Use Case #8 — Validate ZIP Codes, Phones, and Emails

Business Purpose

Propósito de negocio

Apply practical format checks to common contact and location fields.

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 validated dataset

import pandas as pd
from pathlib import Path

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

df = pd.read_csv(input_file, dtype={"Zip_Code": "string"})

ZIP codes are intentionally loaded as text to preserve leading zeros.

Step 2 — Validate ZIP codes

zip_pattern = r"^\d{5}(-\d{4})?$"

df["Valid_Zip_Code"] = (
    df["Zip_Code"].astype("string").str.fullmatch(zip_pattern, na=False)
)

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

Five-digit and ZIP+4 formats pass; malformed values fail.

Step 3 — Validate email addresses

email_pattern = r"^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$"

df["Valid_Email"] = (
    df["Email"].astype("string").str.fullmatch(email_pattern, na=False)
)

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

Basic email structure is checked without claiming full deliverability.

Step 4 — Normalize and validate phone numbers

digits = df["Phone"].astype("string").str.replace(r"\D", "", regex=True)

df["Phone_Digits"] = digits
df["Valid_Phone"] = digits.str.fullmatch(r"\d{10}", na=False)

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

Formatting symbols are removed before checking for ten digits.

Step 5 — Export validation results

df.to_csv(output_file, index=False, encoding="utf-8-sig")
print(f"Saved: {output_file}")

The dataset retains original values plus reusable validity flags.

Use Case #8 Completed

Caso de uso #8 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.