轻量Python开发环境JupyterLab
尝试了很多Python的开发环境(Integrated Development Environment, IDE)后,终于找到了一款轻量的、交互的、基于浏览器的IDE。

Jupyter Lab是一款开源的Python开发环境,定位于:Interactive Data Science and Scientific Computing。
这款软件的主要特点是:
- 开源(Open Source):即是免费。不过现在大部分的IDE是免费的,这也不算是特别的优点。
- 基于浏览器(Web-based):不需要安装额外的软件,因此使用起来会方便快捷,毕竟每台电脑都有浏览器。
- 最重要的特点是可以把代码分成多个独立的Cells,每个Cells可独立运行。我们可以只运行自己想要运行的单元格,不用运行整个代码。
Jupyter的安装也很简单,只需要命令提示符中输入:
pip install jupyterlab
或者(可执行文件的名称为pip3时使用)
pip3 install jupyterlab
安装好后可通过以下命令运行
jupyter lab
为什么Jupyter会适合科学计算呢?
- 这款软件是通过Code Cells来管理代码的,每个Cell可以单独运行。这种设计的话是方便调试。科学计算需要大量探索代码块的运行结果,然后来调试代码。
- JupyterLab 支持多种输出格式,包括文本、图像、图表和交互式小部件,从而能够有效地可视化数据。
使用Jupyter Lab也非常简单,只需要在空白的Code Cell里面输入Python代码,然后按[Shift + Enter]执行。
另外还有一些非常实用的功能:
- Tap Completion: Pressing the Tab key will search the namespace for any variables (objects, functions, etc.) matching the characters you have typed so far and show the results in a convenient drop-down menu. 这功能可通过点击[Tab]键索引之前定义过的变量或者引用过的变量,提升编程的效率。Python自带的IDE是没有此项功能,大部分IDE都有这个功能。
- Introspection: Using a question mark (?) before or after a variable will display some general information about the object. 这个功能可通过变量前后加一个问题,执行后可显示这个变量的信息,完全不用调配type()函数来了解变量的类型。
- 可以通过在代码单元内加上感叹号(!)作为前缀来直接执行 shell 命令。例如在 Jupyter Notebook 中安装
numpy
的包,只需要运行!pip install numpy
,不需要在shell中执行pip instal numpy
。
如何你还是不想安装Jupyter Notebook,那可以直接用Google Colab,效果和Jupyter Notebook一样,还有免费的GPU TPU资源,甚至AI编程。
Python venv
环境如何运行Jupyter?
这种问题可直接问大模型:how to create venv in jupyter?
You don't directly "create" a Python venv
directory within the Jupyter Notebook interface itself. Jupyter is an interactive computing environment, not a file system manager for creating virtual environments. Here's a recap of the process, emphasizing where the venv
directory is actually created on Macbook:
1. Open your Terminal or Command Prompt:
This is the essential first step. You need a shell environment to execute the commands that create the venv
directory.
2. Navigate to your desired project directory:
This is where your venv
directory will be created.
cd /path/to/your/project
(Replace /path/to/your/project
with the actual path to where you want your project and its virtual environment to reside. For instance, if you have a folder named my_jupyter_projects
, you might cd
into that.)
3. Create the Virtual Environment (the venv
directory is created here!):
Creates a new directory (e.g., my_project_env
) within your current working directory. This new directory is your virtual environment.
python3 -m venv my_project_env
4. Activate the Virtual Environment:
This command sets up your shell so that when you run python
or pip
, they use the versions from within your my_project_env
directory.
source my_project_env/bin/activate
5. Install ipykernel
within your activated venv
:
ipykernel
is the package that allows your virtual environment to be recognized as a kernel in Jupyter.
pip install ipykernel
6. Register your venv
as a Jupyter Kernel:
This tells Jupyter that the Python environment located in your my_project_env
directory is available for use.
python -m ipykernel install --user --name=my_project_env --display-name "Python (My Project Environment)"
--name=my_project_env
: This should match the name of thevenv
directory you created.--display-name "Python (My Project Environment)"
: This is the friendly name that will appear in the Jupyter Notebook interface.
7. Launch Jupyter Notebook:
jupyter notebook
8. Select your venv
in Jupyter:
In the Jupyter interface (your web browser):
- Click New -> Python (My Project Environment) (or whatever you named it).
- Or, for an existing notebook, go to Kernel -> Change kernel and select it.
In summary:
The venv
directory is created by the python -m venv
command in your terminal. Jupyter Notebook then acts as an interface to connect to and use the Python interpreter and packages within that already-created venv
directory.