Running Python Code

This module covers the various ways you can execute Python code, from quick testing in the interactive shell to scripting and using popular development environments. Each method has unique use cases, and understanding them will allow you to choose the right one for each coding scenario.

Subtopic 1: Python Interactive Shell

Description: The Python Interactive Shell (also known as the Python REPL) is an environment where you can execute Python commands line-by-line and see results immediately. It’s a quick tool for testing small snippets of code and experimenting with language features.
How to Access:
- Open a terminal or command prompt.
- Type python (or python3 on some systems), and press Enter.

Example:

>>> print("Hello, World!")
Hello, World!
>>> 2 + 3
5
>>> name = "Vijay"
>>> f"Hello, {name}"
'Hello, Vijay'

Common Use Cases:
- Testing individual functions or statements.
- Quick debugging of code snippets.
- Exploring Python libraries and syntax in a low-setup environment.

Subtopic 2: Running Python Scripts from the Command Line

Description: For larger scripts or when building applications, you’ll typically save code in .py files and run them as scripts. This is especially useful for full applications or larger projects.
How to Run a Script:
- Write your code in a text editor and save it with a .py extension, such as hello.py.
- Open a terminal, navigate to the directory where the file is saved, and run:
```
python hello.py  # Use python3 hello.py if required
```

Example (hello.py file):

def greet(name):
    return f"Hello, {name}!"

if __name__ == "__main__":
    print(greet("Vijay"))

Command:
```
python hello.py
```
Output:
```
Hello, Vijay!
```
Common Use Cases:
- Running standalone scripts or batch processing tasks.
- Testing modules with the __name__ == "__main__" construct to prevent code from running when imported.

Subtopic 3: Integrated Development Environments (IDEs) and Text Editors

Description: IDEs and advanced text editors provide a full-featured environment for writing, debugging, and managing Python code. IDEs are especially useful for larger projects due to features like syntax highlighting, debugging tools, and integrated terminal.
Popular IDEs and Editors:
- VS Code:
  - Install the Python extension to add support for syntax highlighting, linting, IntelliSense, and integrated terminal.
  - Example: Open a .py file in VS Code, press F5 to run and debug the code.
- PyCharm:
  - Built specifically for Python with features like code completion, refactoring, and built-in support for virtual environments.
  - Example: Open a project, right-click a file, and select Run or Debug.
- Text Editors:
  - Sublime Text and Atom are popular for lightweight coding needs. Use plugins to add Python support.
Common Use Cases:
- IDEs are beneficial for large-scale application development.
- Text editors are great for smaller scripts or quick changes.

Subtopic 4: Jupyter Notebooks and Python

Description: Jupyter Notebooks provide an interactive environment where code, markdown, and visualizations can all be combined in a single document. This is widely used in data science, machine learning, and educational contexts.
Setting Up Jupyter Notebooks:
- Install Jupyter with pip install notebook.
- Launch Jupyter by typing jupyter notebook in the terminal.
Example:
- Open Jupyter and create a new Python notebook.
- Enter code in a cell:
```
# Cell 1
name = "Vijay"
f"Hello, {name}"
```
- Press Shift + Enter to execute, and it will display:
```
'Hello, Vijay'
```
- Other Features:
  - Markdown cells: Use # to create headings, * for bullet points, and other markdown syntax for formatted text.
  - Data Visualizations: Libraries like Matplotlib and Seaborn can be used to create inline visualizations.
Common Use Cases:
- Analyzing data and creating data visualizations.
- Writing tutorials or sharing code snippets with explanations.
- Prototyping code with quick feedback and inline outputs.