Refactoring Tip 4: Utilize Python's Built-in Functions and Libraries

import csv

def read_csv(file_path):
    data = []
    with open(file_path, 'r') as file:
        reader = csv.reader(file)
        next(reader)  # Skip header
        for row in reader:
            data.append(float(row[1]))  # Assuming the second column has the numerical data
    return data

def calculate_total_and_average(data):
    total = sum(data)
    average = total / len(data)
    return total, average

# Usage
data = read_csv('data.csv')
total, average = calculate_total_and_average(data)
print(f"Total: {total}, Average: {average}")

import pandas as pd

def read_and_summarize_csv(file_path):
    df = pd.read_csv(file_path)
    total = df['column_name'].sum()  # Assuming 'column_name' is the name of the column
    average = df['column_name'].mean()
    return total, average

# Usage
total, average = read_and_summarize_csv('data.csv')
print(f"Total: {total}, Average: {average}")

def find_most_common_elements(items):
    frequency_dict = {}
    for item in items:
        if item in frequency_dict:
            frequency_dict[item] += 1
        else:
            frequency_dict[item] = 1
    most_common = max(frequency_dict.values())
    return [item for item, count in frequency_dict.items() if count == most_common]

# Usage
items = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']
most_common = find_most_common_elements(items)
print(most_common)

from collections import Counter

def find_most_common_elements(items):
    counter = Counter(items)
    most_common_count = counter.most_common(1)[0][1]
    return [item for item, count in counter.items() if count == most_common_count]

# Usage
items = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']
most_common = find_most_common_elements(items)
print(most_common)
 

def file_statistics(file_path):
    with open(file_path, 'r') as file:
        lines = file.readlines()
        num_lines = len(lines)
        num_words = sum(len(line.split()) for line in lines)
        num_chars = sum(len(line) for line in lines)
    return num_lines, num_words, num_chars

# Usage
num_lines, num_words, num_chars = file_statistics('example.txt')
print(f"Lines: {num_lines}, Words: {num_words}, Characters: {num_chars}")

from pathlib import Path

# Usage
file_path = Path('example.txt')
num_lines = sum(1 for _ in file_path.open('r'))
num_words = sum(len(line.split()) for line in file_path.open('r'))
num_chars = file_path.stat().st_size

print(f"Lines: {num_lines}, Words: {num_words}, Characters: {num_chars}")