GeezerVison

Explore step-by-step tutorials and code examples for figure structures, updating figures, displaying figures, and framework integrations to master our Python graphing library.

Find copy-pasteable code snippets and interactive demos to create standard Python charts, including scatter plots, line graphs, bar charts, pie charts, and bubble charts.

Browse code tutorials and live previews for complex data analysis, featuring box plots, histograms, error bars, and 2D density plots.

Access interactive examples and code snippets for domain-specific visualizations. Build heatmaps, contour plots, ternary plots, and log plots.

Explore code guides and live examples for financial data, including candlestick charts, OHLC plots, waterfall diagrams, funnel charts, and time series lines to create interactive graphs in Python.

Learn how to build interactive geographic visualizations with step-by-step code snippets for Mapbox integration, tile choropleth maps, lines on maps, and bubble maps.

Visualize machine learning models with tutorials for ML regression, kNN classification, ROC and PR curves, and PCA visualization.

Access specialized charts for computational biology, including volcano plots, Manhattan plots, clustergrams, and alignment charts.

View high-performance code examples for 3D axes, 3D scatter plots, 3D surface plots, and 3D subplots to enhance your Python graphs.

Find layout tutorials and code examples for combining multi-chart arrangements, map subplots, table subplots, and mixed layouts when plotting graphs in Python.

Learn how to connect your Python graph to interactive Jupyter environments using FigureWidget tutorials, interactive data analysis, and custom click events.

Discover code snippets and tutorials for building interactive UI controls directly into your visualizations, including dropdown menus, custom buttons, sliders, and range selectors to upgrade your graph in Python.

Explore code tutorials and interactive demos for animating data points, building time-series sliders, and creating smooth transitions to build a dynamic Python graph.

Take your interactive plots in Python to the next level with advanced Python graphing techniques, including plotting CSV data, generating random walks, peak finding, data smoothing, and rendering LaTeX.