# Python Compiler Online and Python IDEs: IDLE, Thonny, VS Code, PyCharm, Jupyter, Colab

URL: https://skillaria.top/en/guides/python/where-to-write-python-code-online

You can try Python without installing anything in an online interpreter in the browser or in Google Colab. For regular study, install Python and write code in IDLE or Thonny at the start, in VS Code or PyCharm for projects, and in Jupyter, Colab or Spyder for data analysis.

You can write and run Python code in three kinds of places: in the browser (an online Python compiler, no installation needed), in a simple learning environment such as IDLE or Thonny, and in a professional editor such as VS Code or PyCharm. For data analysis there is a separate world: Jupyter, Google Colab and Spyder from the Anaconda distribution. Below we explain how they differ, how to run your first code in each of them, and what to choose for your task.

## Compiler or interpreter: what actually runs the code

People often search for a "Python compiler", but Python is an interpreted language. The code is executed by an interpreter program: you write plain text in a `.py` file, and the interpreter reads it and runs it. The main implementation is called CPython, written in C. It is the one you install from python.org or through uv.

Internally, CPython first translates the source into bytecode (the `.pyc` files in the `__pycache__` folder) and immediately executes it on its virtual machine. There is no separate "compile to exe" step. So any "online Python compiler" is really an interpreter running on a server or directly in the browser.

That is the main point of this article: the editor and the interpreter are different things. IDLE, Thonny, VS Code and PyCharm are places where you write text. The interpreter always runs it, and in serious environments you choose which interpreter that is.

## Online Python compiler: run code without installing anything

If you need to check a couple of lines, solve a textbook exercise, or you are sitting at someone else's computer, the simplest option is an online Python compiler in the browser. You type code into a window, press Run and see the output. Nothing needs to be installed, and it even works on a tablet.

Online interpreters come in two kinds:

- **Server-side** — your code is sent to a remote machine and executed there (this is how OnlineGDB, Replit and most "python online compiler" sites work). They run a full CPython, but usually there is a time limit and you cannot install your own libraries.
- **Browser-side** — the interpreter is built with WebAssembly (the Pyodide project, used by JupyterLite and PyScript). Your code never leaves your computer, but not every module is available: there is no normal network access and you cannot start processes.

Pay attention to the version: online services often lag behind the latest release. If an example from the course uses Python 3.15 features, it may not run on a site with an older interpreter. You can check the version right in the window:

```
import sys
print(sys.version)
```

### Finding a Python error online

An online interpreter also helps with errors: run the code and Python will show a traceback with the line and the error type. Read it from the bottom up: the last line says what went wrong, and the lines above show where.

```
Traceback (most recent call last):
  File "main.py", line 2, in
    print(total + 1)
          ^^^^^
NameError: name 'total' is not defined
```

Here the variable name is misspelled, or it was never defined. A typical beginner's error is `SyntaxError: 'return' outside function`: `return` is placed outside a function, usually because the indentation is wrong. Check that the line is indented by the same four spaces as the body of the `def`.

### When an online compiler is not enough

Online environments are good for first steps, but for serious study they quickly get in the way: files get lost, you cannot install libraries, there is no debugger, and a project with several modules is awkward to put together. Once you write code every day, install Python locally — it takes about ten minutes — and choose an editor from the list below.

## IDLE: the environment that comes with Python

IDLE (IDLE Python) is included with the python.org installers for Windows and macOS, so you do not need to download IDLE separately: install Python and you get IDLE. On Linux it is usually installed as a package; on Debian and Ubuntu, run `sudo apt install idle3`.

IDLE has two windows. The Shell is an interactive console with the `>>>` prompt, where each line runs immediately. The editor opens through File → New File: you write code, save the file and press F5 (Run → Run Module), and the result appears in the Shell.

The advantages of IDLE are zero setup and syntax highlighting. The drawbacks are a modest editor: there is no proper project-wide autocompletion, and no built-in Git or virtual environment support. It is enough for the first few weeks, but not for projects. There is no online version of IDLE: people searching for "idle python online" are usually looking for a browser-based interpreter.

## Thonny: the best editor for the very first steps

Thonny is a free IDE designed specifically for learning. Its installer includes its own Python, so you can start without installing an interpreter separately. The interface is minimal: an editor window, a console and a run button.

Thonny's key feature is a clear step-by-step debugger. You can see how each expression is evaluated, what values the variables hold and how functions are called. For someone who does not yet understand why a loop behaves the way it does, this is worth more than any advanced feature. Libraries are installed through the Tools → Manage packages menu, without the command line.

If you are teaching a child or learning from scratch and the terminal scares you, start with Thonny. After a couple of months you can comfortably move on to VS Code.

## VS Code: a universal editor for Python and everything else

Visual Studio Code is a free editor from Microsoft that becomes a full Python environment once you install the Python extension (it comes with Pylance for autocompletion and type checking). It is the most popular choice among people who write not only Python: one window holds the code, the terminal, Git, Docker and Markdown.

### How to set up Python in VS Code

1. Install Python itself (from python.org or with `uv python install 3.15`) — VS Code does not include it.
2. Download VS Code from code.visualstudio.com and install it.
3. Open the Extensions panel (Ctrl+Shift+X), find the Python extension by Microsoft and click Install.
4. Open your project folder (File → Open Folder) and create a file called `main.py`.
5. Press Ctrl+Shift+P, choose Python: Select Interpreter and pick the right version or the project's `.venv`.
6. Run the file with the ▷ button in the top right corner, or press F5 to run it under the debugger.

The most common problem is that VS Code runs a different Python, so a library you have just installed "cannot be found". The selected interpreter is shown in the status bar at the bottom. If it shows the system Python while your libraries are installed in the project environment, switch it through Select Interpreter.

## PyCharm: a powerful IDE built for Python

PyCharm from JetBrains is an environment built only for Python. It understands code more deeply than any other: smart refactoring (rename a function across the whole project), navigation to definitions, a built-in debugger, a test runner, database tools and Git. Basic features are free, while advanced features for web and data work require a subscription.

The price of its power is weight: PyCharm starts noticeably more slowly and needs more memory than VS Code. On a weak laptop you will feel it.

### How to set up the Python interpreter in PyCharm

When you create a project (New Project), PyCharm offers to create a virtual environment and asks for the base interpreter: pick the Python 3.15 you have installed. For an existing project: Settings → Project → Python Interpreter → Add Interpreter, then choose an existing environment or create a new one. If the list does not contain the version you need, Python is not installed on the system, and PyCharm does not bundle it.

## Jupyter and Google Colab: notebooks for data and experiments

Jupyter Notebook and JupyterLab work differently: the code lives in cells, each cell can be run on its own, and the result — a table, a chart or text — appears right below it. Variables persist between cells, so you can load data once and keep experimenting. Add it to a project with `uv add jupyterlab` and start it with `uv run jupyter lab`.

Google Colab (Colab Python) is the same kind of Jupyter notebook, but in Google's cloud. All you need is a Google account and a browser; popular data and machine learning libraries are already installed, and for heavy computations you can connect a GPU. Limitations: the session disconnects when idle, files must be saved to Google Drive, and the Python version is set by the service and may lag behind the latest release.

Notebooks are great for data analysis and learning, but poor for applications: it is easy to mix up the order in which cells were run, and an `.ipynb` file is awkward to keep in Git. Working code from a notebook should be moved into regular `.py` modules.

## Anaconda and Spyder: a distribution for scientific computing

Anaconda is a Python distribution for science and data analysis: it includes the interpreter, the conda package manager, hundreds of libraries (NumPy, pandas, Matplotlib), Jupyter and the Spyder IDE. It is installed with a single installer. On Windows, Anaconda does not add itself to PATH by default, and its tools are meant to be launched from Anaconda Prompt or Anaconda Navigator.

Spyder (Spyder Python) is similar to MATLAB or RStudio: an editor, an IPython console, and a variable and plot viewer in one window. Scientists and analysts moving over from these tools will find it most familiar.

Keep two things in mind. The distribution takes several gigabytes, although for learning most of its packages are not needed. And for commercial use inside organisations, read the Anaconda licence terms carefully. A lighter alternative is Miniconda or plain Python with uv, where you install the libraries you need as you go.

## Comparing environments: where to write Python code

| Environment | Installation | Strengths | Best for |
| --- | --- | --- | --- |

| Online Python compiler | not needed | starts in a second, works anywhere | trying things out, solving a task |

| IDLE | comes with Python | nothing to configure, console and editor | the first few weeks |

| Thonny | one program with its own Python | clear step-by-step debugger | beginners, school students |

| VS Code | editor + Python extension | lightweight, universal, many extensions | most developers |

| PyCharm | separate IDE | deep code analysis, refactoring | large Python projects |

| Jupyter | a package in the project | cells, charts under the code | data analysis, experiments |

| Google Colab | not needed, Google account | cloud, ready libraries, GPU | data and ML without a powerful PC |

| Anaconda + Spyder | large distribution | everything for science in one package | scientists, analysts |

## How to choose an environment: a short guide

- Want to see what Python is right now — open an online Python compiler or Colab.
- Learning from scratch and afraid of the terminal — Thonny or IDLE.
- Going to write scripts, bots, websites or work in a team — VS Code or PyCharm.
- Working with tables, charts and models — Jupyter (locally or in Colab), Spyder if you are used to MATLAB.

Switching between environments costs almost nothing: a `.py` file is plain text that opens in any editor. What matters more is getting used to running code from the terminal, without the buttons of an environment:

```
python3 expenses.py      # the system way
uv run expenses.py       # the project's version and environment
```

This way you always know which interpreter runs your program, and the code will behave the same in IDLE, in VS Code and on a server.

## Summary

An online Python compiler is the best way to try the language in a minute, but for regular study it is worth installing Python and choosing an editor: IDLE or Thonny to start, VS Code or PyCharm for projects, and Jupyter, Colab or Spyder for data. Remember that the editor only helps you write text, while the interpreter, whose version you need to know, runs it. Installing Python 3.15, working in the REPL console and running your first script are covered step by step in the first lecture of the course.

## FAQ

**Can I write Python on a phone?**

Yes: you can use an online interpreter or Google Colab in a mobile browser, and on Android there are apps with Python built in. For study it is inconvenient because of the on-screen keyboard, so a phone is better suited for quick checks.

**Which is better for Python, VS Code or PyCharm?**

Both are excellent. VS Code is lighter and a good fit if you write in several languages; PyCharm understands Python code more deeply and is more convenient for large projects. Try both for a week and keep the one you enjoy working in.

**Do I need to install Python if I have PyCharm or VS Code?**

Yes. These editors do not include an interpreter; they need Python installed. The exceptions are Thonny, which bundles Python, and the Anaconda distribution.

**Why does my code run in an online compiler but not on my computer?**

Most often the Python versions differ, or a library is not installed. Check the version with python3 --version and install the missing packages into the project's environment.

**Is Google Colab free?**

Basic Colab is free and needs only a Google account; the free tier is limited in session time and computing power, and the higher-tier plans are paid.


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Source: Skillaria.Top (https://skillaria.top)