课程

Python 工具箱

基础技能水平

更新时间 2025年12月

继续通过学习迭代器和列表推导式来提升你的现代数据科学技能。

免费开始课程

PythonProgramming

4小时

12 视频

46 道练习

3,800 XP

310K+

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课程描述

在本门 Python 工具箱课程中，您将继续提升更高级的 Python 技能。首先，您会学习迭代器，这类对象您已在 for 循环中接触过。接着，您将学习列表推导式，这是所有使用 Python 的数据从业者和开发者都非常实用的工具。课程最后，您将通过一个案例研究，把本课程两部分中学到的所有技术综合应用起来。视频包含实时字幕，您可以点击视频左下角的 "Show transcript" 进行查看。课程术语表位于右侧的资源部分。若要获得 CPE 学分，您需要完成课程并在合格测评中取得 70% 的成绩。您可以点击右侧的 CPE 学分提示进入测评。

先决条件

Introduction to Functions in Python

1

Using iterators in PythonLand

You'll learn all about iterators and iterables, which you have already worked with when writing for loops. You'll learn some handy functions that will allow you to effectively work with iterators. And you’ll finish the chapter with a use case that is pertinent to the world of data science and dealing with large amounts of data—in this case, data from Twitter that you will load in chunks using iterators.

Introduction to iterators

Iterators vs. Iterables

Iterating over iterables (1)

Iterating over iterables (2)

Iterators as function arguments

Playing with iterators

Using enumerate

Using * and zip to 'unzip'

Using iterators to load large files into memory

Processing large amounts of Twitter data

Extracting information for large amounts of Twitter data

Congratulations!

2

List comprehensions and generators

In this chapter, you'll build on your knowledge of iterators and be introduced to list comprehensions, which allow you to create complicated lists—and lists of lists—in one line of code! List comprehensions can dramatically simplify your code and make it more efficient, and will become a vital part of your Python toolbox. You'll then learn about generators, which are extremely helpful when working with large sequences of data that you may not want to store in memory, but instead generate on the fly.

List comprehensions

Write a basic list comprehension

List comprehension over iterables

Writing list comprehensions

Nested list comprehensions

Advanced comprehensions

Using conditionals in comprehensions (1)

Using conditionals in comprehensions (2)

Dict comprehensions

Introduction to generator expressions

List comprehensions vs. generators

Write your own generator expressions

Changing the output in generator expressions

Build a generator

Wrapping up comprehensions and generators.

List comprehensions for time-stamped data

Conditional list comprehensions for time-stamped data

3

Bringing it all together!

This chapter will allow you to apply your newly acquired skills toward wrangling and extracting meaningful information from a real-world dataset—the World Bank's World Development Indicators. You'll have the chance to write your own functions and list comprehensions as you work with iterators and generators to solidify your Python chops.

Welcome to the case study!

Zipping dictionaries

Writing a function to help you

Using a list comprehension

Turning this all into a DataFrame

Using Python generators for streaming data

Processing data in chunks (1)

Writing a generator to load data in chunks (2)

Writing a generator to load data in chunks (3)

Using pandas' read_csv iterator for streaming data

Writing an iterator to load data in chunks (1)

Writing an iterator to load data in chunks (2)

Writing an iterator to load data in chunks (3)

Writing an iterator to load data in chunks (4)

Writing an iterator to load data in chunks (5)

Final thoughts

Python 工具箱

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