conda is a package and environment manager that emerged from the Anaconda distribution around 2012 and became a fixture of scientific computing and data science. Unlike most language-specific package tools, conda is not limited to one language. Its documentation states plainly that “Conda provides package, dependency, and environment management for any language” (https://docs.conda.io/en/latest/).
The reason conda took hold in data science is that many scientific Python libraries depend on compiled code written in C or Fortran, such as numerical and linear-algebra routines. Installing those with Python-only tools often meant compiling them from source, which frequently failed on machines without the right compilers and system libraries. conda ships prebuilt binaries that bundle those non-Python dependencies, so a single command can install a working scientific stack.
conda also manages isolated environments. A developer can create separate environments, each with its own set of packages and even its own Python version, and switch between them without disturbing the rest of the system. The documentation describes environment management as a core capability alongside package and dependency management (https://docs.conda.io/en/latest/).
Around conda a broader ecosystem grew, including conda-build for creating packages, conda-lock for reproducible environments, and the community-driven conda-forge distribution (https://docs.conda.io/en/latest/). conda draws packages from its own channels rather than from PyPI, which means it solved real cross-language problems but also added another parallel packaging world to the Python landscape.