leveldb-py | LevelDB Python interface using ctypes | Database library

I would consider HDF5 for this. It has several advantages:

• Usable from many programming languages.
• Usable from Python via the excellent h5py package.
• Battle tested, including with large data sets.
• Supports variable-length string values.
• Values are addressable by a filesystem-like "path" (/foo/bar).
• Values can be arrays (and usually are), but do not have to be.
• Optional built-in compression.
• Optional "chunking" to allow writing chunks incrementally.
• Does not require loading the entire data set into memory at once.

It does have some disadvantages too:

• Extremely flexible, to the point of making it hard to define a single approach.
• Complex format, not feasible to use without the official HDF5 C library (but there are many wrappers, e.g. h5py).
• Baroque C/C++ API (the Python one is not so).
• Little support for concurrent writers (or writer + readers). Writes might need to lock at a coarse granularity.

You can think of HDF5 as a way to store values (scalars or N-dimensional arrays) inside a hierarchy inside a single file (or indeed multiple such files). The biggest problem with just storing your values in a single disk file would be that you'd overwhelm some filesystems; you can think of HDF5 as a filesystem within a file which won't fall down when you put a million values in one "directory."