Python

python lead generation is a challenge that many sales and recruiting teams face daily. Manually extracting email addresses from LinkedIn profiles or company pages can be time-consuming and error-prone. When teams need to build targeted outreach lists for cold email campaigns, the process often involves hopping between platforms, copying and pasting data, and cross-referencing domains — all of which break workflow momentum. This kind of python lead generation work is ripe for automation, especially when you’re dealing with dozens or hundreds of company URLs....

We just released Professional Network Lead Finder & Email Extractor — find leads from linkedin search urls and extract emails from company websites. What it does This tool automates lead generation by taking a list of LinkedIn profile or company page URLs and extracting publicly available email addresses from their associated websites. It’s for sales teams and recruiters who need to build targeted outreach lists quickly. It works entirely from exported LinkedIn data and avoids any platform scraping....

This tool automates lead generation by taking a list of LinkedIn profile or company page URLs and extracting publicly available email addresses from their associated websites. It’s for sales teams and recruiters who need to build targeted outreach lists quickly. It works entirely from exported LinkedIn data and avoids any platform scraping. Features Parse CSV/JSON files containing LinkedIn profile or company URLs Extract company website domains from LinkedIn URLs Scrape company ‘Contact’ pages for email patterns Validate extracted emails with syntax and domain checks Output clean lead list with name, company, and email to CSV Usage linkedin_leads --input leads....

stock trading automation doesn’t have to mean manual data entry and delayed execution. When you’re managing multiple trades and relying on spreadsheets to track orders, the process becomes error-prone and slow. The Excel trading script approach might feel familiar, but it’s also tedious and leaves room for human mistakes. The Manual Way (And Why It Breaks) Manually copying trade orders from Excel into a trading platform is time-consuming and fragile. You have to open your spreadsheet, select rows, copy data, paste into the platform, and then confirm each transaction....

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....

We just released Spreadsheet-Driven Stock Trading Script — execute trades from an excel sheet using python and stoxkart api. What it does This tool reads buy/sell orders from an Excel file and executes them via the StoxKart trading API. It’s for Python developers who automate trading strategies. Key benefit is moving from manual Excel planning to automated trade execution. Features Reads trade orders from Excel (.xlsx, .xls) files Validates order parameters like symbol, quantity, and type Sends authenticated orders to StoxKart REST API Logs all execution results and errors to a CSV file Supports market and limit order types from spreadsheet Usage python execute_trades....

This tool reads buy/sell orders from an Excel file and executes them via the StoxKart trading API. It’s for Python developers who automate trading strategies. Key benefit is moving from manual Excel planning to automated trade execution. Features Reads trade orders from Excel (.xlsx, .xls) files Validates order parameters like symbol, quantity, and type Sends authenticated orders to StoxKart REST API Logs all execution results and errors to a CSV file Supports market and limit order types from spreadsheet Usage python execute_trades....

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....