Created using remix-ide: Realtime Ethereum Contract Compiler and Runtime. Load this file by pasting this gists URL or ID at https://remix.ethereum.org/#version=soljson-v0.6.12+commit.27d51765.js&optimize=false&gist=

Created using remix-ide: Realtime Ethereum Contract Compiler and Runtime. Load this file by pasting this gists URL or ID at https://remix.ethereum.org/#version=soljson-v0.6.1+commit.e6f7d5a4.js&optimize=false&gist=

This example shows a Solidity smart contract for a Know Your Customer (KYC) system, compiled and run in real time through Remix IDE. It defines three structs—Customer, Bank, and KYCRequest—and uses a mapping from customer usernames to KYCRequest records. The `addRequest` function takes a username and customer data string, then stores a new KYCRequest with the calling bank’s address from `msg.sender`. The code relies on the Solidity compiler version 0.6.1 via Remix’s browser-based environment, which provides immediate feedback on compilation and execution without any external libraries or visualization APIs.

90% match

KYC-ExtensionVersion

This example shows a Solidity smart contract system for managing customer KYC (Know Your Customer) data across banks, with structs defining customers, banks, and KYC requests, plus mappings for storage and an event for tracking new requests. The code uses the `KYCValidations` library with custom modifiers `isValidCustomerUserName` and `isValidCustomerData` to enforce non-empty input validation, and imports it via `import './KYCValidations.sol'`. The `KYCPhase2` contract adds requests through the `addRequest` function, which emits a `KYCRequestAdded` event but does not store the request, as the function returns `false` without modifying state. The implementation relies on Solidity’s mapping data structures and the `msg.sender` address to capture the requesting bank.

80% match

Working on Solidity Practise

This example shows a Solidity smart contract collection practicing basic syntax, data structures, and functions, with a second contract focused on mapping and struct access patterns. The main contract implements arithmetic operations, comparisons with events, summation loops, modifiers with require statements, and a mark-tracking system that calculates percentages from stored struct arrays. It uses Solidity’s built-in types, modifiers, events, and the `log1` low-level logging function, while the mapping contract demonstrates private state variables and public add/access functions. The code highlights common Solidity patterns like struct initialization, visibility qualifiers, and data location requirements, though the getter for a struct currently fails due to missing memory specification.

55% match

Fork of ICE5

This example displays metadata about a credit card customer dataset, showing the file size in kilobytes, the number of rows, and the number of columns in a preformatted text block. The visualization fetches a remote CSV file using the `d3.csv` API, then calculates and formats the metadata with `Math.round` and `d3.csvFormat`. The page is styled with CSS, including a credit card SVG background image, and uses D3.js v7 within a basic HTML structure. The dataset is linked for reference, and the computed information is presented as plain text in a centered `<pre>` element.

Rradhadevarapu

Fork of ICE5

Rradhadevarapu

Fork of ICE5

This example displays metadata about a credit card customer dataset, including file size, row count, and column count, presented as text on a page with a credit card background image. The code loads the CSV data from a GitHub gist using `d3.csv`, then computes the formatted size with `d3.csvFormat` and counts rows and columns. It dynamically updates a `<pre>` element by setting its text content via the DOM API. The page uses D3 v7, CSS styling, and a linked dataset source, with no interactive charts or additional visual encodings.

YYarreddu

Loading and Parsing Bank Customers Dataset

This example shows the basic structure of a bank customer dataset by displaying three summary statistics: file size in kilobytes, row count, and column count. It loads a CSV file from a GitHub gist using `d3.csv` and parses the data, then formats the metrics with `d3.csvFormat` and `Math.round`. The results are rendered as large text inside a `<pre>` element. The visualization relies on D3 v5’s promise-based loading API to handle the asynchronous data fetch.

RRaul Gamez

please copy into repo

This example demonstrates array manipulation techniques in JavaScript, implementing utility functions such as `indexOf`, `sum`, `remove`, `append`, `truncate`, and `findAllOccurrences`. The code defines an object with methods that operate on arrays, including custom implementations for finding element positions, calculating sums, copying arrays without mutations, and detecting duplicates. The visualization relies on plain JavaScript array methods and loops, with the `exports` pattern exposing the functions for use in browser or Node environments. The source data consists of hardcoded test arrays, and the rendering approach uses no external libraries or graphical APIs.