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Welcome to the Lab 🛠️

Manual repetitive tasks can take hours. AI can act as a coding co-pilot, helping you write scripts that automate these tasks. Today, you’ll generate Python scripts for real-world automation, test them, and refine them interactively.

Step 0: Define the Task

Scenario: You receive daily CSV reports and want to automatically rename, filter, and summarize them.

• Input: Folder of CSV files
• Output: Summary CSV containing key stats
• Optional: Log errors or skipped files

Mini-challenge: Write down, in plain English, what you want the script to do.

Step 1: Craft a Prompt for the AI

Good prompts include:

1. Task description
2. Input/output format
3. Optional constraints (performance, logging, style)

Example Prompt:

Write a Python script that reads all CSV files in a folder, filters out rows where "Status" is "Inactive", and writes a summary CSV with the counts of active items. Include error handling and logging.

Step 2: AI Output Example

import pandas as pd
import os

summary = []

for file in os.listdir('data'):
    if file.endswith('.csv'):
        try:
            df = pd.read_csv(os.path.join('data', file))
            active_count = df[df['Status'] == 'Active'].shape[0]
            summary.append({'file': file, 'active_count': active_count})
        except Exception as e:
            print(f"Error processing {file}: {e}")

summary_df = pd.DataFrame(summary)
summary_df.to_csv('summary.csv', index=False)
print("Summary created successfully.")

Step 3: Test the Script

Run the script on sample data:

python automate_reports.py

• Check the summary output
• Note any errors or missing functionality
• Update the prompt to include additional rules (e.g., sorting, multiple columns, or logging to a file)

Step 4: Mini Lab Challenges

1. Add a feature to sort the summary CSV by active_count descending.
2. Modify the script to handle multiple folders automatically.
3. Include logging to a file instead of printing errors.
4. Experiment with prompts for Excel automation, sending emails, or PDF generation.

Step 5: Pro Tips

• Break large automation tasks into smaller steps for the AI.
• Provide examples if the task is complex.
• Test the script thoroughly — AI may miss edge cases.
• Combine AI-generated scripts with your own code for robust automation.
• Iteratively refine prompts to improve readability, efficiency, and error handling.

Lab Summary

• AI can generate full automation scripts from plain-English instructions.
• Clear prompts = task description + inputs/outputs + constraints.
• Iteration is key: refine prompts → test → improve.
• Using AI for scripting saves time and allows you to focus on higher-value tasks.