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

Lists

Dictionaries

Functions, methods, and libraries

Methods

NumPy arrays

Pandas DataFrames

The Basics

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

Loops

Functions

Individual binge drinking function

General binge drinking function

Lambda functions

Comprehensions

Mapping functions

List comprehension

Dictionary comprehension

Control flow, Loops, and Functions

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

Data types

Integers and floats

Strings

Dates (I)

Dates (II)

Advanced Pandas

Missing values

Groupby

Tidy data

Pandas

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

Seaborn

Univariate plots in seaborn

Bivariate plots in seaborn

Facet plots in seaborn

Matplotlib

Univariate and bivariate plots in matplotlib

Subfigures in matplotlib

Working with axes

Polishing up a figure

Plotting

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

NYC flights data

Load multiple data files

Explore

Visualize

Manipulating data

Recode dates

Groupby aggregates

Plots

Dummy variables

Wrap-up

Capstone

Air quality

Country timeseries

Flights (sample)

Inflammation

NYC Flights 2013

Tips

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.

Introduction to Writing Functions in R

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

Python for R Users

Instructor

Whitespace matters

if-elif-else

Let's practice!

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Control flow, Loops, and Functions - Control flow and loops