DEV Community: carrierone

How to Query 9 Million US Healthcare Providers via API (NPI Registry)

carrierone — Tue, 28 Apr 2026 19:01:09 +0000

Querying 9 Million US Healthcare Providers via API: A Developer's Guide to NPI Registry

When developing a healthcare application that requires integration with provider directories or FHIR services, having access to accurate and up-to-date information on US healthcare providers is essential. One of the key resources for this purpose is the National Provider Identifier (NPI) registry.

The NPI Registry provides an API endpoint accessible at api.verilexdata.com/api/v1/npi/sample. This sample endpoint offers a glimpse into what querying the full dataset might look like, enabling developers to explore how they can integrate provider data into their applications. Below is a short Python code snippet that demonstrates how one could make a request to this API and process the response.

import requests

def fetch_npi_data(npi_number):
    url = "https://api.verilexdata.com/api/v1/npi/sample"

    headers = {
        'Content-Type': 'application/json'
    }

    payload = {
        'npi': npi_number
    }

    response = requests.post(url, json=payload, headers=headers)

    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Failed to fetch data: {response.status_code}")

To query the NPI registry using this endpoint, simply replace `

OTC Stock Shell Risk Scoring: How to Screen Penny Stocks Programmatically

carrierone — Fri, 24 Apr 2026 19:01:06 +0000

OTC Stock Shell Risk Scoring: How to Screen Penny Stocks Programmatically

When developing retail trading tools or fintech apps that need to screen penny stocks and OTC (Over The Counter) stock listings, one critical aspect is identifying shell companies. These are often thinly traded stocks run by individuals with little transparency into their operations. Identifying such companies can significantly reduce risk for your investors.

One way to automate this process is through the use of an API that provides sample data on potentially risky OTC stocks. Let’s take a look at how you might implement this in Python:


python
import requests

# Define the URL and API key (obtained from API documentation)
url = "https://api.verilexdata.com/api/v1/otc/sample"
headers = {
    'Authorization': 'Bearer YOUR_API_KEY',  # Replace with your actual API key
}

# Send a GET request to fetch sample OTC data
response = requests.get(url, headers=headers)

# Check if the request was successful (status code 200)
if response.status_code == 200:
    otc_data = response.json()

    # Here we can filter for companies that might be considered shell stocks based on criteria like low volume or no financial reports
    risky_stocks = [company['ticker'] for company in otc_data if company['volume

Federal Contract Award Data (USASpending) via API

carrierone — Tue, 21 Apr 2026 19:01:10 +0000

Federal Contract Award Data (USASpending) via API

Federal agencies award billions of dollars annually through various contracting mechanisms. As developers, we can access these transactions through the USASpending API to build tools for procurement analytics and business development. To search contracts by agency, vendor, or NAICS code, you'll need a Python script that interacts with this API.

Problem: Searching for Federal Contract Award Data

Let's say you're developing software that needs to filter federal contract awards based on the contracting agency or vendor name. Without an API endpoint to query these data points directly, we'd have to scrape websites manually, which is error-prone and might violate terms of service policies.

Solution: Python Code Example

Here’s a simple example using requests and json libraries in Python to search for federal contracts based on the agency name:


python
import requests

def get_federal_contracts_by_agency(agency_name):
    url = f"https://verilexdata.com/api/search?query=contract_award&agencies={agency_name}"
    headers = {
        "User-Agent": "Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.2)"
    }

    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        contracts = response.json

NOAA Weather Data API: Historical and Current Weather for Developers

carrierone — Fri, 17 Apr 2026 19:01:08 +0000

NOAA Weather Data API: Historical and Current Weather for Developers

When developing applications that require historical and current weather data, developers often turn to APIs provided by organizations like the National Oceanic and Atmospheric Administration (NOAA). One such API is available at api.verilexdata.com/api/v1/weather/sample, which offers access to 200 weather stations' historical and current observations.

Problem: Accessing Historical and Current Weather Data

Developers frequently need to integrate weather data into their applications. Whether for real-time updates, historical analysis, or predictive models, having accurate and up-to-date information is crucial. However, accessing this data directly from NOAA's servers can be cumbersome due to rate limits, authentication requirements, and the complexity of handling different datasets.

Solution: Using a Weather API

To simplify access to NOAA weather data, developers can use an intermediary API like api.verilexdata.com/api/v1/weather/sample. This endpoint provides 200 stations' historical and current observations in real-time. The data includes temperature, humidity, wind speed, precipitation, and other meteorological parameters.

Here's a simple Python script to fetch weather data using this API:


python
import requests

def get_weather_data(station_id):
    url = f"https://api.verilexdata.com/api/v1/weather/sample/{station_id}"
    response = requests.get(url)

FRED and BLS Economic Indicator Data for Developers

carrierone — Tue, 14 Apr 2026 19:01:03 +0000

FRED and BLS Economic Indicator Data for Developers

As a developer working on a macro trading or analytics app, you often need access to economic indicators such as CPI (Consumer Price Index), NFP (Non-Farm Payroll), GDP (Gross Domestic Product), PCE (Personal Consumption Expenditure), and unemployment rates. These data points are crucial for making informed decisions in your applications.

Problem: Getting Real-Time Data Feeds

To get the most up-to-date CPI, NFP, GDP, and unemployment figures, you need to consume real-time API feeds. One such resource is the FRED (Federal Reserve Economic Data) service, which provides access to various economic data series from a variety of sources.

Solution: Fetching Data with Python

Here's an example of how you can fetch these indicators using Python:


python
import requests

def get_economic_data(indicator_type):
    url = f"https://api.verilexdata.com/api/v1/econ/stats?indicator={indicator_type}"
    response = requests.get(url)
    if response.status_code == 200:
        data = response.json()
        return data['results'][0] if data else None
    else:
        print(f"Failed to fetch data: {response.text}")
        return None

# Example usage for CPI, NFP, GDP, and unemployment rates

DeFi Liquidation Signals and Whale Wallet Tracking via API

carrierone — Fri, 10 Apr 2026 19:01:23 +0000

DeFi Liquidation Signals and Whale Wallet Tracking via API

In the realm of decentralized finance (DeFi), understanding liquidation signals and tracking whale wallet movements are crucial for developers looking to mitigate risks. These insights can help in making informed decisions about liquidity management, risk mitigation strategies, and overall system stability.

Understanding Liquidation Signals

A common occurrence in DeFi protocols like Aave or Compound is the issuance of new loans against collateral. However, if a user's collateral value falls below a certain threshold set by the protocol, they may be subject to liquidation, where their assets are seized to cover the debt. Developers can monitor these events and notify other systems about impending risks.

Python Example for Monitoring Aave Liquidations

Here is a simple example using Python to fetch and process data from a simulated API endpoint that provides Aave liquidation signals:


python
import requests

# Define API endpoint URL
url = 'https://verilexdata.com/api/v1/defi/aave/liquidation'

# Send GET request to the API
response = requests.get(url)

# Check if the request was successful
if response.status_code == 200:
    # Parse JSON response
    data = response.json()

    # Example: Print out first liquidation signal
    print(data[0]['signal'])
else:
    print(f"Failed to retrieve

Polymarket Data API: Smart Money Tracking and Cross-Market Arbitrage

carrierone — Tue, 07 Apr 2026 19:01:24 +0000

Polymarket Data API: Smart Money Tracking and Cross-Market Arbitrage

If you're a prediction market trader looking to integrate data from Polymarket into your applications for smart money tracking or cross-market arbitrage, you'll need access to the Polymarket Data API. This article will show you how to interact with the api.verilexdata.com/api/v1/pm/stats endpoint using Python.

Tracking Smart Money Signals

To get a sense of market sentiment and potential trading signals, let's start by fetching some data from Polymarkets. Here’s a simple example in Python that retrieves statistics for all active markets:

import requests

def fetch_polymarket_data():
    response = requests.get('https://api.verilexdata.com/api/v1/pm/stats')

    if response.status_code == 200:
        data = response.json()
        print(data)
    else:
        raise Exception("Failed to fetch data from Polymarkets. Status code: ", response.status_code)

fetch_polymarket_data()

This script sends a GET request to the api.verilexdata.com/api/v1/pm/stats endpoint, which should return JSON containing various statistics about active prediction markets on Polymarket.

Cross-Market Arbitrage

Polymarkets often offer similar bets across different exchanges. By leveraging this API

USPTO Patent and Trademark Data via REST API

carrierone — Fri, 03 Apr 2026 19:01:13 +0000

USPTO Patent and Trademark Data via REST API

As developers building patent search tools, IP analytics, and prior art searches, having access to accurate and up-to-date patent data is crucial. The United States Patent and Trademark Office (USPTO) provides a wealth of information through its RESTful API, which can be accessed via api.verilexdata.com/api/v1/patents/sample.

Problem

One common challenge when building these tools is ensuring the data freshness and accuracy. The USPTO grants over 1.6 million patents annually, but keeping track of all changes manually is impractical. Moreover, different inventors, assignees, and technologies require specific filtering to meet unique project requirements.

Python Code Example

Here's a simple Python script using requests library to fetch patent data from the USPTO API:


python
import requests

def get_patent_data(keyword):
    url = "https://api.verilexdata.com/api/v1/patents/sample"

    params = {
        'keyword': keyword,
        'fields': 'patentNumber,title,assignee'
    }

    response = requests.get(url, params=params)
    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Request failed with status: {response.status_code}")

#

Querying Federal Court Records (PACER) Programmatically

carrierone — Tue, 31 Mar 2026 19:01:20 +0000

Querying Federal Court Records (PACER) Programmatically for LegalTech Developers

As a legaltech developer working on e-discovery projects or case monitoring systems, having access to federal court records is crucial. The Public Access to Court Electronic Records (PACER) API provides the necessary data programmatically. Below is an example of how you can fetch PACER data using Python.

Small Python Code Example

First, you need to install the requests library if you don't have it already:

pip install requests

Here's a simple script that demonstrates fetching case information from PACER:

import requests

def get_case_info(case_number):
    url = f"https://api.pacerpc.gov/v1/cases/{case_number}"

    headers = {
        "X-API-Key": "YOUR_PACER_API_KEY",  # Replace with your actual API key.
    }

    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Failed to fetch case info: {response.text}")

# Example usage
case_number = "15-cv-03647"
try:
    data = get_case_info(case_number)
    print(data)
except Exception as e:
    print(e)

Mention

OFAC Sanctions Screening for Developers: How to Check Addresses and Names

carrierone — Fri, 27 Mar 2026 19:01:21 +0000

OFAC Sanctions Screening for Developers: How to Check Addresses and Names

When building applications that involve KYC/AML compliance, one critical aspect is screening against the Office of Foreign Assets Control (OFAC) Specially Designated National (SDN) list. This list includes entities designated by OFAC as being involved in activities that are contrary to U.S. foreign policy or national security interests.

To integrate SDN checks into your application efficiently and securely, you can use an API endpoint like api.verilexdata.com/api/v1/sanctions/stats. For example, you might want to check if a wallet address is on the OFAC list before allowing financial transactions.

Example Python Code for OFAC Sanction Screening


python
import requests

def check_address_on_sanctions(address):
    url = "https://api.verilexdata.com/api/v1/sanctions/stats"

    # Constructing the request payload. In practice, this would include your app's API key and possibly additional parameters.
    headers = {
        'X-API-KEY': 'YOUR_API_KEY',  # Replace with actual API key
        'Content-Type': 'application/json'
    }
    data = {
        "address": address,
        "type": "wallet"
    }

    response = requests.post(url, headers=headers, json=data)

    if response

Getting SEC EDGAR Filings via API Without Scraping

carrierone — Tue, 24 Mar 2026 19:01:15 +0000

Getting SEC EDGAR Filings via API Without Scraping

When developing financial applications that require real-time access to SEC filings such as 10-Ks, 10-Qs, and 13-Fs, one common challenge is obtaining these documents efficiently. One effective way to do this without resorting to web scraping is by using an API service like VeriLexData's api.verilexdata.com/api/v1/sec/sample.

The Problem

Web scraping requires manual setup for each website or dataset and can be time-consuming, especially when dealing with dynamic content that changes frequently. Moreover, it often comes with legal risks and may not always comply with the terms of service of the websites involved.

A Simple Python Example to Fetch Filings

Here's a concise example using Python to fetch SEC filings via the API:


python
import requests

# Replace 'YOUR_API_KEY' with your actual API key from VeriLexData
api_key = "YOUR_API_KEY"
url = f"https://api.verilexdata.com/api/v1/sec/sample?format=json&apikey={api_key}"

response = requests.get(url)
if response.status_code == 200:
    data = response.json()
    # Process the data here, for example printing a sample filing
    print(data[0]['filing'])
else:
    print(f"Failed to

How to Query 9 Million US Healthcare Providers via API (NPI Registry)

carrierone — Sat, 21 Mar 2026 17:21:36 +0000

Query NPI: 9 Million US Healthcare Providers

In today's digital health landscape, integrating accurate and up-to-date information about healthcare providers is crucial for applications like FHIR integrations, provider directories, and prior authorization systems. The National Provider Identifier (NPI) registry houses this essential data, and accessing it programmatically can streamline your development process.

To query NPI numbers, names, specialties, and locations, you'll need to use the API endpoint api.verilexdata.com/api/v1/npi/sample. This simple Python code example demonstrates how to make a request using the requests library:

import requests

def get_npi_data(npi_number):
    url = f"https://api.verilexdata.com/api/v1/npi/{npi_number}"

    response = requests.get(url)

    if response.status_code == 200:
        return response.json()
    else:
        return None

# Example usage
npi_data = get_npi_data('123456789')
if npi_data:
    print(npi_data)
else:
    print("Failed to retrieve NPI data.")

This code sends a GET request to the specified endpoint, where npi_number is replaced with the actual NPI number you're interested in. The response from the API will be in JSON format