Identify geographic concentrations of demand and potential service hotspots.
Caso práctico progresivo para transformar datos municipales sucios en información confiable, analizable y útil para decisiones.
Identify geographic concentrations of demand and potential service hotspots.
Este caso continúa el flujo real de trabajo con solicitudes de servicios municipales y documenta cada transformación de forma trazable.
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.
import pandas as pd
from pathlib import Path
base = Path.home() / "Documents" / "Pandas_Use_Cases"
input_file = base / "city_service_requests_clean_master.csv"
output_file = base / "analysis_by_city_zip.csv"
df = pd.read_csv(input_file, dtype={"Zip_Code": "string"})ZIP codes remain text while geographic dimensions are analyzed.
city_summary = (
df.groupby("City", dropna=False)
.agg(
Request_Count=("Request_ID", "count"),
Average_Resolution_Days=("Resolution_Days", "mean"),
Average_Satisfaction=("Satisfaction_Score", "mean")
)
.round(2)
.sort_values("Request_Count", ascending=False)
)
print(city_summary.to_string())Cities are compared by volume, speed, and satisfaction.
geo_summary = (
df.groupby(["City", "Zip_Code"], dropna=False)
.agg(
Request_Count=("Request_ID", "count"),
Total_Estimated_Cost=("Estimated_Cost", "sum"),
Average_Resolution_Days=("Resolution_Days", "mean")
)
.round(2)
.reset_index()
.sort_values("Request_Count", ascending=False)
)
print(geo_summary.head(20).to_string(index=False))High-volume city–ZIP combinations reveal local demand clusters.
threshold = geo_summary["Request_Count"].quantile(0.90)
geo_summary["Potential_Hotspot"] = (
geo_summary["Request_Count"] >= threshold
)
print(f"Hotspot threshold: {threshold}")The highest-demand geographic groups are flagged using a transparent percentile rule.
geo_summary.to_csv(output_file, index=False, encoding="utf-8-sig")
print(f"Saved: {output_file}")The output can support maps and neighborhood-level planning.
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.
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