# Financial Data Manager
[](https://pypi.org/project/kapfinance/)
[](https://opensource.org/licenses/MIT)
This Python class, `kapfinance`, is designed to read, process, and manage financial `.xls` files (which contain HTML content) located within a specified root folder and its subfolders. It's particularly useful for extracting financial statement data on a **ticker-by-ticker basis**, implementing a **lazy loading** mechanism for efficient memory usage.
---
## Features
* **Automated File Mapping**: Scans a given directory to build a comprehensive map of all available financial statement files, identifying tickers and reporting periods.
* **HTML Content Processing**: Reads `.xls` files (often used for financial reports that are essentially HTML tables), extracts relevant financial account descriptions and their corresponding values.
* **Lazy Loading**: Data for a specific ticker is only loaded into memory when explicitly requested, optimizing memory usage for large datasets.
* **Time-Series Data Retrieval**: Provides a convenient method to retrieve financial data as a **pandas DataFrame**, with account descriptions as rows and reporting periods as columns, sorted chronologically.
* **Period Filtering**: Allows users to filter financial data by specifying a start and end reporting period (in `'YYYY_QQ'` format, e.g., `'2020_01'`, `'2022_04'`).
* **Robust Error Handling**: Includes logging for various scenarios, such as missing folders, file processing errors, and unavailable data.
---
## Getting Started
### Prerequisites
To use this class, you'll need the following Python libraries installed:
```bash
pip install pandas numpy lxml openpyxl
Raw data
{
"_id": null,
"home_page": "https://github.com/mertkurtcu/kapfinance",
"name": "kapfinance",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "financial data manager, financial statements, balance sheet, income statement, html xls, pandas",
"author": "Mert Kurt\u00e7u",
"author_email": "mertkurtcu.official@gmail.com.com",
"download_url": "https://files.pythonhosted.org/packages/36/42/c6c64cc5cd77457c61ee76a82eebb570b45674322873ff5de7456558c9b8/kapfinance-0.1.2.tar.gz",
"platform": null,
"description": "# Financial Data Manager\r\n\r\n[](https://pypi.org/project/kapfinance/)\r\n[](https://opensource.org/licenses/MIT)\r\n\r\nThis Python class, `kapfinance`, is designed to read, process, and manage financial `.xls` files (which contain HTML content) located within a specified root folder and its subfolders. It's particularly useful for extracting financial statement data on a **ticker-by-ticker basis**, implementing a **lazy loading** mechanism for efficient memory usage.\r\n\r\n---\r\n\r\n## Features\r\n\r\n* **Automated File Mapping**: Scans a given directory to build a comprehensive map of all available financial statement files, identifying tickers and reporting periods.\r\n* **HTML Content Processing**: Reads `.xls` files (often used for financial reports that are essentially HTML tables), extracts relevant financial account descriptions and their corresponding values.\r\n* **Lazy Loading**: Data for a specific ticker is only loaded into memory when explicitly requested, optimizing memory usage for large datasets.\r\n* **Time-Series Data Retrieval**: Provides a convenient method to retrieve financial data as a **pandas DataFrame**, with account descriptions as rows and reporting periods as columns, sorted chronologically.\r\n* **Period Filtering**: Allows users to filter financial data by specifying a start and end reporting period (in `'YYYY_QQ'` format, e.g., `'2020_01'`, `'2022_04'`).\r\n* **Robust Error Handling**: Includes logging for various scenarios, such as missing folders, file processing errors, and unavailable data.\r\n\r\n---\r\n\r\n## Getting Started\r\n\r\n### Prerequisites\r\n\r\nTo use this class, you'll need the following Python libraries installed:\r\n\r\n```bash\r\npip install pandas numpy lxml openpyxl\r\n",
"bugtrack_url": null,
"license": null,
"summary": "A Python class for managing financial statement data from HTML-based XLS files.",
"version": "0.1.2",
"project_urls": {
"Homepage": "https://github.com/mertkurtcu/kapfinance"
},
"split_keywords": [
"financial data manager",
" financial statements",
" balance sheet",
" income statement",
" html xls",
" pandas"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "ee2f69eeeb572795aea82dd902accaf7bc4aafe79ae052c38ec87dd05100f941",
"md5": "155ee83dda1e18b52cdf7413d3288b9b",
"sha256": "32ee7094b73239f130430b101a32623ef1191222ef0c87af08ca30a0549b42ac"
},
"downloads": -1,
"filename": "kapfinance-0.1.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "155ee83dda1e18b52cdf7413d3288b9b",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 7407,
"upload_time": "2025-07-29T20:25:04",
"upload_time_iso_8601": "2025-07-29T20:25:04.398934Z",
"url": "https://files.pythonhosted.org/packages/ee/2f/69eeeb572795aea82dd902accaf7bc4aafe79ae052c38ec87dd05100f941/kapfinance-0.1.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "3642c6c64cc5cd77457c61ee76a82eebb570b45674322873ff5de7456558c9b8",
"md5": "14315299bf6a36cd5664a82fcffbb25f",
"sha256": "dadf3c8b33b18633beeeeeb92670573ae95091f1e39e820ad3587a058b30aeba"
},
"downloads": -1,
"filename": "kapfinance-0.1.2.tar.gz",
"has_sig": false,
"md5_digest": "14315299bf6a36cd5664a82fcffbb25f",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 7263,
"upload_time": "2025-07-29T20:25:05",
"upload_time_iso_8601": "2025-07-29T20:25:05.695488Z",
"url": "https://files.pythonhosted.org/packages/36/42/c6c64cc5cd77457c61ee76a82eebb570b45674322873ff5de7456558c9b8/kapfinance-0.1.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-07-29 20:25:05",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "mertkurtcu",
"github_project": "kapfinance",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "kapfinance"
}
JSON
