Geospatial Data Science with Python: Data Visualization

Jupyter Notebook环境中的Pandas和GeoPandas提供了一种替代传统桌面GIS方法进行地理空间分析的强大方法。在分析工作流程中整合表格数据和地理空间数据的可视化的能力是这种方法的一大优势。

本课程详细介绍了如何在 Jupyter Notebook 环境中使用 Matplotlib、Pandas、GeoPandas、Rasterio、Contextily、Seaborn、Plotly、Bokeh 和其他 Python 包创建漂亮的表格和地理空间可视化。

我们从 Matplotlib 开始，因为它是所有其他静态绘图方法所基于的核心。Pandas、GeoPandas、Rasterio、Contextily 和 Seaborn 都生成 Matplotlib 对象作为输出。如果您了解 Matplotlib，您可以使用这些知识来修改任何其他软件包发布的批次。您可以控制标签和标题、在地图上放置注释、包括刻度线和网格线、放置图例、设置 x 和 y 限制等等。并且您可以控制这些输出的每个细节，即使更高级别的绘图 API 可以使用一行代码轻松生成基本输出。

Tabular and Geospatial visualizations with Matplotlib, Pandas, Seaborn, Plotly, Bokeh, and more!

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 42 lectures (7h 58m) | Size: 4.24 GB

What you’ll learn:
How to visualize both tabular and geospatial data with matplotlib, pandas, geopandas, seaborn, plotly, folium, and more.

Requirements
Students are expected to have a working knowledge of Python and Geopandas. This knowledge can be gained in my courses “Survey of Python for GIS applications” and “Geospatial Data Science with Python: GeoPandas”

Description
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.

Who this course is for
Geospatial professionals who want to increase their knowledge of the Python Geospatial Data Science stack, especially with regards to data visualization.