Python预算分析器：自动分类支出、趋势分析与PDF报告生成工具

budget-analyzer by dkyazzentwatwa/chatgpt-skills

116 周安装量

43 GitHub Stars

GitHub

安装命令

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

Python Web框架数据分析金融科技

🇨🇳中文介绍

预算分析器

用于个人理财和商业预算的综合支出分析工具。

功能特性

自动分类 : 根据商户/描述对支出进行分类
趋势分析 : 月度支出模式分析
周期对比 : 比较不同时间段的支出
类别细分 : 按类别生成饼图和条形图
储蓄建议 : 识别可减少支出的领域
预算与实际对比 : 跟踪预算目标完成情况
导出报告 : 生成 PDF 和 HTML 摘要报告

快速开始

from budget_analyzer import BudgetAnalyzer

analyzer = BudgetAnalyzer()

# 加载交易数据
analyzer.load_csv("transactions.csv",
                  date_col="date",
                  amount_col="amount",
                  description_col="description")

# 分析支出
summary = analyzer.analyze()
print(summary)

# 获取类别细分
categories = analyzer.by_category()
print(categories)

# 生成报告
analyzer.generate_report("budget_report.pdf")

命令行使用

# 基础分析
python budget_analyzer.py --input transactions.csv --date date --amount amount

# 使用自定义类别
python budget_analyzer.py --input data.csv --categories custom_categories.json

# 比较两个时间段
python budget_analyzer.py --input data.csv --compare "2024-01" "2024-02"

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

# 设置预算目标
python budget_analyzer.py --input data.csv --budget budget.json --report report.pdf

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自定义类别 (JSON)

{
  "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)

    # 数据加载
    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'

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

    # 分析
    def analyze(self) -> Dict  # 完整摘要
    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

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

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

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

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

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

summary = analyzer.analyze()
# 返回:
# {
#     "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()
# 返回 DataFrame:
#   category        | amount  | percentage | count
#   Food & Dining   | 450.00  | 18.0%      | 15
#   Transportation  | 200.00  | 8.0%       | 8
#   ...

monthly = analyzer.by_month()
# 返回 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")
# 返回:
# {
#     "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
})

预算与实际对比

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

alerts = analyzer.budget_alerts()
# 返回:
# [
#     {"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()
# 返回:
# [
#     "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()
# 返回:
# {
#     "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")
# 创建按类别划分的支出饼图

analyzer.plot_trends("trends.png")
# 创建随时间变化的月度支出折线图

analyzer.plot_budget_comparison("budget.png")
# 创建按类别对比预算与实际支出的条形图

analyzer.generate_report("report.pdf")
# 包含:
# - 执行摘要
# - 带图表的类别细分
# - 月度趋势
# - 主要支出
# - 预算与实际对比 (如果已设置)
# - 建议

analyzer.generate_report("report.html", format="html")
# 包含图表的交互式 HTML 报告

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

# 自动分类交易
analyzer.auto_categorize()

# 设置月度预算
analyzer.set_budget({
    "Food & Dining": 600,
    "Transportation": 250,
    "Entertainment": 200
})

# 获取完整分析
print(analyzer.analyze())
print(analyzer.budget_vs_actual())
print(analyzer.get_recommendations())

# 生成报告
analyzer.generate_report("monthly_review.pdf")

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

# 比较季度数据
q1_vs_q2 = analyzer.compare_periods("2024-Q1", "2024-Q2")

# 主要支出类别
top = analyzer.by_category().head(5)

# 为会计生成报告
analyzer.generate_report("quarterly_expenses.pdf")

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

🇺🇸English

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": {"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

Weekly Installs

Repository

dkyazzentwatwa/…t-skills

GitHub Stars

First Seen

Jan 24, 2026

Security Audits

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Installed on

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DOCX文件创建、编辑与分析完整指南 - 使用docx-js、Pandoc和Python脚本

51,800 周安装

Python预算分析器：自动分类支出、趋势分析与PDF报告生成工具

🇨🇳中文介绍

预算分析器

功能特性

快速开始

命令行使用

相关 Skills

输入格式

交易 CSV 文件

自定义类别 (JSON)

预算目标 (JSON)

API 参考

BudgetAnalyzer 类

分析功能

摘要统计

类别细分

月度趋势

周期对比

预算跟踪

设置预算目标