CBS Analyzer

CBS Analyzer is a financial analysis tool designed to process and analyze Chase Bank PDF statements. This project transforms raw bank statements into structured, analyzable data with powerful aggregation and export capabilities.

Table of Contents

• Installation
• Features
• Parameters
• Attributes
• Methods
• Usage Examples
• Error Handling
• Contributing
• License

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Installation

pip install cbs-analyzer

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Features

• Multi-Statement Processing: Analyze single statements or entire directories
• Comprehensive Financial Metrics:
  • Monthly/Yearly spending trends
  • Cash flow analysis
  • Savings rate calculations
• Flexible Data Export: CSV, Excel, JSON, and Parquet formats
• Advanced Analysis:
  • Time-based aggregation (day/month/year)
  • Statistical summaries
  • Custom column analysis
• Enterprise-Grade Infrastructure:
  • Thread-safe processing
  • Cached properties for performance
  • Comprehensive error handling

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Parameters

Core Parameters

• file_path (PathLike): Path to PDF file or directory
• ascending_date (bool): Sort transactions chronologically (default: False)

Analysis Parameters

• by_year/by_month/by_day (bool): Time-based grouping
• column (str): Specific column to analyze
• minimum (bool): Find minimum values instead of maximum

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Attributes

• transactions: Single statement transactions (DataFrame)
• checking_summary: Single statement summary (DataFrame)
• all_transactions: All statements' transactions (DataFrame)
• all_checking_summaries: All statements' summaries (DataFrame)

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Methods

Core Methods

• analyze_summaries(): Analyze checking account summaries
• analyze_transactions(): Analyze transaction data
• export(): Export data to various formats

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Usage Examples

Basic Analysis

from cbs_analyzer import CBSAnalyzer

# Single statement analysis

analyzer = CBSAnalyzer("path/to/statement.pdf")
print(analyzer.transactions)
# Output transactions from a single statement
#           Date                                        Description  Amount   Balance
# 0   2025-12-31  Card Purchase - Dd/Br.............. .............  -11.81  11940.51
# 1   2025-12-31  Card Purchase - Wendys - ........................  -12.17  11952.32
# 2   2025-12-30  Card Purchase - Walgreens .......................  -4.99  12132.78
# 3   2025-12-30  Recurring Card Purchase 12/30 ...................  -29.25  11964.49
# 4   2025-12-30  Card Purchase - .................................  -31.56  11993.74
# ..         ...                                                ...     ...       ...
# 272 2025-01-02  Card Purchase - Dd *Doordash Wingsto Www.Doord...  -10.16  11930.35
# 273 2025-01-02  Card Purchase - Walgreens .................. ...   -4.43  11925.92
# 274 2025-01-02  Card Purchase - Kings ...........................  -40.28  11859.62
# 275 2025-01-02  Card Purchase - Tst* ...........................   -7.02  11918.90
# 276 2025-01-02  Zelle Payment To ................................  -19.00  11899.90



# Output checking summary for a single statement
print(analyzer.checking_summary)
# Output:
#                        Category    Amount
# 0             Beginning Balance  11679.61
# 1        Deposits and Additions   2955.39
# 2  ATM & Debit Card Withdrawals  -1024.11
# 3        Electronic Withdrawals   -134.62
# 4                Ending Balance  13476.27
# 5             Total Withdrawals   1158.73
# 6                   Net Savings   1796.66
# 7                 % Saving Rate     60.79






# Directory analysis
analyzer = CBSAnalyzer("path/to/statements/")
all_data = analyzer.all_transactions
print(all_data)
# Output all transactions from multiple statements
#            Date                                        Description  Amount   Balance
# 0    2025-12-31  Card Purchase - Dd/Br.............. .............  -12.17  11952.32
# 1    2025-12-31  Card Purchase - Wendys - ........................  -11.81  11940.51
# 2    2025-12-30  Card Purchase - Walgreens .......................  -57.20  12066.25
# 3    2025-12-30  Recurring Card Purchase 12/30 ...................  -31.56  11993.74
# 4    2025-12-30  Card Purchase - .................................  -20.80  12025.30
# ...         ...                                                ...     ...       ...
# 1769 2023-01-03  Card Purchase - Dd *Doordash Wingsto Www.Doord..   -4.00   1837.81
# 1770 2023-01-03  Card Purchase - Walgreens .................. ...   100.00   1765.72
# 1771 2023-01-03  Card Purchase - Kings ..........................   -3.91   1841.81
# 1772 2023-01-03  Card Purchase - Tst* ..........................    70.00   1835.72
# 1773 2023-01-03  Zelle Payment To ...............................   10.00   1845.72


# ----------------------------------------------------


# Output all checking summaries from multiple statements
analyzer = CBSAnalyzer("path/to/statements/")
all_data = analyzer.all_checking_summaries
print(all_data)
#       Date  Beginning Balance  Deposits and Additions  ATM & Debit Card Withdrawals  Electronic Withdrawals  Ending Balance  Total Withdrawals  Net Savings  % Saving Rate
# 0  2025-04           14767.33                 2535.82                      -1183.41                 -513.76        15605.98            1697.17       838.65          33.07
# 1  2025-03           14319.87                 4319.20                      -3620.85                 -250.89        14767.33            3871.74       447.46          10.36
# 2  2025-02           13476.27                 2328.18                       -682.24                 -802.34        14319.87            1484.58       843.60          36.23
# 3  2025-01           11679.61                 2955.39                      -1024.11                 -134.62        13476.27            1158.73      1796.66          60.79

----------------------------------------------------

Advanced Analysis

# Monthly spending analysis
monthly_spending = analyzer.analyze_transactions(
    by_month=True,
    column="Transactions_Count"
)

# Output:
#       Month  Maximum
# 0  February      205




# Annual savings rate
annual_savings = analyzer.analyze_summaries(
    by_year=True,
    column="% Saving Rate_Mean"
)

# Output:
#      Year  Maximum
# 0  2024.0    36.01

Data Export

# Export to different formats
analyzer.all_transactions.export("transactions.xlsx")
analyzer.checking_summary.export("summary.json")
analyzer.checking_summary.export(".json")
analyzer.checking_summary.export("csv")

Various cases for export_path:

1. Export path is empty ("") export_path="" -> "cbsanalyzer.csv"
2. Export path is a directory export_path="path/to/dir/" -> "path/to/dir/cbsanalyzer.csv"
3. Export path is a file name only export_path="file" -> "file.csv"
4. Export path is an extension type export_path=".csv" | "csv" -> "cbsanalyzer.csv"
5. Export path is a file with a given extension export_path="file.csv" -> "file.csv"
6. Export path is a hidden file name export_path="</dir/>.hidden.csv" -> "</dir/>.hidden.csv"
7. Export path is the current (".") or home directory ("") export_path="." -> "</cwdir/>/cbsanalyzer.csv" export_path="" -> "</home/>/cbsanalyzer.csv"

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## Error Handling

The analyzer provides detailed error messages for:
- File access issues
- Malformed PDF content
- Invalid analysis parameters

Example error handling:
```python
try:
    analyzer = CBSAnalyzer("invalid_path.pdf")
except FileNotFoundError as e:
    print(f"Error loading statement: {e}")

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Contributing

1. Fork the repository
2. Create a feature branch
3. Submit a Pull Request

Development Setup

pip install -e ".[dev]"
pytest tests/

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License

Apache 2.0 - See LICENSE for details.

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CBS Analyzer © 2025
Chase is a registered trademark of JPMorgan Chase & Co.
Not affiliated with or endorsed by JPMorgan Chase & Co.