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.
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!
x.append(4)
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().
You can index into lists with integers using trailing
[], but note that indexing is 0-based.
x = [1, 2, 3]
x[0]
#> 1
x[1]
#> 2
x[2]
#> 3
try:
x[3]
except Exception as e:
print(e)
#> list index out of rangeWhen indexing, negative numbers count from the end of the container.
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
type(x)
#> <class 'tuple'>
len(x)
#> 2
x
#> (1, 2)
x = (1,) # tuple of length 1
type(x)
#> <class 'tuple'>
len(x)
#> 1
x
#> (1,)
x = () # tuple of length 0
print(f"{type(x) = }; {len(x) = }; {x = }")
#> type(x) = <class 'tuple'>; len(x) = 0; x = ()
# example of an interpolated string literals
x = 1, 2 # also a tuple
type(x)
#> <class 'tuple'>
len(x)
#> 2
x = 1, # beware a single trailing comma! This is a tuple!
type(x)
#> <class 'tuple'>
len(x)
#> 1Tuples 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:
(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 = 2If 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
#> ValueError: too many values to unpack (expected 2)
a, b, c, d = x # error, x has not enough values to unpack
#> 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)
You can also unpack nested structures:
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
d
#> {'key1': 1, 'key2': 2}
d["key1"]
#> 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).
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.
for
The for statement in Python can be used to iterate over
any kind of container.
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
it
#> <list_iterator object at 0x1402267a0>
# call `next` on the iterator until it is exhausted:
next(it)
#> 1
next(it)
#> 2
next(it)
#> 3
next(it)
#> StopIterationIn R, you can use reticulate to step through an iterator the same way.
library(reticulate)
l <- r_to_py(list(1, 2, 3))
it <- as_iterator(l)
iter_next(it)
#> 1.0
iter_next(it)
#> 2.0
iter_next(it)
#> 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:
print(key)
#> key1
#> key2
for value in d.values():
print(value)
#> 1
#> 2
for key, value in d.items():
print(key, ":", value)
#> key1 : 1
#> key2 : 2Comprehensions 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]
l
#> [101, 102, 103]
# a dict comprehension built from x, where the key is a string.
# Python's str() is like R's as.character()
d = {str(element) : element + 100
for element in x}
d
#> {'1': 101, '2': 102, '3': 103}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)
my_function()
#> Hello World
my_function("Friend")
#> Hello FriendThe equivalent R snippet would be
my_function <- function(name = "World") {
cat("Hello", name, "\n")
}
my_function()
#> Hello World
my_function("Friend")
#> Hello FriendUnlike R functions, the last value in a function is not automatically returned. Python requires an explicit return statement.
(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")
print(x)
my_func()
#> ['was called']
my_func()
#> ['was called', 'was called']
my_func()
#> ['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 do.call() in R.
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.
MyClass
#> <class '__main__.MyClass'>
type(MyClass)
#> <class 'type'>
instance = MyClass()
instance
#> <__main__.MyClass object at 0x14023b260>
type(instance)
#> <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):
dir(MyClass)
#> ['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__']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 0x140266330> is initializing
print(instance)
#> <__main__.MyClass object at 0x140266330>
instance2 = MyClass()
#> <__main__.MyClass object at 0x11e3ad490> is initializing
print(instance2)
#> <__main__.MyClass object at 0x11e3ad490>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()
instance.a_method()
#> MyClass.a_method() was called with <__main__.MyClass object at 0x11e3c7f20>
MyClass.a_method() # error, missing required argument `self`
#> TypeError: MyClass.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 0x11e3c7f20>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!
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
else:
raise StopIteration
for x in MyRange(1, 3):
print(x)
#> 1
#> 2
#> 3
# doing what `for` does, but manually
r = MyRange(1, 3)
it = iter(r)
next(it)
#> 1
next(it)
#> 2
next(it)
#> 3
next(it)
#> StopIterationyield.
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
my_generator_constructor
#> <function my_generator_constructor at 0x1402579c0>
type(my_generator_constructor)
#> <class 'function'>
# But calling it returns something special, a 'generator object'
my_generator = my_generator_constructor()
my_generator
#> <generator object my_generator_constructor at 0x11e3ff530>
type(my_generator)
#> <class 'generator'>
# The generator object is both an iterable and an iterator
# it's __iter__ method is just a stub that returns `self`
iter(my_generator) == my_generator == my_generator.__iter__()
#> True
# step through it like any other iterator
next(my_generator)
#> 1
my_generator.__next__() # next() is just sugar for calling the dunder
#> 2
next(my_generator)
#> 3
next(my_generator)
#> StopIterationEncountering 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 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:
This statement has Python go out to the file system, find an
installed Python module named ‘numpy’, load it (commonly meaning:
evaluate its __init__.py 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")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
sys.path
#> ['', '/Users/tomasz/.pyenv/versions/3.12.4/bin', '/Users/tomasz/.pyenv/versions/3.12.4/lib/python312.zip', '/Users/tomasz/.pyenv/versions/3.12.4/lib/python3.12', '/Users/tomasz/.pyenv/versions/3.12.4/lib/python3.12/lib-dynload', '/Users/tomasz/.virtualenvs/r-reticulate/lib/python3.12/site-packages', '/Users/tomasz/github/rstudio/reticulate/inst/python', '/Users/tomasz/.virtualenvs/r-reticulate/lib/python312.zip', '/Users/tomasz/.virtualenvs/r-reticulate/lib/python3.12', '/Users/tomasz/.virtualenvs/r-reticulate/lib/python3.12/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
os.__file__
#> '/Users/tomasz/.virtualenvs/r-reticulate/lib/python3.12/os.py'
numpy.__path__
#> ['/Users/tomasz/.virtualenvs/r-reticulate/lib/python3.12/site-packages/numpy']Once a module is loaded, you can access symbols from the module using
. (equivalent to ::, or maybe
$.environment, in R).
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
#> TrueIf 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.
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).
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:
library(reticulate)
py$a_strict_Python_function(3) # error
#> x is not an int
py$a_strict_Python_function(3L) # success
#> Yay! x was an int
py$a_strict_Python_function(as.integer(3)) # success
#> Yay! x was an intR 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:
import numpy as np
m = np.arange(12).reshape((3,4))
m
#> 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:
print(row)
#> [0 1 2 3]
#> [4 5 6 7]
#> [ 8 9 10 11]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
@my_decorator
def my_function(): passOne 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()
chdir(self.new_wd)
def __exit__(self, *args):
# __exit__ takes some additional argument that are commonly ignored
chdir(self.original_wd)
getcwd()
#> '/Users/tomasz/github/rstudio/reticulate/vignettes'
with wd_context(".."):
print("in the context, wd is:", getcwd())
#> in the context, wd is: /Users/tomasz/github/rstudio/reticulate
getcwd()
#> '/Users/tomasz/github/rstudio/reticulate/vignettes'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.
https://docs.Python.org/3/library/index.html
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) https://docs.Python.org/3/tutorial/index.html
Finally, don’t forget to solidify your understanding by conducting
small experiments at the reticulate::repl_python().
Thank you for reading!