Data

This tool automates the extraction of property value estimates for lists of addresses or Zillow Property IDs (ZPID). It is for real estate analysts and investors who need to gather market data without manual copying. It processes data from CSV files and outputs structured results. Features Batch process addresses from CSV files Accept Zillow Property IDs (ZPID) as input Extract Zestimate, value range, and last updated date Export results to CSV or JSON format Handle rate limiting and retries for robust scraping Usage python scraper....

This tool processes exported Google Maps search results (saved as HTML files) and extracts business listings into a clean CSV. It’s for researchers, marketers, and developers who need structured location data without manual copying. Works with data exported from Google Maps. Features Parse HTML files containing Google Maps search results Extract business name, address, phone number, and website Export cleaned data to a structured CSV file Handle pagination by processing multiple exported HTML files Configurable output fields via a simple JSON settings file Usage from maps_extractor import process_export process_export('search_results....

A Python CLI tool that processes TecDoc catalog data exports (CSV, Excel) to extract and structure vehicle parts information. For automotive developers and data analysts who need clean, queryable parts data without manual work. Outputs standardized JSON or CSV for integration. Features Parse multi-sheet TecDoc Excel catalogs into structured tables Extract part numbers, descriptions, and vehicle compatibility mappings Clean and normalize manufacturer and OE reference numbers Export to flat CSV or nested JSON for easy database import Filter results by vehicle make, model, year, or part category Usage td_extract --input tecdoc_export....

Python CLI tool that fetches live traffic data using Google Maps API keys. Helps developers and analysts build traffic-aware applications without complex scraping infrastructure. Features Fetch current traffic conditions for specified routes Export traffic data as JSON or CSV files Batch process multiple origin-destination pairs from input file Cache results to minimize API calls and costs Filter results by traffic severity levels Usage python traffic_scraper.py --api-key YOUR_KEY --origin "New York" --destination "Boston" --output traffic_data....

This Python script processes exported Amazon product data (CSV/JSON) to extract and organize brand names and active promotion details. It’s for developers and analysts who need to analyze competitor promotions and brand presence without live scraping. The key benefit is clean, structured data from your own exports. Features Parse exported Amazon product listings from CSV or JSON files Extract and deduplicate brand names from product data Identify and list promotion keywords (e....

This tool processes Instagram data you’ve exported to JSON files and converts it into clean, structured Excel workbooks. It’s for analysts and marketers who need to analyze Instagram insights, posts, or audience data offline. The key benefit is turning messy JSON exports into ready-to-use Excel sheets with charts and tables. Features Parse Instagram JSON export files (posts, profile, insights) Generate separate Excel sheets for posts, followers, and engagement metrics Auto-calculate summary statistics (likes, comments, engagement rate) Create pivot tables and basic charts for visual analysis Export to XLSX with custom formatting and date filters Usage instagram_to_excel --input exported_data....

Generating fake real estate data for testing apps or demos can be a tedious process. Manually crafting property listings with believable MLS-style IDs, accurate pricing, and realistic agent info takes hours — or even days — of work. Whether you’re building a real estate portal, working with a Python fake data generator, or just trying to simulate a property database, the repetition and complexity quickly become a burden. The Manual Way (And Why It Breaks) Creating realistic listings manually often involves copying and pasting from existing real estate websites or using outdated templates....

What’s in This Dataset This dataset provides 90 days of synthetic social media analytics data tailored for Instagram. Each record represents a day’s worth of metrics, resulting in 90 rows of structured data in CSV format. The dataset includes columns for daily impressions, reach, likes, comments, shares, follower growth, and engagement rate. These fields are all modeled to reflect realistic, varied patterns that mirror actual social media performance trends. Data points are consistent and clean, making it easy to load and analyze without preprocessing....

What’s in This Dataset The 1,000 Fake Employee Records dataset includes a comprehensive set of HR-related fields designed to mirror real-world employee data. Each record contains an employee ID, full name, department, job title, salary, hire date, manager ID, and performance score. The dataset is structured in CSV format, making it easy to import into any data analysis or development environment. With 1,000 rows of synthetic data, it offers enough variety to simulate realistic workflows without the risk of using actual employee information....

What’s in This Dataset This dataset contains 3,648 realistic synthetic bank transactions spanning one year, with data for five distinct accounts. Each transaction includes essential fields such as date, account_id, amount, merchant_name, category, running_balance, and transaction_type. The transactions are structured to mimic real-world spending behavior, including regular purchases, recurring bills, and occasional large expenses. The dataset also includes a merchant_name column with realistic names like “Starbucks,” “Amazon,” and “Walmart,” along with category labels like “Food & Dining,” “Shopping,” “Utilities,” and “Transportation....

What’s in This Dataset This dataset contains 10,000 realistic, HIPAA-safe synthetic patient records in standard CSV format. Each record includes a full set of healthcare-related fields such as patient demographics (age, gender, address), ICD-10 diagnosis codes, prescribed medications, insurance provider details, and billing amounts. The dataset is designed to mimic real-world patient data without compromising privacy, making it suitable for testing and development purposes. The CSV includes columns like patient_id, age, gender, diagnosis_code, medication_name, insurance_provider, billing_amount, and admission_date....

What’s in This Dataset This dataset includes 500 realistic synthetic MLS property listings in CSV format, designed to mimic real estate data you’d find in a professional listing system. Each record contains a unique MLS-style listing ID, full property address, price, square footage, number of bedrooms and bathrooms, lot size, listing date, and agent information. The fields are carefully structured to reflect typical real estate listing details, including columns like mls_id, address, price, beds, baths, sqft, lot_size, list_date, and agent_name....