Pandas Dataflow
Hello, I'm using pandas together with IPython (btw, thanks for your library!). At this moment I met a problem of processing big files - there is not enough memory on the server, and the swap is disabled (can't change this). 

As long as I am writing library for data processing / machine learning, I eager to create some pipeline which will involve pandas (as most popular and simple way to operate data) and different classifiers (in particular `sklearn`, but more advanced classifiers also will be available, which will train on large files and predict large files as well). Large files processing also needed to be resolved.

Finally I came to the following concept, which I called `PandasFlow`. Streams are very useful abstraction, this is chunk version of stream. Each operation returns a **lazily-evaluated sequence of DataFrames** (say, ~10000 rows each). Some simplest examples:

```
# nothing done really
PF.from_csv(filename, features)  

# this will return pandas.DataFrame with two columns
PF.from_csv(filename, features).where('width * height > 10').select_columns(width='width', volume='width * height * length').to_pandas()

# this will save with one additional column
PF.from_hdf(filename, path, features).where(lambda x: x.width * x.height > 10).add_columns(prediction=lambda x:classifier.predict(x)).to_csv(new_filename)
```

So, I believe this is
1. not hard to implement
2. easy to use
3. NB: you can prepare a query and pass to classifier - if classifier uses pandas (as `sklearn`) - it will convert this to pandas. If it trains on `csv` - it will save your query to `csv` file. Some other format not a problem as well. No need to think about final format that will be used 

If there is something like that already available? If not, any suggestions/links are appreciated.
