Update python_install_anaconda.md

_docs/guides/python_install_anaconda.md

@@ -93,32 +93,19 @@ Now we can install the packages. Type each of these in sequence, followed by ent
 
 
 Why install these packages in particular?\
-**numpy**:\
-Useful for creating arrays (of XY coordinates or an elevation raster, for example) and manipulating numerical data inside them.\
-**pandas**:\
-Data manipulation package for tabular data (DataFrames). Import, export, clean, aggregate data with pandas.\
-**ipython**:\
-An interactive python shell. Acts as a kernel for Jupyter notebooks.\
-**ipykernel**:\
-Built on top of ipython. In Jupyter notebooks, you’re using ipykernel to execute code interactively.\
-**rtree**:\
-Provides spatial indexing features like nearest neighbour search, intersection, union, joins etc.\
-**gdal**:\
-Geospatial data abstraction library. GDAL provides useful command line utilities for data translation, processing, and conversion. QGIS and ArcGIS uses gdal as a backend library.\
-**fiona**:\
-Streams simple feature data to and from GIS formats like GeoPackage and Shapefile. Used in combination with Shapely.\
-**pyproj**:\
-Can project and transform coordinates across various geographic reference systems.\
-**shapely**:\
-Create and work with vector geometries.\
-**geopandas**:\
-Extension of pandas library but handles GeoDataFrames. These are DataFrames with a geometry column.\
-**matplotlib**:\
-visualizations of geospatial data; scatterplots/heatmaps/choropleths maps etc.\
-**mapclassify**:\
-Handles classifying data e.g for choropleth maps by providing classification schemes. Use in conjunction with geo-visualisation packages like GeoPandas.\
-**folium**:\
-Data visualisation library for creating interactive maps.
+**numpy**: Useful for creating arrays (of XY coordinates or an elevation raster, for example) and manipulating numerical data inside them.\
+**pandas**: Data manipulation package for tabular data (DataFrames). Import, export, clean, aggregate data with pandas.\
+**ipython**: An interactive python shell. Acts as a kernel for Jupyter notebooks.\
+**ipykernel**: Built on top of ipython. In Jupyter notebooks, you’re using ipykernel to execute code interactively.\
+**rtree**: Provides spatial indexing features like nearest neighbour search, intersection, union, joins etc.\
+**gdal**: Geospatial data abstraction library. GDAL provides useful command line utilities for data translation, processing, and conversion. QGIS and ArcGIS uses gdal as a backend library.\
+**fiona**: Streams simple feature data to and from GIS formats like GeoPackage and Shapefile. Used in combination with Shapely.\
+**pyproj**: Can project and transform coordinates across various geographic reference systems.\
+**shapely**: Create and work with vector geometries.\
+**geopandas**: Extension of pandas library but handles GeoDataFrames. These are DataFrames with a geometry column.\
+**matplotlib**: Visualizations of geospatial data; scatterplots/heatmaps/choropleths maps etc.\
+**mapclassify**: Handles classifying data e.g for choropleth maps by providing classification schemes. Use in conjunction with geo-visualisation packages like GeoPandas.\
+**folium**: Data visualisation library for creating interactive maps.