311 skills found · Page 9 of 11
Data-Design-Dimension / Workshop Styles PlotlyEver have that feeling that a lot of data viz you see screams the tool it was made in? Using the Plotly Python libraries, we will take a look under the hood of the style themes available, understand the visual elements like figure and chart backgrounds, and build our own default theme script inspired by 1980's computers.
energee / View CovidQuick chart.js plot to compare countries spread
EarnForex / Overlay ChartOverlay Chart is a simple chart loader to plot another symbol over the current chart.
achntrl / BurndownChartAn app to plot burndown charts
Ringomed / GgvancedAn R package for creating advanced multivariable plots such as spider/radar charts and parallel plots
jmccuen / ThingworxPlotlyChartingSDKThis is an SDK based on the Plotly Javascript library to create charting widgets in Thingworx
mvi-llc / Plotly PanelPlotly-powered chart rendering panel extension for Foxglove Studio
N129BZ / ChartserverDisplay FAA charts and maps using mbtiles databases, can also connect to Stratux to plot traffic and ownship positions
samuelnovaes / Plot ItPlot advanced charts in Node.js
mbejda / PlotlychartexportExport Plot.ly charts on the server
pilotniq / PiChartSoftware for a nautical chart plotter written in javascript, node.js serverside, for a Raspberry Pi. See https://hackaday.io/project/9471-pi-chart
RMI-PACTA / R2dii.plotA package containing functions to create standard PACTA plots using ggplot, together with data processing functions needed for the charts.
kedarghule / Premier League Player Statistics DashboardThis project use multiple visualization techniques and visualizes statistics of each player in the English Premier League on a dashboard using Plotly and Dash and keeping in mind different visualization principles. Visualizations include bar charts, stacked bar charts, pie charts, tables, lollipop charts (to visualize expected goals and assists) and football field heatmaps. This project was done as a capstone project for EECE 5642 Data Visualization at Northeastern University.
deadskull7 / Pokemon Analysis With VisualizationUsed seaborn and matplotlib libraries to deeply visualize the data . Histograms , bar plots , violin plots , pie chart , heat map , box plots , strip plots , swarm plots .... are used to analyse the different categorical variables .
janjoch / InterplotCreate matplotlib and plotly charts with the same few lines of code.
shaadclt / Matplotlib ExercisesThis project provides a collection of Jupyter Notebook exercises for practicing Matplotlib plots, including bar plots, histograms, pie charts, and scatter plots. Matplotlib is a powerful data visualization library in Python that allows for creating a wide range of plots and visualizations.
pearcetm / JscomutAn interactive co-mutation plot generator, in-browser but fully client-side. Load a data file, play with the geometry of the chart, explore your data, and save an svg file.
vikas-kashyap97 / Market Intelligence AgentAn AI-powered Market Intelligence Agent that analyzes live market data, extracts insights using LLMs, generates strategic recommendations, and visualizes trends with interactive charts — built with Streamlit, LangChain, Firecrawl, and Plotly.
vasilyaksenov / QCustomPlotQCustomPlot is a Qt C++ widget for plotting and data visualization. It has no further dependencies and is well documented. This plotting library focuses on making good looking, publication quality 2D plots, graphs and charts, as well as offering high performance for realtime visualization applications. Have a look at the Setting Up and the Basic Plotting tutorials to get started. QCustomPlot can export to various formats such as vectorized PDF files and rasterized images like PNG, JPG and BMP. QCustomPlot is the solution for displaying of realtime data inside the application as well as producing high quality plots for other media.
subhadipml / California Housing Price PredictionBuild a model of housing prices to predict median house values in California using the provided dataset. Train the model to learn from the data to predict the median housing price in any district, given all the other metrics. Predict housing prices based on median_income and plot the regression chart for it.
