title: Timeseries sum of values divided by number of business days
body: <p>Trying to get an understanding how to &quot;standardize&quot; time series data.</p>
  <p>Below is example df:</p>
  <pre><code>string = np.random.choice(np.array([x for x in 'abcde']*100))
  
  visits = np.random.poisson(1000, size=500)
  
  date = pd.Series(pd.date_range(&quot;2020&quot;, freq=&quot;D&quot;, periods=500))
  
  df = pd.DataFrame({'string': string, 'visits': visits, 'date': date})
  </code></pre>
  <p>I would like to be able to add a column for any time range selected. Two examples would be:</p>
  <pre><code>df[Avg_visits_month] = sum of visits in each month/ total US business days in that month
  
  df[Avg_visits_quarter]=sum of visits in each quarter/total US business days in that quarter
  </code></pre>
  <p>Attempts have been made using resample (cut off days in first month), groupby(had trouble getting count of bus_days), transform(could get count of num visits per time period, but not the bus day count), numpy.busday_count. I just couldn't get there.</p>