Notebooks

Jupyter compatible

Notebooks in Datalore are Jupyter compatible, meaning you can upload your existing IPYNB files and continue working with them in Datalore. Furthermore, you can also export notebooks as IPYNB files. Note that data connections and interactive controls won’t be exported.

Python notebooks

Smart coding assistance from PyCharm

Datalore comes bundled with code insight features from PyCharm. For Python notebooks you get first-class code completion, parameter info, inspections, quick-fixes, and refactorings that help you write higher-quality code with less effort.

In-app documentation

Get documentation pop-ups for any method, function, package, or class. Datalore will show you the documentation right where you need it.

Conda and pip support

Datalore supports both pip and conda. Pip is fast and free for everyone, whereas conda is free for non-commercial use only.

Kotlin, Scala, and R notebooks

In Datalore you can create Kotlin, Scala, and R notebooks. You can use magics to install packages, and when writing code you’ll get code completion.

SQL cells Enterprise

Add native SQL cells to query your database connections. In addition to SQL syntax-highlighting, you also get code completion based on the introspected database tables. The query result is automatically transferred to a pandas DataFrame and you can continue working on the dataset in Python.

Environment

Package manager

Datalore comes with an integrated package manager that makes your environment reproducible. The package manager allows you to install and manage new packages and makes sure they persist when you reopen the notebook.

Custom base environments Enterprise

Create multiple base environments from custom Docker images. You can pre-configure all the dependencies, package versions, and build tool configurations so that your team doesn’t spend time manually installing things and syncing the package versions.

Packages from Git repositories

Install a custom pip-compatible package from a Git repository by attaching a Git branch to your notebook.

Initialization scripts

Configure a script that you want to run before the notebook starts. Here you can specify all the necessary build tools and required dependencies.

Visualizations

Visualize tab

Get automatic visualization options inside the Visualize tab for any pandas DataFrame. Point, Line, Bar, Area, and Correlation plots will help you quickly understand the contents of your data. If the dataset is big it will be automatically sampled. All plots can be then extracted to code or Chart cells for further customization.

Support for all Python visualization packages

Create visualizations with the package of your choice. Matplotlib, plotly, altair, seaborn, lets-plot, and many other packages are supported in Datalore notebooks.

Chart cells

Create production-ready visualizations with just a few clicks. The state of the cells is shared with collaborators so you can work on the visualization together.

Interactive controls

Add interactive dropdowns, sliders, and text inputs inside your notebooks and use the input values as variables inside your code.

Terminal

Open Terminal windows inside the editor, execute .py scripts, and access the agent, environment, and file system using standard bash commands.

Variable viewer

Browse notebook variables and built-in parameter values from one place.

Internal versioning

Create custom history checkpoints that enable you to revert the changes at any time using the history tool. When browsing a checkpoint, you will see the difference between the current version of the notebook and the one that is selected.

Computation

Run notebooks on CPUs and GPUs

In Datalore, you can execute your notebooks on CPUs and GPUs. You can choose the machine you need from the UI. The type and amount of resources you get depends on which plan you have. Find more information here.

Private cloud and on-premises machines Enterprise

You can connect any type of server hardware you are already using to Datalore and make it accessible to your users from Datalore’s interface.

Reactive mode for reproducible research

Reactive mode will enforce top-down evaluation order and automatic recalculation of the cells below the modified one. The notebook state will be saved after each cell evaluation and can be restored at any time. Read more about Reactive mode here.

Background computation

Switch on Background computation to keep your notebook running even when you close the browser tab. You will always have access to your running computations list from the User menu or the Admin panel.

Machine usage reports

Download CSV reports containing the amount of time you spent running each machine – it might help you understand which projects you’ve paid most attention to.

Pre-paid machines Professional Soon

Custom computation shutdown time Professional Enterprise Soon