Add the PyGMT ecosystem page (#3475)

Co-authored-by: Michael Grund <[email protected]>
Co-authored-by: Yvonne Fröhlich <[email protected]>

doc/ecosystem.md

@@ -0,0 +1,127 @@
+# Ecosystem
+
+PyGMT provides a Python interface to the Generic Mapping Tools (GMT), which is a command
+line program that provides a wide range of tools for manipulating geospatial data and
+making publication-quality maps and figures. PyGMT integrates well with the
+[scientific Python ecosystem](https://scientific-python.org/), with [NumPy][] for its
+fundamental array data structure, [pandas][] for tabular data I/O and [Xarray][] for
+raster grids/images/cubes I/O.
+
+In addition to these core dependencies, PyGMT also relies on several optional packages to
+provide additional functionality for users.
+
+*This page was adapted from [GeoPandas's Ecosystem](https://geopandas.org/en/latest/community/ecosystem.html) page.*
+
+## PyGMT dependencies
+
+_Asterisk (*) after the package name indicates the package is a required dependency of PyGMT._
+
+### NumPy*
+
+[NumPy][] is the fundamental package for scientific computing in Python. It is a Python
+library that provides a multidimensional array object, various derived objects (such as
+masked arrays and matrices), and an assortment of routines for fast operations on arrays,
+including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete
+Fourier transforms, basic linear algebra, basic statistical operations, random simulation
+and much more.
+
+### pandas*
+
+[pandas][] is a Python package providing fast, flexible, and expressive data structures
+designed to make working with "relational" or "labeled" data both easy and intuitive.
+It aims to be the fundamental high-level building block for doing practical, real-world
+data analysis in Python.
+
+### Xarray*
+
+[Xarray][] is an open source project and Python package that introduces labels in the
+form of dimensions, coordinates, and attributes on top of raw NumPy-like arrays, which
+allows for more intuitive, more concise, and less error-prone user experience.
+
+### IPython
+
+[IPython][] provides a rich toolkit to help you make the most of using Python
+interactively. Its main components are a powerful interactive Python shell and a Jupyter
+kernel to work with Python code in Jupyter notebooks and other interactive frontends.
+
+PyGMT relies on IPython to provide a rich interactive experience in Jupyter notebooks.
+
+### GeoPandas
+
+[GeoPandas][] is an open source project to make working with geospatial data in Python
+easier. GeoPandas extends the datatypes used by [pandas][] to allow spatial operations
+on geometric types. Geometric operations are performed by [Shapely][]. GeoPandas further
+depends on [pyogrio][] for file access and [Matplotlib][] for plotting.
+
+PyGMT doesn't directly rely on GeoPandas, but provides support of GeoPandas's two main
+data structure, {class}`geopandas.GeoDataFrame` and {class}`geopandas.GeoSeries`, which
+can be directly used in data processing and plotting functions/methods of PyGMT.
+
+### contextily
+
+[contextily][] is a small Python package to retrieve tile maps from the internet. It can
+add those tiles as basemap to matplotlib figures or write tile maps to disk into
+geospatial raster files.
+
+In PyGMT, {func}`pygmt.datasets.load_tile_map` and {class}`pygmt.Figure.tilemap` rely
+on it.
+
+### rioxarray
+
+[rioxarray][] is a geospatial [Xarray][] extension powered by [rasterio][]. Built on top
+of rasterio, it enables seamless reading, writing, and manipulation of multi-dimensional
+arrays with geospatial attributes such as coordinate reference systems (CRS) and spatial
+extent (bounds).
+
+Currently, PyGMT relies on [rioxarray][] to save multi-band rasters to temporary files
+in GeoTIFF format, to support processing and plotting 3-D {class}`xarray.DataArray`
+images.
+
+```{note}
+We're working towards removing the dependency of the [rioxarray][] package in
+[PR #3468](https://github.com/GenericMappingTools/pygmt/pull/3468).
+```
+
+### PyArrow
+
+[Apache Arrow][] is a development platform for in-memory analytics. It contains a set of
+technologies that enable big data systems to process and move data fast. It specifies a
+standardized language-independent columnar memory format for flat and hierarchical data,
+organized for efficient analytic operations on modern hardware. The Arrow Python bindings
+(also named "[PyArrow][]") have first-class integration with NumPy, pandas, and built-in
+Python objects. They are based on the C++ implementation of Arrow.
+
+```{note}
+If you have [PyArrow][] installed, PyGMT does have some initial support for
+`pandas.Series` and `pandas.DataFrame` objects with Apache Arrow-backed arrays.
+Specifically, only uint/int/float and date32/date64 are supported for now.
+Support for string Array dtypes, Duration types and GeoArrow geometry types is still a work in progress.
+For more details, see
+[issue #2800](https://github.com/GenericMappingTools/pygmt/issues/2800).
+```
+
+## PyGMT ecosystem
+
+Various packages rely on PyGMT for geospatial data processing, analysis, and visualization.
+Below is an incomplete list (in no particular order) of tools which form the PyGMT-related
+ecosystem.
+
+```{note}
+If your package relies on PyGMT, please
+[let us know](https://github.com/GenericMappingTools/pygmt/issues/new) or
+[add it by yourself](contributing.md).
+```
+
+[apache arrow]: https://arrow.apache.org/
+[contextily]: https://contextily.readthedocs.io/
+[geopandas]: https://geopandas.org/
+[ipython]: https://ipython.org/
+[matplotlib]: https://matplotlib.org/
+[numpy]: https://numpy.org/
+[pandas]: https://pandas.pydata.org/
+[pyarrow]: https://arrow.apache.org/docs/python/
+[pyogrio]: https://pyogrio.readthedocs.io/
+[rasterio]: https://rasterio.readthedocs.io/
+[rioxarray]: https://corteva.github.io/rioxarray/
+[shapely]: https://shapely.readthedocs.io/
+[xarray]: https://xarray.pydata.org/

doc/index.md

@@ -45,6 +45,7 @@ api/index.rst
 techref/index.md
 changes.md
 minversions.md
+ecosystem.md
 ```
 
 ```{toctree}