Name: Budget Analyzer
Author: juancasareto

Skills suchen.../

Budget Analyzer | Skills Pool

# Basic analysis
python budget_analyzer.py --input transactions.csv --date date --amount amount

# With custom categories
python budget_analyzer.py --input data.csv --categories custom_categories.json

# Compare two periods
python budget_analyzer.py --input data.csv --compare "2024-01" "2024-02"

# Generate PDF report
python budget_analyzer.py --input data.csv --report report.pdf

# Set budget targets
python budget_analyzer.py --input data.csv --budget budget.json --report report.pdf

date,amount,description,category
2024-01-15,45.99,Amazon Purchase,Shopping
2024-01-16,12.50,Starbucks,Food & Dining
2024-01-17,150.00,Electric Company,Utilities

{
  "Food & Dining": ["starbucks", "mcdonalds", "restaurant", "uber eats"],
  "Transportation": ["uber", "lyft", "gas station", "shell"],
  "Shopping": ["amazon", "walmart", "target"],
  "Utilities": ["electric", "water", "gas", "internet"]
}

{
  "Food & Dining": 500,
  "Transportation": 200,
  "Shopping": 300,
  "Utilities": 250,
  "Entertainment": 150
}

class BudgetAnalyzer:
    def __init__(self)

    # Data Loading
    def load_csv(self, filepath: str, date_col: str, amount_col: str,
                 description_col: str = None, category_col: str = None) -> 'BudgetAnalyzer'
    def load_dataframe(self, df: pd.DataFrame) -> 'BudgetAnalyzer'

    # Categorization
    def set_categories(self, categories: Dict[str, List[str]]) -> 'BudgetAnalyzer'
    def auto_categorize(self) -> 'BudgetAnalyzer'

    # Analysis
    def analyze(self) -> Dict  # Full summary
    def by_category(self) -> pd.DataFrame
    def by_month(self) -> pd.DataFrame
    def by_day_of_week(self) -> pd.DataFrame
    def top_expenses(self, n: int = 10) -> pd.DataFrame
    def recurring_expenses(self) -> pd.DataFrame

    # Comparison
    def compare_periods(self, period1: str, period2: str) -> Dict
    def year_over_year(self) -> pd.DataFrame

    # Budgeting
    def set_budget(self, budget: Dict[str, float]) -> 'BudgetAnalyzer'
    def budget_vs_actual(self) -> pd.DataFrame
    def budget_alerts(self) -> List[Dict]

    # Insights
    def get_recommendations(self) -> List[str]
    def spending_score(self) -> Dict

    # Visualization
    def plot_categories(self, output: str) -> str
    def plot_trends(self, output: str) -> str
    def plot_budget_comparison(self, output: str) -> str

    # Export
    def generate_report(self, output: str, format: str = "pdf") -> str
    def to_csv(self, output: str) -> str

summary = analyzer.analyze()
# Returns:
# {
#     "total_spent": 2500.00,
#     "transaction_count": 45,
#     "date_range": {"start": "2024-01-01", "end": "2024-01-31"},
#     "average_transaction": 55.56,
#     "largest_expense": {"amount": 500, "description": "Rent"},
#     "categories": {"Food": 450, "Transport": 200, ...}
# }

categories = analyzer.by_category()
# Returns DataFrame:
#   category        | amount  | percentage | count
#   Food & Dining   | 450.00  | 18.0%      | 15
#   Transportation  | 200.00  | 8.0%       | 8
#   ...

monthly = analyzer.by_month()
# Returns DataFrame:
#   month    | total    | avg_transaction | count
#   2024-01  | 2500.00  | 55.56          | 45
#   2024-02  | 2800.00  | 60.87          | 46

comparison = analyzer.compare_periods("2024-01", "2024-02")
# Returns:
# {
#     "period1_total": 2500.00,
#     "period2_total": 2800.00,
#     "difference": 300.00,
#     "percent_change": 12.0,
#     "category_changes": {
#         "Food": {"change": 50, "percent": 11.1},
#         ...
#     }
# }

analyzer.set_budget({
    "Food & Dining": 500,
    "Transportation": 200,
    "Shopping": 300
})

comparison = analyzer.budget_vs_actual()
# Returns DataFrame:
#   category        | budget | actual | difference | status
#   Food & Dining   | 500    | 450    | 50         | under
#   Transportation  | 200    | 250    | -50        | over

alerts = analyzer.budget_alerts()
# Returns:
# [
#     {"category": "Transportation", "status": "over", "amount": 250, "budget": 200, "percent_over": 25},
#     {"category": "Shopping", "status": "warning", "amount": 280, "budget": 300, "percent_used": 93}
# ]

recommendations = analyzer.get_recommendations()
# Returns:
# [
#     "Food & Dining spending increased 15% from last month. Consider meal prepping.",
#     "You have 3 subscription services totaling $45/month. Review for unused subscriptions.",
#     "Transportation costs are 25% over budget. Consider carpooling or public transit.",
#     "Top merchant: Amazon ($350). Set spending limits for online shopping."
# ]

score = analyzer.spending_score()
# Returns:
# {
#     "overall_score": 72,  # 0-100
#     "factors": {
#         "budget_adherence": 65,
#         "spending_consistency": 80,
#         "savings_rate": 70
#     },
#     "grade": "B",
#     "summary": "Good spending habits with room for improvement in budget adherence."
# }

DEFAULT_CATEGORIES = {
    "Food & Dining": ["restaurant", "cafe", "starbucks", "mcdonald", "uber eats", "doordash"],
    "Transportation": ["uber", "lyft", "gas", "shell", "chevron", "parking"],
    "Shopping": ["amazon", "walmart", "target", "costco", "best buy"],
    "Utilities": ["electric", "water", "gas", "internet", "phone", "verizon"],
    "Entertainment": ["netflix", "spotify", "hulu", "movie", "theater"],
    "Healthcare": ["pharmacy", "cvs", "walgreens", "doctor", "hospital"],
    "Travel": ["airline", "hotel", "airbnb", "booking"],
    "Subscriptions": ["subscription", "membership", "monthly"]
}

analyzer.plot_categories("categories.png")
# Creates pie chart of spending by category

analyzer.plot_trends("trends.png")
# Creates line chart of monthly spending over time

analyzer.plot_budget_comparison("budget.png")
# Creates bar chart comparing budget vs actual by category

analyzer.generate_report("report.pdf")
# Includes:
# - Executive summary
# - Category breakdown with charts
# - Monthly trends
# - Top expenses
# - Budget vs actual (if set)
# - Recommendations

analyzer.generate_report("report.html", format="html")
# Interactive HTML report with charts

analyzer = BudgetAnalyzer()
analyzer.load_csv("bank_transactions.csv",
                  date_col="Date",
                  amount_col="Amount",
                  description_col="Description")

# Auto-categorize transactions
analyzer.auto_categorize()

# Set monthly budget
analyzer.set_budget({
    "Food & Dining": 600,
    "Transportation": 250,
    "Entertainment": 200
})

# Get full analysis
print(analyzer.analyze())
print(analyzer.budget_vs_actual())
print(analyzer.get_recommendations())

# Generate report
analyzer.generate_report("monthly_review.pdf")

analyzer = BudgetAnalyzer()
analyzer.load_csv("business_expenses.csv",
                  date_col="date",
                  amount_col="amount",
                  category_col="expense_type")

# Compare quarters
q1_vs_q2 = analyzer.compare_periods("2024-Q1", "2024-Q2")

# Top expense categories
top = analyzer.by_category().head(5)

# Generate report for accounting
analyzer.generate_report("quarterly_expenses.pdf")

Budget Analyzer

Features

Quick Start

Budget Analyzer

Features

Quick Start

CLI Usage

Input Format

Transaction CSV

Custom Categories (JSON)

Budget Targets (JSON)

API Reference

BudgetAnalyzer Class

Analysis Features

Summary Statistics

Category Breakdown

Monthly Trends

Period Comparison

Budget Tracking

Set Budget Targets

Budget vs Actual

Budget Alerts

Recommendations Engine

Spending Score

Auto-Categorization

Visualizations

Category Pie Chart

Spending Trends

Budget Comparison

Report Generation

PDF Report

HTML Report

Example Workflows

Personal Finance Review

Business Expense Tracking

Dependencies

Llm Trading Agent Security

Energy Procurement

Council

Carrier Relationship Management

Market Research

Market Research