Visualizing Appointment Duration Distribution#

Illustrates the length of appointments in minutes, revealing how much time patients typically spend per visit. This visualization helps identify standard session lengths and detect unusual duration patterns in the simulated dataset.

Function Overview#

Function: medscheduler.utils.plotting.plot_appointment_duration_distribution(df)

Inputs:

df (pd.DataFrame) — Appointment table containing an appointment_duration column (in minutes).

Returns: matplotlib.axes.Axes — Histogram showing the distribution of appointment durations.

Validation & error handling:

Missing column → raises ValueError("DataFrame must contain an 'appointment_duration' column.").
All values missing or non-numeric → returns _empty_plot("No valid data available for 'appointment_duration'.").

Output Description#

X-axis: Appointment duration (minutes) grouped in 5-minute intervals.
Y-axis: Number of appointments per duration bin.
Bars: Represent the frequency of appointments lasting within each duration range.
Color scheme: Primary Medscheduler blue (#67A7D4).
Filtering: Bins representing less than 0.1% of total data are automatically excluded for readability.
Annotations: Displays percentage values above each visible bar.
Style: Clean histogram layout with dashed Y-grid, no top/right spines, and left-aligned title.

This chart provides insight into service delivery patterns, helping assess clinic efficiency or verify that simulated durations align with expected operational standards.

Example#

from medscheduler import AppointmentScheduler
from medscheduler.utils.plotting import plot_appointment_duration_distribution

# Generate synthetic appointment dataset
sched = AppointmentScheduler()
slots_df, appts_df, patients_df = sched.generate()

# Visualize the duration breakdown of appointments
ax = plot_appointment_duration_distribution(appts_df)
ax.figure.show()  # optional when running interactively

Output preview:
The following chart illustrates the distribution of appointment durations, showing how frequently each time interval occurs within the simulated dataset.

Appointment Duration Distribution

Next Steps#

Compare with waiting time patterns: Visualizing Patient Waiting Time Distribution
Learn how appointment duration is generated: Appointment timing
Review duration parameters in the appointments table: Appointments table
Adjust simulation realism through customization: Customization options