Python libraries are the ultimate extension in GIS because they allow you to boost its core functionality. By using Python libraries, you can break out of the mold that is GIS and dive into some serious data science. There are 200+ standard libraries in Python. But there are thousands of third-party libraries too. So, it\u2019s endless how far you can take it. Today, it\u2019s all about Python libraries in GIS. Specifically, what are the most popular Python packages that GIS professionals use today? Let\u2019s get started.<\/p>\n
Why Python Libraries<\/strong> By importing these libraries into your Python script, you gain access to an array of functions that may not be inherently available in your core GIS software. This flexibility allows you to expand the capabilities of your GIS workflows and tailor them to your specific needs.<\/p>\n Python Libraries for GIS<\/strong> 1. Arcpy<\/strong> 2. Geopandas<\/strong> 3. GDAL\/OGR<\/strong> 4. RSGISLib<\/strong> 5. PyProj<\/strong> Python Libraries for Data Science<\/strong> 6. NumPy<\/strong> 7. Pandas<\/strong> 8. Matplotlib<\/strong> 9. Re (regular expressions)<\/strong> 10. ReportLab<\/strong> 11. ipyleaflet<\/strong> 12. Folium<\/strong> 13. Geemap<\/strong> Python libraries are the ultimate extension in GIS because they allow you to boost its core functionality. By using Python libraries, you can break out of the mold… <\/p>\n","protected":false},"author":1,"featured_media":183,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[15,2],"tags":[],"class_list":["post-182","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-applications","category-geospatial-technology"],"_links":{"self":[{"href":"https:\/\/geopsyresearch.org\/blogs\/wp-json\/wp\/v2\/posts\/182","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/geopsyresearch.org\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/geopsyresearch.org\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/geopsyresearch.org\/blogs\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/geopsyresearch.org\/blogs\/wp-json\/wp\/v2\/comments?post=182"}],"version-history":[{"count":3,"href":"https:\/\/geopsyresearch.org\/blogs\/wp-json\/wp\/v2\/posts\/182\/revisions"}],"predecessor-version":[{"id":187,"href":"https:\/\/geopsyresearch.org\/blogs\/wp-json\/wp\/v2\/posts\/182\/revisions\/187"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/geopsyresearch.org\/blogs\/wp-json\/wp\/v2\/media\/183"}],"wp:attachment":[{"href":"https:\/\/geopsyresearch.org\/blogs\/wp-json\/wp\/v2\/media?parent=182"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/geopsyresearch.org\/blogs\/wp-json\/wp\/v2\/categories?post=182"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/geopsyresearch.org\/blogs\/wp-json\/wp\/v2\/tags?post=182"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nIt’s no secret that GIS software, despite its many capabilities, may occasionally lack that one specific functionality you require. However, the beauty of Python libraries lies in their ability to fill those gaps and provide the additional capabilities you need. In essence, a Python library is a collection of pre-written code created by developers to simplify tasks for others. These libraries cover a wide range of functionalities, including machine learning, reporting, graphing, and virtually anything you can imagine within the Python ecosystem.<\/p>\n
\nTo create an exceptional lineup of GIS Python libraries that surpasses the conventional tasks of managing, analyzing, and visualizing spatial data, consider the following libraries. Together, they embody the true essence of a Geographic Information System by enabling advanced geospatial capabilities:<\/p>\n
\nIf you use Esri ArcGIS, then you\u2019re probably familiar with the ArcPy library. ArcPy is meant for geoprocessing operations. But it\u2019s not only for spatial analysis, it\u2019s also for data conversion, management, and map production with Esri ArcGIS.<\/p>\n
\nGeopandas is like pandas meet GIS. But instead of straightforward tabular analysis, the Geopandas library adds a geographic component. For overlay operations, Geopandas uses Fiona and Shapely, which are Python libraries of their own.<\/p>\n
\nThe GDAL\/OGR library is used for translating between GIS formats and extensions. QGIS, ArcGIS, ERDAS, ENVI, GRASS GIS and almost all GIS software use it for translation in some way. At this time, GDAL\/OGR supports 97 vector and 162 raster drivers.<\/p>\n
\nThe RSGISLib library is a set of remote sensing tools for raster processing and analysis. To name a few, it classifies, filters, and performs statistics on imagery. My personal favorite is the module for object-based segmentation and classification (GEOBIA).<\/p>\n
\nThe main purpose of the PyProj library is how it works with spatial referencing systems. It can project and transform coordinates with a range of geographic reference systems. PyProj can also perform geodetic calculations and distances for any given datum.<\/p>\n
\nData science extracts insights from data. It takes data and tries to make sense of it, such as by plotting it graphically or using machine learning. This list of Python libraries can do exactly this for you.<\/p>\n
\nNumerical Python (NumPy library) takes your attribute table and puts it in a structured array. Once it\u2019s in a structured array, it\u2019s much faster for any scientific computing. One of the best things about it is how you can work with other Python libraries like SciPy for heavy statistical operations.<\/p>\n
\nThe Pandas library is immensely popular for data wrangling. It\u2019s not only for statisticians. But it\u2019s incredibly useful in GIS too. Computational performance is key for pandas. The success of Pandas lies in its data frame. Data frames are optimized to work with big data. They\u2019re optimized to such a point that it\u2019s something that Microsoft Excel wouldn\u2019t even be able to handle.<\/p>\n
\nWhen you\u2019re working with thousands of data points, sometimes the best thing to do is plot it all out. Enter Matplotlib. Statisticians use the matplotlib library for visual display. Matplotlib does it all. It plots graphs, charts, and maps. Even with big data, it\u2019s decent at crunching numbers.<\/p>\n
\nRegular expressions (Re) are the ultimate filtering tool. When there\u2019s a specific string you want to hunt down in a table, this is your go-to library. But you can take it a bit further like detecting, extracting, and replacing with pattern matching.<\/p>\n
\nReportLab is one of the most satisfying libraries on this list. I say this because GIS often lacks sufficient reporting capabilities. Especially, if you want to create a report template, this is a fabulous option. I don\u2019t know why the ReportLab library falls a bit off the radar because it shouldn\u2019t.<\/p>\n
\nIf you want to create interactive maps, ipyleaflet is a fusion of Jupyter Notebook and Leaflet. You can control an assortment of customizations like loading base maps, geojson, and widgets. It also gives a wide range of map types to pick from including choropleth, velocity data, and side-by-side views.<\/p>\n
\nJust like ipyleaflet, Folium allows you to leverage leaflets to build interactive web maps. It gives you the power to manipulate your data in Python, then you can visualize it with the leading open-source JavaScript library.<\/p>\n
\nGeemap is intended more for science and data analysis using Google Earth Engine (GEE). Although anyone can use this Python library, scientists and researchers specifically use it to explore the multi-petabyte catalog of satellite imagery in GEE for their specific applications and use with remote sensing data.<\/p>\n","protected":false},"excerpt":{"rendered":"