The Conda workflow for managing environments and packages begins by creating a new environment with conda create --name myenv python=3.10 to isolate dependencies and avoid project conflicts. Activate it using conda activate myenv , then install necessary packages like NumPy, Pandas, or Matplotlib with conda install . You can also use channels like conda-forge for additional packages. Use conda list to view installed packages and conda env export > environment.yml to back up or share your setup. When finished, deactivate the environment with conda deactivate , and optionally remove it using conda remove --name myenv --all . You can also list all environments with conda env list or clone one with conda create --name newenv --clone myenv .

Conda Workflow for Working with Environments and Packages


Step 1: Create a New Environment

Use this to isolate dependencies for each project.

conda create --name myenv python=3.10

  • myenv   = Name of your environment
  • python=3.10   = Specific Python version (optional)

This prevents conflicts between projects.


Step 2: Activate the Environment

conda activate myenv

  • This switches your terminal or Anaconda Prompt into the new environment.

 Now all packages you install will only apply to this environment.


Step 3: Install Packages into the Environment

conda install numpy pandas matplotlib

  • Installs packages like NumPy, Pandas, and Matplotlib.


You can also install from specific channels:

conda install -c conda-forge seaborn


Step 4: List Packages and Export Environment

Check which packages are installed:

conda list

Export the environment for sharing or backup:

conda env export > environment.yml

Useful for version control and reproducibility.


Step 5: Deactivate the Environment

conda deactivate

  • This returns you to the base (default) environment.


Step 6: Remove an Environment (Optional Cleanup)

To delete an environment completely:

conda remove --name myenv --all

Do this when you're done with a project or want to clean up disk space.


Bonus Tips

  • List all environments:

conda env list


  • Clone an environment:

conda create --name newenv --clone myenv

Building a Web Site with Flask

By Flaubert Tchouangwa • August 5, 2025
Build a Web Site with Flask Install Flask in a virtual environment and create a simple "Hello World" web app. You’ll learn to modify route definitions, use debug mode, and render HTML pages using Flask routes. You will also work with boilerplate HTML and CSS to customize your site, dynamically generating URLs with url_for , creating and inheriting Jinja templates, and defining multiple routes that link to the same function.

Helpful Conda Commands for Managing Environments and Packages

By Flaubert Tchouangwa • August 1, 2025
When working with Python using Anaconda, managing environments and packages with Conda is essential for keeping your projects organized and conflict-free. Below is a handy table of common Conda commands categorized by functionality:

Install Anaconda

By Flaubert Tchouangwa • August 1, 2025
To easily set up Python and Jupyter Notebook, install the free Anaconda distribution, which includes Python, Jupyter, and essential data science libraries. Download Anaconda from the official website, run the installer, and follow the setup instructions. Once installed, launch Anaconda Navigator or open Jupyter Notebook directly from the Start menu. This all-in-one package simplifies environment management and lets you start coding in Python immediately through an interactive browser-based interface ideal for data analysis, machine learning, and educational projects.

Create an environment for Hugging Face’s Transformers library

By Flaubert Tchouangwa • August 1, 2025
To create an environment for working with Hugging Face’s Transformers library, start by creating a new Conda environment using conda create --name llm_project_env , then activate it with conda activate llm_project_env . Next, install the Transformers library by running conda install transformers . Launch Jupyter Notebook from within this environment using jupyter notebook , and you can begin writing Python code as usual. For example, you can use from transformers import pipeline and create a sentiment analysis pipeline with pipeline("sentiment-analysis") , then test it with a sentence like "I love NLP!" . Once you're done, deactivate the environment with conda deactivate .