Plotly Express

# Plotly Express - High-Level API Plotly Express (px) is a high-level interface for creating data visualizations with minimal code (typically 1-5 lines). ## Installation ```bash uv pip install plotly ``` ## Key Advantages - Concise syntax for common chart types - Automatic color encoding and legends - Works seamlessly with pandas DataFrames - Smart defaults for layout and styling - Returns graph_objects.Figure for further customization ## Basic Usage Pattern ```python import plotly.express as px import pandas as pd # Most functions follow this pattern fig = px.chart_type( data_frame=df, x="column_x", y="column_y", color="category_column", # Auto-color by category size="size_column", # Size by values title="Chart Title" ) fig.show() ``` ## 40+ Chart Types ### Basic Charts - `px.scatter()` - Scatter plots with optional trendlines - `px.line()` - Line charts for time series - `px.bar()` - Bar charts (vertical/horizontal) - `px.area()` - Area charts - `px.pie()` - Pie charts ### Statistical Charts - `px.histogram()` - Histograms with automatic binning - `px.box()` - Box plots for distributions - `px.violin()` - Violin plots - `px.strip()` - Strip plots - `px.ecdf()` - Empirical cumulative distribution ### Maps - `px.scatter_geo()` - Geographic scatter plots - `px.choropleth()` - Choropleth maps - `px.scatter_mapbox()` - Mapbox scatter plots - `px.density_mapbox()` - Density heatmaps on maps ### Specialized - `px.sunburst()` - Hierarchical sunburst charts - `px.treemap()` - Treemap visualizations - `px.funnel()` - Funnel charts - `px.parallel_coordinates()` - Parallel coordinates - `px.scatter_matrix()` - Scatter matrix (SPLOM) - `px.density_heatmap()` - 2D density heatmaps - `px.density_contour()` - Density contours ### 3D Charts - `px.scatter_3d()` - 3D scatter plots - `px.line_3d()` - 3D line plots ## Common Parameters All Plotly Express functions support these styling parameters: ```python fig = px.scatter( df, x="x", y="y", # Dimensions width=800, height=600, # Labels title="Figure Title", labels={"x": "X Axis", "y": "Y Axis"}, # Colors color="category", color_discrete_map={"A": "red", "B": "blue"}, color_continuous_scale="Viridis", # Ordering category_orders={"category": ["A", "B", "C"]}, # Theming template="plotly_dark" # or "simple_white", "seaborn", "ggplot2" ) ``` ## Data Format Plotly Express works with: - **Long-form data** (tidy): One row per observation - **Wide-form data**: Multiple columns as separate traces ```python # Long-form (preferred) df_long = pd.DataFrame({ 'fruit': ['apple', 'orange', 'apple', 'orange'], 'contestant': ['A', 'A', 'B', 'B'], 'count': [1, 3, 2, 4] }) fig = px.bar(df_long, x='fruit', y='count', color='contestant') # Wide-form df_wide = pd.DataFrame({ 'fruit': ['apple', 'orange'], 'A': [1, 3], 'B': [2, 4] }) fig = px.bar(df_wide, x='fruit', y=['A', 'B']) ``` ## Trendlines Add statistical trendlines to scatter plots: ```python fig = px.scatter( df, x="x", y="y", trendline="ols", # "ols", "lowess", "rolling", "ewm", "expanding" trendline_options=dict(log_x=True) # Additional options ) ``` ## Faceting (Subplots) Create faceted plots automatically: ```python fig = px.scatter( df, x="x", y="y", facet_row="category_1", # Separate rows facet_col="category_2", # Separate columns facet_col_wrap=3 # Wrap columns ) ``` ## Animation Create animated visualizations: ```python fig = px.scatter( df, x="gdp", y="life_exp", animation_frame="year", # Animate over this column animation_group="country", # Group animated elements size="population", color="continent", hover_name="country" ) ``` ## Hover Data Customize hover tooltips: ```python fig = px.scatter( df, x="x", y="y", hover_data={ "extra_col": True, # Add column "x": ":.2f", # Format existing "hidden_col": False # Hide column }, hover_name="name_column" # Bold title in hover ) ``` ## Further Customization Plotly Express returns a `graph_objects.Figure` that can be further customized: ```python fig = px.scatter(df, x="x", y="y") # Use graph_objects methods fig.update_layout( title="Custom Title", xaxis_title="X Axis", font=dict(size=14) ) fig.update_traces( marker=dict(size=10, opacity=0.7) ) fig.add_hline(y=0, line_dash="dash") ``` ## When to Use Plotly Express Use Plotly Express when: - Creating standard chart types quickly - Working with pandas DataFrames - Need automatic color/size encoding - Want sensible defaults with minimal code Use graph_objects when: - Building custom chart types not in px - Need fine-grained control over every element - Creating complex multi-trace figures - Building specialized visualizations