conda env remove -n old-env
conda list --export > conda-requirements.txt # Or use conda-lock for exact binaries conda install conda-lock conda-lock -f environment.yml | Practice | Why it matters | |----------|----------------| | Use environment.yml for everything | No manual conda install – guarantees reproducibility. | | Version-lock critical packages | pandas=2.0.3 not just pandas . | | Keep data separate from code | Use data/raw , data/processed , never commit large files. | | Add a Makefile or shell script | Automate conda env create , conda activate , python train.py . | | Test with a fresh environment | conda env create -f environment.yml --prefix ./test_env to verify. | 7. Common Pitfalls & How to Avoid Them ❌ Mixing pip and conda carelessly → Can lead to broken dependencies. If needed, install everything with conda first, then use pip for remaining packages. building data science solutions with anaconda
jupyter notebook Your notebook automatically uses the correct kernel. import pandas as pd from sklearn.ensemble import RandomForestClassifier import joblib df = pd.read_csv("data/raw/churn.csv") X = df.drop("churn", axis=1) y = df["churn"] conda env remove -n old-env conda list --export
conda create -n project-name python=3.10 conda activate project-name conda install jupyter pandas scikit-learn matplotlib Then commit your environment.yml alongside your code. Your future self — and your team — will thank you. : Explore conda build for packaging your own libraries, or anaconda-project for automating multi-step workflows. The foundation you build with Anaconda today enables the production-grade solutions of tomorrow. | | Add a Makefile or shell script
conda env create -f environment.yml One of Conda’s killer features is handling Python itself as a package. You can have one environment with Python 3.8 (legacy code) and another with 3.11 (newer features).