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Python is extremely useful language to learn in terms of GIS since many of the different GIS Software packages (such as ArcGIS, QGIS, PostGIS etc.) provide an interface to do analysis using Python scripting.
Why should use Python for GIS
1.Everything is free: you don’t need to buy and expensive license for example ArcGIS
2.You will learn and understand much more deeply how different geoprocessing operations work
3.Python is highly efficient: used for analyzing Big Data
4.Python is highly flexible: supports all data formats that you can imagine
Using Python supports open source softwares,codes and open science by making it possible for everyone to reproduce your work, free-of-charge.
5.Plug-in and chain different third-party softwares to build e.g. a fancy web-GIS applications as you want (using e.g. GeoDjango with PostGIS as a back-end)
Here are the GIS Python Libraries
1 Arcpy
If you use Esri ArcGIS, then you’re probably familiar with the ArcPy library. ArcPy is meant for geoprocessing operations. But it’s not only for spatial analysis, it’s also for data conversion, management and map production with Esri ArcGIS.
2 Geopandas
Geopandas is like pandas meet GIS. But instead of straight-forward tabular analysis, the geopandas library adds a geographic component. For overlay operations, geopandas uses capabilities of pandas,Fiona and Shapely, which are Python libraries of their own.
3 GDAL/OGR
The GDAL/OGR library is used for translating between GIS formats and extensions. QGIS, ArcGIS, ERDAS, ENVI and 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.
4 RSGISLib
The 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.
5 PyProj
The 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.
6.Shapely
Shapely is Python package for manipulation and analysis of planar geometric objects. It is based on the widely deployed GEOS (the engine of PostGIS) and JTS (from which GEOS is ported) libraries. Shapely is not concerned with data formats or coordinate systems, but can be readily integrated with packages that are.
7.Fiona
Reading and writing spatial data (alternative for geopandas). Fiona is designed to be simple and dependable. It focuses on reading and writing data in standard Python IO style and relies upon familiar Python types and protocols such as files, dictionaries, mappings, and iterators instead of classes specific to OGR.
8.Pysal
PySAL, the Python spatial analysis library, is an open source cross-platform library for geospatial data science with an emphasis on geospatial vector data written in Python. It supports the development of high level applications for spatial analysis, such as
9.Geopy
Geopy makes it easy for Python developers to locate the coordinates of addresses, cities, countries, and landmarks across the globe using third-party geocoders and other data sources.
10.GeoViews
GeoViews is a Python library that makes it easy to explore and visualize any data that includes geographic locations. It has particularly powerful support for multidimensional meteorological and oceanographic datasets, such as those used in weather, climate, and remote sensing research, but is useful for almost anything that you would want to plot on a map
11.Cartopy
Cartopy is a Python package designed to make drawing maps for data analysis and visualisation easy.
It features:
12.Rtree
Rtree is a ctypes Python wrapper of libspatialindex that provides a number of advanced spatial indexing features for the spatially curious Python user. These features include:
13 Rasterio
Rasterio reads and writes geospatial raster data and perfect for Clean and fast and geospatial raster I/O for Python.
Geographic information systems use GeoTIFF and other formats to organize and store gridded, or raster, datasets. Rasterio reads and writes these formats and provides a Python API based on N-D arrays.
Python Libraries for Data Science
1 NumPy
Numerical Python (NumPy library) takes your attribute table and puts it in a structured array. Once it’s in a structured array, it’s 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.
2 Pandas
The Pandas library is immensely popular for data wrangling. It’s not only for statisticians. But it’s 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’re optimized to such a point that it’s something that Microsoft Excel wouldn’t even be able to handle.
3 Matplotlib
When you’re 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’s decent at crunching numbers.
4 Scikit
Lately, machine learning has been all the buzz. And with good reason. Scikit is a Python library that enables machine learning. It’s built in NumPy, SciPy and matplotlib. So, if you want to do any data mining, classification or ML prediction, the Scikit library is a decent choice.
Why should use Python for GIS
1.Everything is free: you don’t need to buy and expensive license for example ArcGIS
2.You will learn and understand much more deeply how different geoprocessing operations work
3.Python is highly efficient: used for analyzing Big Data
4.Python is highly flexible: supports all data formats that you can imagine
Using Python supports open source softwares,codes and open science by making it possible for everyone to reproduce your work, free-of-charge.
5.Plug-in and chain different third-party softwares to build e.g. a fancy web-GIS applications as you want (using e.g. GeoDjango with PostGIS as a back-end)
Here are the GIS Python Libraries
1 Arcpy
If you use Esri ArcGIS, then you’re probably familiar with the ArcPy library. ArcPy is meant for geoprocessing operations. But it’s not only for spatial analysis, it’s also for data conversion, management and map production with Esri ArcGIS.
2 Geopandas
Geopandas is like pandas meet GIS. But instead of straight-forward tabular analysis, the geopandas library adds a geographic component. For overlay operations, geopandas uses capabilities of pandas,Fiona and Shapely, which are Python libraries of their own.
3 GDAL/OGR
The GDAL/OGR library is used for translating between GIS formats and extensions. QGIS, ArcGIS, ERDAS, ENVI and 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.
4 RSGISLib
The 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.
5 PyProj
The 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.
6.Shapely
Shapely is Python package for manipulation and analysis of planar geometric objects. It is based on the widely deployed GEOS (the engine of PostGIS) and JTS (from which GEOS is ported) libraries. Shapely is not concerned with data formats or coordinate systems, but can be readily integrated with packages that are.
7.Fiona
Reading and writing spatial data (alternative for geopandas). Fiona is designed to be simple and dependable. It focuses on reading and writing data in standard Python IO style and relies upon familiar Python types and protocols such as files, dictionaries, mappings, and iterators instead of classes specific to OGR.
8.Pysal
PySAL, the Python spatial analysis library, is an open source cross-platform library for geospatial data science with an emphasis on geospatial vector data written in Python. It supports the development of high level applications for spatial analysis, such as
- Detection of spatial clusters, hot-spots, and outliers
Construction of graphs from spatial data
Spatial regression and statistical modeling on geographically embedded networks
Spatial econometrics
Exploratory spatio-temporal data analysis
9.Geopy
Geopy makes it easy for Python developers to locate the coordinates of addresses, cities, countries, and landmarks across the globe using third-party geocoders and other data sources.
10.GeoViews
GeoViews is a Python library that makes it easy to explore and visualize any data that includes geographic locations. It has particularly powerful support for multidimensional meteorological and oceanographic datasets, such as those used in weather, climate, and remote sensing research, but is useful for almost anything that you would want to plot on a map
11.Cartopy
Cartopy is a Python package designed to make drawing maps for data analysis and visualisation easy.
It features:
- Object oriented projection definitions
Point, line, polygon and image transformations between projections
Integration to expose advanced mapping in Matplotlib with a simple and intuitive interface
powerful vector data handling by integrating shapefile reading with Shapely capabilities
12.Rtree
Rtree is a ctypes Python wrapper of libspatialindex that provides a number of advanced spatial indexing features for the spatially curious Python user. These features include:
- Nearest neighbor search
Intersection search
Multi-dimensional indexes
Clustered indexes
Bulk loading
13 Rasterio
Rasterio reads and writes geospatial raster data and perfect for Clean and fast and geospatial raster I/O for Python.
Geographic information systems use GeoTIFF and other formats to organize and store gridded, or raster, datasets. Rasterio reads and writes these formats and provides a Python API based on N-D arrays.
Python Libraries for Data Science
1 NumPy
Numerical Python (NumPy library) takes your attribute table and puts it in a structured array. Once it’s in a structured array, it’s 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.
2 Pandas
The Pandas library is immensely popular for data wrangling. It’s not only for statisticians. But it’s 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’re optimized to such a point that it’s something that Microsoft Excel wouldn’t even be able to handle.
3 Matplotlib
When you’re 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’s decent at crunching numbers.
4 Scikit
Lately, machine learning has been all the buzz. And with good reason. Scikit is a Python library that enables machine learning. It’s built in NumPy, SciPy and matplotlib. So, if you want to do any data mining, classification or ML prediction, the Scikit library is a decent choice.
