Create to_sqlite.py


Essentially, my goal was to organize 7 years of tweets into counts and averages*
by bank mentioned (extracted earlier from query results) and by week, so the
output had to be several spreadsheets.

*The data being aggregated was a single-value sentiment score (-1 to 1). In
order to preserve more of the polarity of the data, I separated data points into
positive and negative during aggregation.

So I had a positive and negative spreadsheet for each bank, and I aggregated them
into a single, more client-friendly sheet per bank using a subsequent script.