Excel's SUMIFS implemented using PANDAS, the Python Data Analysis Library
The usual approach -- if you want all the projects -- would be

&gt;&gt;&gt; df.groupby("PROJECT")["HOURS"].sum()
PROJECT
A001       15
B002        6
C003       11
Name: HOURS, dtype: float64


This only applies the sum on the desired column, as this constructs an intermediate SeriesGroupBy object:

&gt;&gt;&gt; df.groupby("PROJECT")["HOURS"]
&lt;pandas.core.groupby.SeriesGroupBy object at 0xa94f8cc&gt;


If you're only interested in the total hours of a particular project, then I suppose you could do

&gt;&gt;&gt; df.loc[df.PROJECT == "A001", "HOURS"].sum()
15.0


or if you dislike the repetition of df:

&gt;&gt;&gt; df.query("PROJECT == 'A001'")["HOURS"].sum()
15.0


but I find that I almost always want to be able to access more than one sum, so these are pretty rare patterns in my code.

Aside: .ix has fallen out of favour as it has some confusing behaviour.  These days it's recommended to use .loc or .iloc to be explicit.