A great source of information about GRASS GIS is the GRASS Wiki. One example is this list with GRASS GIS Jupyter notebooks which was just added by Markus Neteler (no introduction needed I guess). There are some really nice tutorials there, which alone is reason enough to check out this list. Continue reading “GRASS GIS Jupyter notebooks”
GRASS GIS offers some useful but basic plotting options for raster data. However, for plotting of data in attribute tables and for more advanced graphs, we need to use other software tools. In this tutorial I explore some of the possibilities offered by Pandas plot() and how we can further tune plots using matplotlib / pyplot library.
In this post I show how to import an attribute table of a vector layer in a GRASS GIS database into a Pandas data frame. Pandas stands for Python Data Analysis Library which provides high-performance, easy-to-use data structures and data analysis tools for the Python programming language. For people familiar with R, the Pandas data frame is an object similar to the R data frame. They are a lot like the most common way in which spreadsheets are used, with the data presented in rectangular form with columns holding variables and rows holding observations. An important characteristic is that the data frame, like a spreadsheet, can hold different types of data in different columns: numbers, character data, dates and so on. Continue reading “GRASS and Pandas – from attribute table to pandas dataframe”