课程

高效编写 Python 代码

中级技能水平

更新时间 2026年1月

学习编写高效代码，快速执行并巧妙分配资源，避免不必要的开销。

免费开始课程

PythonProgramming

4小时

15 视频

52 道练习

4,000 XP

150K+

成就证明

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课程描述

作为一名数据科学家，您的大部分时间应该用来从数据中提炼可操作的洞见，而不是等待代码运行结束。高效的 Python 代码可以缩短运行时间、节省计算资源，从而让您把精力投入到更有价值的工作中。本课程将带您学习如何利用 Python 的内置数据结构、函数和模块，编写更简洁、更快速、更高效的代码。我们将探讨如何对代码进行计时与性能分析，以定位瓶颈。随后，您将练习使用 Python 标准库、NumPy 和 pandas 消除这些瓶颈，并改进不良的设计模式。完成本课程后，您将具备开始编写高效 Python 代码所需的工具！视频包含实时字幕，您可以点击视频左下角的 "Show transcript" 展开查看。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% 的分数。您可以点击右侧的 CPE 学分提示进入测评。

先决条件

Data Types in Python Python Toolbox

1

Foundations for efficiencies

In this chapter, you'll learn what it means to write efficient Python code. You'll explore Python's Standard Library, learn about NumPy arrays, and practice using some of Python's built-in tools. This chapter builds a foundation for the concepts covered ahead.

Pop quiz: what is efficient

A taste of things to come

Zen of Python

Building with built-ins

Built-in practice: range()

Built-in practice: enumerate()

Built-in practice: map()

The power of NumPy arrays

Practice with NumPy arrays

Bringing it all together: Festivus!

2

Timing and profiling code

In this chapter, you will learn how to gather and compare runtimes between different coding approaches. You'll practice using the line_profiler and memory_profiler packages to profile your code base and spot bottlenecks. Then, you'll put your learnings to practice by replacing these bottlenecks with efficient Python code.

Examining runtime

Using %timeit: your turn!

Using %timeit: specifying number of runs and loops

Using %timeit: formal name or literal syntax

Code profiling for runtime

Pop quiz: steps for using %lprun

Using %lprun: spot bottlenecks

Using %lprun: fix the bottleneck

Code profiling for memory usage

Pop quiz: steps for using %mprun

Using %mprun: Hero BMI

Using %mprun: Hero BMI 2.0

Bringing it all together: Star Wars profiling

3

Gaining efficiencies

This chapter covers more complex efficiency tips and tricks. You'll learn a few useful built-in modules for writing efficient code and practice using set theory. You'll then learn about looping patterns in Python and how to make them more efficient.

Efficiently combining, counting, and iterating

Combining Pokémon names and types

Counting Pokémon from a sample

Combinations of Pokémon

Comparing Pokédexes

Searching for Pokémon

Gathering unique Pokémon

Eliminating loops

Gathering Pokémon without a loop

Pokémon totals and averages without a loop

Writing better loops

One-time calculation loop

Holistic conversion loop

Bringing it all together: Pokémon z-scores

4

Basic pandas optimizations

This chapter offers a brief introduction on how to efficiently work with pandas DataFrames. You'll learn the various options you have for iterating over a DataFrame. Then, you'll learn how to efficiently apply functions to data stored in a DataFrame.

Intro to pandas DataFrame iteration

Iterating with .iterrows()

Run differentials with .iterrows()

Another iterator method: .itertuples()

Iterating with .itertuples()

Run differentials with .itertuples()

pandas alternative to looping

Analyzing baseball stats with .apply()

Settle a debate with .apply()

Optimal pandas iterating

Replacing .iloc with underlying arrays

Bringing it all together: Predict win percentage

Congratulations!

高效编写 Python 代码

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