| Name | jpcorpreg JSON |
| Version |
1.8.1
JSON |
| download |
| home_page | None |
| Summary | jpcorpreg is a Python library that downloads corporate registry which is published in the Corporate Number Publication Site as a data frame. |
| upload_time | 2025-08-16 14:57:36 |
| maintainer | None |
| docs_url | None |
| author | new-village |
| requires_python | >=3.8 |
| license | Apache-2.0 |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# jpcorpreg
[](https://github.com/new-village/jp-corpreg-loader/actions/workflows/test.yaml)
**jpcorpreg** is a Python library that downloads corporate registry which is published in the [Corporate Number Publication Site](https://www.houjin-bangou.nta.go.jp/en/) as a data frame.
## Installation
----------------------
jpcorpreg is available on pip installation.
```sh
$ python -m pip install jpcorpreg
```
### GitHub Install
Installing the latest version from GitHub:
```sh
$ git clone https://github.com/new-village/jpcorpreg
$ cd jpcorpreg
$ pip install -e .
```
## Usage
This section demonstrates how to use this library to load and process data from the National Tax Agency's [Corporate Number Publication Site](https://www.houjin-bangou.nta.go.jp/).
### Direct Data Loading
To download data for a specific prefecture, use the `load` function. By passing the prefecture name as an argument, you can obtain a DataFrame containing data for that prefecture.
```python
>>> import jpcorpreg
>>> df = jpcorpreg.load("Shimane")
```
To execute the `load` function without argument, data for all prefectures across Japan will be downloaded.
```python
>>> import jpcorpreg
>>> df = jpcorpreg.load()
```
### Parquet Output
If you prefer to save the downloaded data as a Parquet file instead of returning a DataFrame, pass `format="parquet"`. The function returns the path to the generated `.parquet` file.
```python
>>> import jpcorpreg
>>> parquet_path = jpcorpreg.load("Shimane", format="parquet")
```
You can then read the Parquet file with pandas:
```python
>>> import pandas as pd
>>> df = pd.read_parquet(parquet_path)
```
### CSV Data Loading
If you already have a downloaded CSV file, use the `read_csv` function. By passing the file path as an argument, you can obtain a DataFrame with headers from the CSV data.
```python
>>> import jpcorpreg
>>> df = jpcorpreg.read_csv("path/to/data.csv")
```
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"description": "# jpcorpreg \n[](https://github.com/new-village/jp-corpreg-loader/actions/workflows/test.yaml)\n\n \n**jpcorpreg** is a Python library that downloads corporate registry which is published in the [Corporate Number Publication Site](https://www.houjin-bangou.nta.go.jp/en/) as a data frame.\n \n \n## Installation \n----------------------\njpcorpreg is available on pip installation.\n```sh\n$ python -m pip install jpcorpreg\n```\n \n### GitHub Install\nInstalling the latest version from GitHub: \n```sh\n$ git clone https://github.com/new-village/jpcorpreg\n$ cd jpcorpreg\n$ pip install -e .\n```\n \n## Usage\nThis section demonstrates how to use this library to load and process data from the National Tax Agency's [Corporate Number Publication Site](https://www.houjin-bangou.nta.go.jp/).\n\n### Direct Data Loading\nTo download data for a specific prefecture, use the `load` function. By passing the prefecture name as an argument, you can obtain a DataFrame containing data for that prefecture. \n```python\n>>> import jpcorpreg\n>>> df = jpcorpreg.load(\"Shimane\")\n```\n\nTo execute the `load` function without argument, data for all prefectures across Japan will be downloaded. \n```python\n>>> import jpcorpreg\n>>> df = jpcorpreg.load()\n```\n\n### Parquet Output\nIf you prefer to save the downloaded data as a Parquet file instead of returning a DataFrame, pass `format=\"parquet\"`. The function returns the path to the generated `.parquet` file.\n```python\n>>> import jpcorpreg\n>>> parquet_path = jpcorpreg.load(\"Shimane\", format=\"parquet\")\n```\n\nYou can then read the Parquet file with pandas:\n```python\n>>> import pandas as pd\n>>> df = pd.read_parquet(parquet_path)\n```\n\n### CSV Data Loading\nIf you already have a downloaded CSV file, use the `read_csv` function. By passing the file path as an argument, you can obtain a DataFrame with headers from the CSV data.\n```python\n>>> import jpcorpreg\n>>> df = jpcorpreg.read_csv(\"path/to/data.csv\")\n```\n",
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