pandas performance hotspots
#### Context

I'm writing a text mining toolkit, and decided to try pandas because it offered convenience row-and-column labelled access. Numpy doesn't, and structured numpy arrays only have named columns. 
#### Performance Problem

Indexing text, and manipulating dataframes of word co-occurrence/relevance appeared to be very slow, so I profiled my code using %time, cProfile and pprofile.

The following 2 blogs detail this work:
[http://makeyourowntextminingtoolkit.blogspot.co.uk/2016/10/profiling-indexing-relevance-and-co.html](http://makeyourowntextminingtoolkit.blogspot.co.uk/2016/10/profiling-indexing-relevance-and-co.html)
[http://makeyourowntextminingtoolkit.blogspot.co.uk/2016/10/profiling-indexing-relevance-and-co_16.html](http://makeyourowntextminingtoolkit.blogspot.co.uk/2016/10/profiling-indexing-relevance-and-co_16.html)
#### Key Findings

It seems that some operations are **really** slow:
- growing a pandas dataframe using df.ix[a,b] = value ... 96% time of a profiled function is in indexing.py __setitem__
- performing cell calculations eg df.ix[a,b] += something .. one example saw 60% of a function doing an in-place increment!
- performing calculations on rows eg df.loc[a] = somethng ... 37% of a function is spent doing df.loc[a] = df.loc[a] \* factor
#### Solution?

I know the optimal use-case for pandas is working with whole-arrays and columns at a time .. but this performance seems pretty bad. 

Does the pandas project consider it a priority to improve this?

My initial testing with pure numpy and also h5py (vs pandas.HD5) seems to show massive improvements .. but at a loss of convenience.
