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QGIS Python Programming Cookbook, Second Edition

QGIS Python Programming Cookbook, Second Edition

By : Joel Lawhead
1.5 (2)
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QGIS Python Programming Cookbook, Second Edition

QGIS Python Programming Cookbook, Second Edition

1.5 (2)
By: Joel Lawhead

Overview of this book

QGIS is a desktop geographic information system that facilitates data viewing, editing, and analysis. Paired with the most efficient scripting language—Python, we can write effective scripts that extend the core functionality of QGIS. Based on version QGIS 2.18, this book will teach you how to write Python code that works with spatial data to automate geoprocessing tasks in QGIS. It will cover topics such as querying and editing vector data and using raster data. You will also learn to create, edit, and optimize a vector layer for faster queries, reproject a vector layer, reduce the number of vertices in a vector layer without losing critical data, and convert a raster to a vector. Following this, you will work through recipes that will help you compose static maps, create heavily customized maps, and add specialized labels and annotations. As well as this, we’ll also share a few tips and tricks based on different aspects of QGIS.
Table of Contents (10 chapters)
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Using pie charts for symbols

QGIS has the ability to use dynamic pie charts as symbols describing the statistics in a given region. In this recipe, we'll use pie chart symbols on a polygon layer in QGIS.

Getting ready

For this recipe, download the following zipped shapefile and extract it to a directory named ms in your qgis_data directory from https://github.com/GeospatialPython/Learn/raw/master/County10PopnHou.zip.

How to do it...

As with other renderers, we will build a symbol layer, add it to a renderer, and display the layer on the map. The pie chart diagram renderers are more complex than other renderers but have many more options. Perform the following steps to create a pie chart map:

  1. First, we import the PyQt GUI library:
            from PyQt4.QtGui import * 
    
  2. Then, we load the layer:
            lyr = QgsVectorLayer("/qgis_data/ms/County10PopnHou.shp",
                                 "Population", "ogr") 
    
  3. Next, we set up categories based on attribute names:
       ...
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