What are the differences between pip and conda for data science?

What are the differences between pip and conda for data science?

Pip is a package manager for Python packages, while Conda is a package manager for both Python and non-Python packages. Conda can manage environments, making it more versatile for data science.

Which one should I use for my data science projects?

It depends on your needs. If you're working primarily with Python packages, pip is sufficient. However, if you need to manage environments or use packages from other languages, conda is a better choice.

Can I use both pip and conda together?

Yes, you can use both, but it's recommended to use conda to create an environment and then use pip to install additional packages within that environment to avoid conflicts.

What are some common packages I should install for data science?

Common packages include NumPy, pandas, Matplotlib, and scikit-learn. If you're using conda, you can install them easily with conda install.