Data visualization represents data graphically so people can explore patterns, interpret results, and communicate important findings quickly. In workplace dashboards, charts turn metrics into a focused story for decision-makers. section 6.0 section 6.5
Choose the visual form according to the question:
- Use line charts to show how a metric changes over time. section 6.1.3
- Use pie charts to show how a whole quantity breaks down across a dimension. section 6.1.3
- Place important metrics prominently, then let filters and date ranges control the displayed slice of data. section 6.1.3
A practical workflow is to wrangle data with pandas, create charts with a visualization library, and display them in Streamlit. Supported options include Matplotlib, Plotly, Altair, GraphViz, and PyDeck. section 1.3.7 section 6.5
Interactive histogram
Generate sample values with NumPy, create a Matplotlib histogram, let the user choose the number of bins with a Streamlit number input, and render the figure with Streamlit. section 1.3.7
Figure 1.6 A histogram in Streamlit created using the popular Matplotlib library
Use the histogram figure as a compact example of a chart rendered inside a Streamlit application. section 1.3.7
