课程

Python for R Users

中级技能水平

更新时间 2023年3月

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

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PythonProgramming5 小时15 视频57 练习4,950 经验值15,737成就声明

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课程描述

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.

先决条件

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

50 经验值

Assignment and data types

100 经验值

Arithmetic with strings

100 经验值

Containers - lists and dictionaries

50 经验值

100 经验值

Dictionaries

100 经验值

Functions, methods, and libraries

50 经验值

100 经验值

NumPy arrays

100 经验值

Pandas DataFrames

100 经验值

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

50 经验值

Control flow

100 经验值

100 经验值

50 经验值

Individual binge drinking function

100 经验值

General binge drinking function

100 经验值

Lambda functions

100 经验值

Comprehensions

50 经验值

Mapping functions

100 经验值

List comprehension

100 经验值

Dictionary comprehension

100 经验值

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

50 经验值

Selecting columns

100 经验值

Selecting rows

100 经验值

Selecting rows and columns

100 经验值

50 经验值

Integers and floats

100 经验值

100 经验值

100 经验值

100 经验值

100 经验值

Advanced Pandas

50 经验值

Missing values

100 经验值

100 经验值

100 经验值

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

50 经验值

Univariate plots in pandas

100 经验值

Bivariate plots in pandas

100 经验值

50 经验值

Univariate plots in seaborn

100 经验值

Bivariate plots in seaborn

100 经验值

Facet plots in seaborn

100 经验值

50 经验值

Univariate and bivariate plots in matplotlib

100 经验值

Subfigures in matplotlib

100 经验值

Working with axes

100 经验值

Polishing up a figure

100 经验值

5

Capstone

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

NYC flights data

50 经验值

Load multiple data files

100 经验值

100 经验值

100 经验值

Manipulating data

50 经验值

Recode dates

100 经验值

Groupby aggregates

100 经验值

100 经验值

Dummy variables

100 经验值

50 经验值

Python for R Users

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