Day 30 - Tables in Python

TODO: make an actual CSV with restaurant orders, replace placeholder below

Introduction to Tables in Python with Pandas

1. Introduction (10 minutes)

• Motivation & Overview:
  • Introduce Pandas as a popular package for table-based data analysis in Python.
• Key Concepts:
  • DataFrame: A table with rows and columns.
  • Series: A single column (or row) of data.
• Fresh Example Context:
  • A restaurant order dataset, where each order has a date, dish name, quantity, and order type (dine-in or takeout).

2. Creating and Loading DataFrames (15 minutes)

• Manually Creating a DataFrame:
  • Example Code:

    import pandas as pd
    
    # Create a simple restaurant orders DataFrame manually
    data = {
        'date': ['2023-07-01', '2023-07-01', '2023-07-02', '2023-07-02', '2023-07-03'],
        'dish': ['Pasta', 'Salad', 'Burger', 'Pasta', 'Steak'],
        'quantity': [2, 1, 3, 2, 1],
        'order_type': ['dine-in', 'takeout', 'dine-in', 'dine-in', 'takeout']
    }
    orders = pd.DataFrame(data)
    print(orders)
• Loading Data from CSV:
  • Show how to load a CSV from a URL (use a different CSV URL or assume a local file for this example).

    orders_url = "https://example.com/restaurant_orders.csv"  # replace with a valid URL
    orders = pd.read_csv(orders_url, header=0)
• Accessing Data:
  • Access columns by label (e.g., orders['dish']) and rows by index (e.g., orders.iloc[2]).
• Interactive Exercise:
  • Ask: “What does orders['quantity'][1] return?”
  • Have students experiment using interactive Python sessions.

3. Data Cleaning and Filtering Rows (20 minutes)

• Cleaning Data:
  • Suppose the CSV sometimes uses missing values (NaN) for order type.
  • Example Code to Replace NaN:

    # Replace missing values in order_type with 'unknown'
    orders['order_type'] = orders['order_type'].fillna('unknown')
• Filtering Rows:
  • Filter rows to show only dine-in orders.

    dine_in_orders = orders[orders['order_type'] == 'dine-in']
    print(dine_in_orders)
• Using Boolean Masks:
  • Explain that masks are arrays of booleans. For example:

    mask = orders['quantity'] > 1
    filtered_orders = orders[mask]
    print(filtered_orders)
• Interactive Questions:
  • “What does orders[orders['dish'] == 'Pasta'] return?”
  • “Try filtering orders to show only those from 2023-07-02.”

4. Wrap-Up (5 minutes)

• Recap:
  • We introduced Pandas DataFrames and Series, manually creating DataFrames and loading from CSV files.
  • We practiced basic data cleaning (filling missing values) and filtering rows using boolean masks.