Use Case #7 — Validate Numeric Ranges and Business Rules

Business Purpose

Propósito de negocio

Test numeric fields against realistic operational limits and flag violations instead of silently changing them.

Este caso continúa el flujo real de trabajo con solicitudes de servicios municipales y documenta cada transformación de forma trazable.

Input and Output

Entrada y salida

Results can vary because the source dataset was generated synthetically and randomly. Run the code to obtain the exact values from your local file.

Los resultados pueden variar porque el dataset de origen fue generado de forma sintética y aleatoria. Ejecuta el código para obtener los valores exactos de tu archivo local.

Step 1 — Load the resolved dataset

import pandas as pd
from pathlib import Path

base = Path.home() / "Documents" / "Pandas_Use_Cases"
input_file = base / "request_id_conflicts_resolved.csv"
output_file = base / "numeric_rules_validated.csv"

df = pd.read_csv(input_file)

The working dataset is ready for business-rule validation.

Step 2 — Validate satisfaction scores

df["Invalid_Satisfaction_Score"] = ~(
    df["Satisfaction_Score"].between(1, 5)
    | df["Satisfaction_Score"].isna()
)

print(df["Invalid_Satisfaction_Score"].value_counts())

Scores below 1 or above 5 are flagged.

Step 3 — Validate resolution days

df["Invalid_Resolution_Days"] = (
    (df["Resolution_Days"] < 0)
    | (df["Resolution_Days"] > 365)
)

print(df["Invalid_Resolution_Days"].value_counts())

Negative and extreme resolution durations are identified.

Step 4 — Validate estimated cost

df["Invalid_Estimated_Cost"] = (
    (df["Estimated_Cost"] < 0)
    | (df["Estimated_Cost"] > 100000)
)

print(df["Invalid_Estimated_Cost"].value_counts())

Negative or operationally implausible costs are flagged.

Step 5 — Create a numeric issue count and export

numeric_flags = [
    "Invalid_Satisfaction_Score",
    "Invalid_Resolution_Days",
    "Invalid_Estimated_Cost"
]

df["Numeric_Issue_Count"] = df[numeric_flags].sum(axis=1)

df.to_csv(output_file, index=False, encoding="utf-8-sig")
print(df["Numeric_Issue_Count"].value_counts().sort_index())

Every row receives a count of numeric-rule violations for later prioritization.

Use Case #7 Completed

Caso de uso #7 completado

The output of this module becomes the controlled input for the next stage of the 20-case Pandas workflow.

La salida de este módulo se convierte en la entrada controlada para la siguiente etapa del flujo de 20 casos de Pandas.