Overview of Datalore features

These features help you get the most out of the application. For more details, visit the Datalore Features overview page.

Coding productivity

• Coding assistance provides auto-completion, quick-fixes, and quick reference.

• Ready-to-use environment allows you to start quickly with pre-installed Python packages. You can choose between pip and Conda package managers.

• Automatic plotting helps you quickly generate visualizations for your DataFrames.

• Terminal can be used to execute .py scripts and run sudo commands.

• Statistics tab provides detailed metrics on your dataframes.

• Variable viewer grants quick access to all variables and their values used in your notebook.

• Scheduling allows you to run your notebooks at selected intervals (hourly, daily, weekly, or monthly). You can set up multiple or parameterized schedules for one notebook.

• Supported languages: Python, R, Scala, Kotlin

Data access

• Attached data is a tool used to upload and manage files and folders for your notebooks. All data stays persistent and is stored in the cloud.

• Bucket data sources and SMB/CIFS folders can be mounted directly inside your notebooks.

• Environment variables is a feature that ensures security of your credentials.

• Table viewer allows viewing .csv and .tsv files from Attached data.

Editor

• Table of contents ensures easy navigation through your notebooks.

• Command palette and shortcuts provide quick access to all editor operations.

• View menu allows you to configure the appearance of your editor and notebook cells.

• Reactive mode enables live computation. When you change code in one cell, the kernel automatically recalculates all the dependent cells without you manually running them.

• Interactive controls help you quickly customize the output without manually changing the code.

• Chart cells are specifically used to build multilayered charts based on datasets of any size. The feature also facilitates collaborative work.

• Background computation keeps the computation running after the tab is closed with a cut-off timer option.

• Computation tab helps you manage kernels, machines, and notebook runs from one place.

• Interactive table output ensures interaction with table outputs (sorting, resizing, column renaming, scrolling, etc).

• Metric cells allow you to track numerical values and compare them to others.

• Export to database cells are used to append dataframes to tables attached to your notebooks.

• Ask AI feature supports code generation and modification via commands in natural language.

• Comments allow you and your teammates collaborate more productively on your projects by commenting individual notebook cells.

Collaboration

• Sharing allows your team to edit notebooks in real time.

• Report builder provides interface for preparing and publishing static and interactive reports.

• Workspaces are used to organize your notebooks into collections with datasets that are shareable across teams and notebooks.

• History is a tool for recording and tracking changes in your notebooks with the option of reverting to previous states.

• Comments is an efficient tool to improve your team communication on notebooks and reports.

Presentation and communication

• Markdown cells support LaTex to help you better describe your code.

• Embedding code cells is a quick way to demonstrate your Datalore work on social networks and other platforms.

• Exporting notebooks is supported for a number of file extensions: PDF, PY, HTML, IPYNB.