Which is better for environment management: Conda or Pip?

I'm trying to decide between Conda and Pip for managing my Python environments. Which one is better?

Both Conda and Pip have their strengths. Conda is great for managing packages and environments, especially for data science, while Pip is more widely used in the Python community for package management.

What are the main differences in how they handle dependencies?

Conda manages dependencies more comprehensively, often resolving conflicts better than Pip. Pip installs packages from the Python Package Index, which can lead to dependency issues if not managed carefully.

Can I use both Conda and Pip together?

Yes, you can use both together, but it's recommended to use Conda to create the environment and then use Pip to install additional packages within that environment.

Are there any specific scenarios where one is preferred over the other?

Conda is preferred for data science projects due to its ability to manage complex dependencies, while Pip is often used for general Python development.