Mira como te fue en el Grand Slam 2018

El ranking de Fetemba (python, pandas)

This example shows a ranking table of the top ten players in the Fetemba standings, displaying their position, name, category, club, points, and variation. The data is extracted from a PDF using Tabula and processed with pandas to create a structured DataFrame. The visualization presents the ranked list directly as an HTML table, with columns for each attribute, highlighting the current leaders and their recent point changes. The code relies on pandas for data manipulation and Tabula for table extraction from the source document.

Aaaizemberg

67% match

projet final

This interactive visualization tracks tennis players’ progress through a Grand Slam tournament, showing the ranking difference against each opponent across seven rounds. Each player appears as a node on a line chart, with separate paths for wins and losses, and a button resets the view. Built with D3.js v3, it loads ATP match data from CSV files, processes player rankings and match outcomes, and renders the animated SVG chart with axes, grid lines, and tooltips.

projet v22

This visualization shows the ranking differences between winners and losers across the rounds of a Grand Slam tennis tournament, with each player represented as a line tracking their progress from the first round through the finals. The SVG chart uses two line styles—a solid steelblue line for wins and a dashed line for losses—and includes a reset button to reinitialize the animation. Built with D3 v3, the code loads ATP match data from a CSV file, parses player rankings, and constructs a parallel-coordinates-style plot with an interactive tooltip.

Los pases de Fetemba

Los pases de Fetemba visualizes player transfers between table tennis clubs in Argentina as a directed graph. Each club is a node, with size reflecting the number of players transferred, and each transfer is an edge from the player's previous club ("from") to their new club ("to"). The visualization uses a force-directed layout to reveal clusters and movement patterns, with node colors and positions helping to distinguish clubs and the flow of players between them. The underlying data is drawn from a gist by aaizemberg, listing individual player transfers (identified by name and ID) between named clubs, including entries with "LIBRE" (free agent) status. The graph highlights the structure of player movement across the league.

Aaaizemberg

projet debug v2

This visualization explores the progression of a tennis player through a tournament bracket. It displays the ranking difference between the player and their opponent at each round, from the first round through to the finals. The chart uses a line graph to trace this difference, with a solid line for victories and a dashed line for losses. The code loads match data from a CSV file using `d3.csv`, processes it to extract player rankings and match outcomes, and builds a custom data structure to track each player's journey. It relies on the D3.js version 3 library for potential chart rendering, though the current implementation focuses on data parsing and manipulation. The visualization is designed to compare player performance across different Grand Slam tournaments and years.

projet v13

This visualization tracks the progression of tennis players through a Grand Slam tournament, showing the difference in ranking between each player and their opponents across seven rounds from the first round to the finals. The code processes match data from a CSV file to build a dataset of wins and losses for each player, then plots these ranking differences as lines on an SVG chart. It uses d3.v3 to parse the data, create scales, and render the line paths, with solid lines representing actual wins and dashed lines for predicted or simulated outcomes.

64% match

projet v20

This visualization tracks the progress of tennis players through a tournament, plotting the difference in their ranking against their opponents at each round. It shows the trajectory of individual players with lines, where wins and losses are marked, and a reset button allows the view to be refreshed. The visualization is built with D3 version 3 and uses SVG for rendering, with animation for interactive feedback. Data is loaded from CSV files containing tournament results, and the code uses D3's `d3.csv` function to parse the data, while custom logic calculates ranking differences.

64% match