Generalise discussion of Python.

You happy with this @jannes-m ?

01-introduction.Rmd

@@ -161,12 +161,12 @@ R has unparalled support for statistics, including spatial statistics, with hund
 The major advantage of Python that it is a multi-purpose language.
 It brings together people from diverse fields, explaining its larger user base compared with R's.
 Python's status as a *general purpose* language that bridges many divides in software development has led it to become the 'glue' that holds many GIS programs together.
-In fact, we often advise our students to start with Python just because the major GIS software packages provide Python libraries that lets the user access its geoalgorithms from the Python command line^[`grass.script` for GRASS (https://grasswiki.osgeo.org/wiki/GRASS_and_Python), `saga-python` for SAGA-GIS (http://saga-python.readthedocs.io/en/latest/), `processing` for QGIS and `arcpy` for ArcGIS.
-].
-However, when the teaching moves on to statistical geoprocessing and spatial predictive modeling we guide them towards R where they can take advantage of the concepts already learned through Python.
-Nevertheless, you can also use Python for the most common statistical learning techniques (though R tends to be more on the bleeding edge regarding new statistical development including those in the spatial statistical community).
-In addition, Python also offers excellent support for spatial data analysis and manipulation (see packages **osgeo**, **Shapely**, **NumPy**, **PyGeoProcessing**). 
-We refer you to @garrard_geoprocessing_2016 for an introduction to geoprocessing with Python.
+Many spatial algorithms, including those in QGIS and ArcMap, can be accessed from the Python command line, making it well-suited as a starter language for command-line GIS.^[Python modules providing access to spatial algoriths include `grass.script` for GRASS (https://grasswiki.osgeo.org/wiki/GRASS_and_Python), `saga-python` for SAGA-GIS (http://saga-python.readthedocs.io/en/latest/), `processing` for QGIS and `arcpy` for ArcGIS.
+]
+
+For spatial statistics and predictive modeling, however, R is second-to-none.
+This does not mean you must chose either R or Python: Python supports most common statistical techniques (though R tends to support new developments in spatial statistics earlier) and many concepts learned from Python can be applied to the R world.
+Like R Python also supports spatial data analysis and manipulation with packages such as **osgeo**, **Shapely**, **NumPy**, **PyGeoProcessing** [@garrard_geoprocessing_2016].
 
 ## R's spatial ecosystem