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.
In addition to what is listed above, Datalore Professional provides a number of other features. Here are just a few of them:
Database integration is an interface that allows you to set up database connections to your notebooks and use retrieved data via the SQL cell type.
Powerful CPU and GPU machines
Unlimited Background computation feature
Unlimited number of users with edit access to shared notebooks
Unlimited notebooks running in parallel
Machine CPU and GPU prepaid options
On-premises hosting
Advanced customization options
Teamwork-oriented solutions