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This article mainly explains "What are the Python libraries of GIS". Interested friends may wish to take a look. The method introduced in this paper is simple, fast and practical. Let's let Xiaobian take you to learn "What are the Python libraries of GIS"!

First, why use Python libraries for GIS

Have you ever noticed how GIS lacks a feature you need to do? Since no GIS software can do everything, Python libraries can add additional functionality you need.

In short, Python libraries are code written by others that can make life easier for the rest of us. Developers have written open libraries for machine learning, reporting, graphics, and just about everything in Python.

If you need this extra functionality, you can take advantage of these libraries by importing them into Python scripts. From here, you can invoke functionality that is not included in the native GIS software itself.

Pro Tip: Install and manage packages in Python using pip

Python libraries for GIS

If you want to build an all-star team for your GIS Python library, that's enough. They all help you go beyond typical spatial data management, analysis, and visualization. This is the true definition of GIS.

1.Arcpy

If you use Esri ArcGIS, you're probably familiar with the ArcPy library. ArcPy is suitable for Geoprocessing operations. But this isn't just for spatial analysis; it's also for data conversion, management, and mapping using Esri ArcGIS.

2.geopandas

Geopandas are like pandas meeting GIS. However, the geopandas library does not perform tabular analysis directly, but adds a geographic component. For overlay operations, geopandas uses Fiona and Shapely, which are Python libraries of their own.

3.GDAL / OGR

The GDAL / OGR library is used for conversion between GIS formats and extensions. QGIS, ArcGIS, ERDAS, ENVI, and GRASS GIS, as well as almost all GIS software, use it in some way for format conversion. Currently, GDAL / OGR supports 97 vectors and 162 raster drivers.

9.Scikit

Machine learning has been a hot topic lately. And for good reason. Scikit is a Python library that enables machine learning. It is built into NumPy, SciPy and matplotlib. Therefore, if you want to do any data mining, classification or ML prediction, Scikit library is a good choice.

Re (regular expression)

Regular expressions (Re) are the ultimate filtering tool. This is the library you want to use when you want to look up a particular string in a table. But you can go further, such as detecting, extracting, and replacing pattern matches.

11.ReportLab

ReportLab is one of the most satisfying libraries on this list. I say this because GIS often lacks adequate reporting capabilities. Especially if you want to create report templates, this is a great choice. I don't know why the ReportLab library is a bit unpopular because it shouldn't be.

At this point, I believe that everyone has a deeper understanding of "what are the Python libraries of GIS". Let's actually operate them! Here is the website, more related content can enter the relevant channels for inquiry, pay attention to us, continue to learn!

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