n",
     "\n",
     "For instructions on how to install deep learning frameworks manually or over ArcGIS Image Server, click [here](https://github.com/Esri/deep-learning-frameworks/)."
    ]
@@ -446,31 +447,29 @@
     "Alternatively,  \n",
     "for a cloned environment of ArcGIS Pro's default environment, `deep-learning-essentials` metapackage can be used to install the required dependencies which can be done using the following command, in the _`Python Command Prompt`_ <i>(included with ArcGIS Pro)</i>:\n",
     "\n",
-    "`conda install deep-learning-essentials`\n"
+    "`conda install -c esri deep-learning-essentials`\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "####  For Anaconda users (Windows & Linux platforms):"
+    "####  For Anaconda users (Windows):"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`arcgis_learn` metapackage can be used for both windows and linux installations of Anaconda in a new environment.\n",
+    "`arcgis_learn` metapackage can be used for windows installation of Anaconda in a new environment.\n",
     "\n",
     "The following command will update Anaconda to the latest version.\n",
     "\n",
     "```conda update conda```\n",
     "\n",
     "After that, metapackage can be installed using the command below:\n",
     "\n",
-    "```conda install -c esri arcgis_learn=2.1.0 python=3.9```\n",
-    "\n",
-    "Note: Python version 3.7 and 3.8 are also supported with this command."
+    "```conda install -c esri arcgis_learn=<api_version>```"
    ]
   },
   {
@@ -564,9 +563,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:arcgispro-py3-clone] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-arcgispro-py3-clone-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -578,7 +577,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.13.7"
   },
   "toc": {
    "base_numbering": 1,

guide/02-api-overview/release_notes_242.ipynb

@@ -16,7 +16,7 @@
     "    * [Working with Branch Versioning](../guide/working-with-branch-versioning/)  \n",
     "* [Deep Learning](../)\n",
     "  * [3D Computer Vision](../)\n",
-    "    * [Point cloud object detection using SECOND](../guide/point-cloud-classification-using-second)\n",
+    "    * [Point cloud object detection using SECOND](../guide/point-cloud-object-detection-using-second)\n",
     "    * [Point cloud classification using Point Transformer](../guide/point-cloud-classification-using-point-transformer/)\n",
     "* [Content Management](../)\n",
     "  * **Note:** Available since July '25\n",
@@ -32,7 +32,7 @@
     "* [Deep Learning](/python/samples)\n",
     "  * [2D Computer Vision](/python/samples)\n",
     "    * [Object Detection](/python/samples)\n",
-    "      * [Detecting airplanes in satellite imagery using deep learning](/python/latest/samples/detecting-airplanes-on-satellite-imagery-using-deep-learning/\") \n",
+    "      * [Detecting airplanes in satellite imagery using deep learning](/python/latest/samples/detecting-airplanes-on-satellite-imagery-using-deep-learning/) \n",
     "  * [Tabular Data Forecasting](/python/samples)\n",
     "    * [Leveraging TabPFN for Human Activity Recognition Using Mobile Dataset](/python/latest/samples/classifying-human-activity-using-tabPFN-classifier) \n",
     "\n",

guide/03-the-gis/working-with-different-authentication-schemes.ipynb

@@ -453,6 +453,125 @@
     "The recommended suggestion for non-interactive login scripts is to use the built-in identity provider instead of SAML."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## App Authentication using API Key Credentials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This workflow demonstrates how to use an Administrative connection to register a new Application, define its security boundaries (Privileges, Expiration, and Referers), and subsequently initialize a restricted GIS session using the generated `access token`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 1. Establish Administrative Connection\n",
+    "Before creating credentials, you must connect to your Portal as an Administrator. This \"Master Connection\" is used to manage the lifecycle of your application identities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from arcgis.gis import GIS\n",
+    "\n",
+    "# Initialize the Admin session\n",
+    "gis_admin = GIS(url=\"your_organization_url\", username=\"your_admin_username\", password=\"your_admin_ password\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2. Creating the App Identity\n",
+    "\n",
+    "The `developer_credentials.create` is a method of the `DeveloperCredentialManager` class that registers the app. Here, we define the \"Security Perimeter\" of the application before it ever logs in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from arcgis.gis.admin import TokenPrivilege\n",
+    "import datetime as dt\n",
+    "\n",
+    "# Define the restricted API Key Credentials\n",
+    "api_key_credentials = gis_admin.admin.developer_credentials.create(\n",
+    "    title=\"API Key Credentials\",\n",
+    "    privileges=[TokenPrivilege.PORTAL_USER_VIEWORGUSERS], # Restricted Scope\n",
+    "    expiration=dt.datetime.now() + dt.timedelta(weeks=5), # Time-limit\n",
+    "    referers=[\"http\"]                                     # Domain-locked\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `DeveloperCredential` object has a method called `generate_token` which can be used to generate an `access token`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "token = api_key_credentials.generate_token(slot=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 3. Initializing the App Session (App Authentication)\n",
+    "\n",
+    "Now, we initialize a new GIS object using the `access token`. This session is App-authenticated. It does not represent the Admin; it represents the \"API Key Credentials\" app itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Authenticate as the App\n",
+    "gis_app = GIS(token=token[\"access_token\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After running the above cell, `gis_app` will be app-authenticated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can verify it by running `gis_app.users.me`. It will not return a `User` object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gis_app.users.me"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -655,7 +774,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.5"
+   "version": "3.12.7"
   },
   "toc": {
    "base_numbering": 1,

guide/04-feature-data-and-analysis/working-with-feature-layers-and-features.ipynb

@@ -490,7 +490,9 @@
    "source": [
     "<a id=\"querying-layers\"></a>\n",
     "## Querying feature layers\n",
-    "Querying is a powerful operation that can be performed on a `FeatureLayer` object. Let's take a closer look here. To write meaningful queries, we need to know the names of fields present in the layer. This can be determined by calling the `fields` property:"
+    "Querying is a powerful operation that can be performed on a [`FeatureLayer`](/python/latest/api-reference/arcgis.features.toc.html#arcgis.features.FeatureLayer) object. Let's take a closer look here. To write meaningful queries, we need to know the names of fields present in the layer. This can be determined by calling the `fields` property:\n",
+    "\n",
+    "> **Note:** When leveraging Esri hosted content, organizations should review the [ArcGIS Online terms of use](https://doc.arcgis.com/en/arcgis-online/reference/terms-of-use.htm), as well as the terms of use for the data layer to ensure they are in compliance with extracting data and/or making it available in other systems."
    ]
   },
   {
@@ -1503,7 +1505,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.13.2"
   },
   "toc": {
    "base_numbering": 1,

guide/05-working-with-the-spatially-enabled-dataframe/introduction-to-the-spatially-enabled-dataframe.ipynb

@@ -52,7 +52,11 @@
     "\n",
     "The SEDF integrates with Esri's [`ArcPy` site-package](http://pro.arcgis.com/en/pro-app/arcpy/get-started/what-is-arcpy-.htm) as well as the open source [`pyshp`](https://github.com/GeospatialPython/pyshp/), [`shapely`](https://github.com/Toblerity/Shapely) and [`fiona`](https://github.com/Toblerity/Fiona) packages. This means the ArcGIS API for Python SEDF can use either of these geometry engines to provide you options for easily working with geospatial data regardless of your platform. The SEDF transforms data into the formats you desire so you can use Python functionality to analyze and visualize geographic information.\n",
     "\n",
-    "Data can be read and scripted to automate workflows and just as easily visualized on maps in [`Jupyter Lab notebooks`](../using-the-jupyter-notebook-environment/). The SEDF can export data as feature classes or publish them directly to servers for sharing according to your needs. Let's explore some of the different options available with the versatile `Spatial Enabled DataFrame` namespaces:\n"
+    "Data can be read and scripted to automate workflows and just as easily visualized on maps in [`Jupyter Lab notebooks`](../using-the-jupyter-notebook-environment/). The SEDF can export data as feature classes or publish them directly to servers for sharing according to your needs. \n",
+    "\n",
+    "> **_Note:_** When leveraging Esri hosted content, organizations should review the [ArcGIS Online terms of use](https://doc.arcgis.com/en/arcgis-online/reference/terms-of-use.htm), as well as the terms of use for the data layer to ensure they are in compliance with extracting data and/or making it available in other systems.\n",
+    "\n",
+    "Let's explore some of the different options available with the versatile `Spatial Enabled DataFrame` namespaces:"
    ]
   },
   {
@@ -1418,7 +1422,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.13.2"
   },
   "toc": {
    "base_numbering": 1,

guide/05-working-with-the-spatially-enabled-dataframe/part1_introduction_to_sedf.ipynb

@@ -39,7 +39,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> _Note:_ Spatial Data Engineering using SeDF builds on top of core Data Engineering concepts in Python. If you are new to Pandas, NumPy and related libraries, we recommend you start with the [Introduction to Data Engineering](../part1-introduction-to-dataengineering) guide series and then come here.\n"
+    "> _**Note:**_ Spatial Data Engineering using SeDF builds on top of core Data Engineering concepts in Python. If you are new to Pandas, NumPy and related libraries, we recommend you start with the [Introduction to Data Engineering](../part1-introduction-to-dataengineering) guide series and then come here.\n",
+    "\n",
+    "> **_Note:_** When leveraging Esri hosted content, organizations should review the [ArcGIS Online terms of use](https://doc.arcgis.com/en/arcgis-online/reference/terms-of-use.htm), as well as the terms of use for the data layer to ensure they are in compliance with extracting data and/or making it available in other systems."
    ]
   },
   {
@@ -1543,7 +1545,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.13.2"
   },
   "toc": {
    "base_numbering": 1,