plotly

# Plotly - Interactive Visualization Plotly provides a wide range of interactive charts. Its "Plotly Express" API is designed for speed and ease of use with tidy DataFrames, while "Graph Objects" offers low-level control over every trace and attribute. ## When to Use - Creating interactive charts for web applications or Jupyter notebooks - Visualizing 3D data (surfaces, scatter, mesh) - Geographic maps (scatter on maps, choropleths) with Mapbox integration - Financial charts (candlestick, OHLC) - Exploring large datasets where zooming into specific regions is required - Creating animations (time-series sliders) - Building production-ready dashboards (via Dash) ## Reference Documentation **Official docs**: https://plotly.com/python/ **Plotly Express**: https://plotly.com/python/plotly-express/ **Search patterns**: `px.scatter`, `go.Figure`, `fig.update_layout`, `fig.write_html`, `px.choropleth` ## Core Principles ### Plotly Express (px) vs. Graph Objects (go) | Feature | Plotly Express (px) | Graph Objects (go) | |---------|---------------------|-------------------| | Complexity | High-level, concise. | Low-level, verbose. | | Data Format | Tidy (long-form) DataFrames. | Lists, Arrays, Dicts, or DataFrames. | | Customization | Good (using update_*). | Maximum / Full control. | | Speed of Dev | Very fast. | Slower. | ### Use Plotly For - Interactive exploration (hover, zoom) - 3D and Geospatial visualization - Exporting to standalone interactive HTML files - Integration with Dash ### Do NOT Use For - Publication-quality static LaTeX plots (use Matplotlib) - Very large static image generation (Matplotlib is faster) - Low-memory environments (Plotly's JSON-based figures are memory-heavy) ## Quick Reference ### Installation ```bash pip install plotly pandas ``` ### Standard Imports ```python import plotly.express as px import plotly.graph_objects as go import pandas as pd import numpy as np ``` ### Basic Pattern - Plotly Express ```python import plotly.express as px # Load data df = px.data.iris() # Create interactive scatter plot fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", size="petal_length", hover_data=['petal_width']) # Display fig.show() ``` ## Critical Rules ### ✅ DO - Use Plotly Express first - 90% of tasks are easier with px - Prefer Tidy Data - Ensure one row per observation for easy mapping to colors/axes - Use update_layout - Cleanly modify titles, fonts, and background colors - Save as HTML - Use `fig.write_html("plot.html")` to share interactive charts - Leverage Hover Data - Add context to points without cluttering the plot - Set Figure Templates - Use `template="plotly_dark"` or `"ggplot2"` for instant style - Use marginal_x/y - In px.scatter, quickly add histograms or boxplots to margins ### ❌ DON'T - Pass huge datasets to the browser - Plotting >50k points can lag the UI; use datashader or decimation - Manual looping with go - If px can do it, don't use a for-loop to add traces in go - Forget to set axis labels - px uses column names; rename them in the DataFrame for better labels - Over-animate - Smooth animations are cool, but too many moving parts distract from the data ## Anti-Patterns (NEVER) ```python # ❌ BAD: Over-complicating a simple plot with Graph Objects fig = go.Figure() for species in df['species'].unique(): sub = df[df['species'] == species] fig.add_trace(go.Scatter(x=sub['sepal_w'], y=sub['sepal_l'], name=species)) # ✅ GOOD: Use Plotly Express (One line, automatic legend/colors) fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species") # ❌ BAD: Mixing list-style data with DataFrame-style data in px px.scatter(x=[1,2,3], y=df['column']) # Can lead to alignment issues # ✅ GOOD: Stick to the DataFrame px.scatter(df, x="column_a", y="column_b") ``` ## Plotly Express (px) Deep Dive ### Statistical Charts ```python # Boxplot with points fig = px.box(df, x="day", y="total_bill", color="smoker", points="all") # Violin plot with box inside fig = px.violin(df, x="day", y="total_bill", color="sex", box=True, points="all") # Heatmap (Density Contour) fig = px.density_heatmap(df, x="total_bill", y="tip", marginal_x="histogram", marginal_y="histogram") ``` ### Time Series and Faceting ```python df = px.data.stocks() # Multiple lines from wide data fig = px.line(df, x='date', y=["GOOG", "AAPL", "AMZN"], title="Tech Stocks") # Faceting (Subplots by category) df = px.data.tips() fig = px.scatter(df, x="total_bill", y="tip", color="smoker", facet_col="day", facet_row="time") ``` ## 3D Visualization ### Scatter, Lines, and Surfaces ```python # 3D Scatter fig = px.scatter_3d(df, x='sepal_length', y='sepal_width', z='petal_width', color='species') # 3D Surface (Using Graph Objects) z_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv') fig = go.Figure(data=[go.Surface(z=z_data.values)]) fig.update_layout(title='Mt Bruno Elevation', autosize=False, width=500, height=500, margin=dict(l=65, r=50, b=65, t=90)) ``` ## Geospatial Analysis ### Maps and Choropleths ```python # Scatter on a map df = px.data.gapminder().query("year == 2007") fig = px.scatter_geo(df, locations="iso_alpha", color="continent", hover_name="country", size="pop", projection="natural earth") # Detailed Mapbox Choropleth (Needs token or use open-street-map) fig = px.choropleth_mapbox(df, geojson=counties, locations='fips', color='unemp', color_continuous_scale="Viridis", mapbox_style="carto-positron", zoom=3, center = {"lat": 37.0902, "lon": -95.7129}) ``` ## Layout and Styling (fig.update_*) ### Fine-tuning the appearance ```python fig = px.scatter(df, x="x", y="y") # Global layout updates fig.update_layout( title="Custom Styled Plot", xaxis_title="Dimension X", yaxis_title="Dimension Y", font=dict(family="Courier New, monospace", size=18, color="RebeccaPurple"), legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01), plot_bgcolor="white" ) # Axis specific updates fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='LightPink') fig.update_yaxes(zeroline=True, zerolinewidth=2, zerolinecolor='Black') ``` ## Advanced Interaction: Animations ```python df = px.data.gapminder() fig = px.scatter(df, x="gdpPercap", y="lifeExp", animation_frame="year", animation_group="country", size="pop", color="continent", hover_name="country", log_x=True, size_max=55, range_x=[100, 100000], range_y=[25, 90]) ``` ## Practical Workflows ### 1. Interactive Scientific Report Export ```python def create_interactive_report(df, filename="report.html"): """Generates a multi-chart HTML report.""" fig1 = px.scatter(df, x="A", y="B", color="C") fig2 = px.histogram(df, x="A", color="C") with open(filename, 'a') as f: f.write(fig1.to_html(full_html=False, include_plotlyjs='cdn')) f.write(fig2.to_html(full_html=False, include_plotlyjs='cdn')) # Useful for sharing findings with non-technical stakeholders ``` ### 2. Financial Dashboard Fragment (Candlestick) ```python import pandas as pd from datetime import datetime df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv') fig = go.Figure(data=[go.Candlestick(x=df['Date'], open=df['AAPL.Open'], high=df['AAPL.High'], low=df['AAPL.Low'], close=df['AAPL.Close'])]) # Remove rangeslider for cleaner look fig.update_layout(xaxis_rangeslider_visible=False) ``` ### 3. Mixing Subplots with go.Figure ```python from plotly.subplots import make_subplots fig = make_subplots(rows=1, cols=2, subplot_titles=("Plot A", "Plot B")) fig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=1, col=1) fig.add_trace(go.Bar(x=[1, 2, 3], y=[2, 3, 5]), row=1, col=2) fig.update_layout(height=600, width=800, title_text="Side-by-Side Comparison") ``` ## Performance Optimization ### WebGL for Large Datasets ```python # For scatter plots with >10,000 points, use Scattergl (Graph Objects) # or tell px to use webgl (available in newer versions) fig = px.scatter(df, x="large_x", y="large_y", render_mode="webgl") # WebGL drastically improves performance by using the GPU for rendering. ``` ## Common Pitfalls and Solutions ### JSON Overhead in Notebooks ```python # ❌ Problem: Notebook file size explodes to 50MB # ✅ Solution: Display as static image (requires kaleido) or use a different renderer # fig.show(renderer="png") # Static # OR: Clear output after viewing ``` ### Axis Scaling in Animations ```python # ❌ Problem: Axes jump around during animation # ✅ Solution: Manually fix the ranges fig = px.scatter(df, x="x", y="y", animation_frame="time", range_x=[0, 100], range_y=[0, 100]) ``` ### Handling Missing Categories in Legend ```python # ❌ Problem: Colors change when filtering data because categories disappear # ✅ Solution: Pass a category_orders dictionary fig = px.scatter(df, x="x", y="y", color="category", category_orders={"category": ["A", "B", "C", "D"]}) ``` ## Best Practices 1. **Use Plotly Express first** - Start with `px` for 90% of tasks; only use `go` when you need fine-grained control 2. **Work with tidy DataFrames** - Ensure one row per observation for easy mapping to visual attributes 3. **Use `update_layout` for styling** - Cleanly modify titles, fonts, and background colors without recreating figures 4. **Save as HTML for sharing** - Use `fig.write_html("plot.html")` to share interactive charts with stakeholders 5. **Leverage hover data** - Add context to points without cluttering the plot 6. **Set figure templates** - Use `template="plotly_dark"` or `"ggplot2"` for instant professional styling 7. **Use marginal plots** - In `px.scatter`, use `marginal_x` and `marginal_y` to quickly add histograms or boxplots 8. **Optimize for large datasets** - Use WebGL rendering or datashader for datasets with >50k points 9. **Fix axis ranges in animations** - Use `range_x` and `range_y` to prevent axes from jumping during animations 10. **Set category orders** - Use `category_orders` to maintain consistent colors when filtering data Plotly bridges the gap between static analysis and interactive discovery. It is the best tool for moving scientific insights from a notebook to the web.