Calling Python from R (2024)

Overview

The reticulate package provides an R interface toPython modules, classes, and functions. For example, this code importsthe Python os module and calls some functions withinit:

library(reticulate)os <- import("os")os$listdir(".")

 [1] ".git" ".gitignore" ".Rbuildignore" ".RData" [5] ".Rhistory" ".Rproj.user" ".travis.yml" "appveyor.yml" [9] "DESCRIPTION" "docs" "external" "index.html" [13] "index.Rmd" "inst" "issues" "LICENSE" [17] "man" "NAMESPACE" "NEWS.md" "pkgdown" [21] "R" "README.md" "reticulate.Rproj" "src" [25] "tests" "vignettes" 

Functions and other data within Python modules and classes can beaccessed via the $ operator (analogous to the way you wouldinteract with an R list, environment, or reference class).

The reticulate package is compatible with allversions of Python >= 2.7. Integration with NumPy is optional andrequires NumPy >= 1.6.

Python Version

By default, reticulate uses the version of Python found on yourPATH (i.e.Sys.which("python")). Theuse_python() function enables you to specify an alternateversion, for example:

library(reticulate)use_python("/usr/local/bin/python")

The use_virtualenv() and use_condaenv()functions enable you to specify versions of Python in virtual or condaenvironments, for example:

library(reticulate)use_virtualenv("myenv")

See the article on Python VersionConfiguration for additional details.

Python Packages

You can install any required Python packages using standard shelltools like pip and conda. Alternately,reticulate includes a set of functions for managing and installingpackages within virtualenvs and Conda environments. See the article onInstallingPython Packages for additional details.

Type Conversions

When calling into Python, R data types are automatically converted totheir equivalent Python types. When values are returned from Python to Rthey are converted back to R types. Types are converted as follows:

If a Python object of a custom class is returned then an R referenceto that object is returned. You can call methods and access propertiesof the object just as if it was an instance of an R reference class.

Importing Modules

The import() function can be used to import any Pythonmodule. For example:

difflib <- import("difflib")difflib$ndiff(foo, bar)filecmp <- import("filecmp")filecmp$cmp(dir1, dir2)

The import_main() and import_builtins()functions give you access to the main module where code is executed bydefault and the collection of built in Python functions. Forexample:

main <- import_main()builtins <- import_builtins()builtins$print('foo')

The main module is generally useful if you have executed Python codefrom a file or string and want to get access to its results (see thesection below for more details).

Sourcing Scripts

The source_python() function will source a Python scriptand make the objects it creates available within an R environment (bydefault the calling environment). For example, consider the followingPython script:

def add(x, y): return x + y

We source it using the source_python() function and thencan call the add() function directly from R:

source_python('add.py')add(5, 10)

[1] 15

Executing Code

You can execute Python code within the main module using thepy_run_file and py_run_string functions. Youcan then access any objects created using the py objectexported by reticulate:

library(reticulate)py_run_file("script.py")py_run_string("x = 10")# access the python main module via the 'py' objectpy$x

Object Conversion

By default when Python objects are returned to R they are convertedto their equivalent R types. However, if you’d rather make conversionfrom Python to R explicit and deal in native Python objects by defaultyou can pass convert = FALSE to the importfunction. In this case Python to R conversion will be disabled for themodule returned from import. For example:

# import numpy and specify no automatic Python to R conversionnp <- import("numpy", convert = FALSE)# do some array manipulations with NumPya <- np$array(c(1:4))sum <- a$c*msum()# convert to R explicitly at the endpy_to_r(sum)

As illustrated above, if you need access to an R object at end ofyour computations you can call the py_to_r() functionexplicitly.

Getting Help

You can print documentation on any Python object using thepy_help() function. For example:

os <- import("os")py_help(os$chdir)

Lists, Tuples, and Dictionaries

The automatic conversion of R types to Python types works well inmost cases, but occasionally you will need to be more explicit on the Rside to provide Python the type it expects.

For example, if a Python API requires a list and you pass a singleelement R vector it will be converted to a Python scalar. To overcomethis simply use the R list function explicitly:

foo$bar(indexes = list(42L))

Similarly, a Python API might require a tuple ratherthan a list. In that case you can use the tuple()function:

tuple("a", "b", "c")

R named lists are converted to Python dictionaries however you canalso explicitly create a Python dictionary using the dict()function:

dict(foo = "bar", index = 42L)

This might be useful if you need to pass a dictionary that uses amore complex object (as opposed to a string) as its key.

Numeric Types and Indexes

R and Python have different default numeric types. If you write42 in R it is considered a floating point number whereas42 in Python is considered an integer.

This means that when a Python API expects an integer, you need to besure to use the L suffix within R. For example, if thefoo function requires an integer as its indexargument you would do this:

foo$bar(index = 42L)

Python collections are addressed using 0-based indices rather thanthe 1-based indices you might be familiar with from R. So to address thefirst item of an array in R you would write:

items[[1]]

Whereas if you are calling a method in Python via reticulate thattakes an index you would write this to address the first item:

items$get(0L)

Note the use of the 0-based index as well as the L toindicate t that the value is an integer.

Arrays

R matrices and arrays are converted automatically to and from NumPy arrays.

When converting from R to NumPy, the NumPy array is mapped directlyto the underlying memory of the R array (no copy is made). In this case,the NumPy array uses a column-based in memory layout that is compatiblewith R (i.e.Fortran style rather than C style). When converting fromNumPy to R, R receives a column-ordered copy of the NumPy array.

You can also manually convert R arrays to NumPy using thenp_array() function. For example, you might do this if youneeded to create a NumPy array with C rather than Fortran stylein-memory layout (for higher performance in row-oriented computations)or if you wanted to control the data type of the NumPy array moreexplicitly. Here are some example uses of np_array():

a <- np_array(c(1:8), dtype = "float16")a <- np_array(c(1:8), order = "C")

Reasoning about arrays which use distinct in-memory orders can betricky. The Arrays in R and Python articleprovides additional details.

Also, always remember that when calling NumPy methods array indicesare 0 rather than 1 based and require the L suffix toindicate they are integers.

Data Frames

R data frames can be automatically converted to and from Pandas DataFrames. By default,columns are converted using the same rules governing R array <->NumPy array conversion, but a couple extensions are provided:

If the R data frame has row names, the generated Pandas DataFramewill be re-indexed using those row names (and vice versa). Specialhandling is also available for a DatetimeIndex associatedwith a Pandas DataFrame; however, because R only supports charactervectors for row names they are converted to character first.

df <- data.frame( int = c(NA, 1:4), num = c(NA, rnorm(4)), lgl = c(NA, rep(c(TRUE, FALSE), 2)), string = c(NA, letters[1:4]))options(reticulate.pandas_use_nullable_data_types = TRUE)r_to_py(df)#> int num lgl string#> 0 <NA> <NA> <NA> <NA>#> 1 1 -0.697855 True a#> 2 2 -0.253042 False b#> 3 3 0.385421 True c#> 4 4 0.519933 False d

Sparse Matrices

Sparse matrices created by Matrix R packagecan be converted ScipyCSC matrix, and vice versa. This is often useful when you want topass sparse matrices to Python functions that accepts Scipy CSC matrixto take advantage of this format, such as efficient column slicing andfast matrix vector products.

For example, we first create a sparse matrix usingMatrix::sparseMatrix():

library(Matrix)N <- 5dgc_matrix <- sparseMatrix( i = sample(N, N), j = sample(N, N), x = runif(N), dims = c(N, N))

The sparse matrix looks like this:

> dgc_matrix5 x 5 sparse Matrix of class "dgCMatrix" [1,] 0.2264952 . . . . [2,] . . . . 0.3927282[3,] . . . 0.9215908 . [4,] . . 0.01777771 . . [5,] . 0.05885743 . . . 

Let’s convert it to Scipy CSC matrix usingr_to_py():

> csc_matrix <- r_to_py(x)> csc_matrix (0, 0) 0.226495201467 (4, 1) 0.0588574311696 (3, 2) 0.0177777127828 (2, 3) 0.921590822982 (1, 4) 0.392728160601

Note that the right-hand side contains the non-zero entries of thematrix while the left-hand side represents their locations in thematrix.

We can also use py_to_r() to convert the CSC matrix backto Matrix::dgCMatrix representation that can then bemanipulated easily in R which is the same as the original sparse matrixthat we created earlier using Matrix::sparseMatrix():

> py_to_r(csc_matrix)5 x 5 sparse Matrix of class "dgCMatrix" [1,] 0.2264952 . . . . [2,] . . . . 0.3927282[3,] . . . 0.9215908 . [4,] . . 0.01777771 . . [5,] . 0.05885743 . . . 

With Contexts

The R with generic function can be used to interact withPython context manager objects (in Python you use the withkeyword to do the same). For example:

py <- import_builtins()with(py$open("output.txt", "w") %as% file, { file$write("Hello, there!")})

This example opens a file and ensures that it is automatically closedat the end of the with block. Note the use of the %as%operator to alias the object created by the context manager.

Iterators

If a Python API returns an iterator or a generator, you can interactwith it using the iterate() function. Theiterate() function can be used to apply an R function toeach item yielded by the iterator:

iterate(iter, print)

If you don’t pass a function to iterate the results willbe collected into an R vector:

results <- iterate(iter)

Note that the Iterators will be drained of their valuesby iterate():

a <- iterate(iter) # results are not emptyb <- iterate(iter) # results are empty since items have already been drained

while (TRUE) { item <- iter_next(iter) if (is.null(item)) break}

while (TRUE) { item <- iter_next(iter, completed = NA) if (is.na(item)) break}

# define a generator functionsequence_generator <-function(start) { value <- start function() { value <<- value + 1 value }}# convert the function to a python iteratoriter <- py_iterator(sequence_generator(10))

sequence_generator <-function(start) { value <- start function() { value <<- value + 1 if (value < 100) value else NULL }}

Functions

> inspect <- import("inspect")> converted_func <- r_to_py(function(a, b = 1.5) {})> inspect$getargspec(converted_func)ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)

> wrapped_func <- py_func(function(a, b = 1.5) {})> inspect$getargspec(wrapped_func)ArgSpec(args=['a', 'b'], varargs=None, keywords=None, defaults=(1.5,))

Advanced

There are several more advanced functions available that are usefulprincipally when creating high level R interfaces for Pythonlibraries.

Learning More

The following articles cover additional aspects of usingreticulate:

• R Markdown Python Engine

• Python Version Configuration

• Installing PythonPackages

• Using reticulate in an RPackage

• Arrays in R and Python