Analyze request activity over time and identify seasonality, growth, and unusual monthly changes.
Caso práctico progresivo para transformar datos municipales sucios en información confiable, analizable y útil para decisiones.
Analyze request activity over time and identify seasonality, growth, and unusual monthly changes.
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 / "monthly_operational_trends.csv"
df = pd.read_csv(input_file, parse_dates=["Created_Date"])
df = df.dropna(subset=["Created_Date"]).copy()
df["Year_Month"] = df["Created_Date"].dt.to_period("M").astype(str)
df["Month_Name"] = df["Created_Date"].dt.month_name()
df["Day_of_Week"] = df["Created_Date"].dt.day_name()Calendar dimensions are created from valid request dates.
monthly = (
df.groupby("Year_Month")
.agg(
Request_Count=("Request_ID", "count"),
Average_Resolution_Days=("Resolution_Days", "mean"),
Total_Estimated_Cost=("Estimated_Cost", "sum")
)
.round(2)
.reset_index()
)
print(monthly.tail(12).to_string(index=False))The recent monthly pattern becomes visible.
monthly["Volume_Change"] = monthly["Request_Count"].diff()
monthly["Volume_Change_Percent"] = (
monthly["Request_Count"].pct_change() * 100
).round(2)
print(monthly.tail(12).to_string(index=False))Growth and decline are quantified month over month.
mean_change = monthly["Volume_Change"].mean()
std_change = monthly["Volume_Change"].std()
monthly["Unusual_Change"] = (
(monthly["Volume_Change"] - mean_change).abs()
> 2 * std_change
)
print(monthly[monthly["Unusual_Change"]].to_string(index=False))Months with changes far from the normal pattern are flagged for investigation.
monthly.to_csv(output_file, index=False, encoding="utf-8-sig")
print(f"Saved: {output_file}")The time-series table is ready for charts, forecasting, and operational 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.
Explore additional resources connected to local training, practical skills, employment opportunities, and community growth.
Explora recursos adicionales relacionados con capacitación local, habilidades prácticas, oportunidades de empleo y crecimiento comunitario.
Community-focused resources for trades, skills, local opportunities, and practical career development.
Recursos comunitarios enfocados en oficios, habilidades, oportunidades locales y desarrollo profesional práctico.
hialeahoficios.comMiami-oriented resources for practical learning, employment pathways, and workforce development.
Recursos orientados a Miami para aprendizaje práctico, rutas de empleo y desarrollo de la fuerza laboral.
miamioficios.com