Setting Up Virtual environment in Python Projects with Conda - 1

Creating isolated environments is crucial for managing dependencies and avoiding conflicts in Python projects. This guide will help you install Anaconda, resolve common issues, and set up a virtual environment for your projects.

1. Install Anaconda

a) Follow this guide to install Anaconda using the installer. Ensure Anaconda is added to your shell configuration (~/.zshrc or ~/.bashrc).

b) Verify the installation by running:

   conda --version

c) Disable the default environment activation (optional):

   conda config --set auto_activate_base false

d) Add the pip3 path for convenience (optional):

   echo "alias pip=$(which pip3)" >> ~/.zshrc && source ~/.zshrc

2. Create a Project Folder and Virtual Environment

a) Create and navigate to your project directory:

   mkdir my_project && cd my_project

b) Create a Conda virtual environment named venv with Python 3.10 (or your desired version):

   # Environment created in the current directory
   conda create -p ./venv python=3.10 -y
# OR
   # Environment created in Conda's central directory
   conda create -n venv python=3.10 -y

Check your Python version using:

   python --version

If you encounter errors, remove any broken or partially created environments:

   conda remove --name venv --all

c) Activate the virtual environment:

   # For a directory-specific environment
   conda activate ./venv

   # For a central environment
   conda activate venv

d) To deactivate the environment:

   conda deactivate

3. Why Use Virtual Environments?

• Isolation: Keeps dependencies separate for each project.
• Consistency: Ensures identical environments across systems.
• Reproducibility: Facilitates sharing and replication of the project setup.

4. Manage Dependencies with requirements.txt

Tracking dependencies ensures collaboration and smooth deployment.

a) Save Dependencies to requirements.txt

• Manually list the required libraries:

  langchain
  openai
  python-dotenv
  streamlit

• Or generate the file automatically:

  pip freeze > requirements.txt

b) Install Dependencies from requirements.txt

Install all libraries listed in the file:

pip install -r requirements.txt

To add new libraries, update requirements.txt and re-run the command.

c) Remove Dependencies

Uninstall all libraries listed in the file:

pip uninstall -r requirements.txt -y

List all installed dependencies with:

conda list

5. Install Additional Dependencies (Optional)

For example, to install Jupyter Kernel:

conda install ipykernel -y

6. Finalize the Project Structure

Manually create the necessary files and organize the folder structure as per your project’s requirements. A typical structure may look like this:

my_project/
│
├── app.py
├── requirements.txt
├── venv/
└── ...

To run Python scripts:

python app.py

To run Streamlit applications:

streamlit run app.py

With this setup, you can efficiently manage Python projects using Conda environments. Happy coding!