{"id":348961,"date":"2020-06-30T19:59:51","date_gmt":"2020-07-01T02:59:51","guid":{"rendered":"https:\/\/www.esri.com\/about\/newsroom\/?post_type=arcnews&p=348961"},"modified":"2023-12-11T19:56:07","modified_gmt":"2023-12-12T03:56:07","slug":"spatial-analysis-meets-data-science-with-arcgis-notebooks","status":"publish","type":"arcnews","link":"https:\/\/www.esri.com\/about\/newsroom\/arcnews\/spatial-analysis-meets-data-science-with-arcgis-notebooks","title":{"rendered":"Spatial Analysis Meets Data Science with ArcGIS Notebooks"},"author":5752,"featured_media":0,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"sync_status":"","episode_type":"","audio_file":"","castos_file_data":"","podmotor_file_id":"","cover_image":"","cover_image_id":"","duration":"","filesize":"","filesize_raw":"","date_recorded":"","explicit":"","block":"","itunes_episode_number":"","itunes_title":"","itunes_season_number":"","itunes_episode_type":"","_links_to":"","_links_to_target":""},"categories":[457161,1041,10382],"tags":[1391,23402,278902,198052,201052],"arcnews_issues":[457841],"class_list":["post-348961","arcnews","type-arcnews","status-publish","format-standard","hentry","category-arcgis-notebooks","category-collaboration","category-esri-technology","tag-artificial-intelligence","tag-automation","tag-data-science","tag-jupyter-notebook","tag-python","arcnews_issues-summer-2020","arcnews_sections-esri-technology"],"acf":{"short_description":"ArcGIS Notebooks is now available across the ArcGIS platform.","pdf":{"host_remotely":false,"file":"","file_url":""},"flexible_content":[{"acf_fc_layout":"content","content":"Over the past few years, Jupyter notebooks have grown into a widely used workspace for analysts and data scientists alike. On the developer and data science platform GitHub, for instance, the community has created and publicly shared more than seven million Jupyter notebooks to date.\r\n\r\nGiven the increasing popularity of Jupyter and the continually evolving spatial data science<\/a> community, Esri introduced a spatially optimized Jupyter experience called ArcGIS Notebooks\u2014and it is now available across the ArcGIS platform."},{"acf_fc_layout":"image","image":348971,"image_position":"left","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"Jupyter is best suited for users who want to learn and practice Python, do data engineering and transformation, perform statistical modeling, and develop machine learning and deep learning apps. What makes Jupyter an attractive and popular interface with the analytics community is that it enables data scientists to combine live code with visualizations, videos, and narrative text. This capability also allows them to create transparent and reproducible workflows that can be easily shared with others.\r\n\r\nIn early 2019, Esri introduced ArcGIS Notebooks in ArcGIS Enterprise. The new software was designed and built on top of Jupyter and optimized for spatial data science.\r\n\r\nWith ArcGIS Notebooks, users can access spatial analysis algorithms<\/a> via ArcPy and ArcGIS API for Python and combine them with hundreds of open-source Python libraries, such as pandas, scikit-learn, and TensorFlow. ArcGIS Notebooks helps users ideate and iterate workflows within the secure and scalable environment of ArcGIS while encouraging collaboration across data science ecosystems."},{"acf_fc_layout":"image","image":348981,"image_position":"right","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"Common workflows that users can perform within ArcGIS Notebooks include the following:\r\n