README.md

metadata

title: StreamlitPydeckMapVisualViewStateForLatitudeLongitude
emoji: ⚡
colorFrom: pink
colorTo: gray
sdk: streamlit
sdk_version: 1.17.0
app_file: app.py
pinned: false
license: mit

🧠Visualization with Plotly Sunbursts, Treemaps, and WebGL 🩺Vis🧠

Naming Convention

• Plotly Sunbursts
• Plotly Treemaps
• Plotly WebGL
• Streamlit File Upload

Ontology

Plotly Sunbursts

Plotly Sunburst is a type of interactive visualization that displays hierarchical data in a circular layout. Each ring represents a level in the hierarchy, and the size of each arc represents the proportion of data within that level.

1. 🔍 Data Exploration

   • 🔧 plotly.graph_objects.FigureWidget: create an interactive widget for exploring data
   • 📊 plotly.graph_objects.Histogram: create a histogram to visualize data distribution
   • 📈 plotly.graph_objects.Scatter: create a scatter plot to visualize relationships between variables

2. 📊 Data Presentation

   • 🌞 plotly.graph_objects.Sunburst: create a sunburst chart to display hierarchical data
   • 🌳 plotly.graph_objects.Treemap: create a treemap to display hierarchical data
   • 🌐 plotly.graph_objects.Figure: create a WebGL plot to display large datasets

3. 🎨 Customization

   • 🎨 plotly.graph_objects.Layout: customize the layout of the plot
   • 🎭 plotly.graph_objects.Marker: customize the markers or symbols used in the plot
   • 🖼️ plotly.graph_objects.Image: add images or logos to the plot

4. 📁 File I/O

   • 📥 streamlit.file_uploader: create a file upload widget in Streamlit
   • 📊 pandas.read_csv: read CSV data into a pandas dataframe
   • 🗄️ plotly.graph_objects.Figure.data: assign data to the plot from a pandas dataframe

5. 📈 Dynamic Plotting

   • 🔄 streamlit.cache: cache expensive computations for faster dynamic plotting
   • 🎚️ streamlit.slider: create a slider widget to interact with the plot
   • 📈 plotly.subplots: create a subplot with multiple plots in one figure

6. 📊 Interactive Widgets

   • 📑 streamlit.selectbox: create a dropdown menu to select data for the plot
   • 🧮 streamlit.number_input: create a number input to adjust plot parameters
   • 📄 streamlit.text_area: create a text area to display plot information

7. 🔧 Utilities

   • 📝 plotly.subplots.make_subplots: create a grid of subplots in one figure
   • 📊 plotly.subplots.subplots_adjust: adjust the spacing between subplots
   • 🎞️ plotly.io.write_html: save the plot as an HTML file for sharing or embedding

The Smart Community concept for Africa is an approach to building sustainable and interconnected communities in Africa using technology and data-driven solutions. To visualize this concept, we can use various Plotly visualization tools, such as Sunbursts, Treemaps, and WebGL.

Using Plotly Sunbursts, we can display hierarchical data in a circular layout, where each ring represents a level in the hierarchy, and the size of each arc represents the proportion of data within that level. This can be useful for visualizing data on the different levels of the community, such as the different sectors, departments, or organizations involved.

Using Plotly Treemaps, we can display hierarchical data in a rectangular layout, where each rectangle represents a level in the hierarchy, and the size of each rectangle represents the proportion of data within that level. This can be useful for visualizing data on the different areas or regions within the community.

Using Plotly WebGL, we can display large datasets in a three-dimensional format that allows for interactive exploration and analysis. This can be useful for visualizing data on the different demographics or socio-economic indicators within the community.

To facilitate data exploration, we can use interactive widgets such as the Plotly FigureWidget, Histogram, and Scatter plot. To customize the visualization, we can use the Plotly Layout, Marker, and Image options. To enable file input and output, we can use the Streamlit file uploader and pandas read_csv functions. To enable dynamic plotting and interactive widgets, we can use the Streamlit cache, slider, selectbox, number_input, and text_area functions. Finally, to generate multiple plots in one figure, we can use the Plotly subplots function, and to save and share the plots, we can use the Plotly write_html function.