Reading a pickle file (PANDAS Python Data Frame) in R

Reticulate was quite easy and super smooth as suggested by russellpierce in the comments.

install.packages('reticulate')

After which I created a Python script like this from examples given in their documentation.

Python file:

import pandas as pddef read_pickle_file(file):    pickle_data = pd.read_pickle(file)    return pickle_data

And then my R file looked like:

require("reticulate")source_python("pickle_reader.py")pickle_data <- read_pickle_file("C:/tsa/dataset.pickle")

This gave me all my data in R stored earlier in pickle format.

You can also do this all in-line in R without leaving your R editor (provided your system python can reach pandas)... e.g.

library(reticulate)pd <- import("pandas")pickle_data <- pd$read_pickle("dataset.pickle")

Edit: If you can install and use the {reticulate} package, then this answer is probably outdated. See the other answers below for an easier path.

You could load the pickle in python and then export it to R via the python package rpy2 (or similar). Once you've done so, your data will exist in an R session linked to python. I suspect that what you'd want to do next would be to use that session to call R and saveRDS to a file or RAM disk. Then in RStudio you can read that file back in. Look at the R packages rJython and rPython for ways in which you could trigger the python commands from R.

Alternatively, you could write a simple python script to load your data in Python (probably using one of the R packages noted above) and write a formatted data stream to stdout. Then that entire system call to the script (including the argument that specifies your pickle) can use used as an argument to fread in the R package data.table. Alternatively, if you wanted to keep to standard functions, you could use combination of system(..., intern=TRUE) and read.table.

As usual, there are /many/ ways to skin this particular cat. The basic steps are:

1. Load the data in python
2. Express the data to R (e.g., exporting the object via rpy2 or writing formatted text to stdout with R ready to receive it on the other end)
3. Serialize the expressed data in R to an internal data representation (e.g., exporting the object via rpy2 or fread)
4. (optional) Make the data in that session of R accessible to another R session (i.e., the step to close the loop with rpy2, or if you've been using fread then you're already done).

To add to the answer above: you might need to point to a different conda env to get to pandas:

use_condaenv("name_of_conda_env", conda = "<<result_of `which conda`>>")pd <- import('pandas')df <- pd$read_pickle(paste0(outdir, "df.pkl"))