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ArcGIS API for Python

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Overview
Key features
Install and Setup
System requirements
Python environments
- Understanding Conda
- Using the Jupyter notebook environment
- Using the JupyterLab environment
- Install older versions of the API
Release notes
- 2.0.1
- Prior releases
  - 2.0.0
  - 1.9.1
  - 1.9.0
  - 1.8.5
  - 1.8.4
  - 1.8.3
  - 1.8.2
  - 1.8.1
  - 1.8.0
  - 1.7.1
  - 1.7.0
  - 1.6.2
  - 1.6.1
  - 1.6.0
  - 1.5.3
  - 1.5.2
  - 1.5.1
  - 1.5.0
  - 1.4.2
  - 1.4.1
  - 1.4.0
  - 1.3.0
  - 1.2.4
  - 1.2.2
  - 1.2.1
  - 1.2.0
  - 1.0.1
  - 1.0.0
Using the API
The GIS
- gis module
- Using the GIS
- Working with different authentication schemes
- Accessing and creating content
- Managing your content
- Cloning content
- Accessing and managing groups
- Accessing and managing users
- Accessing Item Resources
- Properties of your GIS
- Building a distributed GIS through collaborations
- Administering your GIS
- Customizing the look and feel of your GIS
- gis.admin module
- Managing your GIS servers
Working with geometries
- Part 1 - Introduction - what is geometry
- Part 2 - Working with geometries
- Part 3 - Spatial operations on geometries
- Part 4 - Applying spatial filters
Feature data and analysis
- features module
- Working with Feature Layers and Features
- Editing Features
- Appending Features
- Updating feature layer properties
- Checking out data from feature layers using replicas
- Using attachments with feature layers
- Analyzing patterns in feature data
- Discovering suitable locations in feature data
- Performing proximity analysis on feature data
- Summarizing feature data
- Part 1 - Introduction to Parcel Fabric layers
- Part 2 - Branch Versioning with Parcel Fabric
- Part 3 - Create a New Parcel Record
- Part 4 - Query Parcels
- Part 5 - Merge Parcels
Introduction to Data Engineering in Python
- Part 1 - Introduction to Data Engineering
- Part 2 - Introduction to NumPy
- Part 3 - Introduction to Pandas
- Part 4 - Productivity with Pandas
- Part 5 - Time series analysis with Pandas
Working with the Spatially Enabled DataFrame
- Part 1 - Introduction to the Spatially enabled DataFrame
- Part-2 Data IO with SeDF - Accessing Data
- Part-3 Data IO with SeDF - Exporting Data
- Introduction to the Spatially Enabled DataFrame
- Visualizing Data with the Spatially Enabled DataFrame
- Spatially Enabled DataFrames - Advanced Topics
Enriching GIS data with thematic information
- Part 1 - Introduction to GeoEnrichment
- Part 2 - Where to enrich - what are study areas?
- Part 3 - Where to enrich - what are Named Statistical Areas?
- Part 4 - What to enrich with - what are Data Collections and Analysis Variables?
- Part 5 - Generating Reports
- Part 6 - Standard Geography Queries
Imagery and raster analysis
- Using imagery layers
- Using raster analysis
- Raster analysis - advanced concepts
- Orthomapping (part 1) - creating image collections
- Orthomapping (part 2) - generating elevation models
- Orthomapping (part 3) - managing image collections
- Image classification and segmentation
- Perform analysis - concepts
- Perform analysis using out of the box tools
- Create Imagery Layers
Working with Big Data
- What is Geospatial big data
- Make your data accessible to the GeoAnalytics Server
- Performing big data analysis
- Analyzing Patterns
- Finding Locations
- Summarizing Data
- Use Proximity
- Performing Data Enrichment
- Managing Big Data
- Extending GeoAnalytics with Custom Tools
Using geoprocessing tools
- An introduction to geoprocessing
- Accessing geoprocessing tools
- Using geoprocessing tools
- Advanced concepts
Finding places with geocoding
- Part 1 - What is geocoding
- Part 2 - Locating Addresses
- Part 3 - Finding Point of Interests
- Part 4 - Batch Geocoding
- Part 5 - Reverse Geocoding
- Part 6 - Working with Custom Geocoders
- Part 7 - Utility Functions for Geocoding
Performing network analyses
- Part 1 - Network Dataset and Network Analysis
- Part 2 - Find Routes
- Part 3 - Generate Service Area
- Part 4 - Find Closest Facilities
- Part 5 - Generate OD Cost Matrix
- Part 6 - Solve Location Allocation
- Part 7 - Vehicle Routing Problem
Mapping and visualization
- Using the map widget
- Advanced map widget usage
- Smart mapping
- Advanced cartography - part 1
- Advanced cartography - part 2
- Working with web maps and web scenes
- Managing offline map areas
- Part 1 - Introduction to using the map widget
- Part 2 - Navigating the map widget
- Part 3 - Visualizing spatial data on the map widget
- Part 4 - Visualizing time enabled data on the map widget
- Part 5 - Saving, embedding and exporting the map widget
- Part 6 - Utility functions for the map widget
Managing ArcGIS applications
- apps module
- Managing Workforce projects
- Configuring Location Tracking
- Authoring ArcGIS Dashboards
Deep Learning with ArcGIS
- How does feature categorization work?
- Object detection and tracking on videos
- How SSD works
- How RetinaNet works
- YOLOv3 Object Detector
- Faster R-CNN Object Detector
- How Mask R-CNN works
- Multi-object Tracking using ObjectTracker
- Track objects using SiamMask
- How U-net Works
- How PSPNet works
- How DeepLabV3 works
- Edge Detection
- How Multi-task road extractor works
- How Change Detection Works
- How CycleGAN works
- How Pix2Pix translation works
- How SuperResolution works
- How Image Captioning works
- Point Cloud Segmentation using PointCNN
- Geo referencing and digitization of scanned maps with arcgis.learn
- Unsupervised Machine Learning using arcgis.learn
- FullyConnectedNetwork and MLModel guide
- TimeseriesModel
- Text classification with arcgis.learn
- Named entity extraction workflow
- Labeling text using Doccano
- How SequenceToSequence works?
- Inference only Text Models
- Training Mobile-Ready models using TensorFlow Lite
- Monitor model training with TensorBoard
- Retraining Windows and Doors Extraction model
- Working with Multispectral Data
- Utilize multiple GPUs to train models
- How AutoDL Works
- How WNet_cGAN works

What's new in version 1.8.2

Fixes¶

Module Changes¶

`arcgis.learn`¶

Fixes classcode predictions in the predict_las() function for point cloud classification
Fixes to SuperResolution model:
- Applies data augmentation to training data
- Normalizes predicted color values in the range of (0,1)
- Bug fixes to data preprocessing step when generating downsampled images

Fixes
- Module Changes

Fixes
- Module Changes

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