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所在平台: Udemy |
课程主页: https://www.udemy.com/course/geospatial-data-science-with-python-data-visualization/
课程评论：没有评论
**课程名称：** 使用Python进行地理空间数据科学：数据可视化 **课程概述：** 本课程旨在教授学员如何利用Pandas和GeoPandas在Jupyter Notebook环境下进行地理空间分析，并将其与数据可视化相结合。这种方法提供了一种强大的替代传统桌面GIS的解决方案。课程重点在于展示如何通过Matplotlib、Pandas、GeoPandas、Rasterio、Contextily、Seaborn、Plotly、Bokeh等Python包，创建精美的表格数据和地理空间数据可视化图表。 **核心内容：** * **Matplotlib基础：** 课程从Matplotlib讲起，因为它是一切其他静态绘图方法的基础。学员将学习如何利用Matplotlib控制图表的标签、标题、注解、刻度线、网格线、图例、坐标轴范围等细节，即使在使用高级绘图API时也能进行精细调整。 * **集成高级绘图库：** 课程将介绍如何将Pandas、GeoPandas、Rasterio、Contextily和Seaborn等库与Matplotlib结合使用，生成各种静态可视化图表。 * **交互式可视化：** 课程还将涵盖Plotly和Bokeh库，这些库基于JavaScript，能够创建交互式可视化，支持鼠标移动、点击等互动功能。 **学习目标：** 通过本课程，学员将能够： * 熟练掌握在Jupyter Notebook中使用Python进行地理空间数据分析。 * 创建高质量的表格数据可视化图表。 * 创建精美的地理空间数据可视化图表。 * 理解并应用Matplotlib以精细控制图表细节。 * 掌握使用Pandas、GeoPandas、Rasterio、Contextily、Seaborn等库进行静态地理空间可视化。 * 了解并实践Plotly和Bokeh创建交互式地理空间可视化。
Pandas and GeoPandas within a Jupyter Notebook environment provide a powerful alternative to traditional desktop GIS methods for geospatial analysis. The ability to incorporate visualizations of both tabular data and geospatial data from within your analysis workflow is one of the big advantages of this approach.This course provides detail on how to create beautiful tabular and geospatial visualizations using Matplotlib, Pandas, GeoPandas, Rasterio, Contextily, Seaborn, Plotly, Bokeh and other Python packages within a Jupyter Notebook environment.We start with Matplotlib because it is the core upon which all of the other static plotting methods are based. Pandas, GeoPandas, Rasterio, Contextily, and Seaborn all produce Matplotlib objects as output. If you understand Matplotlib you can use that knowledge to modify the lots put out by any of these other packages. You can control the labels and titles, place annotation on the maps, include ticks and gridlines, place legends, set the x and y limits and more. And you can control every detail of those outputs, even when the higher level plotting API's make it easy to produce the base output with a single line of code.Plotly and Bokeh produce dynamic output that is based on JavaScript and are able to respond to mouse movements, clicks, etc.
