# bojdata
A comprehensive Python package for accessing ALL Bank of Japan (BOJ) statistical data.
## Overview
`bojdata` provides a complete interface to download and work with ALL data from the Bank of Japan's Time-Series Data Search portal. It handles the complexities of navigating the BOJ website, bulk downloading, and data processing to give you access to the entire BOJ statistical database.
## Features
### Core Features
- Download individual time series by code
- Search for available data series with enhanced parameters
- Automatic data cleaning and formatting
- Date range filtering
- Frequency conversion (daily, monthly, quarterly, yearly)
- Support for multiple series download
### FRED-Compatible Features (NEW)
- **Data Transformations**: All 9 FRED units (percent change, YoY, log, etc.)
- **FRED API Wrapper**: Familiar API for users migrating from FRED
- **Enhanced Search**: Full-text and series ID search with pagination and filters
- **Standardized Errors**: FRED-style error codes and messages
- **Multiple Output Formats**: Pandas DataFrames or FRED-style JSON
- **Batch Operations**: Parallel downloads for multiple series
- **Release Calendar**: Track when data series are updated
- **Series Metadata**: Detailed information about each series
- **Tag System**: Search and organize series by tags
- **Aggregation Methods**: Choose how to aggregate data (avg, sum, eop)
### Advanced Features
- **Bulk Download**: Download ALL available BOJ data at once
- **Comprehensive Search**: Search across all 13 data categories
- **Series Discovery**: Automatically discover all available series codes
- **Unified Database**: Build a searchable database of all BOJ data
- **Command-Line Interface**: Easy CLI access to all functionality
- **Category Navigation**: Browse data by category and subcategory
- **Flat File Support**: Download and process BOJ's flat file archives
## Installation
```bash
pip install bojdata
```
### Development Installation
```bash
git clone https://github.com/caymandev/bojdata.git
cd bojdata
pip install -e .[dev]
```
## Quick Start
### Basic Usage
```python
import bojdata
# Download a single series
df = bojdata.read_boj(series="BS01'MABJMTA")
# Download multiple series
df = bojdata.read_boj(series=["IR01", "FM01"])
# Search for series
results = bojdata.search_series("interest rate")
# Download with data transformations (NEW)
df_pct_change = bojdata.read_boj(series="BS01'MABJMTA", units='pch') # Percent change
df_yoy = bojdata.read_boj(series="CPI", units='pc1') # Year-over-year % change
```
### FRED-Compatible API (NEW)
```python
from bojdata import BOJDataAPI
api = BOJDataAPI()
# Get series metadata (like FRED)
metadata = api.get_series("BS01'MABJMTA")
print(f"Title: {metadata['title']}")
print(f"Frequency: {metadata['frequency']}")
# Get observations with transformations
data = api.get_observations(
"BS01'MABJMTA",
start_date="2020-01-01",
end_date="2023-12-31",
units='pc1', # Year-over-year percent change
frequency='Q' # Convert to quarterly
)
# Enhanced search with FRED parameters
results = api.search_series(
"interest rate",
search_type='full_text',
limit=20,
order_by='relevance'
)
# Get FRED-style JSON output
json_data = api.get_observations("IR01", output_type='dict')
```
### Advanced Usage - Access ALL BOJ Data
```python
from bojdata import BOJBulkDownloader, BOJComprehensiveSearch
# Download ALL available data
downloader = BOJBulkDownloader("./boj_data")
downloader.download_all_flat_files()
downloader.extract_and_process_all()
# Build a unified database
db_path = downloader.build_unified_database(output_format="sqlite")
# Search across ALL categories
searcher = BOJComprehensiveSearch()
results = searcher.search_all_categories("inflation")
# Discover all available series
catalog = searcher.build_series_catalog()
```
### Command-Line Interface
```bash
# Download specific series
bojdata download IR01 FM01 --output ./data
# Bulk download everything
bojdata bulk --build-db --db-format sqlite
# Search across all data
bojdata search "exchange rate" --limit 50
# Build complete series catalog
bojdata catalog --output all_series.csv
```
## Examples
### Download Monetary Base Data
```python
import bojdata
# Download monetary base average amounts outstanding
monetary_base = bojdata.read_boj(series="BS01'MABJMTA")
print(monetary_base.head())
```
### Download Multiple Interest Rate Series
```python
# Download overnight call rate and other interest rates
rates = bojdata.read_boj(series=["IR01", "IR02", "IR03"])
print(rates.columns)
```
### Convert Frequency
```python
# Download daily data and convert to monthly
daily_data = bojdata.read_boj(series="FM01", frequency="M")
```
### Data Transformations (NEW)
```python
# Available transformation units (FRED-compatible):
# 'lin' - Levels (no transformation) [default]
# 'chg' - Change from previous period
# 'ch1' - Change from year ago
# 'pch' - Percent change
# 'pc1' - Percent change from year ago
# 'pca' - Compounded annual rate
# 'cch' - Continuously compounded change
# 'cca' - Continuously compounded annual rate
# 'log' - Natural log
# Examples of transformations
df_levels = bojdata.read_boj("BS01'MABJMTA", units='lin') # Original values
df_pct_chg = bojdata.read_boj("BS01'MABJMTA", units='pch') # Period % change
df_yoy_chg = bojdata.read_boj("BS01'MABJMTA", units='pc1') # YoY % change
df_log = bojdata.read_boj("BS01'MABJMTA", units='log') # Natural log
# Combine with frequency conversion
df_quarterly_yoy = bojdata.read_boj(
series="FM01",
frequency="Q", # Convert to quarterly
units='pc1' # Then calculate YoY change
)
# Different aggregation methods
df_sum = bojdata.read_boj("FLOW_SERIES", frequency="Q", aggregation_method="sum")
df_eop = bojdata.read_boj("STOCK_SERIES", frequency="Q", aggregation_method="eop")
```
### Batch Operations (NEW)
```python
from bojdata import read_boj_batch
# Download multiple series in parallel
series_list = ['IR01', 'IR02', 'IR03', 'FM01', 'FM02', 'BS01', 'MD01']
df = read_boj_batch(series_list, max_workers=5, units='pch')
# Using the API
api = BOJDataAPI()
df = api.get_observations_multi(
['IR01', 'FM01', 'BS01'],
start_date='2020-01-01',
units='pc1'
)
```
### Release Calendar (NEW)
```python
# Get release calendar
releases = api.get_releases(2024)
print(releases[['date', 'series_name', 'frequency']])
# Find when TANKAN is released
tankan_dates = api.get_release_dates('TANKAN')
# Export to calendar
from bojdata import BOJReleaseCalendar
calendar = BOJReleaseCalendar()
calendar.to_ical(2024, 'boj_releases.ics')
```
### Series Metadata & Tags (NEW)
```python
# Get detailed metadata
meta = api.get_series("BS01'MABJMTA")
print(f"Description: {meta['notes']}")
print(f"Units: {meta['units']}")
print(f"Tags: {meta['tags']}")
print(f"Related series: {meta['related_series']}")
# Search by tags
inflation_series = api.search_by_tag('inflation')
monthly_series = api.search_by_tag('monthly')
# Get all tags for a series
tags = api.get_series_tags("PR01'IUQCP001")
```
## For FRED Users
If you're familiar with the FRED API, bojdata now provides a compatible interface:
### Migration Examples
```python
# FRED Python # BOJData Equivalent
from fredapi import Fred from bojdata import BOJDataAPI
fred = Fred(api_key='...') api = BOJDataAPI()
# Get series info
fred.get_series_info('DGS10') api.get_series('FM08')
# Get observations
fred.get_series('DGS10') api.get_observations('FM08')
# With transformations
fred.get_series('DGS10', api.get_observations('FM08',
units='pch') units='pch')
# Search
fred.search('text:interest rate') api.search_series('interest rate')
```
### Key Differences
| Feature | FRED | BOJData |
|---------|------|---------|
| API Key | Required | Not needed |
| Data Source | Multiple sources | Bank of Japan only |
| Geographic Scope | Mainly US data | Japanese data |
| Vintage Data | Available | Not available |
| Rate Limits | Yes | No (but be respectful) |
## Available Data
### Data Categories (13 Total)
1. **Interest Rates on Deposits and Loans**
- Deposit rates, loan rates, interest rate spreads
2. **Financial Markets**
- Stock indices, exchange rates, bond yields, money market rates
3. **Payment and Settlement**
- BOJ-NET, Zengin System, electronic payments
4. **Money and Deposits**
- Monetary base, money stock (M1, M2, M3), deposit aggregates
5. **Loans**
- Bank lending by sector, loan growth, credit conditions
6. **Balance Sheets**
- BOJ accounts, financial institution balance sheets
7. **Flow of Funds**
- Sectoral accounts, financial flows, asset holdings
8. **Other Bank of Japan Statistics**
- Research series, special surveys, historical data
9. **TANKAN (Business Survey)**
- Business conditions, economic outlook, capital expenditure
10. **Prices**
- CPI, PPI, CGPI, service price indices
11. **Public Finance**
- Government debt, fiscal balance, bond issuance
12. **Balance of Payments**
- Current account, trade statistics, international investment
13. **Others**
- Regional statistics, miscellaneous indicators
### Flat File Downloads
Pre-packaged data files updated regularly:
- `prices_m_en.zip` - Monthly price data
- `fof_q_en.zip` - Quarterly flow of funds
- `tankan_q_en.zip` - Quarterly TANKAN survey
- `bp_m_en.zip` - Monthly balance of payments
- `bis_q_en.zip` - Quarterly BIS statistics
## Error Handling
The package provides FRED-compatible exceptions with HTTP error codes:
```python
from bojdata.exceptions import (
BOJSeriesNotFoundError,
InvalidParameterError,
DataUnavailableError
)
# Series not found (404)
try:
df = bojdata.read_boj(series="INVALID_CODE")
except BOJSeriesNotFoundError as e:
print(f"Error: {e}")
print(f"HTTP Code: {e.code}") # 404
# Invalid parameter (400)
try:
df = bojdata.read_boj(series="IR01", units="invalid_unit")
except InvalidParameterError as e:
print(f"Error: {e}")
print(f"Invalid parameter: {e.parameter_name}")
print(f"Valid options: {e.valid_values}")
# Get error as dictionary (for API responses)
try:
api = BOJDataAPI()
api.get_series("INVALID")
except BOJSeriesNotFoundError as e:
error_dict = e.to_dict() # {'error_code': 404, 'error_message': '...'}
```
## Requirements
- Python 3.9+
- pandas
- requests
- beautifulsoup4
- numpy
- lxml
## License
MIT License - see LICENSE file for details.
## Development
### Running Tests
```bash
# Run tests with pytest
pytest
# Run tests with coverage
pytest --cov=bojdata
# Run tests for all Python versions
tox
```
### Code Quality
```bash
# Format code
black bojdata tests
# Sort imports
isort bojdata tests
# Lint code
flake8 bojdata tests
# Type check
mypy bojdata
```
### Releasing New Versions
This project uses `bump2version` and GitHub Actions for versioning and releases:
1. Update version:
```bash
bump2version patch # for bug fixes
bump2version minor # for new features
bump2version major # for breaking changes
```
2. Push to GitHub:
```bash
git push origin main
git push --tags
```
3. GitHub Actions will automatically:
- Run tests on multiple Python versions
- Publish to Test PyPI
- Publish to PyPI (on tag push)
- Create a GitHub release
## Contributing
Contributions are welcome! Please:
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## License
MIT License - see LICENSE file for details.
## Disclaimer
This package is not affiliated with or endorsed by the Bank of Japan. Please refer to the BOJ website for official data and terms of use.
Raw data
{
"_id": null,
"home_page": null,
"name": "bojdata",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "boj, bank-of-japan, economics, finance, data, statistics",
"author": "CaymanDev",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/1e/92/97dbf60c562e92ca98ee972da06d0fb92f3e9e8e929955e72d451ac90f45/bojdata-0.0.1.tar.gz",
"platform": null,
"description": "# bojdata\n\nA comprehensive Python package for accessing ALL Bank of Japan (BOJ) statistical data.\n\n## Overview\n\n`bojdata` provides a complete interface to download and work with ALL data from the Bank of Japan's Time-Series Data Search portal. It handles the complexities of navigating the BOJ website, bulk downloading, and data processing to give you access to the entire BOJ statistical database.\n\n## Features\n\n### Core Features\n- Download individual time series by code\n- Search for available data series with enhanced parameters\n- Automatic data cleaning and formatting\n- Date range filtering\n- Frequency conversion (daily, monthly, quarterly, yearly)\n- Support for multiple series download\n\n### FRED-Compatible Features (NEW)\n- **Data Transformations**: All 9 FRED units (percent change, YoY, log, etc.)\n- **FRED API Wrapper**: Familiar API for users migrating from FRED\n- **Enhanced Search**: Full-text and series ID search with pagination and filters\n- **Standardized Errors**: FRED-style error codes and messages\n- **Multiple Output Formats**: Pandas DataFrames or FRED-style JSON\n- **Batch Operations**: Parallel downloads for multiple series\n- **Release Calendar**: Track when data series are updated\n- **Series Metadata**: Detailed information about each series\n- **Tag System**: Search and organize series by tags\n- **Aggregation Methods**: Choose how to aggregate data (avg, sum, eop)\n\n### Advanced Features\n- **Bulk Download**: Download ALL available BOJ data at once\n- **Comprehensive Search**: Search across all 13 data categories\n- **Series Discovery**: Automatically discover all available series codes\n- **Unified Database**: Build a searchable database of all BOJ data\n- **Command-Line Interface**: Easy CLI access to all functionality\n- **Category Navigation**: Browse data by category and subcategory\n- **Flat File Support**: Download and process BOJ's flat file archives\n\n## Installation\n\n```bash\npip install bojdata\n```\n\n### Development Installation\n\n```bash\ngit clone https://github.com/caymandev/bojdata.git\ncd bojdata\npip install -e .[dev]\n```\n\n## Quick Start\n\n### Basic Usage\n\n```python\nimport bojdata\n\n# Download a single series\ndf = bojdata.read_boj(series=\"BS01'MABJMTA\")\n\n# Download multiple series\ndf = bojdata.read_boj(series=[\"IR01\", \"FM01\"])\n\n# Search for series\nresults = bojdata.search_series(\"interest rate\")\n\n# Download with data transformations (NEW)\ndf_pct_change = bojdata.read_boj(series=\"BS01'MABJMTA\", units='pch') # Percent change\ndf_yoy = bojdata.read_boj(series=\"CPI\", units='pc1') # Year-over-year % change\n```\n\n### FRED-Compatible API (NEW)\n\n```python\nfrom bojdata import BOJDataAPI\n\napi = BOJDataAPI()\n\n# Get series metadata (like FRED)\nmetadata = api.get_series(\"BS01'MABJMTA\")\nprint(f\"Title: {metadata['title']}\")\nprint(f\"Frequency: {metadata['frequency']}\")\n\n# Get observations with transformations\ndata = api.get_observations(\n \"BS01'MABJMTA\",\n start_date=\"2020-01-01\",\n end_date=\"2023-12-31\",\n units='pc1', # Year-over-year percent change\n frequency='Q' # Convert to quarterly\n)\n\n# Enhanced search with FRED parameters\nresults = api.search_series(\n \"interest rate\",\n search_type='full_text',\n limit=20,\n order_by='relevance'\n)\n\n# Get FRED-style JSON output\njson_data = api.get_observations(\"IR01\", output_type='dict')\n```\n\n### Advanced Usage - Access ALL BOJ Data\n\n```python\nfrom bojdata import BOJBulkDownloader, BOJComprehensiveSearch\n\n# Download ALL available data\ndownloader = BOJBulkDownloader(\"./boj_data\")\ndownloader.download_all_flat_files()\ndownloader.extract_and_process_all()\n\n# Build a unified database\ndb_path = downloader.build_unified_database(output_format=\"sqlite\")\n\n# Search across ALL categories\nsearcher = BOJComprehensiveSearch()\nresults = searcher.search_all_categories(\"inflation\")\n\n# Discover all available series\ncatalog = searcher.build_series_catalog()\n```\n\n### Command-Line Interface\n\n```bash\n# Download specific series\nbojdata download IR01 FM01 --output ./data\n\n# Bulk download everything\nbojdata bulk --build-db --db-format sqlite\n\n# Search across all data\nbojdata search \"exchange rate\" --limit 50\n\n# Build complete series catalog\nbojdata catalog --output all_series.csv\n```\n\n## Examples\n\n### Download Monetary Base Data\n\n```python\nimport bojdata\n\n# Download monetary base average amounts outstanding\nmonetary_base = bojdata.read_boj(series=\"BS01'MABJMTA\")\nprint(monetary_base.head())\n```\n\n### Download Multiple Interest Rate Series\n\n```python\n# Download overnight call rate and other interest rates\nrates = bojdata.read_boj(series=[\"IR01\", \"IR02\", \"IR03\"])\nprint(rates.columns)\n```\n\n### Convert Frequency\n\n```python\n# Download daily data and convert to monthly\ndaily_data = bojdata.read_boj(series=\"FM01\", frequency=\"M\")\n```\n\n### Data Transformations (NEW)\n\n```python\n# Available transformation units (FRED-compatible):\n# 'lin' - Levels (no transformation) [default]\n# 'chg' - Change from previous period\n# 'ch1' - Change from year ago\n# 'pch' - Percent change\n# 'pc1' - Percent change from year ago\n# 'pca' - Compounded annual rate\n# 'cch' - Continuously compounded change\n# 'cca' - Continuously compounded annual rate\n# 'log' - Natural log\n\n# Examples of transformations\ndf_levels = bojdata.read_boj(\"BS01'MABJMTA\", units='lin') # Original values\ndf_pct_chg = bojdata.read_boj(\"BS01'MABJMTA\", units='pch') # Period % change\ndf_yoy_chg = bojdata.read_boj(\"BS01'MABJMTA\", units='pc1') # YoY % change\ndf_log = bojdata.read_boj(\"BS01'MABJMTA\", units='log') # Natural log\n\n# Combine with frequency conversion\ndf_quarterly_yoy = bojdata.read_boj(\n series=\"FM01\",\n frequency=\"Q\", # Convert to quarterly\n units='pc1' # Then calculate YoY change\n)\n\n# Different aggregation methods\ndf_sum = bojdata.read_boj(\"FLOW_SERIES\", frequency=\"Q\", aggregation_method=\"sum\")\ndf_eop = bojdata.read_boj(\"STOCK_SERIES\", frequency=\"Q\", aggregation_method=\"eop\")\n```\n\n### Batch Operations (NEW)\n\n```python\nfrom bojdata import read_boj_batch\n\n# Download multiple series in parallel\nseries_list = ['IR01', 'IR02', 'IR03', 'FM01', 'FM02', 'BS01', 'MD01']\ndf = read_boj_batch(series_list, max_workers=5, units='pch')\n\n# Using the API\napi = BOJDataAPI()\ndf = api.get_observations_multi(\n ['IR01', 'FM01', 'BS01'],\n start_date='2020-01-01',\n units='pc1'\n)\n```\n\n### Release Calendar (NEW)\n\n```python\n# Get release calendar\nreleases = api.get_releases(2024)\nprint(releases[['date', 'series_name', 'frequency']])\n\n# Find when TANKAN is released\ntankan_dates = api.get_release_dates('TANKAN')\n\n# Export to calendar\nfrom bojdata import BOJReleaseCalendar\ncalendar = BOJReleaseCalendar()\ncalendar.to_ical(2024, 'boj_releases.ics')\n```\n\n### Series Metadata & Tags (NEW)\n\n```python\n# Get detailed metadata\nmeta = api.get_series(\"BS01'MABJMTA\")\nprint(f\"Description: {meta['notes']}\")\nprint(f\"Units: {meta['units']}\")\nprint(f\"Tags: {meta['tags']}\")\nprint(f\"Related series: {meta['related_series']}\")\n\n# Search by tags\ninflation_series = api.search_by_tag('inflation')\nmonthly_series = api.search_by_tag('monthly')\n\n# Get all tags for a series\ntags = api.get_series_tags(\"PR01'IUQCP001\")\n```\n\n## For FRED Users\n\nIf you're familiar with the FRED API, bojdata now provides a compatible interface:\n\n### Migration Examples\n\n```python\n# FRED Python # BOJData Equivalent\nfrom fredapi import Fred from bojdata import BOJDataAPI\nfred = Fred(api_key='...') api = BOJDataAPI()\n\n# Get series info\nfred.get_series_info('DGS10') api.get_series('FM08')\n\n# Get observations\nfred.get_series('DGS10') api.get_observations('FM08')\n\n# With transformations\nfred.get_series('DGS10', api.get_observations('FM08',\n units='pch') units='pch')\n\n# Search\nfred.search('text:interest rate') api.search_series('interest rate')\n```\n\n### Key Differences\n\n| Feature | FRED | BOJData |\n|---------|------|---------|\n| API Key | Required | Not needed |\n| Data Source | Multiple sources | Bank of Japan only |\n| Geographic Scope | Mainly US data | Japanese data |\n| Vintage Data | Available | Not available |\n| Rate Limits | Yes | No (but be respectful) |\n\n## Available Data\n\n### Data Categories (13 Total)\n\n1. **Interest Rates on Deposits and Loans**\n - Deposit rates, loan rates, interest rate spreads\n \n2. **Financial Markets**\n - Stock indices, exchange rates, bond yields, money market rates\n \n3. **Payment and Settlement**\n - BOJ-NET, Zengin System, electronic payments\n \n4. **Money and Deposits**\n - Monetary base, money stock (M1, M2, M3), deposit aggregates\n \n5. **Loans**\n - Bank lending by sector, loan growth, credit conditions\n \n6. **Balance Sheets**\n - BOJ accounts, financial institution balance sheets\n \n7. **Flow of Funds**\n - Sectoral accounts, financial flows, asset holdings\n \n8. **Other Bank of Japan Statistics**\n - Research series, special surveys, historical data\n \n9. **TANKAN (Business Survey)**\n - Business conditions, economic outlook, capital expenditure\n \n10. **Prices**\n - CPI, PPI, CGPI, service price indices\n \n11. **Public Finance**\n - Government debt, fiscal balance, bond issuance\n \n12. **Balance of Payments**\n - Current account, trade statistics, international investment\n \n13. **Others**\n - Regional statistics, miscellaneous indicators\n\n### Flat File Downloads\n\nPre-packaged data files updated regularly:\n- `prices_m_en.zip` - Monthly price data\n- `fof_q_en.zip` - Quarterly flow of funds\n- `tankan_q_en.zip` - Quarterly TANKAN survey\n- `bp_m_en.zip` - Monthly balance of payments\n- `bis_q_en.zip` - Quarterly BIS statistics\n\n## Error Handling\n\nThe package provides FRED-compatible exceptions with HTTP error codes:\n\n```python\nfrom bojdata.exceptions import (\n BOJSeriesNotFoundError,\n InvalidParameterError,\n DataUnavailableError\n)\n\n# Series not found (404)\ntry:\n df = bojdata.read_boj(series=\"INVALID_CODE\")\nexcept BOJSeriesNotFoundError as e:\n print(f\"Error: {e}\")\n print(f\"HTTP Code: {e.code}\") # 404\n\n# Invalid parameter (400)\ntry:\n df = bojdata.read_boj(series=\"IR01\", units=\"invalid_unit\")\nexcept InvalidParameterError as e:\n print(f\"Error: {e}\")\n print(f\"Invalid parameter: {e.parameter_name}\")\n print(f\"Valid options: {e.valid_values}\")\n\n# Get error as dictionary (for API responses)\ntry:\n api = BOJDataAPI()\n api.get_series(\"INVALID\")\nexcept BOJSeriesNotFoundError as e:\n error_dict = e.to_dict() # {'error_code': 404, 'error_message': '...'}\n```\n\n## Requirements\n\n- Python 3.9+\n- pandas\n- requests\n- beautifulsoup4\n- numpy\n- lxml\n\n## License\n\nMIT License - see LICENSE file for details.\n\n## Development\n\n### Running Tests\n\n```bash\n# Run tests with pytest\npytest\n\n# Run tests with coverage\npytest --cov=bojdata\n\n# Run tests for all Python versions\ntox\n```\n\n### Code Quality\n\n```bash\n# Format code\nblack bojdata tests\n\n# Sort imports\nisort bojdata tests\n\n# Lint code\nflake8 bojdata tests\n\n# Type check\nmypy bojdata\n```\n\n### Releasing New Versions\n\nThis project uses `bump2version` and GitHub Actions for versioning and releases:\n\n1. Update version:\n ```bash\n bump2version patch # for bug fixes\n bump2version minor # for new features\n bump2version major # for breaking changes\n ```\n\n2. Push to GitHub:\n ```bash\n git push origin main\n git push --tags\n ```\n\n3. GitHub Actions will automatically:\n - Run tests on multiple Python versions\n - Publish to Test PyPI\n - Publish to PyPI (on tag push)\n - Create a GitHub release\n\n## Contributing\n\nContributions are welcome! Please:\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## License\n\nMIT License - see LICENSE file for details.\n\n## Disclaimer\n\nThis package is not affiliated with or endorsed by the Bank of Japan. Please refer to the BOJ website for official data and terms of use.\n",
"bugtrack_url": null,
"license": null,
"summary": "Comprehensive Python package for accessing all Bank of Japan statistical data",
"version": "0.0.1",
"project_urls": {
"Documentation": "https://bojdata.readthedocs.io",
"Homepage": "https://github.com/caymandev/bojdata",
"Issues": "https://github.com/caymandev/bojdata/issues",
"Repository": "https://github.com/caymandev/bojdata"
},
"split_keywords": [
"boj",
" bank-of-japan",
" economics",
" finance",
" data",
" statistics"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "ba01967ec7508c2250ca2bcc3eb3b6e13297384ad7fa8b27046ec779b11b6c44",
"md5": "bbe1bed3c3df79b9a44bb8bb6d77c72e",
"sha256": "2b00525cf2972b8b28b9732217a2f5b7cfc2ac02d2c02e03b4864688e6d3025d"
},
"downloads": -1,
"filename": "bojdata-0.0.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "bbe1bed3c3df79b9a44bb8bb6d77c72e",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 46088,
"upload_time": "2025-07-25T06:34:13",
"upload_time_iso_8601": "2025-07-25T06:34:13.901498Z",
"url": "https://files.pythonhosted.org/packages/ba/01/967ec7508c2250ca2bcc3eb3b6e13297384ad7fa8b27046ec779b11b6c44/bojdata-0.0.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "1e9297dbf60c562e92ca98ee972da06d0fb92f3e9e8e929955e72d451ac90f45",
"md5": "001ee9c68aa3a899e2ea68145bd12b24",
"sha256": "701f1ed39b2b5390d5fe6d7104ad98a923c264c377e9bd330ba67f8cfaaab25e"
},
"downloads": -1,
"filename": "bojdata-0.0.1.tar.gz",
"has_sig": false,
"md5_digest": "001ee9c68aa3a899e2ea68145bd12b24",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 48920,
"upload_time": "2025-07-25T06:34:15",
"upload_time_iso_8601": "2025-07-25T06:34:15.637558Z",
"url": "https://files.pythonhosted.org/packages/1e/92/97dbf60c562e92ca98ee972da06d0fb92f3e9e8e929955e72d451ac90f45/bojdata-0.0.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-07-25 06:34:15",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "caymandev",
"github_project": "bojdata",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"tox": true,
"lcname": "bojdata"
}
JSON
