Because of this, a user with a working installation of, for example TensorFlow, can find that it stops working after using pip to install a different package that requires a different version of the dependent NumPy library than the one used by TensorFlow. It will install a package and any of its dependencies regardless of the state of the existing installation. When pip installs a package, it automatically installs any dependent Python packages without checking if these conflict with previously installed packages. The big difference between conda and the pip package manager is in how package dependencies are managed, which is a significant challenge for Python data science. Navigator can search for packages, install them in an environment, run the packages and update them. Anaconda Navigator is included in the Anaconda distribution, and allows users to launch applications and manage conda packages, environments and channels without using command-line commands. It also includes a GUI (graphical user interface), Anaconda Navigator, as a graphical alternative to the command line interface. Over 7500 additional open-source packages can be installed from PyPI as well as the conda package and virtual environment manager. The Anaconda distribution comes with over 250 packages automatically installed. Package versions in Anaconda are managed by the package management system, conda, which analyzes the current environment before executing an installation to avoid disrupting other frameworks and packages. Anaconda is an open-source distribution of the Python and R programming languages for data science that aims to simplify package management and deployment.
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