python environment settings

Install python

https://www.python.org/

 

for example:

python-3.14.0-amd64.exe

 

install

 

 

 

List all python installed in local machine

> py -0
-V:3.14 * Python 3.14 (64-bit)
-V:3.13 Python 3.13 (64-bit)
-V:3.12 Python 3.12 (64-bit)
-V:3.11 Python 3.11 (64-bit)

　　

 

Create virtual enviornement for python project

> python --version
Python 3.9.13

 

 

 

> py -m venv myenv_3_14

 

 

> .\myenv_3_14\Scripts\activate
(myenv_3_14) PS D:\

 

(myenv_3_14) PS D:\> python --version
Python 3.14.0
(myenv_3_14)

 

 

> deactivate

　　

 Jupyter Notebook

 

 

> py --version
Python 3.14.0

 

> py -m pip install --upgrade pip

 

> py -m pip install notebook

> jupyter notebook
[I 2025-11-25 20:46:28.199 ServerApp] Extension package jupyter_server_terminals took 0.6866s to import
[I 2025-11-25 20:46:28.706 ServerApp] jupyter_lsp | extension was successfully linked.
[I 2025-11-25 20:46:28.712 ServerApp] jupyter_server_terminals | extension was successfully linked.
[I 2025-11-25 20:46:28.727 ServerApp] jupyterlab | extension was successfully linked.
[I 2025-11-25 20:46:28.738 ServerApp] notebook | extension was successfully linked.
...

Or copy and paste one of these URLs:http://localhost:8888/tree?token=a7b0746c1f0cd96588efa4198781436a3d4cf4ff2e7dbdf6http://127.0.0.1:8888/tree?token=a7b0746c1f0cd96588efa4198781436a3d4cf4ff2e7dbdf6

 

 

 

✅ 1. Jupyter Notebook

• What it is:
  An interactive web-based environment for writing and running code, especially popular in data science and machine learning.
• Key Features:
  • Runs in your browser.
  • Supports Markdown for documentation alongside code.
  • Allows inline visualization (charts, plots, images).
  • Supports multiple languages via kernels (Python, R, Julia, etc.).
  • Ideal for exploratory programming, data analysis, and sharing notebooks.
• Use Case:
  Great for data science, machine learning, prototyping, and teaching.

---

✅ 2. Regular Python Notebook / IDE (e.g., VS Code, PyCharm)

• What it is:
  A traditional development environment for writing Python scripts (.py files).
• Key Features:
  • Focused on software development rather than interactive exploration.
  • No built-in Markdown or inline visualization (though IDEs can integrate tools).
  • Better for large projects, debugging, and version control.
• Use Case:
  Ideal for production code, backend development, and complex applications.

---

✅ Main Differences

Feature	Jupyter Notebook	Python IDE / Notebook
Interface	Web-based	Desktop application
File Type	.ipynb	.py
Documentation	Markdown + Code	Comments only
Visualization	Inline plots/images	Separate windows
Best For	Data analysis, ML	Full-scale development

 

 User VS code Edit .ipynb code

add file test.ipynb

 

 

 

ipykernel is a key component of the Jupyter ecosystem. Here’s what it is and why it matters:

---

✅ What is ipykernel?

• Definition:
  ipykernel is the Python kernel for Jupyter. A kernel is the computational engine that executes the code contained in your Jupyter Notebook cells.
• It allows Jupyter Notebook (or JupyterLab) to run Python code and communicate with the notebook interface.

---

✅ How it works

• When you start a Jupyter Notebook, it launches a kernel in the background.
• The kernel:
  • Executes Python code.
  • Sends results back to the notebook interface.
  • Handles variables, imports, and state during your session.

---

✅ Why do you need it?

• Without ipykernel, Jupyter cannot run Python code.
• It’s installed automatically with Jupyter, but if you want to add Python to Jupyter from a virtual environment, you need to install ipykernel in that environment.

 

 

PS,

VS code extention used