Visualization with Python
Comprehensive guide to data visualization in Python—from static charts to interactive dashboards.
Key Concepts
- Line, bar, scatter, histogram, box, violin plots
- Subplots and figure layout
- Color palettes and theming
- Interactive tooltips and zoom
- Dashboard frameworks: Dash, Streamlit
- Export to SVG, PNG, PDF
Libraries & Tools
- Matplotlib: Static plots, publications, fine-grained control
- Seaborn: Statistical plots, heatmaps, distribution visualizations
- Plotly: Interactive charts, dashboards, web embeds
- Altair: Declarative Vega-Lite, clean grammar of graphics
- Bokeh: Interactive web visualizations, streaming data
- Folium: Leaflet maps, geospatial data
Code Example
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species')
fig.show()📺 20 Curated YouTube Videos
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▶ Customization
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