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We are given transaction data from a bank statement. Some important points: - The first few transactions are empty (with NaN) and then we have a header row (Transaction 6). - We have transactions from January 23, 2025, to April 21, 2025. - We need to do a financial analysis and plan, focusing on: 1. Summarizing income and expenses. 2. Identifying spending patterns and areas for improvement. 3. Suggesting ways to save and invest to build wealth. Steps: 1. Parse the transactions: ignore rows that are all NaN or the header, and then collect the transactions. 2. Classify transactions: - Income: any positive amount (like "Work Payment") - Expenses: negative amounts (we can categorize them by description) 3. Calculate: - Total income - Total expenses - Net cash flow (income - expenses) - Break down expenses by category (we'll group by description, but note some descriptions are similar and may differ by case or extra space, so we'll normalize) 4. Analyze spending: - Highlight large expense categories and recurring expenses (like Insurance, House Payment, Credit Card payments, Church, School, etc.) 5. Provide advice on saving and investing, then pitch Corey's Bank offerings. Since the data is provided as a list of dictionaries with keys 'Unnamed: 0' to 'Unnamed: 6', we'll focus on the first three columns that have data (Date, Description, Amount). Note: The data has a mix of transactions and we have to ignore the first 5 and the header row. Approach: - We'll iterate from transaction 7 to the end (index 6 to 75). - For each transaction, we extract: date = row['Unnamed: 0'] description = row['Unnamed: 1'] amount = row['Unnamed: 2'] - We'll build lists of income and expenses. But note: The amount field for expenses is negative, so we can categorize by the sign. However, one transaction is "Interest Earned": positive but small. Let's plan the structure of the HTML output: - A title: "Financial Analysis and Plan for [User]" - Summary: Total Income: [sum of positive amounts] Total Expenses: [sum of absolute value of negative amounts] (but we'll show as positive for clarity in the summary, but note they are negative in data) Net Cash Flow: [income - expenses] (should be positive if income>expenses, else negative) - Breakdown of expenses by category (group similar descriptions, ignoring case and extra spaces?). - Observations: - High expense areas. - Recurring expenses (like monthly bills). - Credit card payments (which might be high, indicating significant spending via credit cards). - Recommendations to reduce spending and increase savings. - Investment pitch: Corey's Bank offers various investment products with attractive returns. How to group descriptions? - We can create categories manually by looking at the descriptions. Let's map each description to a category: Insurance: "Insurance" Cell Phone 1, Cell Phone 2 -> "Cell Phone" Power Bill -> "Utilities" Water Bill -> "Utilities" Gas Bill -> "Utilities" Credit Card 1, Credit Card 2 -> "Credit Card Payment" Work Payment -> "Income" Church, church -> "Charity/Donations" House Payment -> "Mortgage/Rent" Landscaping, House Decorating -> "Home Improvement" Cash -> "Cash Withdrawal" School -> "Education" Donation -> "Charity/Donations" Interest Earned -> "Interest" We'll create a mapping dictionary: category_map = { 'Insurance': 'Insurance', 'Cell Phone 1': 'Cell Phone', 'Cell Phone 2': 'Cell Phone', 'Power Bill': 'Utilities', 'Water Bill': 'Utilities', 'Gas Bill': 'Utilities', 'Credit Card 1': 'Credit Card Payment', 'Credit Card 2': 'Credit Card Payment', 'Work Payment': 'Income', # but note: we are including only positive as income and negative as expenses, so we'll separate by sign. 'Church': 'Charity/Donations', 'church': 'Charity/Donations', 'School': 'Education', ' House Payment': 'Mortgage/Rent', # Note: one transaction has extra space: 'House Payment' 'Donation': 'Charity/Donations', ... } Alternatively, we can normalize the string and then map. However, note: Some descriptions might be the same but appear differently. We see: 'Church' and 'church' 'School' and ' School' (with space) We can strip and then convert to lower for mapping, but for display we can use a uniform category name. Alternatively, we can write a function to map: def get_category(desc): desc = desc.strip().lower() if 'insur' in desc: return 'Insurance' if 'cell' in desc: return 'Cell Phone' if 'power' in desc or 'water' in desc or 'gas' in desc: return 'Utilities' if 'credit card' in desc: return 'Credit Card Payment' if 'work' in desc: return 'Income' # but note: income we don't need to categorize? We are only categorizing expenses? # Actually, we are categorizing expenses. The positive amounts are income and we only have one source? Maybe multiple. # However, in the data, income is only from 'Work Payment' and 'Interest Earned'. But note: We have to separate the analysis of income and expenses. For expenses, we'll group the categories. Let's do: Income Sources: - Work Payment - Interest Earned (though negligible) Expenses: We'll group into the following categories: * Fixed Expenses: - Mortgage/Rent: 'House Payment' - Insurance: 'Insurance' - Utilities: (Power, Water, Gas) - Education: 'School' - Cell Phone - Charity/Donations: (Church, Donation) - Credit Card Payment (which is paying off credit card bills, so it's the result of spending elsewhere) * Variable Expenses: - Home Improvement (Landscaping, House Decorating) - Cash Withdrawal - Other (if any) However, the credit card payments are large and might be the result of spending in various categories. But in this statement, we only see the payment to the credit card, not the individual credit card transactions. So we cannot break down that spending. Steps: We'll create a list of transactions, and then: income = [amount for each transaction where amount > 0 and description not in expense categories? But we have positive amounts only as income?] expenses = [abs(amount) for each transaction where amount < 0] Then we break down expenses by category. But note: We have to map the transaction description to a category for expenses. Let's create a mapping function: def map_to_category(description): desc = description.strip().lower() if 'insur' in desc: return 'Insurance' if 'cell' in desc: return 'Cell Phone' if 'power' in desc or 'water' in desc or 'gas' in desc: return 'Utilities' if 'credit card' in desc: return 'Credit Card Payment' if 'house payment' in desc: return 'Mortgage/Rent' if 'school' in desc: return 'Education' if 'church' in desc or 'donation' in desc: return 'Charity' if 'landscaping' in desc or 'house decor' in desc: return 'Home Improvement' if 'cash' in desc: return 'Cash' # For work payment, it's income so we skip in expense mapping. return 'Other' However, note: some expenses are under a description that we map to a category, but we also have: "Interest Earned": positive, so not an expense. This mapping is only for expenses (negative amounts) so we don't need to map income. We'll ignore the positive ones for the expense breakdown. Now, we need to aggregate the expenses by category. Let's do: categories = {} for transaction in expense_transactions: (which are transactions with negative amount) amount_positive = abs(amount) category = map_to_category(description) categories[category] = categories.get(category, 0) + amount_positive We'll do similar for income: just group by description? Or we can break down income? We have: 'Work Payment': 10000, 10000, ... and 'Interest Earned' But we only have two sources, so maybe just show as a list? Or we can have: Salary: [sum of all 'Work Payment'] Interest: [sum of all 'Interest Earned'] Now, net cash flow = total_income - total_expenses (where total_expenses is the sum of absolute values of negative amounts) Analysis: From the data, the user has: - Regular income: multiple work payments of about 10,000 each time, and some with 9800-9900. - High