**Course Title:** Modern Python Programming: Best Practices and Trends **Section Title:** Functional Programming in Python **Topic:** Solve real-world problems using functional programming principles (Lab topic) **Introduction** ===================================================================== Functional programming is a paradigm that emphasizes the use of pure functions, immutability, and avoidance of changing state. In this lab topic, we will apply functional programming principles to solve real-world problems. **Lab Problem 1:** Text Processing ------------------------------------ Given a text file containing reviews, and each review is a single line in the file. Write a Python program using functional programming principles to: * Read the text file line by line * Remove punctuation from each line * Convert the text to lowercase * Split the text into individual words * Count the frequency of each word **Solution** ------------ ```python import re from collections import Counter def read_file(file_path): try: with open(file_path, 'r') as file: content = file.readlines() return content except FileNotFoundError: print("File not found.") return [] def process_text(text): # Remove punctuation text = re.sub(r'[^\w\s]', '', text) # Convert to lowercase text = text.lower() # Split into individual words words = text.split() return words def count_word_frequency(words): return Counter(words) def main(): file_path = 'reviews.txt' # replace with your file path text_lines = read_file(file_path) word_frequencies = [] for line in text_lines: words = process_text(line) word_frequencies.extend(words) frequency_count = count_word_frequency(word_frequencies) print(frequency_count) if __name__ == "__main__": main() ``` **Explanation** --------------- In this solution, we define four functions: * `read_file`: reads the text file line by line using a `with` statement to ensure the file is properly closed after it is no longer needed. * `process_text`: removes punctuation using a regular expression, converts the text to lowercase using the `lower` method, and splits the text into individual words using the `split` method. * `count_word_frequency`: counts the frequency of each word using the `Counter` class from the `collections` module. * `main`: orchestrates the entire process, reading the file, processing each line, counting the word frequencies, and printing the result. **Lab Problem 2:** Data Analysis ------------------------------- Given a dataset containing information about employees, including their names, ages, and salaries. Write a Python program using functional programming principles to: * Calculate the average age of all employees * Find the highest-paid employee * Filter out employees who earn more than $100,000 **Solution** ------------ ```python import pandas as pd def calculate_average_age(employees): return sum(employee['age'] for employee in employees) / len(employees) def find_highest_paid_employee(employees): return max(employees, key=lambda x: x['salary']) def filter_employees(employees, threshold): return [employee for employee in employees if employee['salary'] > threshold] def main(): data = [ {'name': 'John', 'age': 30, 'salary': 80000}, {'name': 'Jane', 'age': 25, 'salary': 120000}, {'name': 'Bob', 'age': 40, 'salary': 90000}, ] average_age = calculate_average_age(data) highest_paid = find_highest_paid_employee(data) filtered_employees = filter_employees(data, 100000) print("Average age:", average_age) print("Highest paid employee:", highest_paid) print("Filtered employees:", filtered_employees) if __name__ == "__main__": main() ``` **Explanation** --------------- In this solution, we define three functions: * `calculate_average_age`: calculates the average age of all employees using a generator expression to sum up the ages and then dividing by the length of the list. * `find_highest_paid_employee`: finds the highest-paid employee using the `max` function with a lambda function as the key to specify the comparison criteria. * `filter_employees`: filters out employees who earn more than $100,000 using a list comprehension. **Conclusion** ---------- In this lab topic, we applied functional programming principles to solve real-world problems involving text processing and data analysis. We defined pure functions, used immutability, and avoided changing state to write concise and readable code. **What to do next?** -------------------- * Try to solve more complex problems using functional programming principles. * Experiment with different functional programming libraries, such as `toolz` and `cytoolz`. * Share your experiences and ask for feedback in the comments below. **External Resources** -------------------- * [Python's official documentation on functional programming](https://docs.python.org/3/library/functional.html) * [Wikipedia's article on functional programming](https://en.wikipedia.org/wiki/Functional_programming) * [Haskell's tutorial on functional programming](https://www.haskell.org/tutorial/functions.html) **Ask for help** --------------- If you have any questions or need help with a specific problem, feel free to ask in the comments below. **Next Topic** ------------- In the next topic, we will introduce concurrent programming in Python. We will cover the basics of concurrency, including threads, processes, and synchronization primitives. You can find the next topic [here](link-to-next-topic). **Leave a comment** ------------------ Please leave a comment below with your thoughts on this topic, and what you would like to see covered in future topics. Thank you for reading!