Adding Custom Categorical Variables#

Overview#

Beyond demographic and scheduling parameters, AppointmentScheduler allows extending the synthetic dataset with new categorical variables — such as insurance type, geographic region, or clinic branch — using the add_custom_column() method.
These additional fields enrich the simulated patient table (patients_df) without altering the core data-generation process.

You will explore:

  1. Adding a balanced attribute (uniform distribution).

  2. Adding a skewed regional attribute (normal distribution).

  3. Adding a highly imbalanced attribute (Pareto distribution).

Example 1 – Uniform distribution (balanced attribute)#

A new variable insurance_type evenly splits the population into Public and Private coverage.

from medscheduler import AppointmentScheduler
from medscheduler.utils.plotting import plot_custom_column_distribution

# Generate baseline dataset
sched_uniform = AppointmentScheduler()
slots_df, appts_df, patients_df = sched_uniform.generate()

# Add insurance type with uniform probability
sched_uniform.add_custom_column(
    column_name="insurance_type",
    categories=["Public", "Private"],
    distribution_type="uniform"
)

plot_custom_column_distribution(patients_df, column="insurance_type")

Output preview:
Below are the main results for this configuration:

  1. Category distribution – Nearly equal proportions across insurance groups.
    Custom column distribution – Uniform

Interpretation:
Uniform distributions are ideal for balanced segmentation variables, ensuring each group is equally represented — useful for fair testing or visualization purposes.

Example 2 – Normal distribution (regional variation)#

Now we introduce a three-region attribute, with the central region most frequent and the outer ones less represented.

sched_region = AppointmentScheduler()
slots_df, appts_df, patients_df = sched_region.generate()

sched_region.add_custom_column(
    column_name="region",
    categories=["North", "Center", "South", "West", "East"],
    distribution_type="normal"
)

plot_custom_column_distribution(patients_df, column="region")

Output preview:

  1. Category distribution – Bell-shaped distribution with a dominant “Center” group.
    Custom column distribution – Normal

Interpretation:
The normal model generates a realistic middle-heavy pattern, useful for representing geographically centered populations or other naturally clustered attributes.

Example 3 – Pareto distribution (dominant providers)#

Finally, we simulate a field representing health insurance providers, where a few dominate the market while others serve smaller shares.

sched_pareto = AppointmentScheduler()
slots_df, appts_df, patients_df = sched_pareto.generate()

sched_pareto.add_custom_column(
    column_name="insurance_provider",
    categories=[
        "Vitalynx Orbit", "Carebubble Spectrum", "CurativeWhale",
        "HealthZenotron", "Mediflora Nexus", "BioCrest Harmony",
        "MediNimbus", "QuantumPetal", "Heliospring Vital",
        "MediSpectra Flux", "CuraQuark Alliance"
    ],
    distribution_type="pareto"
)

plot_custom_column_distribution(patients_df, column="insurance_provider")

Output preview:

  1. Category distribution – Heavily right-skewed proportions showing a few dominant insurers.
    Custom column distribution – Pareto

Interpretation:
The Pareto model mirrors real-world skewness, where a small number of categories account for most observations.
Such variables are useful when simulating unequal resource distribution or modeling provider market share.

Summary#

Scenario

Distribution type

Typical shape

Use case

Insurance type

Uniform

Flat

Balanced segmentation

Region

Normal

Bell-shaped

Central dominance

Insurance provider

Pareto

Right-skewed

Market inequality

Notes#

  • Each column is reproducible under the same seed.

  • Probabilities can also be manually supplied via custom_probs.

  • Added fields integrate seamlessly into patients_df for analysis, joining, or visualization.

Next Steps#

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