What all are different documents that a tester should have for entire SDL Cphase?
I am currently engaged in a particular task in which I am managing a data visualization project where I have a large number if categories and the stakeholders are finding it challenging to interpret a pie chart due to the complexity. What are the other alternative data visualization options should I propose them to improve the clarity and understanding for the stakeholders?
Here are the other some alternative data visualization options or techniques you can propose to them:-
Horizontal bar chart
You can suggest them to use horizontal bar chart instead of using a pie chart. Thus format can offer easier comparison between categories and it is particularly effective when you are dealing with a large number of categories.
Import matplotlib.pyplot as plt
Categories = [‘Category 1’, ‘Category 2’, ‘Category 3’, ‘Category 4’, ‘Category 5’]
Values = [20, 35, 15, 25, 30]
Plt.barh(categories, values)
Plt.xlabel(‘Values’)
Plt.ylabel(‘Categories’)
Plt.title(‘Horizontal Bar Chart’)
Plt.show()Stacked bar chart
If the categories possibly van be grouped in sub categories then you can use a stacked bar chart for showing the total value. You can also highlight the contribution of each subcategories in it.
Import matplotlib.pyplot as plt
Categories = [‘Category 1’, ‘Category 2’, ‘Category 3’, ‘Category 4’, ‘Category 5’]
Subcategories = [‘Subcategory A’, ‘Subcategory B’, ‘Subcategory C’, ‘Subcategory D’, ‘Subcategory E’]
Values = [[10, 5, 5, 0, 10], [15, 10, 5, 5, 0], [5, 10, 0, 5, 10], [10, 5, 5, 5, 0], [15, 5, 10, 0, 10]]
Plt.bar(categories, values[0], label=subcategories[0])
Plt.bar(categories, values[1], bottom=values[0], label=subcategories[1])
Plt.bar(categories, values[2], bottom=[sum(x) for x in zip(values[0], values[1])], label=subcategories[2])
Plt.bar(categories, values[3], bottom=[sum(x) for x in zip(values[0], values[1], values[2])], label=subcategories[3])
Plt.bar(categories, values[4], bottom=[sum(x) for x in zip(values[0], values[1], values[2], values[3])], label=subcategories[4])
Plt.xlabel(‘Categories’)
Plt.ylabel(‘Values’)
Plt.title(‘Stacked Bar Chart’)
Plt.legend()
Plt.show()Treemap
A treemap is another important and useful chart when you are trying to visualise hierarchical data with a focus on proportionality. It would represent categories as rectangle with size proportional to their values.
Import matplotlib.pyplot as plt
Import squarify
Categories = [‘Category 1’, ‘Category 2’, ‘Category 3’, ‘Category 4’, ‘Category 5’]
Values = [20, 35, 15, 25, 30]
Squarify.plot(sizes=values, label=categories, alpha=0.7)
Plt.axis(‘off’)
Plt.title(‘Treemap’)
Plt.show()