PyCharm vs Anaconda is one of the most common comparisons in the Python community, and it’s also one of the most misleading. These two tools don’t actually compete. One is an IDE built by JetBrains. The other is a Python distribution and package management platform.
But they overlap just enough to cause real confusion, especially for developers setting up their first Python development environment for data science or machine learning work.
This guide breaks down what each tool does, where they differ, how they handle packages and environments, and when it makes sense to use both together. By the end, you’ll know exactly which setup fits your workflow.
What Is PyCharm?
PyCharm is a Python IDE built by JetBrains. It handles code writing, debugging, testing, and deployment inside a single interface.
Two editions exist. The Community Edition is free, open-source under the Apache 2.0 license, and covers core Python development. The Professional Edition adds database tools, web framework support for Django and Flask, and scientific computing features behind a paid subscription.
According to Stack Overflow’s 2025 Developer Survey, PyCharm holds 15% IDE usage across all developers. Among Python developers specifically, the 2023 PSF/JetBrains survey showed PyCharm and VS Code split the top two spots almost evenly at 31% and 32%.
The built-in toolset is what separates PyCharm from lighter editors. Intelligent code completion, automated code refactoring, an integrated debugger, a terminal emulator, and native Git version control all come standard. You don’t install half a dozen extensions just to get a working Python environment.
Companies like Amazon, Walmart, and Apple use PyCharm in their tech stacks, according to Datanyze. The top industries using it are software development, artificial intelligence, and machine learning.
One Gartner reviewer put it plainly: PyCharm is powerful but resource-intensive. That tradeoff between features and system demand follows the tool everywhere.
What Is Anaconda?
Anaconda is a Python distribution platform built for data science, machine learning, and scientific computing. It is not an IDE. That distinction trips up a lot of people.
Developed by Anaconda, Inc. (founded in 2012 in Austin, Texas), the platform bundles Python, the conda package manager, Anaconda Navigator, and over 300 pre-installed packages tested to work together out of the box. NumPy, pandas, scikit-learn, Matplotlib, and Jupyter Notebook all ship with the default installation.
The Anaconda repository gives access to over 33,000 open-source packages across multiple platforms. Anaconda’s own download page now reports the platform is trusted by over 50 million users. Wikipedia’s entry on Anaconda cites a user base of 45 million as of 2024, with a $1.5 billion valuation following a Series C round in mid-2025.
Conda vs the Anaconda Distribution
Conda is the open-source package and environment manager. It remains free for everyone, regardless of organization size. Conda handles dependency resolution, virtual environment creation, and package installation across Python and other languages like R.
Anaconda Distribution is the full installer. It wraps conda with Navigator, pre-built libraries, and access to Anaconda’s curated repositories. This is the part that now carries licensing restrictions for larger organizations.
Miniconda sits between the two. A minimal installer that includes just conda, Python, and a few base packages. It gives you the environment management without the multi-gigabyte footprint.
What Ships with Anaconda Navigator
Navigator is a desktop GUI that lets you launch applications and manage environments without touching the command line.
From Navigator, you can open Jupyter Notebook, JupyterLab, Spyder, and VS Code. You can also create, clone, and switch between conda environments visually. For someone new to Python who finds terminal commands intimidating, Navigator removes that friction.
Why PyCharm and Anaconda Get Compared
They solve different problems. PyCharm is where you write Python code. Anaconda is how you manage Python environments and packages. But the comparison keeps happening, and there’s a real reason for it.
Both tools touch virtual environment management. PyCharm lets you configure project interpreters, create virtualenvs, and even use conda environments directly. Anaconda bundles Spyder (a lightweight IDE) and Jupyter Notebook for writing code. That overlap creates the impression they compete.
The 2024 Python Developers Survey, which collected responses from over 30,000 developers, found that 42% use three or more IDEs or editors simultaneously. Only 23% stick to a single tool. Developers mix and match constantly. And since Anaconda ships with its own code editing tools while PyCharm can manage conda environments, both tools end up in the same conversation.
The Beginner Confusion
Beginners hit this wall hard. They Google “best Python tools,” see PyCharm and Anaconda recommended side by side, and assume they need to pick one. A quarter of Python developers have been using the language for less than a year, according to the same PSF survey. Those newcomers don’t yet understand the difference between an IDE and a distribution.
The real answer? You probably want both. Or at least, you want to understand what each one actually does before deciding your setup.
Code Editing and Development Features
This is where the gap between PyCharm and Anaconda becomes obvious. PyCharm is a full integrated development environment. Anaconda ships with code editing tools, but they’re bundled applications, not the core product.
PyCharm’s IDE Capabilities
PyCharm gives you intelligent code completion that understands Python types, function signatures, and imported modules. It catches errors before you run anything.
The built-in debugger lets you set breakpoints, inspect variables, step through execution, and evaluate expressions on the fly. There’s a built-in terminal, a Python console, and native source control management through Git, Mercurial, and Subversion.
The Professional Edition adds database tools, remote interpreter support, Docker and containerization integration, and framework-specific features for Django, Flask, and FastAPI. Scientific Mode gives you an interactive data viewer alongside your code.
| Feature | PyCharm Community | PyCharm Professional |
|---|---|---|
| Code completion | Yes | Yes (enhanced) |
| Debugger | Yes | Yes |
| Jupyter support | No | Yes |
| Database tools | No | Yes |
| Docker integration | No | Yes |
| Web framework support | No | Django, Flask, FastAPI |
Anaconda’s Code Editing Tools
Jupyter Notebook is Anaconda’s flagship coding interface. It’s cell-based, interactive, and built for data exploration rather than traditional software development. You write a chunk of code, run it, see the output immediately. Great for prototyping. Less great for building production applications.
Spyder ships with Anaconda as a more traditional IDE. It has a variable explorer, an IPython console, and basic debugging. But it doesn’t come close to PyCharm in refactoring support, project navigation, or plugin depth.
The 2024 Python Developers Survey found that 37% of PyCharm users take advantage of its Jupyter support. So even within PyCharm, notebook-style work is common.
Jupyter Notebooks Inside PyCharm
PyCharm Professional runs Jupyter notebooks natively. You get the cell-based workflow with PyCharm’s code intelligence layered on top. Autocomplete, error highlighting, and debugging all work inside notebook cells.
This is where the line between the two tools blurs. If you’re paying for PyCharm Professional, you’re getting Jupyter support plus everything else PyCharm offers. Running Jupyter through Anaconda gives you the notebook experience without the IDE overhead.
Took me a while to figure out that PyCharm’s Jupyter integration still has some quirks with certain kernel configurations. It works well for most cases, but if you’re running exotic conda environments with non-standard packages, you might hit friction that the standalone Jupyter won’t give you.
Package and Environment Management
Anaconda was built for this. PyCharm supports it. That’s the simplest way to frame this section.
How Conda Handles Packages and Environments
Conda creates isolated environments where each project gets its own Python version, its own set of libraries, and its own dependencies. No cross-project conflicts.
The real advantage is dependency resolution. Conda resolves the entire dependency tree before installing anything, including non-Python libraries like C extensions, CUDA toolkits, and system-level binaries. Pip can’t do that. As the conda documentation states, conda provides “prebuilt packages which avoid the need to deal with compilers or figuring out how to set up a specific tool.”
The Anaconda repository hosts packages compiled for Windows, Linux, and macOS. Conda-forge, a community-driven channel, adds thousands more. Between the two, most data science and machine learning workflows are covered.
Conda vs Pip in Practice
When conda wins: Installing packages with compiled C/C++ dependencies. Think TensorFlow with GPU support, SciPy, or OpenCV. Conda ships prebuilt binaries for specific hardware configurations, including NVIDIA CUDA versions. Pip sometimes requires you to compile from source, which can fail on machines without the right build tools.
When pip wins: Access to PyPI’s full catalog. PyPI hosts over 500,000 packages. Conda’s repository, while extensive, is smaller. Some niche or newly published Python packages only exist on PyPI.
Many developers use both. Install your heavy scientific packages with conda, then fill in Python-specific gaps with pip inside the same environment. The PSF’s 2024 survey notes that Anaconda and Conda remain popular choices for data scientists specifically because they provide a stable cross-platform experience.
One thing worth mentioning: conda environments consume more disk space. A basic conda environment with Jupyter and pandas can take up around 1.7 GB, compared to roughly 208 MB for an equivalent pip-based setup. If you’re running multiple projects on a laptop with limited storage, that adds up. Miniconda helps, but you should plan for it.
PyCharm’s Environment Support
PyCharm doesn’t replace conda or pip. It wraps them.
When you create a new project in PyCharm, you choose your interpreter: a system Python, a virtualenv, a pipenv, or a conda environment. PyCharm detects installed packages, manages installations through its package manager UI, and tracks dependencies per project.
Starting in 2024, PyCharm also added uv integration, the Rust-based package manager that hit 11% adoption in its first year. That’s a signal JetBrains is keeping up with how Python’s packaging tools are changing.
The real value here is convenience. You’re managing environments without leaving your editor. But the underlying tools (conda, pip, virtualenv) do the actual work.
Data Science and Machine Learning Workflows
If you picked up Python for data analysis or machine learning, this section decides your tool choice more than anything else.
Anaconda’s Data Science Stack
Anaconda ships ready to go. Install it, and you have NumPy, pandas, scikit-learn, Matplotlib, SciPy, Jupyter, and dozens more libraries waiting. No pip install commands. No dependency errors on day one.
The 2024 PSF survey found that 48% of Python developers work on data exploration and processing, with 32% training ML models or generating predictions. For those users, Anaconda’s pre-configured environment removes setup friction that would otherwise eat into productive time.
The platform also handles GPU-accelerated deep learning frameworks. You can install TensorFlow and PyTorch with CUDA support through conda without manually configuring NVIDIA drivers and toolkit versions. That alone saves hours of troubleshooting for anyone working on machine learning projects.
PyCharm’s Scientific Mode
PyCharm Professional includes Scientific Mode, which splits your editor into code, documentation, and an interactive data viewer. You can inspect DataFrames visually, plot inline graphs, and run code cell-by-cell in a console that behaves like a lightweight notebook.
The 2023 survey data showed that among PyCharm users, 47% used its Jupyter support (this dropped slightly to 37% in 2024). So a significant chunk of PyCharm’s user base already does data science work inside the IDE.
But here’s where I’d push back a bit. If your primary workflow is exploratory data analysis (loading CSVs, plotting distributions, testing hypotheses), Jupyter through Anaconda is faster and lighter. You don’t need PyCharm’s full IDE overhead for that kind of work.
Where PyCharm earns its keep is when your data science project grows into something that needs structure. When you’re writing reusable modules, building build pipelines, or preparing code for app deployment, that’s when a real IDE matters.
Notebook-Driven vs Production-Code Development
| Workflow | Better Fit | Why |
|---|---|---|
| Data exploration | Anaconda + Jupyter | Interactive, visual, low setup |
| Model prototyping | Anaconda + Jupyter | Quick iteration, inline output |
| Production ML code | PyCharm Professional | Refactoring, testing, version control |
| API development | PyCharm Professional | Framework support, debugging |
| Mixed workflow | Both together | Conda environments + PyCharm editor |
The software development process for a machine learning project typically moves through stages. You explore in a notebook, validate your approach, then structure the code for production. Using Anaconda for the first phase and PyCharm for the second isn’t just common. It’s probably the most practical approach.
Performance and System Resources
Both tools are heavy. Just in different ways.
PyCharm consumes RAM because it’s doing real-time code analysis, indexing your entire project, and running background inspections. Anaconda eats disk space because it ships with 300+ packages you might never touch.
PyCharm’s Memory Footprint
PyCharm runs on the Java Virtual Machine. The default heap allocation varies by platform, but JetBrains documentation shows the IDE typically starts around 1 GB of RAM with a single project open.
That number climbs fast. JetBrains support forums are full of reports from developers seeing 2.5 to 5 GB of memory usage during normal work. One 2024 report described PyCharm consuming 30-40% CPU on a Ryzen 9 5950X with just 10 Python scripts open.
You can adjust the heap size through Help > Change Memory Settings. But the reality is, if you’re running PyCharm alongside a browser, a database client, and a terminal, 8 GB of RAM feels tight. 16 GB is more realistic for comfortable development.
Anaconda’s Disk Space Demands
Anaconda’s system requirements page lists a minimum of 5 GB disk space to download and install the distribution. Typical usage runs between 5 and 10 GB once you start adding packages and creating environments.
One source measured a full Anaconda install at 4.4 GB before any additional packages. Conda environments compound this. Each new environment copies packages rather than sharing them (though conda does use hardlinks where possible).
Miniconda needs just 400 MB. That’s the lightweight alternative for people who want conda’s environment management without the bloat. You install only what you actually need.
| Tool | Install Size | RAM Usage (Typical) | Lighter Alternative |
|---|---|---|---|
| PyCharm Professional | ~1 GB | 1–4 GB | PyCharm Community |
| Anaconda Distribution | 3–5 GB | Low (no persistent process) | Miniconda (400 MB) |
| Miniconda + Jupyter | ~1 GB total | Varies by notebook | Miniforge |
Startup Times
PyCharm’s startup is notoriously slow. Opening a large project means waiting for indexing, which can take minutes on first load. A G2 reviewer in 2025 noted that startup can exceed one minute on lower-end machines.
Anaconda Navigator also launches slowly (30+ seconds, depending on the machine). But most experienced users skip Navigator entirely and work from the command line, where conda activate is instant.
Pricing and Licensing
This section has gotten more complicated over the past two years. Both tools changed their licensing terms, and if you’re at a mid-size or large organization, the cost picture matters more than it used to.
PyCharm’s Cost Structure
Community Edition: Free and open-source under Apache 2.0. Covers core Python development, debugging, testing, and version control. No time limits, no feature expiration.
Professional Edition: Subscription-based. According to Vendr’s 2025 pricing analysis, JetBrains individual product licenses range from $89 to $249 per user per year for the first year, with continuation discounts in subsequent years.
- Individual developers pay roughly $24.90/month or $249/year
- Organizations get volume discounts starting at 10+ users
- Students, educators, and open-source contributors qualify for free licenses
JetBrains also offers the All Products Pack at $289/user/year, which includes every JetBrains IDE. For teams using both PyCharm and IntelliJ IDEA or WebStorm, the bundle often makes more sense.
Anaconda’s Licensing Landscape
This is where things got messy. Anaconda updated its Terms of Service in 2020, then again in March 2024, requiring commercial licenses for organizations with 200+ employees.
According to Software Finder, the current pricing tiers are:
- Free: $0. Individuals, students, and organizations under 200 employees
- Starter: $15/month per user
- Business: $50/month per user
- Enterprise: Custom pricing
The enforcement came as a surprise to many. The Register reported in 2024 that Mass General Brigham, a non-profit hospital system, had to notify researchers that Anaconda would no longer be available in their general software repositories without purchased licenses.
Conda itself remains free. Conda-forge, the community channel, is also free. The licensing applies specifically to Anaconda’s distribution and default package repository. If you use Miniforge or configure Miniconda to pull from conda-forge only, you avoid the commercial license requirement entirely.
Cost Comparison at a Glance
| Scenario | PyCharm Cost | Anaconda Cost |
|---|---|---|
| Individual developer | Free (Community) or ~$249/yr (Pro) | Free |
| Startup (under 200 employees) | $249/yr per seat (Pro) | Free |
| Enterprise (200+ employees) | $149–249/yr per seat (with discounts) | $50/month per user ($600/yr) |
| Student or educator | Free | Free |
Using PyCharm and Anaconda Together
Picking one doesn’t mean dropping the other. The most practical setup for Python development, especially in data science, uses both tools side by side.
How to Connect Conda Environments to PyCharm
Step 1: Install Anaconda or Miniconda and create a conda environment with your project’s dependencies.
Step 2: Open PyCharm, go to Settings > Project > Python Interpreter, and click “Add Interpreter.”
Step 3: Select “Conda Environment” and point it to your existing environment. PyCharm detects the installed packages automatically.
From that point, PyCharm uses conda as the backend for package management while giving you the full IDE experience. You get intelligent code completion on top of conda-managed libraries. Debugging, unit testing, and code review all work against your conda environment.
When This Combined Setup Makes Sense
The combo works best for projects that need both clean dependency management and a full-featured web development IDE.
Think machine learning projects heading toward production. You prototype in Jupyter through Anaconda, then move structured code into PyCharm for proper testing, version control with Git, and continuous integration setup.
The 2024 Python Developers Survey found that 42% of developers use three or more tools simultaneously. Running PyCharm as your editor with conda managing your environments fits squarely into how most Python developers actually work.
Which One to Pick Based on Your Work
Stop treating this like an either/or decision. Your work dictates the tool.
Scenario-Based Recommendations
Web development or backend engineering with Python: PyCharm Professional. You need Django/Flask support, database integration, and strong refactoring tools. Anaconda adds nothing to this workflow.
Data exploration and quick analysis: Anaconda with Jupyter. The notebook-driven workflow is faster for loading datasets, testing transformations, and visualizing results. PyCharm’s overhead isn’t worth it here.
Machine learning projects heading to production: Both. Conda manages your environment and heavy dependencies (TensorFlow, PyTorch with CUDA). PyCharm handles the structured code, test-driven development, and deployment preparation.
Complete beginners learning Python: PyCharm Community Edition. It’s free, it teaches good habits (project structure, version control, debugging), and it doesn’t dump 300+ packages on your system before you know what pandas is.
Quick Comparison Table
| Criteria | PyCharm | Anaconda |
|---|---|---|
| Primary function | Python IDE | Python distribution + package manager |
| Best for | Writing, debugging, deploying code | Environment setup, data science |
| Cost (individual) | Free (Community) / ~$249/yr (Pro) | Free |
| Learning curve | Moderate (many features) | Low (Navigator) to moderate (CLI) |
| Environment management | Wraps conda/pip/virtualenv | Built-in via conda |
The JetBrains/PSF survey showed that 86% of respondents use Python as their main language. With that much of the community focused on Python, your choice of tools matters. But the good news is that both PyCharm and Anaconda are flexible enough that you can start with one and add the other when your projects demand it.
If you’re building apps with Python across different areas, from web backends to data pipelines, you’ll probably end up using pieces of both. And that’s fine. That’s how most developers actually work.
FAQ on PyCharm vs Anaconda
Can I use PyCharm and Anaconda together?
Yes. PyCharm can use conda environments as its project interpreter. You manage packages with conda and write code in PyCharm. This is actually the most common setup for Python developers working on data science or machine learning projects.
Is Anaconda an IDE like PyCharm?
No. Anaconda is a Python distribution and package management platform. It ships with code editors like Jupyter Notebook and Spyder, but its core function is managing environments and libraries, not writing code.
Which is better for data science, PyCharm or Anaconda?
Anaconda is better for getting started with data science. It bundles NumPy, pandas, scikit-learn, and Jupyter out of the box. PyCharm Professional adds value later when your project needs structured code, debugging, and proper testing.
Is PyCharm free to use?
PyCharm Community Edition is completely free and open-source. The Professional Edition requires a paid subscription starting around $249 per year. Students and open-source contributors get free access to Professional.
Is Anaconda still free in 2025?
For individuals and organizations with fewer than 200 employees, yes. Organizations above that threshold now need a paid Business license. Conda itself and the conda-forge community channel remain free for everyone.
Do I need Anaconda if I already have PyCharm?
Not necessarily. PyCharm supports pip and virtualenv for package management. But if you work with complex dependencies, especially compiled C libraries or GPU-accelerated frameworks like TensorFlow, conda handles those better than pip alone.
Can PyCharm run Jupyter Notebooks?
PyCharm Professional supports Jupyter notebooks natively. You get the cell-based workflow with PyCharm’s code intelligence on top. The Community Edition does not include this feature.
What is Miniconda and how does it relate to Anaconda?
Miniconda is a lightweight version of Anaconda. It includes only conda, Python, and a few base packages. You install what you need manually. It uses about 400 MB compared to Anaconda’s 3-5 GB footprint.
Which tool should a Python beginner choose?
PyCharm Community Edition. It teaches good habits like project structure, version control with Git, and using a debugger. Anaconda can overwhelm beginners with hundreds of pre-installed packages they don’t understand yet.
Does Anaconda work with VS Code instead of PyCharm?
Yes. VS Code supports conda environments through its Python extension. Many developers pair Anaconda with VS Code as a lighter alternative. The 2025 Stack Overflow survey shows VS Code at 75.9% usage across all developers.
Conclusion
The PyCharm vs Anaconda debate only exists because people misunderstand what each tool does. PyCharm is your coding workspace. Anaconda is your Python environment and dependency manager. They solve different problems.
For web developers and backend engineers, PyCharm Community or Professional covers everything you need. For data scientists running Jupyter notebooks and managing scientific computing libraries, Anaconda’s conda package manager is the faster path.
Most developers end up using both. Configure a conda environment for your project’s dependencies, then point PyCharm’s interpreter at it. That gives you clean isolation with a full-featured IDE on top.
Your workflow decides the tool. Not the other way around. Start with what your project actually requires, and add from there.
Many of his resources are available on various design marketplaces and for free on Codepen.
Over the years, he's worked with a range of clients and contributed to design publications like Design Your Way, Designmodo, WebDesignerDepot, WPDean, Speckyboy, and Slider Revolution among others.
- CSS Cheat Sheet - May 18, 2026
- How to Set Up VSCode for Python Development - May 16, 2026
- How Using One Platform Can Simplify Order Fulfillment - May 15, 2026
