Python Mini-Projects, Part 1

#How This Lesson Works

For each project, you will get:

1. A problem statement. What you are building and why
2. Concepts used. Which earlier lessons are relevant
3. Starter code — a skeleton with TODOs to guide you
4. A full solution — to compare against after you try it yourself
5. Challenge extensions — harder tasks for when you want more

#Project 1: Calculator with History

#Problem Statement

This is a classic first project because it ties together user input, functions, conditionals, and data structures in a way that feels like a real application.

#Concepts Used

• Functions. Each operation is a separate function (from the Functions lesson)
• Lists as stacks. History uses append() and pop() (from the Data Structures lesson)
• Control flow — menu loop with if/elif (from the Control Flow lesson)
• String processing — parsing user input

#Starter Code

Try to fill in the TODOs before looking at the solution.

#Challenge Extensions

1. Scientific operations. Add power(a, b), sqrt(a), and modulo(a, b) functions
2. Expression parser — instead of separate a, op, b values, parse a string like "10 + 5" using .split()
3. Persistent history. Save history to a text file so it survives between runs (use what you learned in the File I/O lesson)
4. Redo functionality. Add a separate "redo stack" that stores undone items and lets you redo them

#Project 2: Contact Book

#Problem Statement

This project combines data structures with file I/O and introduces you to the kind of CRUD (Create, Read, Update, Delete) operations that are the foundation of almost every software application.

#Concepts Used

• Dictionaries. Contacts stored as name-to-info mappings (from the Dicts & Hash Tables lesson)
• OOP — the ContactBook as a class with methods (from the Classes & OOP lesson)
• File I/O — saving and loading contacts with JSON (from the File I/O lesson)
• Exception handling — graceful error handling for missing files and invalid input

#Starter Code

#Challenge Extensions

1. Update contact. Add an update_contact(name, phone=None, email=None) method that updates only the provided fields
2. Group contacts. Add a "group" field (family, work, friends) and a list_by_group(group) method
3. Export to CSV. Add a save_to_csv(filename) method that writes contacts in comma-separated format
4. Input validation — validate that phone numbers contain only digits and dashes, and emails contain an @ symbol
5. Favorite contacts. Add a boolean favorite field and a list_favorites() method

#Project 3: Word Frequency Analyzer

#Problem Statement

Build a tool that reads text, counts the frequency of each word, filters out common "stop words" (like "the", "a", "is"), and reports the top N most frequent words.

This is the exact foundation of how text processing works in NLP. When you hear about "tokenization" and "term frequency" in machine learning, this is what is happening at the simplest level. Every NLP pipeline starts with exactly this kind of word counting — you are building the first stage of a natural language processing system.

#Concepts Used

• Strings — splitting, lowering, stripping punctuation (from the Strings Deep Dive lesson)
• Dictionaries — manual word counting (from the Dicts & Hash Tables lesson)
• collections.Counter — efficient counting (from the Collections Module lesson)
• File I/O — reading text from files (from the File I/O lesson)
• Functions — clean decomposition into reusable pieces

#Connection to Machine Learning

This is how tokenization works at the simplest level. In NLP, the first step of any text pipeline is breaking text into tokens (words) and counting their frequency. The techniques you build here — lowercasing, removing punctuation, filtering stop words, counting frequencies — are the exact preprocessing steps used before feeding text into models like BERT, GPT, and other transformers. The only difference is that production systems use more sophisticated tokenizers (like BPE or WordPiece), but the principle is identical.

#Starter Code

#Challenge Extensions

1. Bigrams. Instead of single words, count pairs of consecutive words (e.g., "machine learning" appears together). This is called bigram analysis and is used heavily in NLP
2. TF-IDF. If you have multiple documents, compute Term Frequency-Inverse Document Frequency to find words that are important to a specific document but not common across all documents
3. Read from file. Modify analyze_text to accept a filename instead of a string, and read the file contents
4. Sentiment words. Create a list of positive words and negative words, then count how many of each appear in the text to get a basic sentiment score
5. Zipf's Law — plot word rank vs. frequency and observe that it follows Zipf's law (the nth most common word appears roughly 1/n times as often as the most common word)

#What You Have Built

Take a moment to appreciate what you just did. In three projects, you used:

#Design Principles You Practiced

Even though this lesson was about building, you were also practicing software design without realizing it:

#1. Separation of Concerns

#2. DRY (Don't Repeat Yourself)

#3. Encapsulation (OOP)

#4. Graceful Error Handling

Division by zero, missing files, invalid input — your code handles these cases instead of crashing. This is what separates a script from software.

#5. Pipeline Architecture