JetBrains DataSpell is a database IDE designed for data analysts

DataSpell is a database IDE designed by JetBrains for data scientists and analysts. It perfectly combines JetBrains’ classic intelligent editor with the flexibility of Jupyter Notebook, making data processing and analysis easier and more efficient than ever before.

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https://www.jetbrains.com/dataspell/download

Updates to JetBrains DataSpell For Mac v2024.3.2 include:

1. AI Cells : Introduced in DataSpell 2024.2, AI Cells enable AI-assisted code generation, code editing, and bug fixing in Jupyter Notebooks.
2. Enhanced Jupyter Notebook experience :
   • Simplified cell execution: A new “Run Cell” button is now available next to each cell, along with a “Go To” button for quickly navigating to previously executed cells.
   • Cell Tags: Tags can be assigned and viewed for each cell in the notebook editor, and notebook metadata can be accessed and edited from the context menu.
   • Expand and collapse capabilities for code and Markdown cells: Helps users focus on the most important steps in their analysis. Cell contents and outputs can be expanded or collapsed by clicking the blue line on the cell workline.
3. New UI enabled by default : The new UI is now the default option for all users, bringing a clearer, modern interface to users.
4. Geographic Data Viewer : A new Geographic Viewer has been added, which is a graphical tool for exploring geospatial data in tabular data files, such as CSV and XLSX formats.
5. Mathematical syntax support in Markdown files : DataSpell can now render mathematical expressions natively in Markdown files, insert $inline mathematical expressions, and $$insert code blocks containing mathematical content.

These updates significantly enhance DataSpell’s functionality and user experience, particularly in Jupyter Notebook interaction, geospatial data processing, and rendering of mathematical expressions.

JetBrains DataSpell For Mac v2024.1.3 updates the following:

1. ML-powered code autocompletion : Updated local models significantly improve Python’s full-line code autocompletion capabilities, providing longer suggestions and considering broader context, all handled locally to ensure data privacy.
2. SQL querying data frames and CSV files : In Jupyter notebooks, users can now directly query data frames and CSV files using SQL, by creating a SQL cell and selecting the data source, with best-in-class SQL coding assistance.
3. Import Data Cells : The new Import Data Cells feature allows users to start processing tabular data by dragging and dropping files, using visual controls or Python code to operate, greatly simplifying the data processing process.
4. Improved dbt Core Support : Improved support for dbt Core, including better chart viewing and code completion. Users can now run, preview, and test models directly in SQL files, improving development efficiency.

These updates are designed to significantly improve the productivity of data analysis and data science work, bringing a more convenient and efficient development experience.

DataSpell 2024.1: Full-line code completion, querying DataFrames with SQL cells, Import Data cells, and improved dbt support

DataSpell 2024.1 brings a host of exciting updates and improvements to make data analysis more efficient and convenient. Here’s a detailed look at the latest changes.

Full-line code completion

In DataSpell 2024.1, we’ve enhanced the local model that powers Python’s machine learning (ML)-assisted full-line code completion. The model’s suggestions are now more comprehensive and take into account a wider range of context, providing more accurate suggestions and speeding up your typing. Furthermore, this purely local model ensures data privacy, never sending any data to the internet, and is completely free.

Querying a DataFrame with SQL Cells

In DataSpell 2024.1, you can now use SQL to query DataFrames and CSV files directly in Jupyter Notebooks. We’ve introduced a new Import Data cell type that makes it easy to import files and start working with data.

Import Data Unit

The Import Data cell is a new Jupyter Notebook feature in DataSpell 2024.1. By dragging and dropping a file containing tabular data onto the Import Data cell, you can easily start working with your data, either using visual controls or Python code.

dbt support improvements

The latest version of DataSpell includes significant improvements to support for dbt Core. You can now view the DAG graph of your dbt Core projects directly in DataSpell, giving you a better understanding of your project’s dependencies and structure. Additionally, we’ve improved code completion for dbt Core projects, and you can now run, preview, and test models directly in SQL files.

AI Assistant Official Version

The AI Assistant preview has ended, and now we are officially launching the AI Assistant, providing more powerful features and improvements for JetBrains IDEs to further improve your work efficiency in the IDE.

Understanding DataFrames with AI Assistant

Now, with the Explain Code feature, you can easily explore DataFrames in Jupyter Notebooks and Python scripts. Simply right-click and select the option from the context menu, or click the AI Assistant icon in the upper-right corner of the interactive table to expand the tool. Once activated, the AI Assistant will obtain basic information about your dataset, such as column names and descriptive statistics. This enables the AI Assistant to provide detailed information and analysis about your DataFrame. You can also choose to engage in a deeper conversation with the AI Assistant for further data analysis.

Subscribe to JetBrains AI Service and use AI Assistant as a plug-in in DataSpell.

dbt Core support

DataSpell now officially supports dbt Core, a new data transformation framework that is becoming increasingly popular in the data community. dbt Core simplifies the data transformation process and promotes best engineering practices in data analysis, such as modularization, testing, and documentation. If you’re already familiar with SQL, dbt Core is easy to get started with.

The support of dbt Core in DataSpell brings the following benefits:

• Simplified project startup: You can easily start a dbt project using preconfigured templates.
• Simplified run, build, and debug process: Use Run Configurations to easily execute, build, or debug your project with just a few clicks.
• Smart code completion: DataSpell provides smart code completion for SQL and YML files.

SQL Unit and Python

DataSpell 2024.1 introduces the concept of SQL cells to strengthen the connection between SQL and Python. In addition to the strong SQL support provided by existing database tools and SQL plugins, SQL cells allow you to better combine SQL and Python in Jupyter Notebooks. With SQL cells, you can easily retrieve data from the database and automatically convert it to a pandas DataFrame for direct use in Notebooks. In addition, the smart code completion feature also fully supports SQL code and SQL objects (such as tables and columns), improving the SQL coding experience. Creating a SQL cell is as simple as clicking “Add SQL Cell”.

Interactive Table

Interactive tables in DataSpell 2024.1 receive some significant improvements and enhancements.

Statistics in the table

Now, you can easily access basic data insights such as missing values, mean, standard deviation, etc. by clicking the table header. This improvement helps data specialists improve the efficiency of data analysis.

Categorical data distribution statistics

Now you can easily view the distribution of your categorical data. This feature allows you to quickly see a list of the most frequently occurring values and their percentages. If you have a large number of unique values, you can also easily get the count of each distinct entry in the column.

Histogram of data distribution in the table

Data distribution histograms are an important tool in data analysis. They provide a visual snapshot of data distribution and aid in pattern recognition, outlier detection, and data quality assessment. In DataSpell, you can easily view these histograms directly from the table header. You can choose to view the default compact mode or a detailed view.

Simplified visualization of table data

To simplify your data analysis workflow, we’ve introduced a handy graph builder. Now, you can easily create graphs by clicking icons on table headers for quick and easy data visualization.

In-table AI assistant

By clicking the AI Assistant icon in the upper right corner of the interactive table, you can get valuable insights into your DataFrame. The Assistant provides real-time information, and you can also explore your data analysis further by chatting with the Assistant.

UI and Navigation

Hide the main toolbar

DataSpell 2024.1 now offers the option to hide the main toolbar to help unclutter your workspace and better utilize screen space. You can hide the main toolbar by selecting Appearance and unchecking the Toolbar option.

Improved navigation

To enhance the navigation experience when working with multiple file types in the editor, we’ve added color-coded highlighting for editor tabs to mirror their appearance in the Project tool window.

Quick Search

Now you can use shortcuts to quickly search and navigate directly in tool windows and dialogs. Simply focus on a tree or list and use ⌘ F on macOS, Ctrl+F on Windows or Linux, or enter a query and invoke search from the Options menu.

These are the latest changes in DataSpell 2024.1, which you can download from our website, update via your IDE or the free Toolbox App, or use the Ubuntu Snap package. Enjoy the new features!

Intelligent Jupyter Notebook environment

DataSpell has a built-in enhanced version of Jupyter Notebook, making data work easier:

# 导入数据分析模块
import pandas as pd
import numpy as np

# 载入数据集
df = pd.read_csv('data.csv') 

# 数据预览
df.head()

• Supports Notebook commands and editing modes, and is fully compatible with shortcut keys
• Free navigation between cells, automatic code completion, built-in data browsing
• Connect to Jupyter Server locally or remotely, fully compatible with the ecosystem

Convenient Python editing environment

DataSpell provides a professional Python integrated development environment:

# 导入数据分析模块
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# 载入数据集
df = pd.read_csv('data.csv')

# 数据分析操作
df['column_name'].value_counts()

# 可视化
plt.plot(df['column_1'], df['column_2'])
plt.show() 

• Smart code prompts, built-in debugger, and unit testing support
• Interactive Python console for easy data exploration
• Supports mainstream visualization libraries such as Plotly and Bokeh

Professional data processing tools

DataSpell has a rich set of built-in database tools, enabling a one-stop workflow from data acquisition to cleaning:

• Database connection and management, SQL editor auto-completion
• Import and export data, convenient data cleaning
• Version control system support to achieve repeatable data analysis

DataSpell makes data science intuitive and efficient. It allows you to focus on valuable analytical work rather than technical details. If you want to boost your data team’s productivity, DataSpell is the right choice.

Smart Jupyter Notebook

DataSpell has added many features to enhance the user experience based on the basic Jupyter Notebook functionality:

• Smart cell navigation : supports using arrow keys to quickly navigate between cells, making jump editing more convenient
• Cell editing mode : supports Jupyter Notebook’s code editing mode, which allows for easy input of multiple lines of code, and also has code highlighting, auto-completion, and other functions
• Markdown rendering : support writing documents in Markdown cells and rendering Markdown content in real time
• Interactive output : Calculation results are displayed in table or graphic form, supporting interactive operations directly in the IDE, such as filtering, sorting, etc.
• Kernel connection : One-click connection to local or remote Jupyter kernel, perfect integration with the ecosystem

Smart Python editing

DataSpell leverages JetBrains’ deep experience in IDE design to provide a very intelligent Python editing experience:

• Instant error checking : Syntax and semantic errors are found in real time during code writing
• Smart prompts : automatically prompt methods, parameters, etc. according to the context to improve coding efficiency
• One-click debugging : built-in complete debugger, supports breakpoint debugging, viewing variables, etc.
• Refactoring support : refactor code while safely preserving program semantics

Professional data tools

DataSpell has built-in tools for database management and data processing:

• Visual database management : intuitively view tables, indexes and other information in the database
• SQL auto-completion : quickly write SQL statements and prompt statement structure and syntax
• Data import and export : support importing data into tables or exporting data to formats such as CSV
• Data version control : Combine with Git or SVN to track data changes and ensure reproducibility

Let me explain in detail the main programming languages and usage scenarios of DataSpell:

1. DataSpell mainly supports Python language.

Python is widely used in data science. DataSpell provides many intelligent editing and analysis features for Python, such as Jupyter Notebook integration, an interactive Python console, and Plotly/Bokeh visualization support. This greatly improves the efficiency of using Python for data acquisition, cleaning, analysis, modeling, and visualization.

2. DataSpell also supports the SQL database language.

Data scientists often need to work with structured databases. DataSpell simplifies database connections and management, allowing you to write query statements through an intelligent SQL editor. This speeds up the workflow of acquiring and processing data from databases.

3. DataSpell assists with version control.

Data projects typically require Git or SVN for version control. DataSpell provides a graphical interface for version control, allowing you to commit or update directly in the IDE. This helps make data analysis projects more repeatable and traceable.

4. DataSpell is used to improve the workflow of data scientists.

Traditionally, data acquisition, cleaning, analysis, modeling, and visualization required switching between various tools. DataSpell integrates the entire workflow into a single IDE, significantly optimizing data work efficiency. Data scientists can focus on value-creating analysis without being delayed by switching between tools.

In summary, DataSpell is committed to improving data scientists’ productivity. Its intelligent editing and integrated design can greatly improve the daily workflow of data scientists. This is also the main design purpose and usage scenario of DataSpell.

DataSpell is a database integrated development environment (IDE) launched by JetBrains. It is specially optimized for the workflow of data scientists, making the entire data analysis process more efficient.

Support Python data analysis

DataSpell has a built-in Jupyter Notebook for interactive Python data analysis:

• Import data
• Data cleaning and transformation
• Modeling analysis
• Visual presentation

It also provides smart prompts, built-in debugger and other functions to improve Python programming efficiency.

Convenient data access

DataSpell makes it easy to retrieve data from a database. It provides:

• Database connection management
• SQL statement intelligent prompt
• Import query results into a data frame

Workflow optimization

DataSpell integrates the entire workflow of data scientists, including:

• Jupyter Notebook for Python
• Built-in database tools
• Support version control and collaboration

This reduces the effort of switching between different tools and allows data scientists to focus more on creating value.

Improve work efficiency

In summary, DataSpell can effectively improve the efficiency of data workflows :

• Reduce the loss of switching tools
• Automate repetitive tasks
• Intelligent assistants , such as code prompts

Let data scientists focus on analysis , not technical details.

The above summarizes how DataSpell optimizes the workflow of data scientists.