“Data-driven decision making.”
Cool.
Now go find the data. 😐
That was one of the reasons I started building Company Data Crawler, an open-source Python library for collecting company information from public sources.
Give it a company name or stock ticker and get structured company data such as:
- Company details and industry
- Employees and key executives
- Funding and financial information
- Locations
- Social profiles
- Similar companies
- Operating metrics and more
The data is returned as a Pydantic model, so you get validated, typed data instead of another giant dictionary to deal with.
A simple example
from company_data_crawler import CompanyDataCrawler
crawler = CompanyDataCrawler(cache_dir="./cache")
# 1. Find a company by name
results = crawler.search_company("stripe", source="craft")
print(results[0].company_name) # Stripe
print(results[0].source_url) # https://craft.co/stripe
# 2. Scrape its public company page into a validated model
company = crawler.get_company_data(results[0].source_url, source="craft")
print(company.company_name) # Stripe
print(company.company_domain) # stripe.com
print(company.company_founded_year) # 2010
print(company.company_funding_info) # [CompanyFundingInfo(funding_amount=..., ...)]
print(company.company_locations) # [CompanyLocation(city=..., is_headquarter=True), ...]
print(company.key_executives) # [KeyExecutive(name=..., title=...), ...]
You can also search using a stock ticker:
company = crawler.get_company_data_by_symbol(
"MSFT",
source="craft"
)
Currently, it supports Craft and Owler, with more sources planned.
The collected data can also be:
- Stored in PostgreSQL or MongoDB
- Exported to JSON, CSV, Excel or Parquet
- Cached to avoid unnecessary requests
- Used for data pipelines, RAG, company research or AI agents
The idea is simple: make company data easier to collect and actually use.
Open source and available on PyPI.
GitHub: https://github.com/shaikhsajid1111/company-data-crawler
