# How to Install Python in R Studio (Easy)

Below we look at how to install Python in R Studio.

For those who like using both R and Python and enjoy using R Studio as an IDE, this is a convenient way to do things.

## How to Install Python in R Studio

To install Python in R Studio just copy-paste our scripts below.

This is an R Script (not a Python Script since Python is not yet installed).

library(reticulate) version <- "3.12.0" install_python(version = version) virtualenv_create("my-python", python_version = version) use_virtualenv("my-python", required = TRUE) virtualenv_install(envname = "my-python", "matplotlib",ignore_installed = FALSE, pip_options = character() ) virtualenv_install(envname = "my-python", "numpy",ignore_installed = FALSE, pip_options = character() ) virtualenv_install(envname = "my-python", "pandas",ignore_installed = FALSE, pip_options = character() ) virtualenv_install(envname = "my-python", "scipy",ignore_installed = FALSE, pip_options = character() ) virtualenv_install(envname = "my-python", "seaborn",ignore_installed = FALSE, pip_options = character() ) virtualenv_install(envname = "my-python", "scikit-learn",ignore_installed = FALSE, pip_options = character() ) virtualenv_install(envname = "my-python", "keras",ignore_installed = FALSE, pip_options = character() ) virtualenv_install(envname = "my-python", "statsmodels",ignore_installed = FALSE, pip_options = character() ) virtualenv_install(envname = "my-python", "plotly",ignore_installed = FALSE, pip_options = character() )

This is how it looks in R Studio:

**Notes:**

You will need to install the package **reticulate** manually in R Studio for this to work (go to the **Packages** tab -> **Install**).

Note that for the Python version, please look up the latest version on the web.

We simply used 3.12.0 as an example since that’s what we currently use.

## What Packages to Install

You may not need all the packages that we show in the code.

For example, you may not need **plotly **if it’s not relevant to your work.

Below we briefly explain what each of these packages does:

### matplotlib

A plotting library for Python, used for creating static, interactive, and animated visualizations in Python.

### numpy

A fundamental package for scientific computing in Python.

It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

### pandas

A data manipulation and analysis library for Python.

Offers data structures and operations for manipulating numerical tables and time series.

### scipy

A Python-based ecosystem of open-source software for mathematics, science, and engineering.

Has various tools for optimization, integration, interpolation, eigenvalue problems, algebraic equations, and other tasks.

### seaborn

A statistical data visualization library in Python that builds on matplotlib and integrates closely with pandas data structures.

### scikit-learn

A machine learning library for Python.

Provides simple and efficient tools for data mining and data analysis.

Includes regression, classification, clustering, and dimensionality reduction.

### keras

An open-source software library that provides a Python interface for artificial neural networks.

Built on top of TensorFlow and designed for easy and fast experimentation.

### statsmodels

A Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and exploring data.

### plotly

An interactive graphing library for Python (and other languages) that lets you create and publish interactive, web-based data visualizations.

## To Setup a Python Script in R Studio

To run Python in R, you need a Python Script (be sure it’s not an R Script).

Go to the top left of your R Studio module and choose Python Script:

And you’re ready to code in Python!

For those interested, we have a sample script you can try out below, geared toward finance.

## Example of a Python Script in R

In a separate article we looked at how to model fixed-income attribution as a Python script.

We use a simple example to demonstrate the basic concept using Python.

This example assumes you have a fixed-income portfolio and market data, including bond prices, yields, durations, and convexities.

You’ll need libraries like **pandas** and **numpy** for data handling and calculations.

pip install pandas numpy

So, let’s write a basic Python script for fixed-income attribution:

import pandas as pd import numpy as np # Sample data: Replace this with your actual portfolio and market data # Assuming data contains columns: 'Bond', 'Duration', 'Convexity', 'Yield', 'Weight' portfolio_data = pd.DataFrame({ 'Bond': ['Bond1', 'Bond2', 'Bond3'], 'Duration': [5, 7, 4], 'Convexity': [60, 80, 50], 'Yield': [0.02, 0.03, 0.025], 'Weight': [0.4, 0.35, 0.25] }) # Market movements delta_yield = -0.001 # Change in yield, e.g., -10 basis points market_duration = sum(portfolio_data['Duration'] * portfolio_data['Weight']) market_convexity = sum(portfolio_data['Convexity'] * portfolio_data['Weight']) # Calculate attribution interest_rate_effect = -market_duration * delta_yield convexity_effect = 0.5 * market_convexity * delta_yield**2 total_effect = interest_rate_effect + convexity_effect # Output the results print("Interest Rate Effect:", interest_rate_effect) print("Convexity Effect:", convexity_effect) print("Total Effect on Portfolio:", total_effect) # Extend this model to include other factors like credit spread movements, sector allocation effects, and active management decisions

You will see the results in the Console output:

**Related**