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Python for R Users

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

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5 Hours15 Videos57 Exercises12,904 Learners

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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.
  1. 1

    The Basics

    Free

    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.

    Play Chapter Now
    Introduction
    50 xp
    Assignment and data types
    100 xp
    Arithmetic with strings
    100 xp
    Containers - lists and dictionaries
    50 xp
    Lists
    100 xp
    Dictionaries
    100 xp
    Functions, methods, and libraries
    50 xp
    Methods
    100 xp
    NumPy arrays
    100 xp
    Pandas DataFrames
    100 xp
  2. 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!

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    Control flow and loops
    50 xp
    Control flow
    100 xp
    Loops
    100 xp
    Functions
    50 xp
    Individual binge drinking function
    100 xp
    General binge drinking function
    100 xp
    Lambda functions
    100 xp
    Comprehensions
    50 xp
    Mapping functions
    100 xp
    List comprehension
    100 xp
    Dictionary comprehension
    100 xp
  3. 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.

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    Selecting data in pandas
    50 xp
    Selecting columns
    100 xp
    Selecting rows
    100 xp
    Selecting rows and columns
    100 xp
    Data types
    50 xp
    Integers and floats
    100 xp
    Strings
    100 xp
    Category
    100 xp
    Dates (I)
    100 xp
    Dates (II)
    100 xp
    Advanced Pandas
    50 xp
    Missing values
    100 xp
    Groupby
    100 xp
    Tidy data
    100 xp
  4. 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.

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    Plotting directly using pandas
    50 xp
    Univariate plots in pandas
    100 xp
    Bivariate plots in pandas
    100 xp
    Seaborn
    50 xp
    Univariate plots in seaborn
    100 xp
    Bivariate plots in seaborn
    100 xp
    Facet plots in seaborn
    100 xp
    Matplotlib
    50 xp
    Univariate and bivariate plots in matplotlib
    100 xp
    Subfigures in matplotlib
    100 xp
    Working with axes
    100 xp
    Polishing up a figure
    100 xp
  5. 5

    Capstone

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

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    NYC flights data
    50 xp
    Load multiple data files
    100 xp
    Explore
    100 xp
    Visualize
    100 xp
    Manipulating data
    50 xp
    Recode dates
    100 xp
    Groupby aggregates
    100 xp
    Plots
    100 xp
    Dummy variables
    100 xp
    Wrap-up
    50 xp

Datasets

Air qualityCountry timeseriesFlights (sample)InflammationNYC Flights 2013Tips

Collaborators

Hugo Bowne-Anderson
Eunkyung Park
Sumedh Panchadhar

Prerequisites

Introduction to Writing Functions in R
Daniel Chen HeadshotDaniel Chen

Data Science Consultant at Lander Analytics

Daniel is a Software Carpentry instructor and a doctoral student in Genetics, Bioinformatics, and Computational Biology at Virginia Tech, where he works in the Social and Decision Analytics Laboratory under the Biocomplexity Institute. He received his MPH at the Mailman School of Public Health in Epidemiology and is interested in integrating hospital data in order to perform predictive health analytics and build clinical support tools for clinicians. An advocate of open science, he aspires to bridge data science with epidemiology and health care.
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