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.

`get_data_range(data)`

Calculates the minimum and maximum values of a numeric data series, providing a quick summary range for data inspection before plotting.

Parameters:

Name	Type	Description	Default
`data`	`list`	A list of quantitative (int or float) values.	required

Returns:

Name	Type	Description
`tuple`	`tuple[int \| float, int \| float]`	A tuple containing the minimum and maximum values (min, max).

Raises:

Type	Description
`ValueError`	If `data` is empty or contains non-numeric values.

Example

The Py_simple WayThe Traditional Way

from py_simple.easy_data_visualization import get_data_range

min_val, max_val = get_data_range([5, 2, 9, 1, 7])

data = [5, 2, 9, 1, 7]
if not data:
    raise ValueError("Data series cannot be empty.")
min_val = min(data)
max_val = max(data)

`plot_bar_chart(labels, values, title=None, x_label=None, y_label=None)`

Displays a bar chart with an optional title and axis labels.

Unlike plot_data, which picks a chart type for you, this function always draws a bar chart and lets you name the chart and its axes, so the result is ready to share or screenshot.

Parameters:

Name	Type	Description	Default
`labels`	`list[str]`	The name of each bar (shown on the x-axis).	required
`values`	`list[int \| float]`	The height of each bar.	required
`title`	`str`	Text shown above the chart. Defaults to `None` (no title).	`None`
`x_label`	`str`	Text shown under the x-axis. Defaults to `None` (no label).	`None`
`y_label`	`str`	Text shown beside the y-axis. Defaults to `None` (no label).	`None`

Returns:

Name	Type	Description
`None`	`None`	The bar chart is rendered directly via `plt.show()`.

Raises:

Type	Description
`ValueError`	If `labels` or `values` is empty, if they have different lengths, or if any value is not a number.

Example

The Py_simple WayThe Traditional Way

from py_simple import plot_bar_chart

plot_bar_chart(
    ["Mon", "Tue", "Wed"],
    [3.5, 2.0, 4.5],
    title="My Screen Time",
    x_label="Day",
    y_label="Hours",
)

import matplotlib.pyplot as plt

labels = ["Mon", "Tue", "Wed"]
values = [3.5, 2.0, 4.5]
fig, ax = plt.subplots()
ax.bar(labels, values)
ax.set_title("My Screen Time")
ax.set_xlabel("Day")
ax.set_ylabel("Hours")
ax.spines[["top", "right"]].set_visible(False)
plt.show()

`plot_box_plot(data)`

Displays a box-and-whisker plot for a numeric data series.

A box plot shows the middle half of the data, the median, and possible outliers, making it useful for quickly understanding the distribution of a list of numbers.

Parameters:

Name	Type	Description	Default
`data`	`list[float]`	The numeric values to visualize.	required

Returns:

Name	Type	Description
`None`	`None`	The box plot is rendered directly via `plt.show()`.

Raises:

Type	Description
`ValueError`	If `data` is empty.

Example

The Py_simple WayThe Traditional Way

from py_simple import plot_box_plot

plot_box_plot([12, 14, 15, 15, 16, 18, 30])

import matplotlib.pyplot as plt

data = [12, 14, 15, 15, 16, 18, 30]
fig, ax = plt.subplots()
ax.boxplot(data)
ax.set_title("Box plot")
plt.show()

`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()

`plot_heatmap(data, title=None, x_label=None, y_label=None)`

Displays a heatmap for a two-dimensional numeric data series.

A heatmap represents values in a grid using color intensity, making it useful for quickly spotting patterns, differences, and high or low values across a two-dimensional dataset.

Parameters:

Name	Type	Description	Default
`data`	`list[list[int \| float]]`	A rectangular two-dimensional list containing numeric values.	required
`title`	`str`	Text shown above the heatmap. Defaults to `None` (no title).	`None`
`x_label`	`str`	Text shown under the x-axis. Defaults to `None` (no label).	`None`
`y_label`	`str`	Text shown beside the y-axis. Defaults to `None` (no label).	`None`

Returns:

Name	Type	Description
`None`	`None`	The heatmap is rendered directly via `plt.show()`.

Raises:

Type	Description
`ValueError`	If `data` is empty, is not two-dimensional, is not rectangular, or contains non-numeric values.

Example

The Py_simple WayThe Traditional Way

from py_simple import plot_heatmap

plot_heatmap(
    [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
    title="Example Heatmap",
    x_label="Columns",
    y_label="Rows",
)

import matplotlib.pyplot as plt

data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
fig, ax = plt.subplots()
image = ax.imshow(data)
fig.colorbar(image, ax=ax)
ax.set_title("Example Heatmap")
ax.set_xlabel("Columns")
ax.set_ylabel("Rows")
plt.show()