Use ronnywang/pdf-table-extractor CLI to extract CSV files

Loading and parsing CSV data

This example demonstrates the simplest possible workflow for fetching an external CSV file from a GitHub gist URL using JavaScript's built-in `fetch` API. The page shows no visualization, instead logging the raw `Response` object to the console, which would contain the CSV text if parsed. The code relies on the modern `fetch` function and a hardcoded URL pointing to a CSS color dataset hosted on GitHub, but it does not parse or render the data, making it a minimal first step toward loading external tabular data.

RRonandLion

Gist 6ec7169c37cdd07ba6003e3004741d6d

This example demonstrates a workflow for sending academic PDFs to a reMarkable tablet directly from a DOI reference on a webpage. It shows how a DOI retrieved from a hovered reference is used to fetch and download the corresponding PDF, then transfer it to the device via SSH. The code uses Express with CORS to handle HTTP requests, `execSync` for shell commands like `curl` and `wget`, and `scp` for file transfer. The visualization aspect is minimal, focusing on the automated pipeline rather than graphical output.

UUtopiah

Convert csv to json using d3

This example demonstrates converting a CSV file into JSON using the D3.js library. It loads survey data from a GitHub gist, transforms each row into an object containing the properties "broers" and "zussen", and logs the resulting JSON array to the browser console. The code uses d3.csv with a row-accessor function to parse and reshape the data. The page displays the title "Convert csv to json using d3" with no visual chart, focusing solely on the data conversion process.

LLaurens

CSV to JSON

This example demonstrates how to convert flat CSV data into a nested JSON structure using D3’s nesting and mapping functions. It shows the raw CSV text alongside the resulting JSON, grouping records by name and transforming year–amount pairs into arrays for two time-series variables. The code loads `data.csv` via `d3.csv`, coerces numeric columns, then applies `d3.nest()` and `.map()` to reshape the data. Output is displayed in HTML paragraphs using D3 selections.

SSpaceActuary

Useful csvkit commands

This example demonstrates the versatility of csvkit, a command-line toolkit for working with tabular data, by presenting a collection of common workflows. It shows how to inspect, filter, merge, and transform CSV files using commands like `csvstat`, `csvcut`, `csvlook`, and `csvgrep`. The visualization highlights chaining commands with pipes to combine operations, such as using `csvsql` for SQL queries and joins across multiple files, `csvjson` to convert data into JSON or GeoJSON, and `csvstack` to merge datasets. It also covers importing and exporting data to and from SQL databases via `csvsql` and `sql2csv`, using Postgres and MSSQL connection strings.

WWilliamQLiu

Parsing CSV Data with D3

This example demonstrates how to parse CSV data using D3 within an HTML page. The page fetches a CSV file of named CSS colors from a GitHub gist, parses it with `d3.csvParse`, and displays the total number of rows, columns, and file size in kilobytes as large text. The code relies on the Fetch API to retrieve the raw text asynchronously, then uses D3 v7’s `csvParse` to structure the data. This highlights a minimal workflow for loading and summarizing tabular data before visualization.

AAndy Chang

Programming_languages_demand

This example demonstrates a simple D3.js data-loading and parsing workflow. It fetches a CSV file from a GitHub gist, cleans its formatting, and uses `d3.csvParse` to convert the text into a tabular data structure. The visualization then displays a summary of the dataset—its size in kilobytes, row count, and column count—in a styled HTML box, while also logging this information to the console. Finally, it appends a static SVG containing a circle and a rectangle to the page, using D3's selection and attribute methods.

AAparna Desi Reddy

Fork of Loading and Parsing CSV Data

This example demonstrates how to fetch a remote CSV file and log its contents to the browser console. The page uses the native Fetch API to request the dataset from a GitHub gist URL, then processes the response with the `.text()` method to print the raw CSV string. No external JavaScript libraries are used, and the implementation relies on standard browser APIs for HTTP requests and text handling. The visualization is purely demonstrative, showing the foundational step of loading tabular data without any rendering or charting logic.

Rrishyossarian