Gist 901f4da6657c981e68620127130e3f23

100%

Gist 901f4da6657c981e68620127130e3f23

✓ Published0🌍 Public

Last edited Jan 11, 2022

Created on Jan 11, 2022

This example presents a heuristic strategy for a word-guessing game, prioritizing candidate words based on letter values and positional constraints. The visualization shows a sorted list of possible words, with scores derived from Scrabble-like letter values, adjusted by a penalty for duplicate letters and a bonus for letters already eliminated from the vocabulary. The code uses pandas to map scores across columns (first through fifth) and applies custom logic via `special_heuristic` to filter vacant positions and remaining letters. The approach relies on DataFrame operations for sorting and filtering, with no external visualization library, as the output is a single recommended word.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 0d3ef677475862ba439fd5dacfd4a60d

The visualization maps the scoring patterns of five-letter words, showing how letter positions and letter values contribute to total word scores. It displays a grid or distribution of words, likely sorted or clustered by score, with each word’s score calculated from per-letter point values and a bonus for non-repeated letters. The implementation uses Python’s pandas to load and transform the word list, adding columns for each letter’s position and score, while the letter-to-value mapping follows standard Scrabble tile distributions. The final score sums individual letter scores and adds a two-point bonus when all letters are unique, as seen in the `duplicates` logic. The rendering itself is not shown in the code, but the data processing relies on pandas dataframes and string operations to derive the visualization’s underlying dataset.

CConorAspell

80% match

Loading thumbnail…

Gist 2ddd23e185defb909f5b269d22e7849e

This example demonstrates a Wordle-solving filter tool that narrows down possible answer words based on player feedback. It shows how the filter function progressively eliminates candidates using three types of constraints: letters known to be absent, letters confirmed in specific positions, and letters that are present but misplaced. The code uses pandas DataFrame operations with string containment checks and column-based equality comparisons to refine the word list. While no visualization library is used, the logic relies on pandas’ vectorized string methods and boolean indexing to efficiently process the word list, making it a practical data-cleaning component for a word-guessing game interface.

CConorAspell

78% match

Loading thumbnail…

Gist 44d4055a4886e06298020ea9a02e7d48

The visualization recommends a word by identifying the row with the lowest score in a dataset, then randomly selecting one word from that row’s matching entries. It shows a data-driven selection process where the lowest-scoring candidate is chosen. The code uses pandas to filter rows by the minimum score and samples one word from the resulting subset. The example relies on a CSV-like data frame, with the `recommend_word` function operating on that data to produce a single word output. No external rendering libraries are used; the logic is purely computational in Python.

CConorAspell

70% match

Loading thumbnail…

Gist 9cae4888d477011e1083bb00dc773a1d

This visualization displays a grid of five-letter words, each row representing a word from the supplied CSV file. It shows how each word’s letters and their scores relate, with columns for each position and a total score. The code uses `d3.forceSimulation` to position the text elements, while the data comes from the embedded `word_pool.csv` file. Each word’s letter scores are visually encoded, and the `duplicates` flag is included in the dataset. The rendering relies on SVG text elements updated via D3’s data join, with no external libraries beyond D3.

CConorAspell

68% match

Loading thumbnail…

Make a Word

The visualization is a word-guessing game that displays seven letters and prompts the user to submit a valid word. The user can guess a word, shuffle the letters, or generate a new random set of letters. The game tracks the number of wins and losses, and shows a message indicating whether each guess is correct or wrong. The code uses plain JavaScript DOM methods like `querySelector`, `getElementById`, and `innerHTML` to manage the interface, with the letter set generated by combining random consonants and vowels.

FFrankieFabuloso

62% match

Loading thumbnail…

Gist b0f64c51570ca06b6602

This example demonstrates a recursive Boggle word-finding algorithm that visualizes its search process by printing the game board to the console at each step. The code uses Ruby’s `Array#each_with_index` to scan the 4x4 letter grid, and recursively calls the `checker` method to trace valid adjacent letter paths for a given word. A `draw` function outputs the board state, marking previously visited cells with the string `"NO"` to show the algorithm’s backtracking. The visualization relies on terminal text output rather than graphical rendering, using only the `p` method for display.

AAndrelton

60% match

Loading thumbnail…

Hexagon Word List

A hexagon outline occupies the center of the screen, with seven randomly generated gibberish words stacked along its vertical axis. The words re-sort every two seconds as their associated scores change, causing entries to grow, shrink, and swap positions. The code uses d3.hexbin, Chance.js for word and integer generation, and underscore.js for sorting. SVG text elements animate font-size and y-position via d3 transitions.

BBrideau

56% match

Loading thumbnail…

Gist f284381bb142623d89f9aa34c2a7b15d

This example visualizes the upcoming gameweek’s fixture odds and player selection data, showing the probability of each team winning or drawing as derived from betting odds. The code fetches player and fixture data from the Fantasy Premier League API, merges it with daily odds stored in an S3 bucket, and calculates a “diff” metric comparing home and away win chances. Using pandas for data manipulation and the `requests` library for API calls, it outputs a combined player–fixture dataframe that highlights favorable matchups. The visualization runs as an AWS Lambda function, triggered on demand to refresh the data.

CConorAspell

56% match