budget-analyzer

仓库: dkyazzentwatwa/chatgpt-skills
安装量: 84
排名: #9377

安装

npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill budget-analyzer

Budget Analyzer

Comprehensive expense analysis tool for personal finance and business budgeting.

Features Auto-Categorization: Classify expenses by merchant/description Trend Analysis: Month-over-month spending patterns Period Comparison: Compare spending across time periods Category Breakdown: Pie charts and bar graphs by category Savings Recommendations: Identify areas to reduce spending Budget vs Actual: Track against budget targets Export Reports: PDF and HTML summaries Quick Start from budget_analyzer import BudgetAnalyzer

analyzer = BudgetAnalyzer()

Load transaction data

analyzer.load_csv("transactions.csv", date_col="date", amount_col="amount", description_col="description")

Analyze spending

summary = analyzer.analyze() print(summary)

Get category breakdown

categories = analyzer.by_category() print(categories)

Generate report

analyzer.generate_report("budget_report.pdf")

CLI Usage

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

Input Format Transaction CSV 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

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

Budget Targets (JSON) { "Food & Dining": 500, "Transportation": 200, "Shopping": 300, "Utilities": 250, "Entertainment": 150 }

API Reference BudgetAnalyzer Class 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

Analysis Features Summary Statistics 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":

}

Category Breakdown categories = analyzer.by_category()

Returns DataFrame:

category | amount | percentage | count

Food & Dining | 450.00 | 18.0% | 15

Transportation | 200.00 | 8.0% | 8

...

Monthly Trends 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

Period Comparison 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},

...

}

}

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

Budget vs Actual comparison = analyzer.budget_vs_actual()

Returns DataFrame:

category | budget | actual | difference | status

Food & Dining | 500 | 450 | 50 | under

Transportation | 200 | 250 | -50 | over

Budget Alerts 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 Engine 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."

]

Spending Score 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."

}

Auto-Categorization

Built-in category patterns:

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"] }

Visualizations Category Pie Chart analyzer.plot_categories("categories.png")

Creates pie chart of spending by category

Spending Trends analyzer.plot_trends("trends.png")

Creates line chart of monthly spending over time

Budget Comparison analyzer.plot_budget_comparison("budget.png")

Creates bar chart comparing budget vs actual by category

Report Generation PDF Report analyzer.generate_report("report.pdf")

Includes:

- Executive summary

- Category breakdown with charts

- Monthly trends

- Top expenses

- Budget vs actual (if set)

- Recommendations

HTML Report analyzer.generate_report("report.html", format="html")

Interactive HTML report with charts

Example Workflows Personal Finance Review 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")

Business Expense Tracking 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")

Dependencies pandas>=2.0.0 numpy>=1.24.0 matplotlib>=3.7.0 reportlab>=4.0.0

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