课程

Matplotlib 数据可视化入门

基础技能水平

更新时间 2025年6月

学习如何使用 Matplotlib 创建、自定义并分享数据可视化。

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PythonData Visualization

4小时

14 视频

44 道练习

3,600 XP

230K+

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课程描述

通过图表和图形可视化数据，可以揭示数据中的模式并获得洞见。好的可视化还能帮助您向他人传达数据，对于数据分析师和其他数据使用者都很实用。在本课程中，您将学习如何使用 Matplotlib，这是一款功能强大的 Python 数据可视化库。Matplotlib 提供了构建不同类型数据集的丰富可视化所需的基础组件。您将学习如何为不同类型的数据创建可视化，并了解如何自定义、自动化以及共享这些可视化。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评估中达到 70% 的得分。您可以点击右侧的 CPE 学分提示进入评估。

先决条件

Introduction to Python

1

Introduction to Matplotlib

This chapter introduces the Matplotlib visualization library and demonstrates how to use it with data.

Introduction to data visualization with Matplotlib

Using the matplotlib.pyplot interface

Adding data to an Axes object

Customizing your plots

Customizing data appearance

Customizing axis labels and adding titles

Small multiples

Creating a grid of subplots

Creating small multiples with plt.subplots

Small multiples with shared y axis

2

Plotting time-series

Time series data is data that is recorded. Visualizing this type of data helps clarify trends and illuminates relationships between data.

Plotting time-series data

Read data with a time index

Plot time-series data

Using a time index to zoom in

Plotting time-series with different variables

Plotting two variables

Defining a function that plots time-series data

Using a plotting function

Annotating time-series data

Annotating a plot of time-series data

Plotting time-series: putting it all together

3

Quantitative comparisons and statistical visualizations

Visualizations can be used to compare data in a quantitative manner. This chapter explains several methods for quantitative visualizations.

Quantitative comparisons: bar-charts

Stacked bar chart

Quantitative comparisons: histograms

Creating histograms

"Step" histogram

Statistical plotting

Adding error-bars to a bar chart

Adding error-bars to a plot

Creating boxplots

Quantitative comparisons: scatter plots

Simple scatter plot

Encoding time by color

4

Sharing visualizations with others

This chapter shows you how to share your visualizations with others: how to save your figures as files, how to adjust their look and feel, and how to automate their creation based on input data.

Preparing your figures to share with others

Selecting a style for printing

Switching between styles

Saving your visualizations

Saving a file several times

Save a figure with different sizes

Automating figures from data

Unique values of a column

Automate your visualization

Where to go next

Matplotlib 数据可视化入门

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