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Can PyCharm Handle Data Science Workflows?

PyCharm supports data-science workflows with Python scripts, Jupyter notebooks, data inspection, and plots. Learn how to configure a project and check edition and integration details.
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Yes. PyCharm supports data-science work in Python, including Jupyter notebooks, data inspection, and plots. You’ll need to configure a Python interpreter and install the libraries your project uses; PyCharm is the development environment, not a replacement for those libraries.

What you can do for data science in PyCharm

Work with Python scripts and project libraries

PyCharm can be used to build and maintain Python data projects. Configure an interpreter for the project, then install the required packages into that environment. The available features depend in part on which packages are installed there. JetBrains’ Python support guide covers interpreter setup.

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Edit, run, and debug Jupyter notebooks

PyCharm supports editing and executing Jupyter notebooks, displaying outputs such as text streams and images, and debugging notebook code. See the Jupyter notebook support guide for setup and workflow details.

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Inspect data and view plots

PyCharm’s scientific features include data views for NumPy arrays and pandas dataframes, with table views, column statistics, and charts. Its Plots tool window supports visualization workflows with Matplotlib and Plotly. Install the relevant libraries in the selected interpreter to use them. The scientific features documentation and data science and machine-learning tools overview describe the available tools.

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How to get started

  1. Install Python and create or open a PyCharm project.

  2. Configure the project’s Python interpreter and environment.

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  3. Install the packages needed for the workflow, such as NumPy, pandas, Matplotlib, or Plotly, into that interpreter.

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  4. Use a Python file for script-based work or create/open a Jupyter notebook for cell-based exploration. Run code, inspect supported data structures, and view plots in the IDE.

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JetBrains’ scientific project tutorial walks through a sample setup using conda, NumPy, and Matplotlib.

Which PyCharm features are free?

JetBrains combined the former Community and Professional editions into unified PyCharm starting with version 2025.1. Core functionality, including Jupyter Notebook support, is free; Pro adds advanced features, and the current unified-product overview describes a 30-day Pro trial. If a particular capability matters to your workflow, check the current PyCharm overview and quick start guide rather than assuming it is included in the free core.

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Check integrations before relying on them

Some older guides may describe integrations that have since changed. In its PyCharm 2026.2.1 release notes, JetBrains said Data Wrangler, Hugging Face, and Google Colab support were unbundled and were no longer bundled or actively maintained by the PyCharm team. Compatible versions may still be available from JetBrains Marketplace, but check their current availability and maintenance status before making them part of your workflow. Read the PyCharm 2026.2.1 release notes.

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When PyCharm is a good fit

PyCharm is a reasonable choice if you want to develop and maintain Python data projects in an IDE that also supports code navigation, debugging, notebooks, and data inspection. Decide based on the work you actually do:

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  • Whether notebook editing and debugging meet your needs.

  • Whether its data views and plotting workflow suit the way you inspect results.

  • Whether you can manage the interpreter and packages for each project.

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  • Whether a required feature is part of free core PyCharm, requires Pro, or depends on an external integration.

These capabilities make PyCharm suitable for many Python data-science workflows, but they do not establish that it is better than every other environment.

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