课程

Python 中级

基础技能水平

更新时间 2026年1月

通过使用 Matplotlib 创建可视化并用 pandas 操作 DataFrame，提升你的数据科学技能。

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PythonProgramming

4小时

18 视频

87 道练习

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课程描述

提升你的 Python 技能

学习 Python 对任何有志于从事数据科学的人来说都至关重要。学习使用 Matplotlib 的函数可视化真实数据，并熟悉字典和 pandas DataFrame 等数据结构。这门四小时的中级课程将帮助你在现有的 Python 技能基础上继续提升，并探索新的 Python 应用和函数，扩展你的技能组合，帮助你更高效地工作。

学习使用 Python 字典和 pandas

字典提供了 Python 列表的另一种替代方案，而 pandas 数据框是处理表格数据最流行的方式。在本课程的第二章中，你将了解如何创建和操作数据集，以及如何使用这些结构访问它们。整个课程中的实践将帮助你在各个领域建立信心。

探索 Python 布尔逻辑和 Python 循环

在本课程的后半部分，你将学习逻辑、控制流、筛选和循环。这些函数用于控制 Python 程序中的决策过程，并帮助你对数据执行更多操作，包括重复语句。你将通过使用黑客统计来计算你赢得赌注的概率，从而完成这门课程。

完成所有章节后，您将能够在工作、新职业或个人项目中运用新学到的技能，并为进一步学习更高级的 Python 做好准备。

先决条件

Introduction to Python

1

Matplotlib

Data visualization is a key skill for aspiring data scientists. Matplotlib makes it easy to create meaningful and insightful plots. In this chapter, you’ll learn how to build various types of plots, and customize them to be more visually appealing and interpretable.

Basic plots with Matplotlib

Line plot (1)

Line Plot (2): Interpretation

Line plot (3)

Scatter Plot (1)

Scatter plot (2)

Build a histogram (1)

Build a histogram (2): bins

Build a histogram (3): compare

Choose the right plot (1)

Choose the right plot (2)

Customization

Additional Customizations

Interpretation

2

Dictionaries & Pandas

Learn about the dictionary, an alternative to the Python list, and the pandas DataFrame, the de facto standard to work with tabular data in Python. You will get hands-on practice with creating and manipulating datasets, and you’ll learn how to access the information you need from these data structures.

Dictionaries, Part 1

Motivation for dictionaries

Create dictionary

Access dictionary

Dictionaries, Part 2

Dictionary Manipulation (1)

Dictionary Manipulation (2)

Dictionariception

Pandas, Part 1

Dictionary to DataFrame (1)

Dictionary to DataFrame (2)

CSV to DataFrame (1)

CSV to DataFrame (2)

Pandas, Part 2

Square Brackets (1)

Square Brackets (2)

loc and iloc (1)

loc and iloc (2)

loc and iloc (3)

3

Logic, Control Flow and Filtering

Boolean logic is the foundation of decision-making in Python programs. Learn about different comparison operators, how to combine them with Boolean operators, and how to use the Boolean outcomes in control structures. You'll also learn to filter data in pandas DataFrames using logic.

Comparison Operators

Greater and less than

Compare arrays

Boolean Operators

and, or, not (1)

and, or, not (2)

Boolean operators with NumPy

if, elif, else

Customize further: elif

Filtering pandas DataFrames

Driving right (1)

Driving right (2)

Cars per capita (1)

Cars per capita (2)

4

Loops

There are several techniques you can use to repeatedly execute Python code. While loops are like repeated if statements, the for loop iterates over all kinds of data structures. Learn all about them in this chapter.

while: warming up

Basic while loop

Add conditionals

Loop over a list

Indexes and values (1)

Indexes and values (2)

Loop over list of lists

Loop Data Structures Part 1

Loop over dictionary

Loop over NumPy array

Loop Data Structures Part 2

Loop over DataFrame (1)

Loop over DataFrame (2)

Add column (1)

Add column (2)

5

Case Study: Hacker Statistics

This chapter will allow you to apply all the concepts you've learned in this course. You will use hacker statistics to calculate your chances of winning a bet. Use random number generators, loops, and Matplotlib to gain a competitive edge!

Random Numbers

Random float

Roll the dice

Determine your next move

Random Walk

The next step

How low can you go?

Visualize the walk

Distribution

Simulate multiple walks

Visualize all walks

Implement clumsiness

Plot the distribution

Calculate the odds

Python 中级

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