Normalize spaces, capitalization, spelling variations, and equivalent business categories without changing the underlying meaning.
Caso práctico progresivo para transformar datos municipales sucios en información confiable, analizable y útil para decisiones.
Normalize spaces, capitalization, spelling variations, and equivalent business categories without changing the underlying meaning.
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 / "messy_city_service_requests.csv"
output_file = base / "standardized_city_service_requests.csv"
df = pd.read_csv(input_file)
print(f"Rows loaded: {len(df):,}")
print(f"Columns loaded: {df.shape[1]}")Rows loaded: 530 Columns loaded: 22
text_columns = df.select_dtypes(include="object").columns
for column in text_columns:
df[column] = df[column].str.strip()
print(f"Text columns processed: {len(text_columns)}")Leading and trailing spaces removed from every text column.
maps = {
"City": {
"HIALEAH": "Hialeah",
"MIAMI": "Miami",
"DORAL": "Doral",
"Miami-Lakes": "Miami Lakes"
},
"State": {
"fl": "FL",
"Fla": "FL",
"Florida": "FL"
},
"Priority": {
"HIGH": "High",
"high": "High",
"Med": "Medium",
"medium": "Medium",
"LOW": "Low",
"Normal": "Medium"
},
"Status": {
"open": "Open",
"OPEN": "Open",
"closed": "Closed",
"In progress": "In Progress"
},
"Request_Channel": {
"phone": "Phone",
"Website": "Web",
"App": "Mobile App"
},
"Department": {
"public works": "Public Works",
"PUBLIC WORKS": "Public Works",
"Sanitation Dept": "Sanitation",
"Water and Sewer": "Water & Sewer"
},
"Service_Type": {
"pothole": "Pothole",
"POTHOLE": "Pothole",
"Streetlight": "Street Light",
"street light": "Street Light",
"Garbage Pickup": "Garbage",
"Trash": "Garbage",
"water leak": "Water Leak",
"Noise": "Noise Complaint"
}
}
for column, mapping in maps.items():
df[column] = df[column].replace(mapping)Equivalent labels are consolidated into one approved value per category.
review_columns = [
"City", "State", "Priority", "Status",
"Request_Channel", "Department", "Service_Type"
]
for column in review_columns:
print(f"\n{column}: {df[column].nunique(dropna=False)} values")
print(df[column].value_counts(dropna=False).to_string())Category counts should decrease because duplicate spellings and formats are merged.
df.to_csv(output_file, index=False, encoding="utf-8-sig")
print(f"Saved: {output_file}")
print(f"Rows exported: {len(df):,}")A new standardized CSV is created while the raw source remains unchanged.
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