visualization

## What I do - Create effective data visualizations - Select appropriate chart types - Design interactive dashboards - Handle large datasets efficiently - Apply color and typography - Tell stories with data ## When to use me When presenting data, building dashboards, or creating visual reports. ## Key Concepts ### Chart Selection ``` Comparison: Bar, Column, Radar Distribution: Histogram, Box plot Relationship: Scatter, Bubble Composition: Pie, Stacked area Trend: Line, Area Part-to-whole: Treemap, Sunburst ``` ### Data Visualization Libraries ```python # Python import matplotlib.pyplot as plt import seaborn as sns import plotly.express as px from bokeh.plotting import figure # JavaScript import * as d3 from 'd3'; import Chart from 'chart.js/auto'; import { Vega } from 'vega'; ``` ### Design Principles 1. Show data, not decoration 2. Maximize data-ink ratio 3. Avoid 3D effects 4. Use color purposefully 5. Include clear labels 6. Provide context ### Interactive Dashboards ```javascript // Plotly Dash example import dash from dash import dcc, html app = dash.Dash(__name__) app.layout = html.Div([ dcc.Graph(id='chart'), dcc.Interval(interval=5000) ]) ``` ### Accessibility in Visualization - Colorblind-safe palettes - Patterns + colors - Screen reader support - High contrast mode - Tooltips for detail ### Best Practices - Start with clean data - Choose the simplest chart - Label axes clearly - Add legends - Provide context - Test with real users