Python for Data & Automation: Combining Data Skills with Automation in Canada

What You'll Learn: By the end of this guide, you'll understand how to automate Excel reports, build SQL data pipelines, create automated analytics dashboards, and combine pandas with automation for real-world business projects used in Canadian companies.

Benefits of Python Data Automation

• ⚡ Save Time: Automate repetitive data tasks that take hours manually. Weekly reports that took 4 hours now run in 2 minutes.
• 📊 Real-Time Insights: Automated data pipelines provide up-to-the-minute analytics instead of outdated manual reports.
• 🎯 Eliminate Errors: Automated data processing is consistent and error-free, unlike manual copy-paste operations.
• 📈 Scale Analysis: Process datasets with millions of rows that would crash Excel or take days manually.
• 💼 Career Value: Data automation skills are highly valued in Canadian job markets (Toronto, Vancouver, Montreal) with salaries $10,000-$25,000 higher than pure data analysts.

import pandas as pd
import numpy as np

# Reading data from multiple sources
df_csv = pd.read_csv('sales_data.csv')
df_excel = pd.read_excel('quarterly_report.xlsx', sheet_name='Q1')
df_sql = pd.read_sql_query('SELECT * FROM customers', connection)

# Data cleaning automation
df['date'] = pd.to_datetime(df['date'])  # Standardize dates
df = df.drop_duplicates()  # Remove duplicates
df = df.fillna(df.mean())  # Handle missing values

# Data transformation for automation
df['month'] = df['date'].dt.month
df['revenue'] = df['quantity'] * df['price']
monthly_summary = df.groupby('month')['revenue'].sum()

# Automated filtering and selection
high_value_customers = df[df['revenue'] > 10000]
recent_orders = df[df['date'] > '2026-01-01']

# Export for automation pipelines
df.to_csv('processed_data.csv', index=False)
df.to_excel('monthly_report.xlsx', sheet_name='Summary')

For Beginners: Start by manually working with pandas on small datasets. Once you understand data cleaning and transformation, you can automate these operations with scheduling tools like cron (Linux/Mac) or Task Scheduler (Windows).

import pandas as pd
from openpyxl import load_workbook
from openpyxl.styles import Font, PatternFill
from openpyxl.chart import BarChart, Reference

# Automated monthly sales report
def generate_monthly_sales_report(data_file, output_file):
    # Read and process data with pandas
    df = pd.read_csv(data_file)
    df['date'] = pd.to_datetime(df['date'])
    df['month'] = df['date'].dt.to_period('M')

    # Calculate monthly metrics
    monthly_sales = df.groupby('month').agg({
        'revenue': 'sum',
        'quantity': 'sum',
        'order_id': 'count'
    }).reset_index()

    # Export to Excel
    monthly_sales.to_excel(output_file, sheet_name='Sales', index=False)

    # Format Excel with openpyxl
    wb = load_workbook(output_file)
    ws = wb['Sales']

    # Style header row
    header_fill = PatternFill(start_color="3B82F6", fill_type="solid")
    header_font = Font(bold=True, color="FFFFFF")
    for cell in ws[1]:
        cell.fill = header_fill
        cell.font = header_font

    # Add chart
    chart = BarChart()
    chart.title = "Monthly Revenue"
    data = Reference(ws, min_col=2, min_row=1, max_row=ws.max_row)
    categories = Reference(ws, min_col=1, min_row=2, max_row=ws.max_row)
    chart.add_data(data, titles_from_data=True)
    chart.set_categories(categories)
    ws.add_chart(chart, "F2")

    wb.save(output_file)
    print(f"✅ Report generated: {output_file}")

# Run automation
generate_monthly_sales_report('sales_data.csv', 'monthly_report.xlsx')

Real-World Use Case (Toronto Finance Company): A financial analyst automated their monthly budget report that previously took 6 hours. Now it runs automatically every Monday morning and emails stakeholders. Time saved: 24 hours per month.

import pandas as pd
from sqlalchemy import create_engine
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

# Automated daily sales report from database
def automated_sales_report():
    # Connect to database
    engine = create_engine('postgresql://user:password@localhost/sales_db')

    # Extract data with SQL query
    query = """
    SELECT
        DATE(order_date) as date,
        SUM(total_amount) as daily_revenue,
        COUNT(DISTINCT customer_id) as unique_customers,
        COUNT(*) as order_count
    FROM orders
    WHERE order_date >= CURRENT_DATE - INTERVAL '7 days'
    GROUP BY DATE(order_date)
    ORDER BY date DESC
    """

    df = pd.read_sql(query, engine)

    # Transform data with pandas
    df['avg_order_value'] = df['daily_revenue'] / df['order_count']
    total_revenue = df['daily_revenue'].sum()

    # Generate HTML report
    html_report = f"""
    
Weekly Sales Summary
    
Total Revenue: ${total_revenue:,.2f}
    {df.to_html(index=False)}
    """

    # Email report automatically
    send_email_report("sales@company.com", "Weekly Sales Report", html_report)

    # Also save to Excel for backup
    df.to_excel('weekly_sales.xlsx', index=False)

    print("✅ Automated report completed and emailed")

def send_email_report(to_email, subject, html_body):
    # Email configuration (simplified)
    msg = MIMEMultipart()
    msg['Subject'] = subject
    msg.attach(MIMEText(html_body, 'html'))
    # Send email logic here
    pass

# Schedule this script to run daily using cron or Task Scheduler
automated_sales_report()

import pandas as pd
import requests
from sqlalchemy import create_engine
import logging
from datetime import datetime

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class DataPipeline:
    def __init__(self):
        self.engine = create_engine('postgresql://user:pass@localhost/analytics')

    def extract_api_data(self):
        """Extract data from REST API"""
        logger.info("Extracting data from API...")
        response = requests.get('https://api.example.com/sales')
        data = response.json()
        return pd.DataFrame(data)

    def extract_database_data(self):
        """Extract data from database"""
        logger.info("Extracting data from database...")
        query = "SELECT * FROM customers WHERE created_at >= CURRENT_DATE - 30"
        return pd.read_sql(query, self.engine)

    def transform_data(self, sales_df, customers_df):
        """Transform and merge datasets"""
        logger.info("Transforming data...")

        # Data cleaning
        sales_df['date'] = pd.to_datetime(sales_df['date'])
        sales_df = sales_df[sales_df['amount'] > 0]  # Remove invalid amounts

        # Merge datasets
        merged = sales_df.merge(customers_df, on='customer_id', how='left')

        # Calculate metrics
        merged['customer_lifetime_value'] = merged.groupby('customer_id')['amount'].transform('sum')

        return merged

    def validate_data(self, df):
        """Validate data quality"""
        logger.info("Validating data...")

        # Check for nulls
        null_count = df.isnull().sum().sum()
        if null_count > 0:
            logger.warning(f"Found {null_count} null values")

        # Check for duplicates
        dup_count = df.duplicated().sum()
        if dup_count > 0:
            logger.warning(f"Found {dup_count} duplicate rows")
            df = df.drop_duplicates()

        return df

    def load_data(self, df):
        """Load data to destination"""
        logger.info("Loading data to database...")
        df.to_sql('sales_analytics', self.engine, if_exists='replace', index=False)
        logger.info(f"✅ Loaded {len(df)} rows successfully")

    def run_pipeline(self):
        """Execute full pipeline"""
        try:
            logger.info("Starting data pipeline...")

            # Extract
            sales_df = self.extract_api_data()
            customers_df = self.extract_database_data()

            # Transform
            transformed_df = self.transform_data(sales_df, customers_df)

            # Validate
            validated_df = self.validate_data(transformed_df)

            # Load
            self.load_data(validated_df)

            logger.info("✅ Pipeline completed successfully")

        except Exception as e:
            logger.error(f"❌ Pipeline failed: {str(e)}")
            # Send alert email here
            raise

# Run pipeline
if __name__ == "__main__":
    pipeline = DataPipeline()
    pipeline.run_pipeline()

Top Business Automation Use Cases

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import joblib
from sqlalchemy import create_engine

class AutomatedMLPipeline:
    def __init__(self):
        self.engine = create_engine('postgresql://user:pass@localhost/ml_db')
        self.model = RandomForestClassifier()

    def get_training_data(self):
        """Extract latest training data"""
        query = """
        SELECT * FROM customer_features
        WHERE created_at >= CURRENT_DATE - 90
        """
        return pd.read_sql(query, self.engine)

    def preprocess_data(self, df):
        """Feature engineering and preprocessing"""
        # Automated feature creation
        df['days_since_signup'] = (pd.Timestamp.now() - df['signup_date']).dt.days
        df['purchase_frequency'] = df['total_purchases'] / df['days_since_signup']

        # Handle categorical variables
        df = pd.get_dummies(df, columns=['province', 'customer_segment'])

        return df

    def train_model(self, X, y):
        """Train and save model"""
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

        self.model.fit(X_train, y_train)

        # Evaluate
        accuracy = accuracy_score(y_test, self.model.predict(X_test))
        print(f"Model accuracy: {accuracy:.2%}")

        # Save model
        joblib.dump(self.model, 'customer_churn_model.pkl')

    def run_automated_training(self):
        """Full automated training pipeline"""
        # Extract
        df = self.get_training_data()

        # Preprocess
        df = self.preprocess_data(df)

        # Split features and target
        X = df.drop('churned', axis=1)
        y = df['churned']

        # Train
        self.train_model(X, y)

        print("✅ Automated model training completed")

# Schedule this to run weekly to retrain model on fresh data
pipeline = AutomatedMLPipeline()
pipeline.run_automated_training()

1. Automated Excel Report Generator: Read CSV data, analyze with pandas, create formatted Excel reports with charts
2. Database to Excel ETL: Extract data from PostgreSQL/MySQL, transform with pandas, export to Excel automatically
3. Web Scraping Data Pipeline: Scrape data from websites, store in database, analyze trends with pandas
4. Email Report Automation: Query database daily, generate pandas analysis, email HTML reports to stakeholders
5. CSV File Consolidator: Merge multiple CSV files, clean data with pandas, create unified dataset
6. Automated Data Quality Checker: Scan datasets for nulls, duplicates, outliers, generate quality reports
7. API to Database Pipeline: Fetch data from REST APIs, transform with pandas, load into database
8. Excel to SQL Automation: Monitor folder for Excel files, automatically import to database with data validation
9. Automated Data Visualization: Generate charts and dashboards automatically from live data sources
10. Multi-Source Data Aggregator: Combine data from APIs, databases, and Excel into unified analytics dataset

Step-by-Step Learning Path

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