Easy Data Visualization

`py_simple.easy_data_visualization`

easy_data_visualization aims to simplify data visualization. without requiring users to memorize every chart type or matplotlib function.

`plot_data(X, Y=None)`

Infers the type of the given data (quantitative or categorical) and automatically plots the most appropriate chart(s) for it, handling the chart-type selection, axis setup, and matplotlib boilerplate every data visualization needs.

Parameters:

Name	Type	Description	Default
`X`	`list`	The primary data series to plot.	required
`Y`	`list`	A second data series to plot against `X`. If omitted, only `X` is visualized on its own. Defaults to `None`.	`None`

Returns:

Name	Type	Description
`None`		The chart(s) are rendered directly via `plt.show()`. One or two subplots are created depending on how many chart types are suggested for the given data combination (e.g. a categorical `X` alone suggests both a bar chart and a pie chart).

Raises:

Type	Description
`KeyError`	If the inferred type combination of `X` and `Y` has no matching entry in `CHART_SUGGESTIONS` (e.g. two categorical series).
`ValueError`	If `X` or `Y` is an empty list (raised internally by `_infer_type`).

Example

The Py_simple WayThe Traditional Way

from py_simple import plot_data

plot_data([1, 2, 2, 3, 5, 5, 5, 8])

import matplotlib.pyplot as plt

data = [1, 2, 2, 3, 5, 5, 5, 8]
fig, ax = plt.subplots()
ax.hist(data)
ax.set_title("Histogram")
ax.spines[['top', 'right']].set_visible(False)
plt.show()