> ## Documentation Index
> Fetch the complete documentation index at: https://ai.tharung.in/llms.txt
> Use this file to discover all available pages before exploring further.

# Python

***

## 1. Introduction: Python for AI

* Python is widely used in AI because of:
  * Simple syntax
  * Strong libraries (NumPy, Pandas, TensorFlow, PyTorch)
* Focus is on building a **professional setup**

***

## 2. Installing Python

### Windows

* Download from [python.org](http://python.org)
* Check **“Add Python to PATH”**

### Mac

```text theme={null}
python3 --version
```

* Install if not available

***

## 3. Installing VS Code

* Free and powerful code editor
* Supports extensions for Python and AI work

***

## 4. VS Code Setup

### Extensions

* Python (Microsoft)
* Pylance
* Jupyter

### Setting

* Enable: Run Python in file directory

***

## 5. Project Setup

* Use **kebab-case** naming
  * Example: `ai-project-demo`
* Save as `.code-workspace`

***

## 6. First Python File

* Extension: `.py`
* Example:

```text theme={null}
print("Hello World")
```

***

## 7. Running Python Code

```text theme={null}
python file.py
```

***

## 8. Python Environments

* Use **virtual environments (venv)**
* Avoid dependency conflicts

```text theme={null}
python -m venv .venv
```

***

## 9. Pip and Packages

* Install libraries using pip

```text theme={null}
pip install requests
```

***

## 10. Using Packages (Your Example)

```text theme={null}
import requests

response = requests.get('https://api.github.com')
print(response.status_code)
```

* `requests.get()` → sends HTTP request
* `status_code` → shows response status (200 = success)

***

## 11. Interpreter Selection

* Select correct Python interpreter in VS Code
* Must match your virtual environment

***

## 12. Jupyter Interactive Mode

```text theme={null}
pip install ipykernel
```

* Run code using **Shift + Enter**
* Helps in debugging and step-by-step execution

***

## 13. Anaconda (Optional)

* Alternative to pip + venv
* Pre-installed data science libraries

***

## 14. Final Recap

* Install Python + VS Code
* Add extensions
* Create project
* Setup venv
* Install packages
* Run code and use Jupyter

***