Python Foundations Workshop

You've been using Python all semester, but a lot of it has felt like following a recipe. This workshop strips away the pandas magic and lets you work with the language directly — writing real code from scratch, making mistakes, and fixing them.

There are two parts. Part 1 walks you through the fundamentals by typing and running small snippets. Part 2 is a debugging challenge where you fix a broken script against real data. Start with Part 1 and move to Part 2 when you're ready.

Part 1 Python Fundamentals

Open a Python terminal (type python in your command prompt) or create a new .py file and run these examples. Type them yourself — don't copy and paste. Typing builds muscle memory.

Variables: Naming Your Data

A variable is just a name you attach to a value. You pick the name. Python remembers the value.

type this
revenue = 75000
company = "Acme Corp"
tax_rate = 0.065

print(revenue)           # 75000
print(company)           # Acme Corp
print(revenue * tax_rate) # 4875.0

Types: Why "50" Is Not the Same as 50

Every value in Python has a type. The type determines what you can do with it.

type this
print(type(142))          # <class 'int'>     whole number
print(type(0.065))        # <class 'float'>   decimal
print(type("Cash"))       # <class 'str'>     text
print(type(True))         # <class 'bool'>    true/false

# This breaks:
price = "29.99"
total = price + 10        # TypeError!

# Fix: convert the type
total = float(price) + 10 # 39.99
print(total)

Lists: Storing Multiple Values

A list is an ordered collection. You access items by their position, starting at zero.

type this
accounts = ["Cash", "AR", "Revenue", "COGS"]

print(accounts[0])   # Cash   (first item)
print(accounts[2])   # Revenue (third item)
print(len(accounts)) # 4

# Add an item
accounts.append("Utilities")
print(accounts)      # [..., 'Utilities']

Dictionaries: Labeled Lookups

A dictionary maps labels (keys) to values. Think of it as a lookup table — like a chart of accounts.

type this
account = {
    "number": 1010,
    "name": "Cash",
    "balance": 50000,
    "type": "Asset"
}

print(account["name"])     # Cash
print(account["balance"])  # 50000

# This is exactly what df["column"] does in pandas

For Loops: Doing Something for Each Item

A for loop takes a collection and runs a block of code once for each item.

type this
expenses = ["Rent", "Salaries", "Utilities", "Supplies"]

for expense in expenses:
    print(f"Processing: {expense}")

# Output:
# Processing: Rent
# Processing: Salaries
# Processing: Utilities
# Processing: Supplies

If/Else: Making Decisions

Python checks conditions top to bottom and runs the first block that's true.

type this
balance = -15000

if balance > 0:
    print("Debit balance")
elif balance < 0:
    print("Credit balance")
else:
    print("Zero balance")

# Output: Credit balance

Functions: Reusable Blocks of Code

A function is a named recipe. You define it once and call it whenever you need it. This is what pd.read_csv() is — just a function someone else wrote.

type this
def calculate_tax(amount, rate=0.065):
    """Calculate sales tax for a given amount."""
    return amount * rate

# Use the default rate
print(calculate_tax(1000))       # 65.0

# Override the rate
print(calculate_tax(5000, 0.08)) # 400.0

Putting It All Together

Here's a small program that uses everything above. Type it into a file called practice.py and run it.

practice.py
# A mini journal entry checker

entries = [
    {"account": "Cash",       "debit": 5000, "credit": 0},
    {"account": "Revenue",    "debit": 0,    "credit": 5000},
    {"account": "Rent",       "debit": 3000, "credit": 0},
    {"account": "Cash",       "debit": 0,    "credit": 3000},
]

def check_balance(entries):
    total_debits = 0
    total_credits = 0
    for entry in entries:
        total_debits = total_debits + entry["debit"]
        total_credits = total_credits + entry["credit"]
        print(f"  {entry['account']:<15} DR: {entry['debit']:>8,}  CR: {entry['credit']:>8,}")

    print()
    print(f"  Total Debits:  {total_debits:>8,}")
    print(f"  Total Credits: {total_credits:>8,}")

    if total_debits == total_credits:
        print("  Balanced!")
    else:
        print(f"  DISCREPANCY: {abs(total_debits - total_credits):,}")

check_balance(entries)

Part 2 Debug Challenge

Now that you've written Python from scratch, let's use those skills. You have a script that validates journal entries from CSV files. It's broken. Your job is to fix it, then run it against five data files. Each file introduces a different real-world data problem.

Use an LLM if you want, Google it, ask a neighbor, or just stare at it until it clicks. There's no wrong way to work through this.

Download everything into the same folder. Then run:

python validate_journal_entries.py entries_jan.csv

It will break immediately. Read the error. That's your starting point.