View Project in Designer hero This guide will use three Prefabs and one custom Workflow Object to load a CSV file from GitHub over HTTP and visualize it as a bar chart using Plotly. Begin by importing the net.fetch Prefab by draging it from the Prefab Library on the left-hand side of the workspace into the workflow graph. prefab.gif In this example, we will be using a sample dataset of population statistics with the following columns:
  • id - A unique identifier for each row
  • gender - Possible values are Male, Female, Bigender, Agender, and Genderfluid.
  • age - A positive integer
  • country - A two letter country code
The dataset is available as a CSV file on GitHub at the following URL: https://raw.githubusercontent.com/mainly-ai/the-lab/main/datasets/population_stats.csv Enter this dataset URL into the URL field of the net.fetch Prefab. Then import the util.show_text prefab and connect the Body transmitter on the net.fetch Prefab to the input_2 (which takes a String) receiver on the miranda_test.printer Prefab. connecting.gif Now let’s run the project and look at the output in the logs using the Processor panel on the right of the workspace. running.gif You should now see the text contents of the CSV file in the logs. To visualize this data, we will first need to parse it into a format that Plotly can understand, such as a Pandas DataFrame. To do this, we will use the pandas.from_csv Prefab. Import it from the Prefab Library and connect the Body transmitter on the net.fetch Prefab to the CSV receiver on the pandas.from_csv Prefab. Then you can connect the Dataframe transmitter on the pandas.from_csv Prefab to the input_1 (which takes a Dataframe) receiver on the miranda_test.printer Prefab. However, if we try to plot this data directly using Plotly, we will get an error or incohorent results. This is because highly dimensional data. Let’s write a custom Workflow Object (Node) to aggregate the data and visualize it as a bar chart. In this example, we will group the data by country and average the age column. Create a new Node by right clicking on the workspace and selecting Create Node. Then right click the node and select Edit Code to begin implementing our own logic. By default, the new Node contains some boilerplate code to get you started. 
from mirmod import miranda

@wob.init()
def init(self):
  self.value = None

@wob.receiver("value","input")
def receive_value(self, value):
  self.value = value

@wob.transmitter("value", "output")
def transmit_value(self):
  return self.value

@wob.execute()
def execute(self):
  print(f"self.value = '{self.value}'")
These are the four main parts of a Workflow Object, which are evaluated in the following order:
  1. init - This is the constructor for the Workflow Object. It is called when the object is created and can be used to initialize any variables.
  2. receiver - Recieves data from other Workflow Objects or from Controls.
  3. execute - This is the main function of the Workflow Object. It is called when all the receivers have been called.
  4. transmitter - Sends data to other Workflow Objects.
Let’s initialize our Workflow Object. We’re gonna want two variables, self.df to store the DataFrame recieved from the pandas.from_csv Prefab and self.transformed to store the transformed DataFrame. 
@wob.init()
def init(self):
  self.df = None
  self.transformed = None
The default code is configured to recieve and transmit strings. We will need to modify this to use DataFrames. Let’s also change the names to better reflect the purpose of the Node and make sure we’re setting and returningthe right variables. 
@wob.receiver("data", "Dataframe")
def receive_value(self, value):
  self.df = value

@wob.transmitter("data", "Population by Country")
def transmit_value(self):
  return self.transformed
Now let’s write the logic to transform the DataFrame. We will use the groupby method to group the data by country and then use the mean method to average the age column. 
@wob.execute()
def execute(self):
  self.transformed = self.df.groupby(['country'])['age'].mean().sort_values()
Now lets plot this data using Plotly. Import the plotly.bar Prefab and connect the Population by Country transmitter on the custom Workflow Object to the Dataset receiver on the plotly.bar Prefab. Then run the project, and you should see a bar chart of the average age by country appear on the plotly.bar node. final.gif