Jupyter notebook toolbar: provides quick access to the most popular actions. Notebook editorĪ Jupyter notebook opened in the editor has its specific UI elements: Mind the following user interface features when working with Jupyter notebooks in IntelliJ IDEA. To start working with Jupyter notebooks in IntelliJ IDEA:Ĭreate a new project, specify a virtual environment, and install the jupyter package.Įxecute any of the code cells to launch the Jupyter server. Quick start with the Jupyter notebook in IntelliJ IDEA Shortcuts for basic operations with Jupyter notebooks.Ībility to recognize. Notebook support in IntelliJ IDEA includes:Ībility to create line comments Control+/.Ībility to run cells and preview execution results. With Jupyter Notebook integration available in IntelliJ IDEA through the Python plugin, you can easily edit, execute, and debug notebook source code and examine execution outputs including stream data, images, and other media. We encourage you to share your feedback on the new features on X (formerly Twitter) or in our issue tracker, where you can also report any bugs you find in the EAP versions.The following is only valid when the Python plugin is installed and enabled. These are the most important updates for DataSpell 2023.3 EAP 3. In DataSpell, you can now easily access these histograms directly within your tables. Data distribution histograms in tablesĪ data distribution histograms is an essential tool in data analysis, providing a visual snapshot of data distribution, as well as aiding in pattern recognition, outlier detection, and data quality assessment. The feature is available in both Jupyter notebooks and Python scripts, with both pandas and Polars supported. You can now view essential data, such as missing values, mean, standard deviation, and more directly within the table. In DataSpell, we’ve simplified this process. Quickly accessing descriptive statistics for a dataframe can significantly streamline the work of data professionals, as it is a frequently used operation in data analysis. If you want to learn more about JetBrains AI Assistant, you can read this post. Now, you can access code explanation, documentation creation, error finding, and many more with just a few clicks. To make it easier to use, we’ve added AI actions to the context menu in Jupyter. Give it a try today, and don’t forget to share your feedback with us! JetBrains AI Assistant in Jupyter via the context menuĪI Assistant has become an important tool for many users. Intelligent code completion: DataSpell offers smart code completion for SQL and YML files.Easy Run, Build, and Debug: Execute, build, or debug your project with Run Configurations in just a few clicks.Effortless project creation: You can easily kickstart your dbt® project using a predefined template.Here are several benefits of using dbt® in DataSpell: dbt® is especially user-friendly for those familiar with SQL. It simplifies the data transformation process and promotes good engineering practices in data analysis, like modularization, testing, and documentation. Please try them out and share your feedback in the comments below or by using our issue tracker.ĭownload DataSpell 2023.3 EAP Introducing dbt® supportĭataSpell now supports dbt®, a modern framework for data transformation that’s gaining popularity in the data community. You can also manually download the EAP builds from our website.īelow, you can find the most interesting features available in DataSpell 2023.3 EAP 3. The Toolbox App is the easiest way to get the EAP builds and keep both your stable and EAP versions up to date. To catch up on all of the new features in DataSpell 2023.3, check out our previous EAP blog posts. The third EAP build for DataSpell 2023.3 brings dbt® support, JetBrains AI Assistant actions via the context menu in Jupyter, and easy access to column statistics and data distribution histograms in tables.
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