R python что это

R Interface to Python

The reticulate package provides a comprehensive set of tools for interoperability between Python and R. The package includes facilities for:

Calling Python from R in a variety of ways including R Markdown, sourcing Python scripts, importing Python modules, and using Python interactively within an R session.

Translation between R and Python objects (for example, between R and Pandas data frames, or between R matrices and NumPy arrays).

Flexible binding to different versions of Python including virtual environments and Conda environments.

Reticulate embeds a Python session within your R session, enabling seamless, high-performance interoperability. If you are an R developer that uses Python for some of your work or a member of data science team that uses both languages, reticulate can dramatically streamline your workflow!

Getting started

Installation

Install the reticulate package from CRAN as follows:

Python version

By default, reticulate uses the version of Python found on your PATH (i.e. Sys.which(«python») ).

The use_python() function enables you to specify an alternate version, for example:

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

See the article on Python Version Configuration for additional details.

Python packages

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

Calling Python

There are a variety of ways to integrate Python code into your R projects:

Python in R Markdown — A new Python language engine for R Markdown that supports bi-directional communication between R and Python (R chunks can access Python objects and vice-versa).

Importing Python modules — The import() function enables you to import any Python module and call it’s functions directly from R.

Sourcing Python scripts — The source_python() function enables you to source a Python script the same way you would source() an R script (Python functions and objects defined within the script become directly available to the R session).

Python REPL — The repl_python() function creates an interactive Python console within R. Objects you create within Python are available to your R session (and vice-versa).

Each of these techniques is explained in more detail below.

Python in R Markdown

The reticulate package includes a Python engine for R Markdown with the following features:

Run Python chunks in a single Python session embedded within your R session (shared variables/state between Python chunks)

Printing of Python output, including graphical output from matplotlib.

Access to objects created within Python chunks from R using the py object (e.g. py$x would access an x variable created within Python from R).

Access to objects created within R chunks from Python using the r object (e.g. r.x would access to x variable created within R from Python)

Built in conversion for many Python object types is provided, including NumPy arrays and Pandas data frames. For example, you can use Pandas to read and manipulate data then easily plot the Pandas data frame using ggplot2:

Note that the reticulate Python engine is enabled by default within R Markdown whenever reticulate is installed.

See the R Markdown Python Engine documentation for additional details.

Importing Python modules

You can use the import() function to import any Python module and call it from R. For example, this code imports the Python os module and calls the listdir() function:

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

Imported Python modules support code completion and inline help:

See Calling Python from R for additional details on interacting with Python objects from within R.

Sourcing Python scripts

You can source any Python script just as you would source an R script using the source_python() function. For example, if you had the following Python script flights.py:

Then you can source the script and call the read_flights() function as follows:

See the source_python() documentation for additional details on sourcing Python code.

Python REPL

If you want to work with Python interactively you can call the repl_python() function, which provides a Python REPL embedded within your R session. Objects created within the Python REPL can be accessed from R using the py object exported from reticulate. For example:

Enter exit within the Python REPL to return to the R prompt.

Note that Python code can also access objects from within the R session using the r object (e.g. r.flights ). See the repl_python() documentation for additional details on using the embedded Python REPL.

Type conversions

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

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

Learning more

The following articles cover the various aspects of using reticulate:

Calling Python from R — Describes the various ways to access Python objects from R as well as functions available for more advanced interactions and conversion behavior.

R Markdown Python Engine — Provides details on using Python chunks within R Markdown documents, including how call Python code from R chunks and vice-versa.

Python Version Configuration — Describes facilities for determining which version of Python is used by reticulate within an R session.

Installing Python Packages — Documentation on installing Python packages from PyPI or Conda, and managing package installations using virtualenvs and Conda environments.

Using reticulate in an R Package — Guidelines and best practices for using reticulate in an R package.

Arrays in R and Python — Advanced discussion of the differences between arrays in R and Python and the implications for conversion and interoperability.

Python Primer — Introduction to Python for R users.

Why reticulate?

From the Wikipedia article on the reticulated python:

The reticulated python is a species of python found in Southeast Asia. They are the world’s longest snakes and longest reptiles…The specific name, reticulatus, is Latin meaning “net-like”, or reticulated, and is a reference to the complex colour pattern.

From the Merriam-Webster definition of reticulate:

1: resembling a net or network; especially : having veins, fibers, or lines crossing a reticulate leaf. 2: being or involving evolutionary change dependent on genetic recombination involving diverse interbreeding populations.

The package enables you to reticulate Python code into R, creating a new breed of project that weaves together the two languages.

What exactly do "u" and "r" string prefixes do, and what are raw string literals?

While asking this question, I realized I didn’t know much about raw strings. For somebody claiming to be a Django trainer, this sucks.

I know what an encoding is, and I know what u» alone does since I get what is Unicode.

But what does r» do exactly? What kind of string does it result in?

And above all, what the heck does ur» do?

Finally, is there any reliable way to go back from a Unicode string to a simple raw string?

Ah, and by the way, if your system and your text editor charset are set to UTF-8, does u» actually do anything?

7 Answers 7

There’s not really any «raw string«; there are raw string literals, which are exactly the string literals marked by an ‘r’ before the opening quote.

A «raw string literal» is a slightly different syntax for a string literal, in which a backslash, \ , is taken as meaning «just a backslash» (except when it comes right before a quote that would otherwise terminate the literal) — no «escape sequences» to represent newlines, tabs, backspaces, form-feeds, and so on. In normal string literals, each backslash must be doubled up to avoid being taken as the start of an escape sequence.

This syntax variant exists mostly because the syntax of regular expression patterns is heavy with backslashes (but never at the end, so the «except» clause above doesn’t matter) and it looks a bit better when you avoid doubling up each of them — that’s all. It also gained some popularity to express native Windows file paths (with backslashes instead of regular slashes like on other platforms), but that’s very rarely needed (since normal slashes mostly work fine on Windows too) and imperfect (due to the «except» clause above).

r’. ‘ is a byte string (in Python 2.*), ur’. ‘ is a Unicode string (again, in Python 2.*), and any of the other three kinds of quoting also produces exactly the same types of strings (so for example r’. ‘ , r»’. »’ , r». » , r»»». «»» are all byte strings, and so on).

Not sure what you mean by «going back» — there is no intrinsically back and forward directions, because there’s no raw string type, it’s just an alternative syntax to express perfectly normal string objects, byte or unicode as they may be.

And yes, in Python 2.*, u’. ‘ is of course always distinct from just ‘. ‘ — the former is a unicode string, the latter is a byte string. What encoding the literal might be expressed in is a completely orthogonal issue.

E.g., consider (Python 2.6):

The Unicode object of course takes more memory space (very small difference for a very short string, obviously ;-).

Python Raw Strings

Summary: in this tutorial, you will learn about Python raw strings and how to use them to handle strings that treat backslashes as literal characters.

Introduction to the Python raw strings

In Python, when you prefix a string with the letter r or R such as r’. ‘ and R’. ‘ , that string becomes a raw string. Unlike a regular string, a raw string treats the backslashes ( \ ) as literal characters.

Raw strings are useful when you deal with strings that have many backslashes, for example, regular expressions or directory paths on Windows.

To represent special characters such as tabs and newlines, Python uses the backslash ( \ ) to signify the start of an escape sequence. For example:

However, raw strings treat the backslash ( \ ) as a literal character. For example:

A raw string is like its regular string with the backslash ( \ ) represented as double backslashes ( \\ ):

In a regular string, Python counts an escape sequence as a single character:

However, in a raw string, Python counts the backslash ( \ ) as one character:

Since the backslash ( \ ) escapes the single quote ( ‘ ) or double quotes ( » ), a raw string cannot end with an odd number of backslashes.

Use raw strings to handle file path on Windows

Windows OS uses backslashes to separate paths. For example:

If you use this path as a regular string, Python will issue a number of errors:

Python treats \u in the path as a Unicode escape but couldn’t decode it.

Now, if you escape the first backslash, you’ll have other issues:

In this example, the \t is a tab and \n is the new line.

To make it easy, you can turn the path into a raw string like this:

Convert a regular string into a raw string

To convert a regular string into a raw string, you use the built-in repr() function. For example:

Note that the result raw string has the quote at the beginning and end of the string. To remove them, you can use slices:

R python что это

In this article, we will see how to take care of a backslash combined with certain alphabet forms literal characters which can change the entire meaning of the string using Python. Here, we will see the various ways to use the string text as it is without its meaning getting changed.

When to use raw strings in Python ?

Raw strings are particularly useful when working with regular expressions, as they allow you to specify patterns that may contain backslashes without having to escape them. They are also useful when working with file paths, as they allow you to specify paths that contain backslashes without having to escape them. Raw strings can also be useful when working with strings that contain characters that are difficult to type or read, such as newline characters or tabs. In general, raw strings are useful anytime you need to specify a string that contains characters that have special meaning in Python, such as backslashes, and you want to specify those characters literally without having to escape them.

Example

Count occurrences of escape sequence in string

In this example, we will find the length of the string by using the Python len() function. Then, we replace backslash ( \ ) with double backslashes ( \\ ) in the string and now find the length of the update string. Later, we find the difference between the lengths of the updated string and the original string to calculate the count of escape sequences in the string.