Python Range Bar Plots

I have some data in a json file that contains a list of timings for different tests. Each test has a name, start timestamp and duration in milliseconds. It looks something like this:

	{
	"timings": [
	{
	"testname": "Test A",
	"start": 123456,
	"duration": 121
	},
	{
	"testname": "Control",
	"start": 123599,
	"duration": 25
	},
	{
	"testname": "Test B",
	"start": 123400,
	"duration": 220
	}
	]
	}

view raw BarPlotData.json hosted with ❤ by GitHub

Now, I love the Matplotlib Python library. I used it a lot when learning Machine Learning during the 2020 lockdowns. It is an incredibly rich and powerful tool  for creating professional data visualizations. So, as I'm new to it I thought I'd see if I could create a Range Bar plot of these data. 

In essence I want to show each test as a individual bar, the size of which corresponds to the duration and each bar's left most edge corresponding to the start time.

Here is the script:

	# I have a json file with a list of tests. Each test has a name, start time and duration
	# I want to display these in a bar plot
	import numpy as np
	import matplotlib.pyplot as plt
	import json
	# Read the json file
	f = open('data.json')
	data = json.load(f)
	test_names = []
	start_times = []
	durations = []
	colors = []
	for timing in data['timings']:
	test_names.append(timing['testname'])
	start_times.append(timing['start'])
	durations.append(timing['duration'])
	# Change the color of the longest duration bar
	max_duration = max(durations)
	for duration in durations:
	if duration == maxduration:
	colors.append("red")
	else:
	colors.append("blue")
	# Now create the plot
	plt.title('Test metrics')
	plt.xlabel('Start time/duration (ms)')
	plt.barh(test_names, durations, left=start_times, color=colors)
	plt.show()

view raw BarPlot.py hosted with ❤ by GitHub

It's very simple. Most of the script is concerned with reading the data from the file and generating the data structures. I thought it would be nice to highlight the longest duration test in red. This would be useful for seeing if the times for different tests were close.

 

And the output:

Very simple. There are of course many options to polish this. I'll update the blog as I refine them.