tidyxbrl 1.2.1

A python package to parse the XBRL file format & interface with XBRL APIs in a tidy format. A powerful tool for automated financial analysis, accounting, and investment research using Pandas.

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tidyxbrl: The tidy Python XBRL Interface

What is it?

tidyxbrl is a Python package that parses XBRL data files and returns dynamic structures that succinctly store the underlying data. This package additionally can interface with the XBRL API and SEC EDGAR interfaces, with further expansion to other XBRL data providers planned for the near future. This package aims to the be the simplest and most effective method to parse XBRL data in Python.

The XBRL Standard

eXtensible Business Reporting Language (XBRL) is a standardized financial reporting framework to structure financial reporting in a way that enables automation and machine processing. This package aims to enable its users to realize the full capabilities of the XBRL standard through parsing files and interfacing with the applicable APIs.

Applicable documentation can be found below:

XBRL

• XBRL API Overview
• XBRL API Documentation
• XBRL API Examples

EDGAR

• SEC Edgar Information
• SEC API Documentation
• SEC Flat Files

Where to get it

The source code is currently hosted on GitHub at: https://github.com/cowboycodeman/tidyxbrl/

The tidyxbrl package is publically available for download at: https://pypi.org/project/tidyxbrl/

# PyPI
pip install tidyxbrl

Examples

xbrl_parse - Parse xbrl files or website urls

tidyxbrl.xbrl_parse("https://www.sec.gov/Archives/edgar/data/320193/000032019321000010/aapl-20201226_htm.xml")

xbrl_query - Query the XBRL API

response = tidyxbrl.xbrl_apikey(username=username, password=password, client_id=client_id, client_secret=client_secret, platform='pc', grant_type='password', refresh_token='')
dataresponse = tidyxbrl.xbrl_query(access_token=response.access_token.values[0], 
               baseapiurl='https://api.xbrl.us/api/v1/report/search?',
               queryparameters = {'report.entity-name': "APPLE INC.",
                                  'fields': "report.id,report.entity-name,report.filing-date,report.base-taxonomy,report.document-type,report.accession,entity.ticker,report.sic-code,entity.cik,report.entry-type,report.period-end,report.sec-url,report.checks-run,report.accepted-timestamp.sort(DESC),report.limit(20),report.offset(0),dts.id,report.entry-url",
                                  'report.document-type': "10-K"
                        })

edgar_query - Query SEC data using the Central Index Key (CIK)

companycik = tidyxbrl.edgar_cik("ZILLOW GROUP, INC")
desiredcorp = str(companycik[companycik.company.str.contains("ZILLOW GROUP, INC.")]['cik_str'].unique()[0])
tidyxbrl.edgar_query(desiredcorp, query_type = 'submissions')
tidyxbrl.edgar_query(desiredcorp, query_type = 'companyconcept', queryextension = '/us-gaap/AccountsPayableCurrent')
tidyxbrl.edgar_query(desiredcorp, query_type = 'companyfacts')

edgar_frames - Aggregates one fact for each reporting entity

tidyxbrl.edgar_frames(urldescriptor = 'us-gaap/NonoperatingIncomeExpense/USD/CY2019Q1I')

Data Visualization

Dependencies

• pandas - A fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
• NumPy - The fundamental package for scientific computing with Python
• requests - An elegant and simple HTTP library for Python, built for human beings.
• bs4 - For pulling data out of HTML and XML files
• tqdm - Instantly make your loops show a smart progress meter - just wrap any iterable with tqdm(iterable), and you're done!