Python Dependency Manager Companion

========================= Conda for data scientists ========================= Conda is useful for any packaging process but it stands out from other package and environment management systems through its utility for data science. Conda’s benefits include: * Providing prebuilt packages which avoid the need to deal with compilers or figuring out how to set up a specific tool. * Managing one-step installation of tools that are more challenging to install (such as TensorFlow or IRAF). * Allowing you to provide your environment to other people across different platforms, which supports the reproducibility of research workflows. * Allowing the use of other package management tools, such as pip, inside conda environments where a library or tools are not already packaged for conda. * Providing commonly used data science libraries and tools, such as R, NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s CUDA) which speed up performance without code changes. `Read more about how conda supports data scientists <https://carpentries-incubator.github.io/introduction-to-conda-for-data-scientists/>`_.