Pandas sum over duplicated indices with sum
<p>I have a data frame indexed by date</p>

<pre><code>transactions_ind
Out[25]: 
                   Ticker     Transaction  Number_of_units      Price
Date                                                                 
2012-10-11  ROG VX Equity             Buy            12000  182.00000
2012-10-16  ROG VX Equity            Sell            -5000  184.70000
2012-11-16  ROG VX Equity            Sell            -5000  175.51580
2012-12-07  ROG VX Equity             Buy             5000  184.90000
2012-12-11  ROG VX Equity            Sell            -3000  188.50000
2012-12-11  ROG VX Equity  Reversal: Sell             3000  188.50000
2012-12-11  ROG VX Equity            Sell            -3000  188.50000
2012-12-11  ROG VX Equity  Reversal: Sell             3000  188.50000
2012-12-11  ROG VX Equity            Sell            -3000  188.50000
2012-12-20  ROG VX Equity            Sell            -5000  185.80000
</code></pre>

<p>I want to sum over the duplicated index values (2012-12-11) but only over the column "Number_of_units".</p>

<pre><code>transactions_ind
Out[25]: 
                   Ticker     Transaction  Number_of_units      Price
Date                                                                 
2012-10-11  ROG VX Equity             Buy            12000  182.00000
2012-10-16  ROG VX Equity            Sell            -5000  184.70000
2012-11-16  ROG VX Equity            Sell            -5000  175.51580
2012-12-07  ROG VX Equity             Buy             5000  184.90000
2012-12-11  ROG VX Equity            Sell            -3000  188.50000
2012-12-20  ROG VX Equity            Sell            -5000  185.80000
</code></pre>

<p>Using </p>

<pre><code>transactions_ind.groupby(transactions_ind.index).sum()
</code></pre>

<p>deletes the columns "Ticker" and "Transaction" since those are filled with non-numeric values. Also I would olike to know how to deal with the different strings in the "Transactions" column when I sum over the "Number_of_units" column. Hope there exists a one-liner in pandas. Thanks for your help!</p>