title: Removing Empty Dataframes with pandas
body: <p>I have written the following code to use regex to request pages, and look for strings that resemble interest rates. The overall code works; however, <strong>it is creating multiple empty dataframes and I can't get the code to drop the empty frames to clean up my output.</strong> I have been trying to use .dropna, .drop, and .empty to try and deprecate the dataframes but the output remains unchanged and keeps printing the empty dataframes with the information I have already. Is there an method I am not aware of that could get rid of these empty frames. Code and output below:</p>
  
  <pre><code>plcompetitors = ['https://www.lendingclub.com/loans/personal-loans',
                  'https://www.marcus.com/us/en/personal-loans',
                  'https://www.discover.com/personal-loans/']
  
  #cycle through links in array until it finds APR rates/fixed or variable using regex
  for link in plcompetitors:
      cdate = datetime.date.today()
      l = r.get(link)
      l.encoding = 'utf-8'
      data = l.text
      soup = bs(data, 'html.parser')
      paragraph = soup.find_all(text=re.compile('[0-9]%'))
      for n in paragraph:
          matches = []
          matches.extend(re.findall('(?i)\d+(?:\.\d+)?%\s*(?:to|-)\s*\d+(?:\.\d+)?%', n.string))
          sint = pd.Series(matches)
          qdate = pd.Series([datetime.datetime.now()]*len(sint))
          slink = pd.Series([link]*len(sint))
          df = pd.concat([qdate,sint,slink],axis=1)
          df.columns = ['Date','Interest Rate', 'URL']
          print(df)
  </code></pre>
  
  <p>Output:</p>
  
  <pre><code>  ...
  0 ...
  1 ...
  
  [2 rows x 3 columns]
   ...
  0 ...
  
  [1 rows x 3 columns]
   ...
  0 ...
  1 ...
  2 ...
  3 ...
  
  [4 rows x 3 columns]
  Empty DataFrame
  Columns: [Date, Interest Rate, URL]
  Index: []
  Empty DataFrame
  Columns: [Date, Interest Rate, URL]
  Index: []
  Empty DataFrame
  Columns: [Date, Interest Rate, URL]
  Index: []
  Empty DataFrame
  Columns: [Date, Interest Rate, URL]
  Index: []
    ...
  0 ...
  
  [1 rows x 3 columns]
  Empty DataFrame
  Columns: [Date, Interest Rate, URL]
  Index: []
  Empty DataFrame
  Columns: [Date, Interest Rate, URL]
  Index: []
  Empty DataFrame
  Columns: [Date, Interest Rate, URL]
  Index: []
  Empty DataFrame
  Columns: [Date, Interest Rate, URL]
  Index: []
  </code></pre>