Coding-For-MBA

In business analytics, text data is everywhere—customer names, product reviews, addresses, and report narratives. In Python, we handle text using strings.

Key String Concepts

Method Description Business Use Case
.lower()/.upper() Converts case. Standardizing categories.
.strip() Removes whitespace from the beginning and end. Cleaning user-entered data.
.replace() Replaces a substring with another. Correcting or reformatting data.
.split() Splits the string into a list of substrings. Parsing comma-separated data.
.startswith() Checks if the string starts with a substring. Identifying invoice numbers.
.endswith() Checks if the string ends with a substring. Checking file types.

Environment Setup

Before you begin, ensure you have followed the setup instructions in the main README.md to set up your virtual environment and install the required libraries.

Exploring the Refactored Code

The script for this lesson, strings.py, has been refactored into functions to make each string manipulation a reusable and testable unit of logic.

  1. Review the Code: Open Day_04_Strings/strings.py. Each data transformation (e.g., generate_report_header(), clean_and_format_name()) is now its own function.
  2. Run the Script: From the root directory of the project (Coding-For-MBA), run the script to see the functions in action: 
    python Day_04_Strings/strings.py
  3. Run the Tests: You can run the tests for this lesson to verify the correctness of each function: 
    pytest tests/test_day_04.py

💻 Exercises: Day 4

  1. Generate a Report Header:

    • In a new script (my_solutions_04.py), create a function format_report_title(title, date).
    • The function should take a title string and a date string and return a formatted header like: --- MONTHLY MARKETING REPORT: 2024-07 ---.
    • Call the function and print the result.

  2. Clean Up Product Codes:

    • You have a list of raw product codes: [" prod-001 ", "prod-002", " Prod-003 "].
    • Create a function clean_product_codes(codes) that takes a list of codes.
    • Inside the function, loop through the list, and for each code, remove whitespace and convert it to uppercase.
    • The function should return a new list of cleaned codes.
    • Call the function and print the cleaned list.

  3. Validate Email Addresses:

    • Create a function is_valid_email(email) that performs two simple checks:
      • Does the email contain an @ symbol?
      • Does the email end with .com?
    • The function should return True if both conditions are met, otherwise False.
    • Test your function with a valid email ("test@example.com") and an invalid one ("test-example.com").

🎉 Fantastic! You can now manipulate text data, which is a massive part of any real-world data analysis task. Cleaning, formatting, and parsing strings are skills you’ll use every single day.

Interactive Notebooks

Run this lesson’s code interactively in your browser:

!!! tip “About JupyterLite” JupyterLite runs entirely in your browser using WebAssembly. No installation or server required! Note: First launch may take a moment to load.

Additional Materials

???+ example “solutions.py” View on GitHub

```python title="solutions.py"
"""
Day 4: Solutions to Exercises
"""

# --- Exercise 1: Generate a Report Header ---
print("--- Solution to Exercise 1 ---")
report_title = "Quarterly Sales Report"
fiscal_year = 2024

# Using .upper() to make the title all caps for emphasis
# and an f-string to combine everything.
header = f"*** {report_title.upper()} - FY{fiscal_year} ***"
print(header)
print("-" * 20)


# --- Exercise 2: Clean Up Customer Data ---
print("--- Solution to Exercise 2 ---")
customer_name = "  john doe  "

# .strip() removes the leading/trailing whitespace
# .title() capitalizes the first letter of each word
cleaned_name = customer_name.strip().title()

print(f"Original name: '{customer_name}'")
print(f"Cleaned name: '{cleaned_name}'")
print("-" * 20)


# --- Exercise 3: Parse Product SKU ---
print("--- Solution to Exercise 3 ---")
sku = "PROD-GADGET-001"

# .split('-') breaks the string into a list of substrings,
# using the hyphen as the separator.
sku_parts = sku.split("-")

# We can access the parts of the list by their index.
product_type = sku_parts[0]
product_name = sku_parts[1]
product_id = sku_parts[2]

print(f"Original SKU: {sku}")
print(f"Product Type: {product_type}")
print(f"Product Name: {product_name}")
print(f"Product ID: {product_id}")
print("-" * 20)
```

???+ example “strings.py” View on GitHub

```python title="strings.py"
"""
Day 4: Manipulating Business Text Data with Strings (Refactored)

This script demonstrates common string manipulations and methods
applied to business-related text data. This version is refactored
into functions for better organization and testability.
"""


def generate_report_header(title, year):
    """Creates a formatted report header."""
    return f"*** {title.upper()} - FY{year} ***"


def clean_and_format_name(raw_name):
    """Cleans and capitalizes a raw name string."""
    return raw_name.strip().title()


def format_date_string(date_str, old_separator="-", new_separator="/"):
    """Replaces separators in a date string."""
    return date_str.replace(old_separator, new_separator)


def parse_sku(sku):
    """Parses a SKU string into its component parts."""
    parts = sku.split("-")
    if len(parts) == 3:
        return {"type": parts[0], "name": parts[1], "id": parts[2]}
    return None


def is_transaction_type(transaction_id, prefix):
    """Checks if a transaction ID starts with a given prefix."""
    return transaction_id.startswith(prefix)


def has_file_extension(filename, extension):
    """Checks if a filename ends with a given extension."""
    return filename.endswith(extension)


def feedback_contains_keyword(feedback, keyword):
    """Checks if a feedback string contains a specific keyword."""
    return feedback.find(keyword) != -1


if __name__ == "__main__":
    # --- Formatting Strings for Reports ---
    print("--- Generating Report Headers ---")
    header_text = generate_report_header("Quarterly Sales Report", 2024)
    print(header_text)
    print("-" * 20)

    # --- Cleaning Customer and Product Data ---
    print("--- Data Cleaning Examples ---")
    customer_name = "  john doe  "
    formatted_customer_name = clean_and_format_name(customer_name)
    print(
        f"Raw name: '{customer_name}', Final formatted name: '{formatted_customer_name}'"
    )

    date_string = "2023-Jan-15"
    formatted_date_str = format_date_string(date_string)
    print(f"Original date: {date_string}, Formatted date: {formatted_date_str}")
    print("-" * 20)

    # --- Parsing and Extracting Information from Strings ---
    print("--- Parsing Product and Transaction IDs ---")
    product_sku = "PROD-GADGET-001"
    parsed_sku = parse_sku(product_sku)
    if parsed_sku:
        print(f"SKU: {product_sku}")
        print(f"  Product Type: {parsed_sku['type']}")
        print(f"  Product Name: {parsed_sku['name']}")
        print(f"  Product ID: {parsed_sku['id']}")
    print()

    trans_id = "INV-2024-03-15-998"
    is_inv = is_transaction_type(trans_id, "INV")
    print(f"Transaction '{trans_id}' is an invoice: {is_inv}")

    report_filename = "q1_sales_report.pdf"
    is_a_pdf = has_file_extension(report_filename, ".pdf")
    print(f"Report file '{report_filename}' is a PDF: {is_a_pdf}")
    print("-" * 20)

    # --- Searching for keywords ---
    customer_feedback_text = "The new CRM is great, but the reporting feature is slow."
    if feedback_contains_keyword(customer_feedback_text, "slow"):
        print("Feedback contains the word 'slow'. Action may be required.")
    else:
        print("Feedback does not contain the word 'slow'.")
```
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