budget-analyzer

name	budget-analyzer
description	Analyze personal or business expenses from CSV/Excel. Categorize spending, identify trends, compare periods, and get savings recommendations.

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() -> pd.DataFrame
     () -> pd.DataFrame

    
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     () -> []

    
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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": {"Food": 450, "Transport": 200, ...}
# }

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