Passing Data Between R and Python: Converting Arrow Table to Tibble/Dataframe

Introduction

As a data scientist, working with multiple programming languages is inevitable. R and Python are two popular choices for data analysis, but they have different data structures. In this post, we will explore how to pass data between R and Python, specifically converting between Arrow tables and Tibbles/dataframes.

Background

R: The R language is a high-level, interpreted language with an extensive collection of libraries and packages for statistical computing.
Python: Python is a general-purpose programming language that has become widely used in data science due to its simplicity, flexibility, and extensive libraries.
Arrow: Arrow is a cross-language development platform for in-memory data. It provides a set of libraries (Arrow.NET and PyArrow) for working with tabular data.
Tibble: A tibble is an R implementation of the arrow table concept. Tibbles are designed to be faster, more memory-efficient, and easier to use than traditional R data frames.

The Problem

In the original Stack Overflow post, the user was trying to pass data between R and Python using Arrow tables and Tibbles/dataframes. However, they encountered issues with converting the data back to a tibble in R.

Solution

To solve this problem, we will use the py_to_r package in R to convert Python objects to R, and the r_to_py package to convert R objects to Python.

We will also use the Arrow libraries (PyArrow and arrow) to work with tabular data. PyArrow is a Python implementation of the Arrow library, while arrow is the R version.

Here’s an example code snippet that demonstrates how to pass data between R and Python:

## Step 1: Load Required Libraries

```r
library(reticulate)
library(dplyr)
library(arrow)

load required libraries in python

import pyarrow
import pandas

Step 2: Create a Tibble Dataframe in R and Convert it to an Arrow Table

Create a tibble dataframe in R:

arrow_dat <- arrow::as_arrow_table(tibble(col = c(1,2,3)))

Convert the tibble dataframe to an arrow table using as_arrow_table function from arrow package

Step 3: Convert the Arrow Table to a Python Object

Use the r_to_py function in R to convert the arrow table to a Python object:

py_taxa_arrow <- r_to_py(arrow_dat)

This will create a Python object py_taxa_arrow that represents the same data as the arrow table.

Step 4: Perform Operations on the Python Object

Perform operations on the Python object, such as converting it to a pandas dataframe using the to_pandas function:

py_taxa_arrow_to_pd <- py_taxa_arrow$to_pandas()

This will create a pandas dataframe that represents the same data as the arrow table.

Step 5: Modify the Pandas DataFrame

Modify the pandas dataframe by adding an extra column and performing some operations:

py_taxa_arrow_edited <- py_taxa_arrow_to_pd + 1

Step 6: Convert the Python Object Back to a Tibble Dataframe in R

Use the py_to_r function in R to convert the python object back to a tibble dataframe:

as_tibble(py_taxa_arrow_edited)

In order to do that we will first need to call

pa <- import('pyarrow')
pd <- import('pandas')
py_taxa_arrow_edited_converted <- pa$Table$from_pandas(py_taxa_arrow_edited)

Step 7: Final Solution

Combine all the steps to create a final solution:

library(reticulate)
library(dplyr)
library(arrow)

pa <- import('pyarrow')
pd <- import('pandas')

arrow_dat <- arrow::as_arrow_table(tibble(col = c(1,2,3)))

# Convert to python
py_taxa_arrow <- r_to_py(arrow_dat)

# Do stuff
py_taxa_arrow_to_pd <- py_taxa_arrow$to_pandas()
py_taxa_arrow_edited <- py_taxa_arrow_to_pd + 1

# Convert back
py_taxa_arrow_edited_converted <- pa$Table$from_pandas(py_taxa_arrow_edited)

as_tibble(py_taxa_arrow_edited_converted)

Conclusion

In this post, we explored how to pass data between R and Python using Arrow tables and Tibbles/dataframes. We used the py_to_r package in R to convert Python objects to R and the r_to_py package to convert R objects to Python.

By following these steps, you can easily pass data between R and Python while working with tabular data structures like Arrow tables and Tibbles/dataframes.

Advice

When working with multiple programming languages, it’s essential to use libraries that provide cross-language compatibility.
Always check the documentation of each library to ensure you’re using the correct functions and classes.
Practice makes perfect! Try out this solution on a sample dataset to get a feel for how it works.

Last modified on 2023-12-22