Python Pandas to R dataframe

If standard text-based formats (csv) are too slow or bulky, I'd recommend feather, a serialization format built on Apache Arrow. It was explicitly developed by the creators of RStudio/ggplot2/etc (Hadley Wickham) and pandas (Wes McKinney) for performance and interoperability between Python and R (see here).

You need pandas verson 0.20.0+, pip install feather-format, then you can use the to_feather/read_feather operations as drop-in replacements for to_csv/read_csv:

df_R = pd.read_feather('filename.feather')

The R equivalents (using the package feather) are

df <- feather::read_feather('filename.feather')
feather::write_feather(df, 'filename.feather')

Besides some minor tweaks (e.g. you can't save custom DataFrame indexes in feather, so you'll need to call df.reset_index() first), this is a fast and easy drop-in replacement for csv, pickle, etc.

EDIT: Today (Juni 2022) the feather development moved to arrow. It means don't use feather library but arrow.

df <- arrow::read_feather('filename.feather')

The recent documentation has a section about interacting with pandas.

Otherwise objects of type rpy2.robjects.vectors.DataFrame have a method to_csvfile, not to_csv:

If wanting to pass data between Python and R, there are more efficient ways than writing and reading CSV files. Try the conversion system:

from rpy2.robjects import pandas2ri

from rpy2.robjects.packages import importr

base = importr('base')
# call an R function on a Pandas DataFrame

Once you have your data.frame you can save it using write.table or one of the wrappers of the latter, for example writee.csv.

In rpy2 :

import rpy2.robjects as robjects
## get a reference to the R function 
write_csv = robjects.r('write.csv')
## save