title: How to store multiple related time series in Pandas
body: <p>I'm new to Pandas and would like some insight from the pros. I need to perform various statistical analyses (multiple regression, correlation etc) on >30 time series of financial securities' daily Open, High, Low, Close prices. Each series has 500-1500 days of data. As each analysis looks at multiple securities, I'm wondering if it's preferable from an ease of use and efficiency perspective to store each time series in a separate df, each with date as the index, or to merge them all into a single df with a single date index, which would effectively be a 3d df. If the latter, any recommendations on how to structure it?</p>
  
  <p>Any thoughts much appreciated.</p>
  
  <p>PS. I'm working my way up to working with intraday data across multiple timezones but that's a bit much for my first pandas project; this is a first step in that direction.</p>