Julius can create a wide variety of visualizations using different Python libraries like matplotlib, seaborn, plotly, and others.
Visualizations created with Julius AI
Visualization | |
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Line Charts | Time series data, trends over time |
Bar Charts | Categorical comparisons, frequency distributions |
Scatter Plots | Relationships between two continuous variables |
Histograms | Distribution of single variables |
Box Plots | Statistical summaries, outlier detection |
Pie Charts | Part-to-whole relationships |
Heatmaps | Correlation matrices, 2D data intensity |
Violin Plots | Distribution shapes with density |
Visualizations created with Julius AI
Visualization | |
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3D Plots | Surface plots, 3D scatter plots |
Geographic Maps | Choropleth maps, point maps using geopandas |
Network Graphs | Node-link diagrams, social networks |
Sankey Diagrams | Flow visualizations |
Treemaps | Hierarchical data representation |
Radar/Spider Charts | Multi-dimensional comparisons |
Candlestick Charts | Financial data visualization |
Gantt Charts | Project timelines |
Parallel Coordinates | Multi-dimensional data |
Sunburst Charts | Hierarchical data in circular format |
Visualization made with Julius AI
Visualization | |
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Plotly | Interactive web-based charts |
Bokeh | Interactive dashboards and applications |
Altair | Grammar of graphics approach |
Streamlit/ Dash | Full web applications |
Visualization made with Julius AI
Visualization | |
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Seaborn | Statistical visualizations |
Plotly | Interactive and 3D plots |
Geopandas + Contextily | Geographic mapping |
NetworkX | Network analysis and visualization |
Wordcloud | Text visualization |
Matplotlib | Foundation for most plotting |