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Visualization

QuickLearnKit provides teaching-friendly wrappers around seaborn + matplotlib.

These wrappers are designed to:

  • Reduce repetitive plotting boilerplate
  • Optionally display numeric values directly on plots
  • Return a matplotlib.Axes object for full customization
  • Automatically display plots by default (ideal for notebooks)

Common Parameters

All plotting functions share a consistent interface:

Parameter Description
data pandas DataFrame used for plotting
x Column name for x-axis
y Column name for y-axis (if applicable)
title Optional plot title
show_values "yes" or "no" to display numeric values
fmt Format string for value labels (e.g., {:.2f})
show If False, returns Axes without displaying

Why it matters:
Students can quickly learn plotting concepts without memorizing seaborn/matplotlib boilerplate, while instructors can demonstrate clarity by showing values directly on charts.

bar_plot

bar_plot(data, x, y, title=None, show_values="no", fmt="{:.1f}", show=True)

Example

from quicklearnkit import bar_plot
import seaborn as sns

df = sns.load_dataset("tips")

bar_plot(df, x="day", y="total_bill", show_values="yes")

line_plot

line_plot(data, x, y, title=None, show_values="no", fmt="{:.2f}", show=True)

scatter_plot

scatter_plot(data, x, y, title=None, show_values="no", fmt="{:.2f}", show=True)

count_plot

count_plot(data, x, title=None, show_values="no", show=True)

box_plot

Displays mean values when show_values="yes".

box_plot(data, x=None, y=None, title=None, show_values="no", fmt="{:.2f}", show=True)

hist_plot

Displays bin counts when show_values="yes".

hist_plot(data, x, bins=10, title=None, show_values="no", fmt="{:.0f}", show=True)

Customization Example

Because all functions return a matplotlib.Axes object, you can customize further:

ax = bar_plot(df, x="day", y="total_bill", show_values="yes", show=False)

ax.set_xlabel("Day of Week")
ax.set_ylabel("Average Bill")
ax.set_ylim(0, 40)

import matplotlib.pyplot as plt
plt.show()

Why it matters:
Learners get the simplicity of QuickLearnKit wrappers, but advanced users retain full matplotlib control for professional-quality plots.

Design Intent

Visualization wrappers are designed to:

  • Help students see numeric values clearly
  • Reduce repetitive plotting code
  • Maintain compatibility with standard matplotlib workflows
  • Simplify — but never restrict — customization

✨ In short: QuickLearnKit visualization tools make plots teaching-ready out of the box. They balance simplicity for beginners with flexibility for advanced users, ensuring clarity without sacrificing control.