Easy Data Visualization

Working with data visualization often means choosing the right chart and writing several lines of plotting code. The easy_data_visualization module simplifies this process by automatically identifying whether your data is quantitative or categorical and suggesting an appropriate chart.

A small real-world example

Imagine you're analyzing a set of values and want to quickly visualize the data without deciding which chart type to use or writing the usual Matplotlib setup yourself.

from py_simple import plot_data

data = [1, 2, 2, 3, 5, 5, 5, 8]

plot_data(data)

Example output:

Plotting data...

The function automatically creates suitable visualizations, such as a histogram and a line chart, for the quantitative data.

What happened?

plot_data() examines the data and determines whether it is quantitative or categorical before selecting appropriate chart types.

For example, numerical data can be visualized with a histogram and line chart:

plot_data([1, 2, 3, 4, 5])

Categorical data can be visualized with a bar chart and pie chart:

plot_data(["Python", "Python", "Java", "C++"])

You can also provide two data series. When both are quantitative, plot_data() creates a scatter plot:

x = [1, 2, 3, 4, 5]
y = [2, 4, 5, 8, 10]

plot_data(x, y)

The module uses _infer_type() internally to classify each series as either quantitative or categorical, allowing plot_data() to choose the appropriate visualization automatically.

Why use these helpers?

Instead of manually checking your data, choosing a chart, creating Matplotlib figures, and configuring each axis, you can simply write:

plot_data(data)

or:

plot_data(x, y)

This keeps data visualization simple, readable, and beginner-friendly while letting easy_data_visualization handle the chart selection and Matplotlib boilerplate for you.