Primer on Python for R users

You may find yourself wanting to read and understand some Python, or even port some Python to R. This guide is designed to enable you to do these tasks as quickly as possible. As you’ll see, R and Python are similar enough that this is possible without necessarily learning all of Python. We start with the basics of container types and work up to the mechanics of classes, dunders, the iterator protocol, the context protocol, and more!


Whitespace matters in Python. In R, expressions are grouped into a code block with {}. In Python, that is done by making the expressions share an indentation level. For example, an expression with an R code block might be:

if (TRUE) {
  cat("This is one expression. \n")
  cat("This is another expression. \n")
#> This is one expression. 
#> This is another expression.

The equivalent in Python:

if True:
  print("This is one expression.")
  print("This is another expression.")
#> This is one expression.
#> This is another expression.

Python accepts tabs or spaces as the indentation spacer, but the rules get tricky when they’re mixed. Most style guides suggest (and IDE’s default to) using spaces only.

Container Types

In R, the list() is a container you can use to organize R objects. R’s list() is feature packed, and there is no single direct equivalent in Python that supports all the same features. Instead there are (at least) 4 different Python container types you need to be aware of: lists, dictionaries, tuples, and sets.


Python lists are typically created using bare brackets []. The Python built-in list() function is more of a coercion function, closer in spirit to R’s as.list(). The most important thing to know about Python lists is that they are modified in place. Note in the example below that y reflects the changes made to x, because the underlying list object which both symbols point to is modified in place.

x = [1, 2, 3]
y = x    # `y` and `x` now refer to the same list!
print("x is", x)
#> x is [1, 2, 3, 4]
print("y is", y)
#> y is [1, 2, 3, 4]

One Python idiom that might be concerning to R users is that of growing lists through the append() method. Growing lists in R is typically slow and best avoided. But because Python’s list are modified in place (and a full copy of the list is avoided when appending items), it is efficient to grow Python lists in place.

Some syntactic sugar around Python lists you might encounter is the usage of + and * with lists. These are concatenation and replication operators, akin to R’s c() and rep().

x = [1]
#> [1]
x + x
#> [1, 1]
x * 3
#> [1, 1, 1]

You can index into lists with integers using trailing [], but note that indexing is 0-based.

x = [1, 2, 3]
#> 1
#> 2
#> 3
except Exception as e:
#> list index out of range

When indexing, negative numbers count from the end of the container.

x = [1, 2, 3]
#> 3
#> 2
#> 1

You can slice ranges of lists using the : inside brackets. Note that the slice syntax is not inclusive of the end of the slice range. You can optionally also specify a stride.

x = [1, 2, 3, 4, 5, 6]
x[0:2] # get items at index positions 0, 1
#> [1, 2]
x[1:]  # get items from index position 1 to the end
#> [2, 3, 4, 5, 6]
x[:-2] # get items from beginning up to the 2nd to last.
#> [1, 2, 3, 4]
x[:]   # get all the items (idiom used to copy the list so as not to modify in place)
#> [1, 2, 3, 4, 5, 6]
x[::2] # get all the items, with a stride of 2
#> [1, 3, 5]
x[1::2] # get all the items from index 1 to the end, with a stride of 2
#> [2, 4, 6]


Tuples behave like lists, except they are not mutable, and they don’t have the same modify-in-place methods like append(). They are typically constructed using bare (), but parentheses are not strictly required, and you may see an implicit tuple being defined just from a comma separated series of expressions. Because parentheses can also be used to specify order of operations in expressions like (x + 3) * 4, a special syntax is required to define tuples of length 1: a trailing comma. Tuples are most commonly encountered in functions that take a variable number of arguments.

x = (1, 2) # tuple of length 2
#> <class 'tuple'>
#> 2
#> (1, 2)
x = (1,) # tuple of length 1
#> <class 'tuple'>
#> 1
#> (1,)
x = () # tuple of length 0
print(f"{type(x) = }; {len(x) = }; {x = }")
# example of an interpolated string literals
#> type(x) = <class 'tuple'>; len(x) = 0; x = ()
x = 1, 2 # also a tuple
#> <class 'tuple'>
#> 2
x = 1, # beware a single trailing comma! This is a tuple!
#> <class 'tuple'>
#> 1
Packing and Unpacking

Tuples are the container that powers the packing and unpacking semantics in Python. Python provides the convenience of allowing you to assign multiple symbols in one expression. This is called unpacking.

For example:

x = (1, 2, 3)
a, b, c = x
#> 1
#> 2
#> 3

(You can access similar unpacking behavior from R using zeallot::`%<-%`).

Tuple unpacking can occur in a variety of contexts, such as iteration:

xx = (("a", 1),
      ("b", 2))
for x1, x2 in xx:
  print("x1 = ", x1)
  print("x2 = ", x2)
#> x1 =  a
#> x2 =  1
#> x1 =  b
#> x2 =  2

If you attempt to unpack a container to the wrong number of symbols, Python raises an error:

x = (1, 2, 3)
a, b, c = x # success
a, b = x    # error, x has too many values to unpack
#> Error in py_call_impl(callable, dots$args, dots$keywords): ValueError: too many values to unpack (expected 2)
a, b, c, d = x # error, x has not enough values to unpack
#> Error in py_call_impl(callable, dots$args, dots$keywords): ValueError: not enough values to unpack (expected 4, got 3)

It is possible to unpack a variable number of arguments, using * as a prefix to a symbol. (You’ll see the * prefix again when we talk about functions)

x = (1, 2, 3)
a, *the_rest = x
#> 1
#> [2, 3]

You can also unpack nested structures:

x = ((1, 2), (3, 4))
(a, b), (c, d) = x


Dictionaries are most similar to R environments. They are a container where you can retrieve items by name, though in Python the name (called a key in Python’s parlance) does not need to be a string like in R. It can be any Python object with a hash() method (meaning, it can be almost any Python object). They can be created using syntax like {key: value}. Like Python lists, they are modified in place. Note that r_to_py() converts R named lists to dictionaries.

d = {"key1": 1,
     "key2": 2}
d2 = d
#> {'key1': 1, 'key2': 2}
#> 1
d["key3"] = 3
d2 # modified in place!
#> {'key1': 1, 'key2': 2, 'key3': 3}

Like R environments (and unlike R’s named lists), you cannot index into a dictionary with an integer to get an item at a specific index position. Dictionaries are unordered containers. (However—beginning with Python 3.7, dictionaries do preserve the item insertion order).

d = {"key1": 1, "key2": 2}
d[1] # error
#> Error in py_call_impl(callable, dots$args, dots$keywords): KeyError: 1

A container that closest matches the semantics of R’s named list is the OrderedDict, but that’s relatively uncommon in Python code so we don’t cover it further.


Sets are a container that can be used to efficiently track unique items or deduplicate lists. They are constructed using {val1, val2} (like a dictionary, but without :). Think of them as dictionary where you only use the keys. Sets have many efficient methods for membership operations, like intersection(), issubset(), union() and so on.

s = {1, 2, 3}
#> <class 'set'>
#> {1, 2, 3}
#> {1, 2, 3}

Iteration with for

The for statement in Python can be used to iterate over any kind of container.

for x in [1, 2, 3]:
#> 1
#> 2
#> 3

R has a relatively limited set of objects that can be passed to for. Python by comparison, provides an iterator protocol interface, which means that authors can define custom objects, with custom behavior that is invoked by for. (We’ll have an example for how to define a custom iterable when we get to classes). You may want to use a Python iterable from R using reticulate, so it’s helpful to peel back the syntactic sugar a little to show what the for statement is doing in Python, and how you can step through it manually.

There are two things that happen: first, an iterator is constructed from the supplied object. Then, the new iterator object is repeatedly called with next() until it is exhausted.

l = [1, 2, 3]
it = iter(l) # create an iterator object

# call `next` on the iterator until it is exhausted:
#> <list_iterator object at 0x7f48127ae640>
#> 1
#> 2
#> 3
#> Error in py_call_impl(callable, dots$args, dots$keywords): StopIteration

In R, you can use reticulate to step through an iterator the same way.

l <- r_to_py(list(1, 2, 3))
it <- as_iterator(l)

#> 1.0
#> 2.0
#> 3.0
iter_next(it, completed = "StopIteration")
#> [1] "StopIteration"

Iterating over dictionaries first requires understanding if you are iterating over the keys, values, or both. Dictionaries have methods that allow you to specify which.

d = {"key1": 1, "key2": 2}
for key in d:
#> key1
#> key2
for value in d.values():
#> 1
#> 2
for key, value in d.items():
  print(key, ":", value)
#> key1 : 1
#> key2 : 2


Comprehensions are special syntax that allow you to construct a container like a list or a dict, while also executing a small operation or single expression on each element. You can think of it as special syntax for R’s lapply.

For example:

x = [1, 2, 3]

# a list comprehension built from x, where you add 100 to each element
l = [element + 100 for element in x]

# a dict comprehension built from x, where the key is a string.
# Python's str() is like R's as.character()
#> [101, 102, 103]
d = {str(element) : element + 100
     for element in x}
#> {'1': 101, '2': 102, '3': 103}

Defining Functions with def

Python functions are defined with the def statement. The syntax for specifying function arguments and default values is very similar to R.

def my_function(name = "World"):
  print("Hello", name)

#> Hello World
#> Hello Friend

The equivalent R snippet would be

my_function <- function(name = "World") {
  cat("Hello", name, "\n")

#> Hello World
#> Hello Friend

Unlike R functions, the last value in a function is not automatically returned. Python requires an explicit return statement.

def fn():
#> None
def fn():
  return 1
#> 1

(Note for advanced R users: Python has no equivalent of R’s argument “promises”. Function argument default values are evaluated once, when the function is constructed. This can be surprising if you define a Python function with a mutable object as a default argument value, like a Python list!)

def my_func(x = []):
  x.append("was called")

#> ['was called']
#> ['was called', 'was called']
#> ['was called', 'was called', 'was called']

You can also define Python functions that take a variable number of arguments, similar to ... in R. A notable difference is that R’s ... makes no distinction between named and unnamed arguments, but Python does. In Python, prefixing a single * captures unnamed arguments, and two ** signifies that keyword arguments are captured.

def my_func(*args, **kwargs):
  print("args = ", args) # args is a tuple
  print("kwargs = ", kwargs) # kwargs is a dictionary

my_func(1, 2, 3, a = 4, b = 5, c = 6)
#> args =  (1, 2, 3)
#> kwargs =  {'a': 4, 'b': 5, 'c': 6}

Whereas the * and ** in a function definition signature pack arguments, in a function call they unpack arguments. Unpacking arguments in a function call is equivalent to using in R.

def my_func(a, b, c):
  print(a, b, c)

args = (1, 2, 3)
#> 1 2 3
kwargs = {"a": 1, "b": 2, "c": 3}
#> 1 2 3

Defining Classes with class

One could argue that in R, the preeminent unit of composition for code is the function, and in Python, it’s the class. You can be a very productive R user and never use R6, reference classes, or similar R equivalents to the object-oriented style of Python class’s.

In Python, however, understanding the basics of how class objects work is requisite knowledge, because class’s are how you organize and find methods in Python. (In contrast to R’s approach, where methods are found by dispatching from a generic). Fortunately, the basics of class’s are accessible.

Don’t be intimidated if this is your first exposure to object oriented programming. We’ll start by building up a simple Python class for demonstration purposes.

class MyClass:
  pass # `pass` means do nothing.

#> <class '__main__.MyClass'>
#> <class 'type'>
instance = MyClass()
#> <__main__.MyClass object at 0x7f4812738580>
#> <class '__main__.MyClass'>

Like the def statement, the class statement binds a new callable symbol, MyClass. First note the strong naming convention, classes are typically CamelCase, and functions are typically snake_case. After defining MyClass, you can interact with it, and see that it has type 'type'. Calling MyClass() creates a new object instance of the class, which has type 'MyClass' (ignore the __main__. prefix for now). The instance prints with its memory address, which is a strong hint that it’s common to be managing many instances of a class, and that the instance is mutable (modified-in-place by default).

In the first example, we defined an empty class, but when we inspect it we see that it already comes with a bunch of attributes (dir() in Python is equivalent to names() in R):

#> ['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__']

What are all the underscores?

Python typically indicates that something is special by wrapping the name in double underscores. A special double-underscore-wrapped token is commonly called a “dunder”. “Special” is not a technical term, it just means that the token invokes a Python language feature. Some dunder tokens are merely ways code authors can plug into specific syntactic sugars, others are values provided by the interpreter that would be otherwise hard to acquire, yet others are for extending language interfaces (e.g., the iteration protocol), and finally, a small handful of dunders are truly complicated to understand. Fortunately, as an R user looking to use some Python features through reticulate, you only need to know about a few easy-to-understand dunders.

The most common dunder method you’ll encounter when reading Python code is __init__(). This is a function that is called when the class constructor is called, that is, when a class is instantiated. It is meant to initialize the new class instance. (In very sophisticated code bases, you may also encounter classes where __new__ is also defined, this is called before __init__).

class MyClass:

  print("MyClass's definition body is being evaluated")

  def __init__(self):
    print(self, "is initializing")
#> MyClass's definition body is being evaluated
print("MyClass is finished being created")
#> MyClass is finished being created
instance = MyClass()
#> <__main__.MyClass object at 0x7f481273bbe0> is initializing
#> <__main__.MyClass object at 0x7f481273bbe0>
instance2 = MyClass()
#> <__main__.MyClass object at 0x7f4812738580> is initializing
#> <__main__.MyClass object at 0x7f4812738580>

A few things to note:

  • the class statement takes a code block that is defined by a common indentation level. The code block has the same exact semantics as any other expression that takes a code block, like if and def. The body of the class is evaluated only once, when the class constructor is first being created. Beware that any objects defined here are shared by all instances of the class!

  • __init__ is just a normal function, defined with def like any other function. Except it’s inside the class body.

  • __init__ take an argument: self. self is the class instance being initialized (note the identical memory address between self and instance). Also note that we didn’t provide self when call MyClass() to create the class instance, self was spliced into the function call by the interpreter.

  • __init__ is called each time a new instance is created.

Functions defined inside a class code block are called methods, and the important thing to know about methods is that each time they are called from a class instance, the instance is spliced into the function call as the first argument. This applies to all functions defined in a class, including dunders. (The sole exception is if the function is decorated with something like @classmethod or @staticmethod).

class MyClass:
  def a_method(self):
    print("MyClass.a_method() was called with", self)

instance = MyClass()
#> MyClass.a_method() was called with <__main__.MyClass object at 0x7f481273ff70>
MyClass.a_method()     # error, missing required argument `self`
#> Error in py_call_impl(callable, dots$args, dots$keywords): TypeError: a_method() missing 1 required positional argument: 'self'
MyClass.a_method(instance) # identical to instance.a_method()
#> MyClass.a_method() was called with <__main__.MyClass object at 0x7f481273ff70>

Other dunder’s worth knowing about are:

  • __getitem__: the function invoked when subsetting an instance with [ (Equivalent to defining a [ S3 method in R.

  • __getattr__: the function invoked when subsetting with . (Equivalent to defining a $ S3 method in R.

  • __iter__ and __next__: functions invoked by for.

  • __call__: invoked when a class instance is called like a function (e.g., instance()).

  • __bool__: invoked by if and while (equivalent to as.logical() in R, but returning only a scalar, not a vector).

  • __repr__, __str__, functions invoked for formatting and pretty printing (akin to format(), dput(), and print() methods in R).

  • __enter__ and __exit__: functions invoked by with.

  • Many built-in Python functions are just sugar for invoking the dunder. For example: calling repr(x) is identical to x.__repr__(). Other builtins that are just sugar for invoking the dunder are next(), iter(), str(), list(), dict(), bool(), dir(), hash() and more!

Iterators, revisited

Now that we have the basics of class, it’s time to revisit iterators. First, some terminology:

iterable: something that can be iterated over. Concretely, a class that defines an __iter__ method, whose job is to return an iterator.

iterator: something that iterates. Concretely, a class that defines a __next__ method, whose job is to return the next element each time it is called, and then raises a StopIteration exception once it’s exhausted.

It’s common to see classes that are both iterables and iterators, where the __iter__ method is just a stub that returns self.

Here is a custom iterable / iterator implementation of Python’s range (similar to seq in R)

class MyRange:
  def __init__(self, start, end):
    self.start = start
    self.end = end

  def __iter__(self):
    # reset our counter.
    self._index = self.start - 1
    return self

  def __next__(self):
    if self._index < self.end:
      self._index += 1 # increment
      return self._index
      raise StopIteration

for x in MyRange(1, 3):

# doing what `for` does, but manually
#> 1
#> 2
#> 3
r = MyRange(1, 3)
it = iter(r)
#> 1
#> 2
#> 3
#> Error in py_call_impl(callable, dots$args, dots$keywords): StopIteration

Defining Generators with yield.

Generators are special Python functions that contain one or more yield statements. As soon as yield is included in a code block passed to def, the semantics change substantially. You’re no longer defining a mere function, but a generator constructor! In turn, calling a generator constructor creates a generator object, which is just another type of iterator.

Here is an example:

def my_generator_constructor():
  yield 1
  yield 2
  yield 3

# At first glance it presents like a regular function
#> <function my_generator_constructor at 0x7f48127975e0>

# But calling it returns something special, a 'generator object'
#> <class 'function'>
my_generator = my_generator_constructor()
#> <generator object my_generator_constructor at 0x7f480af47f90>

# The generator object is both an iterable and an iterator
# it's __iter__ method is just a stub that returns `self`
#> <class 'generator'>
iter(my_generator) == my_generator == my_generator.__iter__()

# step through it like any other iterator
#> True
#> 1
my_generator.__next__() # next() is just sugar for calling the dunder
#> 2
#> 3
#> Error in py_call_impl(callable, dots$args, dots$keywords): StopIteration

Encountering yield is like hitting the pause button on a functions execution, it preserves the state of everything in the function body and returns control to whatever is iterating over the generator object. Calling next() on the generator object resumes execution of the function body until the next yield is encountered, or the function finishes.

Iteration closing remarks

Iteration is deeply baked into the Python language, and R users may be surprised by how things in Python are iterable, iterators, or powered by the iterator protocol under the hood. For example, the built-in map() (equivalent to R’s lapply()) yields an iterator, not a list. Similarly, a tuple comprehension like (elem for elem in x) produces an iterator. Most features dealing with files are iterators, and so on.

Any time you find an iterator inconvenient, you can materialize all the elements into a list using the Python built-in list(), or reticulate::iterate() in R. Also, if you like the readability of for, you can utilize similar semantics to Python’s for using coro::loop().

import and Modules

In R, authors can bundle their code into shareable extensions called R packages, and R users can access objects from R packages via library() or ::. In Python, authors bundle code into modules, and users access modules using import. Consider the line:

import numpy

This statement has Python go out to the file system, find an installed Python module named ‘numpy’, load it (commonly meaning: evaluate its file and construct a module type), and bind it to the symbol numpy.

The closest equivalent to this in R might be:

dplyr <- loadNamespace("dplyr")

Where are modules found?

In Python, the file system locations where modules are searched can be accessed (and modified) from the list found at sys.path. This is Python’s equivalent to R’s .libPaths(). sys.path will typically contain paths to the current working directory, the Python installation which contains the built-in standard library, administrator installed modules, user installed modules, values from environment variables like PYTHONPATH, and any modifications made directly to sys.path by other code in the current Python session (though this is relatively uncommon in practice).

import sys
#> ['', '/opt/hostedtoolcache/Python/3.9.13/x64/bin', '/opt/hostedtoolcache/Python/3.9.13/x64/lib/', '/opt/hostedtoolcache/Python/3.9.13/x64/lib/python3.9', '/opt/hostedtoolcache/Python/3.9.13/x64/lib/python3.9/lib-dynload', '/home/runner/.virtualenvs/r-reticulate/lib/python3.9/site-packages', '/home/runner/work/_temp/Library/reticulate/python', '/home/runner/.virtualenvs/r-reticulate/lib/', '/home/runner/.virtualenvs/r-reticulate/lib/python3.9', '/home/runner/.virtualenvs/r-reticulate/lib/python3.9/lib-dynload']

You can inspect where a module was loaded from by accessing the dunder __path__ or __file__ (especially useful when troubleshooting installation issues):

import os
#> '/opt/hostedtoolcache/Python/3.9.13/x64/lib/python3.9/'
#> ['/home/runner/.virtualenvs/r-reticulate/lib/python3.9/site-packages/numpy']

Once a module is loaded, you can access symbols from the module using . (equivalent to ::, or maybe $.environment, in R).

#> 1

There is also special syntax for specifying the symbol a module is bound to upon import, and for importing only some specific symbols.

import numpy        # import
import numpy as np  # import and bind to a custom symbol `np`
np is numpy         # test for identicalness, similar to identical(np, numpy)
#> True
from numpy import abs # import only `numpy.abs`, bind it to `abs`
abs is numpy.abs
#> True
from numpy import abs as abs2 # import only `numpy.abs`, bind it to `abs2`
abs2 is numpy.abs
#> True

If you’re looking for the Python equivalent of R’s library(), which makes all of a package’s exported symbols available, it might be using import with a * wildcard, though it’s relatively uncommon to do so. The * wildcard will expand to include all the symbols in module, or all the symbols listed in __all__, if it is defined.

from numpy import *

Python doesn’t make a distinction like R does between package exported and internal symbols. In Python, all module symbols are equal, though there is the naming convention that intended-to-be-internal symbols are prefixed with a single leading underscore. (Two leading underscores invoke an advanced language feature called “name mangling”, which is outside the scope of this introduction).

Integers and Floats

R users generally don’t need to be aware of the difference between integers and floating point numbers, but that’s not the case in Python. If this is your first exposure to numeric data types, here are the essentials:

  • integer types can only represent whole numbers like 1 or 2, not floating point numbers like 1.2.

  • floating-point types can represent any number, but with some degree of imprecision.

In R, writing a bare literal number like 12 produces a floating point type, whereas in Python, it produces an integer. You can produce an integer literal in R by appending an L, as in 12L. Many Python functions expect integers, and will error when provided a float.

For example, say we have a Python function that expects an integer:

def a_strict_Python_function(x):
  assert isinstance(x, int), "x is not an int"
  print("Yay! x was an int")

When calling it from R, you must be sure to call it with an integer:

py$a_strict_Python_function(3)             # error
#> Error in py_call_impl(callable, dots$args, dots$keywords): AssertionError: x is not an int
py$a_strict_Python_function(3L)            # success
py$a_strict_Python_function(as.integer(3)) # success

What about R vectors?

R is a language designed for numerical computing first. Numeric vector data types are baked deep into the R language, to the point that the language doesn’t even distinguish scalars from vectors. By comparison, numerical computing capabilities in Python are generally provided by third party packages (modules, in Python parlance).

In Python, the numpy module is most commonly used to handle contiguous arrays of data. The closest equivalent to an R numeric vector is a numpy array, or sometimes, a list of scalar numbers (some Pythonistas might argue for array.array() here, but that’s so rarely encountered in actual Python code we don’t mention it further).

Teaching the NumPy interface is beyond the scope of this primer, but it’s worth pointing out some potential tripping hazards for users accustomed to R arrays:

  • When indexing into multidimensional numpy arrays, trailing dimensions can be omitted and are implicitly treated as missing. The consequence is that iterating over arrays means iterating over the first dimension. For example, this iterates over the rows of a matrix.
import numpy as np
m = np.arange(12).reshape((3,4))
#> array([[ 0,  1,  2,  3],
#>        [ 4,  5,  6,  7],
#>        [ 8,  9, 10, 11]])
m[0, :] # first row
#> array([0, 1, 2, 3])
m[0]    # also first row
#> array([0, 1, 2, 3])
for row in m:
#> [0 1 2 3]
#> [4 5 6 7]
#> [ 8  9 10 11]
  • Many numpy operations modify the array in place! This is surprising to R users, who are used to the convenience and safety of R’s copy-on-modify semantics. Unfortunately, there is no simple scheme or naming convention you can rely on to quickly determine if a particular method modifies in-place or creates a new array copy. The only reliable way is to consult the documentation, and conduct small experiments at the reticulate::repl_python().


Decorators are just functions that take a function as an argument, and then typically returns another function. Any function can be invoked as a decorator with the @ syntax, which is just sugar for this simple action:

def my_decorator(func):
  func.x = "a decorator modified this function by adding an attribute `x`"
  return func

def my_function(): pass
my_function = my_decorator(my_function)

# @ is just fancy syntax for the above two lines
def my_function(): pass

One decorator you might encounter frequently is:

  • @property, which automatically calls a class method when the attribute is accessed (similar to makeActiveBinding() in R).

with and context management

Any object that defines __enter__ and __exit__ methods implements the “context” protocol, and can be passed to with. For example, here is a custom implementation of a context manager that temporarily changes the current working directory (equivalent to R’s withr::with_dir())

from os import getcwd, chdir

class wd_context:
  def __init__(self, wd):
    self.new_wd = wd

  def __enter__(self):
    self.original_wd = getcwd()

  def __exit__(self, *args):
    # __exit__ takes some additional argument that are commonly ignored

#> '/home/runner/work/reticulate/reticulate/vignettes'
with wd_context(".."):
  print("in the context, wd is:", getcwd())
#> in the context, wd is: /home/runner/work/reticulate/reticulate
#> '/home/runner/work/reticulate/reticulate/vignettes'

Learning More

Hopefully, this short primer to Python has provided a good foundation for confidently reading Python documentation and code, and using Python modules from R via reticulate. Of course, there is much, much more to learn about Python. Googling questions about Python reliably brings up pages of results, but not always sorted in order of most useful. Blog posts and tutorials targeting beginners can be valuable, but remember that Python’s official documentation is generally excellent, and it should be your first destination when you have questions.

To learn Python more fully, the built-in official tutorial is also excellent and comprehensive (but does require a time commitment to get value out of it)

Finally, don’t forget to solidify your understanding by conducting small experiments at the reticulate::repl_python().

Thank you for reading!