gbizinfo (youseiushida/gbizinfo)

gbizinfo

https://github.com/youseiushida/gbizinfo
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gbizinfo is a Python client library for the gBizINFO (Corporate Activity Information) REST API v2,...

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# gbizinfo

gbizinfo is a Python client library for the gBizINFO REST API v2, providing access to Japanese corporate information (business activity data). The library supports all API endpoints including corporate search, retrieval by corporate number, and differential updates. It offers both synchronous (`GbizClient`) and asynchronous (`AsyncGbizClient`) clients with built-in features like automatic pagination, local file caching, retry with exponential backoff, and rate limiting.

The library uses httpx for HTTP communication and Pydantic for data validation. It provides type-safe enum-based parameters for prefectures, corporate types, ministries, and other search criteria. Key features include automatic corporate number validation with check digit verification, transparent pagination for large result sets, and a `to_flat_dict()` method for converting nested API responses into flat dictionaries suitable for pandas DataFrames.

## Client Initialization

Initialize a synchronous or asynchronous client with API token and optional configuration for caching, retry, rate limiting, and HTTP settings.

```python
from gbizinfo import GbizClient, AsyncGbizClient
from gbizinfo.config import CacheMode

# Basic initialization (token from environment variable GBIZINFO_API_TOKEN)
with GbizClient() as client:
    result = client.search(name="Toyota")

# Full configuration
client = GbizClient(
    api_token="YOUR_TOKEN",
    cache_dir="./cache",
    cache_mode=CacheMode.READ_WRITE,
    cache_ttl=60 * 60 * 12,        # 12 hours
    retry_max_attempts=3,
    retry_base_delay=1.0,
    retry_cap_delay=16.0,
    rate_limit_per_sec=1.5,
    timeout=60.0,
    http2=True,
    proxy="http://my.proxy:8080",
)

# Async client
async with AsyncGbizClient(api_token="YOUR_TOKEN", max_concurrent=10) as client:
    result = await client.search(name="Sony")
```

## Corporate Search

Search for corporations using various criteria including name, prefecture, corporate type, capital, employee count, and more. Supports 30+ search parameters with type-safe enum values.

```python
from gbizinfo import GbizClient
from gbizinfo.enums import Prefecture, CorporateType, Ministry, Source

with GbizClient(api_token="YOUR_TOKEN") as client:
    # Basic name search
    result = client.search(name="Toyota", limit=5)
    for item in result.items:
        print(f"{item.corporate_number}: {item.name}")
    # Output: 1180301018771: Toyota Motor Corporation

    # Search by prefecture and corporate type
    result = client.search(
        prefecture=Prefecture.東京都,
        corporate_type=CorporateType.株式会社,
        limit=10,
    )

    # Multiple corporate types
    result = client.search(
        corporate_type=[CorporateType.株式会社, CorporateType.合同会社],
        prefecture=Prefecture.大阪府,
    )

    # Capital and employee range
    result = client.search(
        capital_stock_from=100_000_000,
        capital_stock_to=500_000_000,
        employee_number_from=1000,
        prefecture=Prefecture.愛知県,
    )

    # Filter by source and ministry
    result = client.search(source=Source.調達, ministry=Ministry.国税庁)

    # Subsidy and procurement keyword search
    result = client.search(subsidy="環境", prefecture=Prefecture.東京都)
```

## Get Corporate Details by Corporate Number

Retrieve detailed information for a specific corporation using its 13-digit corporate number. The library automatically validates the check digit.

```python
from gbizinfo import GbizClient
import gbizinfo

with GbizClient(api_token="YOUR_TOKEN") as client:
    try:
        info = client.get("7000012050002")  # National Tax Agency
        print(f"Name: {info.name}")
        print(f"Corporate Number: {info.corporate_number}")
        print(f"Location: {info.location}")
        print(f"Capital: {info.capital_stock}")
        print(f"Employees: {info.employee_number}")
        print(f"Established: {info.date_of_establishment}")
    except gbizinfo.GbizCorporateNumberError as e:
        print(f"Invalid corporate number: {e}")
    except gbizinfo.GbizNotFoundError as e:
        print(f"Corporation not found: {e}")
```

## Get Sub-Resources

Retrieve specific categories of information for a corporation including certification, commendation, finance, patent, procurement, subsidy, and workplace data.

```python
from gbizinfo import GbizClient

with GbizClient(api_token="YOUR_TOKEN") as client:
    corporate_number = "7000012050002"

    # Certifications and filings
    cert = client.get_certification(corporate_number)

    # Awards and commendations
    comm = client.get_commendation(corporate_number)

    # Financial information
    fin = client.get_finance(corporate_number)

    # Patent information
    pat = client.get_patent(corporate_number)

    # Procurement information
    proc = client.get_procurement(corporate_number)

    # Subsidy information
    sub = client.get_subsidy(corporate_number)

    # Workplace information
    work = client.get_workplace(corporate_number)
```

## Differential Updates

Retrieve corporations updated within a specified date range. Supports category-specific update endpoints.

```python
from datetime import date, timedelta
from gbizinfo import GbizClient

with GbizClient(api_token="YOUR_TOKEN") as client:
    to_date = date.today()
    from_date = to_date - timedelta(days=3)

    # General update info
    result = client.get_update_info(from_date=from_date, to_date=to_date)
    print(f"Total count: {result.total_count}")
    print(f"Total pages: {result.total_page}")

    for item in result.items:
        print(f"{item.corporate_number}: {item.name}")

    # Category-specific updates
    client.get_update_certification(from_date=from_date, to_date=to_date)
    client.get_update_commendation(from_date=from_date, to_date=to_date)
    client.get_update_finance(from_date=from_date, to_date=to_date)
    client.get_update_procurement(from_date=from_date, to_date=to_date)
    client.get_update_workplace(from_date=from_date, to_date=to_date)
```

## Automatic Pagination

Transparently iterate through all pages of search results or update info. Automatically handles pagination up to 50,000 records (10 pages x 5,000 limit).

```python
from datetime import date
from gbizinfo import GbizClient
from gbizinfo.enums import Prefecture
import gbizinfo

with GbizClient(api_token="YOUR_TOKEN") as client:
    # Paginate search results
    for item in client.paginate_search(
        prefecture=Prefecture.東京都,
        limit=2000
    ):
        print(f"{item.corporate_number}: {item.name}")

    # Paginate update info
    for item in client.paginate_update_info(
        from_date=date(2025, 2, 1),
        to_date=date(2025, 2, 5),
    ):
        print(f"{item.corporate_number}: {item.update_date}")

    # Helper: get recent updates (past N days)
    try:
        for item in client.get_recent_updates(days=7):
            print(f"{item.corporate_number}: {item.name}")
    except gbizinfo.PaginationLimitExceededError as e:
        print(f"Too many results: {e.total_count} (max: {e.max_retrievable})")
```

## Flatten Nested Data

Convert nested API responses to flat dictionaries suitable for pandas DataFrames using the `to_flat_dict()` method.

```python
from gbizinfo import GbizClient
import pandas as pd

with GbizClient(api_token="YOUR_TOKEN") as client:
    info = client.get("1180301018771")

    # List handling strategies
    flat = info.to_flat_dict(lists="count")    # Lists as counts only
    flat = info.to_flat_dict(lists="first")    # First element only
    flat = info.to_flat_dict(lists="json")     # JSON string
    flat = info.to_flat_dict(lists="explode")  # Indexed fields (_0, _1, ...)

    # Bulk flatten from SearchResult
    result = client.search(name="Toyota", limit=10)
    dicts = result.to_flat_dicts(lists="count")

    # Convert to pandas DataFrame
    df = pd.DataFrame(dicts)
    print(df.columns.tolist())
```

## Async Client Usage

Use `AsyncGbizClient` for asynchronous operations with the same API as the synchronous client.

```python
import asyncio
from gbizinfo import AsyncGbizClient

async def main():
    async with AsyncGbizClient(
        api_token="YOUR_TOKEN",
        max_concurrent=10  # Limit concurrent requests
    ) as client:
        # Search
        result = await client.search(name="Sony", limit=3)
        for item in result.items:
            print(item.name)

        # Get by corporate number
        info = await client.get("1180301018771")
        print(info.name)

        # Async pagination
        async for item in client.paginate_search(name="Honda"):
            print(f"{item.corporate_number}: {item.name}")

        # Async recent updates
        async for item in client.get_recent_updates(days=3):
            print(f"{item.corporate_number}: {item.name}")

asyncio.run(main())
```

## Error Handling

Handle API errors using the exception hierarchy. All exceptions inherit from `GbizError`.

```python
import gbizinfo
from gbizinfo import GbizClient

with GbizClient(api_token="YOUR_TOKEN") as client:
    try:
        info = client.get("7000012050002")
    except gbizinfo.GbizBadRequestError as e:
        # 400: Invalid parameters (includes errors array)
        print(f"Status: {e.context.status_code}")
        for error in e.errors:
            print(f"Error: {error}")
    except gbizinfo.GbizUnauthorizedError as e:
        # 401: Invalid token
        print(f"Unauthorized: {e.context.status_code}")
    except gbizinfo.GbizNotFoundError as e:
        # 404: Corporation not found
        print(f"Not found: {e.context.status_code}")
    except gbizinfo.GbizRateLimitError as e:
        # 429: Rate limit exceeded
        print(f"Rate limited, retry after: {e.context.retry_after}s")
    except gbizinfo.GbizServerError as e:
        # 5xx: Server error
        print(f"Server error: {e.context.status_code}")
    except gbizinfo.GbizTransportError as e:
        # Network connection error
        print(f"Transport error: {e.original}")
    except gbizinfo.GbizTimeoutError as e:
        # Timeout error
        print(f"Timeout ({e.timeout_type}): {e}")
    except gbizinfo.GbizValidationError as e:
        # Pre-request validation error
        print(f"Validation error: {e}")
    except gbizinfo.GbizCorporateNumberError as e:
        # Invalid corporate number format
        print(f"Invalid corporate number: {e}")
    except gbizinfo.PaginationLimitExceededError as e:
        # Pagination limit exceeded
        print(f"Too many results: {e.total_count}")
```

## Enums for Type-Safe Parameters

Use enums for type-safe parameter specification with IDE completion support.

```python
from gbizinfo.enums import (
    Prefecture,
    CorporateType,
    Region,
    Ministry,
    Source,
    AverageAge,
    AverageContinuousServiceYears,
    MonthAverageOvertimeHours,
    FemaleWorkersProportion,
    BusinessItem,
    PatentClassification,
)

# Prefecture enum (47 prefectures)
Prefecture.東京都 == "13"  # True - StrEnum subclass

# Corporate type
CorporateType.株式会社  # "301"

# Region with prefectures
Region.関東.prefectures  # Tuple of prefectures in Kanto region

# Workplace information enums
AverageAge.歳30以下
AverageContinuousServiceYears.年21以上
MonthAverageOvertimeHours.時間20未満
FemaleWorkersProportion.割合61以上

# Patent classification
PatentClassification.食品_食料品
```

## Summary

The gbizinfo library provides comprehensive access to Japan's gBizINFO corporate information API. Primary use cases include corporate research and due diligence, building company databases, tracking government procurement and subsidies, analyzing corporate financial data, and monitoring patent portfolios. The library is particularly useful for applications that need to process large volumes of corporate data, with automatic pagination handling up to 50,000 records per query.

Integration patterns typically involve using the synchronous client for simple scripts and CLI tools, while the async client is better suited for web applications and high-throughput data pipelines. The caching feature reduces API calls for repeated queries, and the `to_flat_dict()` method simplifies data analysis workflows with pandas. For production deployments, configure appropriate retry settings, rate limiting, and error handling to ensure robust operation against the gBizINFO API's known limitations and rate restrictions.