title: correct way to sum values of second column for all unique values of first column pandas dataframe
body: <p>I am new to pandas. I have a dataframe that has the days of the week in the first column and a list of values in the second. I wish to sum up the total value for each week day. so:</p>
  
  <pre><code>        day               values 
  0   Thursday                 3
  1   Thursday                 0
  2   Friday                   0
  2   Friday                   1
  4   Saturday                 3
  5   Saturday                 1
  etc...
  </code></pre>
  
  <p>would become :</p>
  
  <pre><code>    day               values 
  0   Thursday                 3
  1   Friday                   1
  2   Saturday                 4
  etc...
  </code></pre>
  
  <p>Using <a href="https://stackoverflow.com/questions/17706109/summing-the-number-of-occurrences-per-day-pandas">summing the number of occurrences per day pandas</a> I achieved what I wanted:
  - where the original df is called value_frame </p>
  
  <pre><code>values_on_day =pd.DataFrame(value_frame.groupby(value_frame.day).apply(lambda subf: subf['values'].sum()))
  </code></pre>
  
  <p>however the values and the weekdays are stuffed into one cell so that:
      print dict(values_on_day)
  equals:</p>
  
  <pre><code>    {0: day
  Friday        3
  Monday        4
  Saturday      7
  Sunday       22
  Thursday     26
  Tuesday       2
  Wednesday     4
  Name: 0, dtype: int64}
  </code></pre>
  
  <p>I have coded a workaround by converting columns into dicts then lists and then back into a dict and converting back into a df but obviously this is not the way to do it. </p>
  
  <p>Please would you show me the correct way to achieve total values for each day of the week in the original dataframe?</p>