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Course

Python for R Users

IntermediateSkill Level

4.7+

Updated 03/2023

This course is for R users who want to get up to speed with Python!

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PythonProgramming

5 hr

15 videos

57 Exercises

4,950 XP

15,846

Statement of Accomplishment

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Course Description

Python and R have seen immense growth in popularity in the "Machine Learning Age". They both are high-level languages that are easy to learn and write. The language you use will depend on your background and field of study and work. R is a language made by and for statisticians, whereas Python is a more general purpose programming language. Regardless of the background, there will be times when a particular algorithm is implemented in one language and not the other, a feature is better documented, or simply, the tutorial you found online uses Python instead of R.In either case, this would require the R user to work in Python to get his/her work done, or try to understand how something is implemented in Python for it to be translated into R. This course helps you cross the R-Python language barrier.

Prerequisites

Introduction to Writing Functions in R

1

The Basics

Learn about some of the most important data types (integers, floats, strings, and booleans) and data structures (lists, dictionaries, numpy arrays, and pandas DataFrames) in Python and how they compare to the ones in R.

Introduction

Assignment and data types

Arithmetic with strings

Containers - lists and dictionaries

Dictionaries

Functions, methods, and libraries

NumPy arrays

Pandas DataFrames

2

Control flow, Loops, and Functions

This chapter covers control flow statements (if-else if-else), for loops and shows you how to write your own functions in Python!

Control flow and loops

Control flow

Individual binge drinking function

General binge drinking function

Lambda functions

Comprehensions

Mapping functions

List comprehension

Dictionary comprehension

3

Pandas

In this chapter you will learn more about one of the most important Python libraries, Pandas. In addition to DataFrames, pandas provides several data manipulation functions and methods.

Selecting data in pandas

Selecting columns

Selecting rows

Selecting rows and columns

Integers and floats

Advanced Pandas

Missing values

4

Plotting

You will learn about the rich ecosystem of visualization libraries in Python. This chapter covers matplotlib, the core visualization library in Python along with the pandas and seaborn libraries.

Plotting directly using pandas

Univariate plots in pandas

Bivariate plots in pandas

Univariate plots in seaborn

Bivariate plots in seaborn

Facet plots in seaborn

Univariate and bivariate plots in matplotlib

Subfigures in matplotlib

Working with axes

Polishing up a figure

5

Capstone

As a final capstone, you will apply your Python skills on the NYC Flights 2013 dataset.

NYC flights data

Load multiple data files

Manipulating data

Recode dates

Groupby aggregates

Dummy variables

Python for R Users

Course
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*4.7

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Toan

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Constantin

3 weeks ago

Jacklyn

3 weeks ago

Very easy to understand, useful, practical course.

Zoey Lu

4 weeks ago

Julio

5 weeks ago

Toan

Constantin

"Very easy to understand, useful, practical course."

Jacklyn

FAQs

Is Python for R Users suitable for someone with no R experience?

No. This course is designed for people who already know R and want to learn Python by drawing parallels. You need experience with R, the tidyverse, and writing functions in R.

What Python data structures and libraries are covered?

You will learn about lists, dictionaries, NumPy arrays, and pandas DataFrames, plus visualization with matplotlib, pandas plotting, and seaborn.

Does the course include a hands-on capstone project?

Yes. The final chapter is a capstone where you apply all your new Python skills to analyze the NYC Flights 2013 dataset.

How does the course teach Python control flow coming from an R background?

Chapter 2 covers if-else statements, for loops, and function definitions in Python, with direct comparisons to how these constructs work in R.

How long should I expect to spend on this course?

The course is 5 chapters with 57 exercises. Most learners finish in about 4 to 5 hours, making it a manageable transition from R to Python.

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