Those are all fine and easy dependencies to install in Python too though, lxml comes in as part of the Python standard lib, and database adaptors like psycopg all bundle the full source code needed.
Where it breaks down in Python is when the dependencies are enormous source code projects with their own dependencies in their own right, that you can't reasonably bundle in with your own project. Conda aimed to solve that by being cross-platform. In the old Python days you'd do 'pip install X' and it would fail because it expected the package to be available, on your system, and in the PATH/LD_LIBRARY_PATH. Then wheels (python packages that could bundle libraries rather than just source code) came along and people started bundling lowest-common-denominator versions that most people could use but at non-optimal performance, and that's roughly still where we are today.
Where it breaks down in Python is when the dependencies are enormous source code projects with their own dependencies in their own right, that you can't reasonably bundle in with your own project. Conda aimed to solve that by being cross-platform. In the old Python days you'd do 'pip install X' and it would fail because it expected the package to be available, on your system, and in the PATH/LD_LIBRARY_PATH. Then wheels (python packages that could bundle libraries rather than just source code) came along and people started bundling lowest-common-denominator versions that most people could use but at non-optimal performance, and that's roughly still where we are today.