I have a dataframe in Pandas, and I want to do some statistics on it using R functions. No problem! RPy makes it easy to send a dataframe from Pandas into R:

```
import pandas as pd
df = pd.DataFrame(index=range(100000),columns=range(100))
from rpy2 import robjects as ro
ro.globalenv['df'] = df

```

And if we're in IPython:

```
%load_ext rmagic
%R -i df

```

For some reason the `ro.globalenv` route is slightly slower than the `rmagic` route, but no matter. What matters is this: The dataframe I will ultimately be using is ~100GB. This presents a few problems:

1. Even with just 1GB of data, the transfer is rather slow.
2. If I understand correctly, this creates two copies of the dataframe in memory: one in Python, and one in R. That means I'll have just doubled my memory requirements, and I haven't even gotten to running statistical tests!

Is there any way to:

1. transfer a large dataframe between Python and R more quickly?
2. Access the same object in memory? I suspect this asking for the moon.