#Use graphviz code#

#Use graphviz code#

In the code below, I changed the shape of node for the CEO to oval, box shape for the two team leads, and just plaintext for the staff. Moreover, it is also possible to get different shape for different nodes. These shapes could be polygon-based, record-based, or user-defined. It is possible to get a wide variety of shapes for nodes using graphviz, which can be found here. The default shape of node in the graph created using graphviz is ellipse. Some of these possibilities are described below in detail: Node shapes Styling and attributes with the graphviz packageДЄs mentioned earlier, the graphviz package offers various styling options and attributes to customise the graphs. This was not the case with the NetworkX package.

In the graph above, the size of each node is adapted automatically to fit the labels within the node. The code for the same is as given below: import graphviz f = graphviz.Digraph(filename = вЂњoutput/plain organogram 1.gvвЂќ) names = positions = for name, position in zip(names, positions): f.node(name, position) #Specify edges f.edge(вЂњAвЂќ,вЂќBвЂќ) f.edge(вЂњAвЂќ,вЂќCвЂќ) #CEO to Team Leads f.edge(вЂњBвЂќ,вЂќDвЂќ) f.edge(вЂњBвЂќ,вЂќEвЂќ) #Team A relationship f.edge(вЂњCвЂќ,вЂќFвЂќ) f.edge(вЂњCвЂќ,вЂќGвЂќ) f.edge(вЂњCвЂќ,вЂќHвЂќ) #Team B relationship fДЄs a result, I get a plain organogram graph as shown below:

I specified the edges between the CEO and two team leads, and team leads and their corresponding staff. Next, by going through a for loop, I created eight nodes for each name and added the corresponding position of the individual in the organisation as labels. I created a list of eight names starting from A to H, and the list of all the positions held in the company. To create a plain organogram using graphviz package, I start with initializing a graph object say f as graphviz.Digraph(filename), where filename is the name of the output file created. I am going to depict this by reconstructing the organogram created using NetworkX in the previous post, but using graphviz in this post. In this post, I am going to share how the graphviz package not only overcomes those limitations, but also offers much more flexibility options for graph visualisation. In my previous post, I pointed out the different limitations of graph visualisation with the NetworkX package, such as limited options for node shapes, limitation for getting unique shape and bounding box (bbox) attributes for individual nodes, etc. But in graphviz, a node can only be defined as a string. For example, in the NetworkX package, the node can be defined as any hashable object (except None). However, there are some key differences as well. This package allows to create both undirected and directed graphs using the DOT language.Д¬onstructing the Graph or DiGraph object using graphviz is similar to that using NetworkX in the sense that one needs to simply define the nodes and edges of the graph object and assign the attributes accordingly. The graphviz package, which works under Python 3.7+ in Python, provides a pure-Python interface to this software. Graphviz is an open-source graph visualisation software.