Produce an analysis-ready master table while preserving audit and quality metadata.
Caso práctico progresivo para transformar datos municipales sucios en información confiable, analizable y útil para decisiones.
Produce an analysis-ready master table while preserving audit and quality metadata.
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 / "data_quality_scored.csv"
output_file = base / "city_service_requests_clean_master.csv"
df = pd.read_csv(
input_file,
parse_dates=["Created_Date", "Closed_Date"]
)The complete working dataset is loaded with parsed dates.
master_columns = [
"Request_ID", "Created_Date", "Closed_Date",
"Service_Type", "Department", "Status", "Priority",
"Request_Channel", "Street_Address", "City", "State",
"Zip_Code", "Latitude", "Longitude", "Estimated_Cost",
"Resolution_Days", "Satisfaction_Score",
"Data_Quality_Score", "Data_Quality_Band",
"Has_Any_Data_Quality_Issue"
]
master = df[master_columns].copy()The master table includes core analytics fields and high-level quality metadata.
master["Request_ID"] = master["Request_ID"].astype("string")
master["Zip_Code"] = master["Zip_Code"].astype("string")
master["Status"] = master["Status"].astype("category")
master["Priority"] = master["Priority"].astype("category")
print(master.dtypes.to_string())Explicit types improve consistency, memory use, and downstream behavior.
assert master["Request_ID"].is_unique
assert master.shape[0] > 0
assert set(["Request_ID", "Created_Date", "Status"]).issubset(master.columns)
print("Integrity checks passed.")The script stops if core master-table assumptions fail.
master.to_csv(output_file, index=False, encoding="utf-8-sig")
print(f"Saved: {output_file}")
print(f"Rows: {len(master):,}")
print(f"Columns: {master.shape[1]}")A documented analysis-ready master dataset is created.
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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Recursos comunitarios enfocados en oficios, habilidades, oportunidades locales y desarrollo profesional práctico.
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Recursos orientados a Miami para aprendizaje práctico, rutas de empleo y desarrollo de la fuerza laboral.
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