xbrl-us 0.0.42

Python wrapper for xbrl.us API

About

The XBRL US Python Wrapper is a powerful tool for interacting with the XBRL US API, providing seamless integration of XBRL data into Python applications. This wrapper simplifies the process of retrieving and analyzing financial data in XBRL format, enabling users to access a wide range of financial information for companies registered with the U.S. Securities and Exchange Commission (SEC).

It’s important to note that while the XBRL US Python Wrapper is free and distributed under the permissive MIT license, the usage of the underlying XBRL US API is subject to the policies and terms defined by XBRL US. These policies govern the access, usage, and restrictions imposed on the API data and services. Users of the XBRL US Python Wrapper should review and comply with the XBRL US policies to ensure appropriate usage of the API and adherence to any applicable licensing terms.

Important

Any and all use of the XBRL APIs to return data from the XBRL US Database of Public Filings is in explicit consent and agreement with the XBRL API Terms of Agreement.

Note

If you are utilizing the XBRL US Python Wrapper for research purposes, we kindly request that you cite the following paper:

[FILL: Insert Paper Title]

[FILL: Authors]

[FILL: Publication Details]

Tutorial ✏️📖📚

This tutorial will guide you through using the XBRL-US Python library to interact with the XBRL API. The XBRL-US library provides a convenient way to query and retrieve financial data from the XBRL API using Python.

1. Prerequisites

Before you begin, ensure you have the following:

• Python installed on your system. The XBRL-US library supports Python 3.8 and above.

• XBRL-US API credentials. You can obtain your credentials by registering for a free XBRL-US account at https://xbrl.us/home/use/xbrl-api/.

• XBRL-US OAuth2 Access. You can obtain your client ID and client secret by registering for a filling the request form at https://xbrl.us/home/use/xbrl-api/access-token/.

You can install this package using pip:

pip install xbrl-us

If you are using Jupyter Notebook, you can install the package using the following command:

!pip install xbrl-us

Caution!

The XBRL US Python Wrapper is currently in beta and is subject to change. We welcome your feedback and suggestions for improvement. Please submit any issues or feature requests to the GitHub repository.

Documentation

For detailed information about the XBRL-US Python library, you can refer to the documentation at https://python-xbrl-us.readthedocs.io/en/latest/.

Official Documentation

For more information about the XBRL API and its endpoints, refer to the original API documentation at https://xbrlus.github.io/xbrl-api.

2. Choose Your Preferred Approach

There are two distinct ways to use the XBRL-US Python package:

• Code-Based Approach: Import the XBRL-US Python package directly into your Python environment for in-depth, custom analysis (see Code-Based Approach)

• Browser Interface: For a no-code experience, navigate to the Browser Interface. This interface allows for easy exploration and analysis of XBRL data directly in your web browser.

2.1. Code-Based Approach

Import the XBRL Library

To start using the XBRL-US library, you need to import it into your Python script:

from xbrl_us import XBRL

Create an Instance of XBRL Class

Next, you need to create an instance of the XBRL class, providing your authentication credentials (client ID, client secret, username, and password) as parameters:

xbrl = XBRL(
client_id='Your client id',
client_secret='Your client secret',
username='Your username',
password='Your password'
)

Make sure to replace Your client id, Your client secret, Your username, and Your password with your actual credentials.

Query the XBRL API

The XBRL-US library provides a query method to search for data from the XBRL API. You can specify various parameters and fields to filter and retrieve the desired data.

Here’s an example of using the query method to search for specific financial facts:

response = xbrl.query(
    method='fact search',
    parameters={
        "concept.local-name": [
            'OperatingIncomeLoss',
            'GrossProfit',
            'OperatingExpenses',
            'OtherOperatingIncomeExpenseNet'
        ],
        "period.fiscal-year": [2009, 2010],
        "report.sic-code": range(2800, 2899)
    },
    fields=[
        'report.accession',
        'period.fiscal-year',
        'period.end',
        'period.fiscal-period',
        'fact.ultimus',
        'unit',
        'concept.local-name',
        'fact.value',
        'fact.id',
        'entity.id',
        'entity.cik',
        'entity.name',
        'report.sic-code',
    ],
    limit=100,
    as_dataframe=True
)

In this example, we are searching for facts related to specific concepts, fiscal years, and SIC codes. We are also specifying the fields we want to retrieve in the response. The limit parameter restricts the number of facts returned to 100, and as_dataframe=True ensures the response is returned as a Pandas DataFrame.

Alternatively, you can use the Parameters and Fields classes provided by the library to make the query more readable, less prone to errors, and easier to maintain:

from xbrl_us.utils import Parameters, Fields

response = xbrl.query(
    method='fact search',
    parameters=Parameters(
        concept_local_name=[
            'OperatingIncomeLoss',
            'GrossProfit',
            'OperatingExpenses',
            'OtherOperatingIncomeExpenseNet'
        ],
        period_fiscal_year=[2009, 2010],
        report_sic_code=range(2800, 2899)
    ),
    fields=[
        Fields.REPORT_ACCESSION,
        Fields.PERIOD_FISCAL_YEAR,
        Fields.PERIOD_END,
        Fields.PERIOD_FISCAL_PERIOD,
        Fields.FACT_ULTIMUS,
        Fields.UNIT,
        Fields.CONCEPT_LOCAL_NAME,
        Fields.FACT_VALUE,
        Fields.FACT_ID,
        Fields.ENTITY_ID,
        Fields.ENTITY_CIK,
        Fields.ENTITY_NAME,
        Fields.REPORT_SIC_CODE,
    ],
    limit=100,
    as_dataframe=True
)

This alternative approach also allows you to take advantage of the autocomplete feature of your IDE to easily find the parameters and fields.

Perform Additional Queries

You can use the same query method to call other API endpoints by changing the method parameter and providing the relevant parameters and fields.

Here’s an example of using the query method to search for a specific fact by its ID:

response = xbrl.query(
method='fact id',
parameters={'fact.id': 123},
fields=[
    'report.accession',
    'period.fiscal-year',
    'period.end',
    'period.fiscal-period',
    'fact.ultimus',
    'unit',
    'concept.local-name',
    'fact.value',
    'fact.id',
    'entity.id',
    'entity.cik',
    'entity.name',
    'report.sic-code',
],
as_dataframe=False
)

Congratulations! You have learned how to use the XBRL-US Python library to interact with the XBRL API. In this example you will receive the data in json format as the as_dataframe parameter is set to False.

2.2 Browser Interface 🖥️

This feature is designed to make our package even more user-friendly, allowing users to interact and work with XBRL data directly through a graphical interface, in addition to the existing code-based methods.

The browser interface streamlines data visualization, simplifies navigation, and enhances user interactions. With this intuitive, user-friendly interface, you can easily explore, interpret, and analyze XBRL data in real-time, right from your web browser.

Key Features:

• Create Real-time queries right in your browser

• Intuitive navigation and search features

• Filtering and sorting options

• Seamless integration with the existing XBRL-US Python API

Getting started is as simple as ever. Update your XBRL-US Python package to the latest version and launch the new Browser Interface from the package menu.

Getting Started with the Browser Interface

Getting started is as simple as ever. First, ensure you have the latest version of xbrl-us installed by running the following code:

pip install xbrl-us --upgrade

or if you are on a Jupyter Notebook:

!pip install xbrl-us --upgrade

Next, launch the new Browser Interface from the package menu:

python -m xbrl_us

or if you are on a Jupyter Notebook:

!python -m xbrl_us

That is it! You should now see the new Browser Interface open in your default web browser.

Happy data exploring!

Note

Please note, while we have tested the interface extensively, this is its initial release. We encourage users to provide feedback to help us further improve the tool. We value your input! You can also find tutorials, example codes, and more resources to help you get started.

Development

To run all the tests run:

tox

Note, to combine the coverage data from all the tox environments run:

Windows	set PYTEST_ADDOPTS=--cov-append tox
Other	PYTEST_ADDOPTS=--cov-append tox

Changelog

0.0.42 (2023-08-05)

• Bug fixes

• Improved Browser Interface

• Added new methods to the API for Browser Interface

0.0.41 (2023-08-04)

• Bug fixes

0.0.40 (2023-08-03)

• Improved Browser Interface

• improved error handling for requests

• Bug fixes

0.0.32 (2023-07-17)

• Improved Browser Interface

• Added unique keyword to query method

• Bug fixes

0.0.31 (2023-07-14)

• fixed dependency issues

• Bug fixes

0.0.3 (2023-07-14)

• Backward compatibility with Python 3.8 and 3.9

• Bug fixes

0.0.2 (2023-07-12)

• Bug fixes

• Enhanced error handling

• Improved methods attributes

• Added the ability to print the query string

• Implemented a feature to handle queries with large limits

• NEW: Introduced a web interface for the API, making it even easier to use

0.0.1 (2023-07-09)

• First release on PyPI.