Return multi-indexed DataFrame, load data from, and save it to local file, many other improvements

Multi-indexed DataFrame:

If you call the data() method with 'concat = True' (meaning concatenate, well, we could call it 'join'), data() will return a tuple of global metadata such as the frequency, and a multi-indexed DataFrame. This feature of pandas is very powerful. I prefer it over columns with namedtuples as titles. This is really a breakthrough. One feature is missing: the ability to sort the index levels. 

list of series with namedtuples:
For the list of series, the metadata is now attached to each series through its name attribute. I learnt about the name attribute only a few days ago. Please ignore my earler comment. 

date_parser:
This function is no longer used. pandas.to_dates is much much faster and more powerful. Please check if it covers all cases, especially those that prompted you to write the date_parser function in the first place.

Methods data, codes and dataflows have to_file and from_file arguments allowing to pass a filename for local storage. You can work offline now and save download time.

observation status:
The multi-indexed DataFrame would allow for elegant integration of observation data as well. I haven't done it yet. Would it be useful? Do we need it?

Enjoy!

Leo