Plotly and Dask Cheat Sheet

Updated 2026-04-27

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Plotly is an interactive, open-source visualization library for Python that creates publication-quality graphs with minimal code, while Dask is a flexible parallel computing library that scales Python data science workflows from single machines to clusters. Together, they form a powerful toolkit for modern data analysis: Plotly excels at transforming data into compelling visual stories with interactivity built-in, while Dask enables you to handle datasets larger than RAM by intelligently chunking and parallelizing computations. The key mental model to keep in mind is that Plotly operates on computed results (whether from pandas or Dask), while Dask operates lazily — building a task graph that only executes when you explicitly call .compute() or .persist(). Since Dask 2024.3.0, the DataFrame backend uses dask-expr with logical query planning enabled by default, bringing significant performance improvements.

What This Cheat Sheet Covers

This topic spans 22 focused tables and 157 indexed concepts, 109 flashcards. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Plotly Core Chart TypesTable 2: Plotly Specialized Chart TypesTable 3: Plotly Express vs Graph ObjectsTable 4: Plotly Figure CustomizationTable 5: Plotly Interactivity and ControlsTable 6: Plotly Layout and StylingTable 7: Plotly Subplots and FacetingTable 8: Plotly AnimationsTable 9: Plotly Export and DisplayTable 10: Dask Core CollectionsTable 11: Dask DataFrame OperationsTable 12: Dask Array OperationsTable 13: Dask Distributed ComputingTable 14: Dask Execution ControlTable 15: Dask Scheduling OptionsTable 16: Dask Memory ManagementTable 17: Dask I/O OperationsTable 18: Dask Best PracticesTable 19: Dask with Machine LearningTable 20: Dask Debugging and DiagnosticsTable 21: Dask-Specific GotchasTable 22: Plotly with Dash Applications

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Table 1: Plotly Core Chart Types

The everyday chart vocabulary lives here — the plots you'll build ninety percent of the time. They span the four basic questions of visualisation: how two variables relate (scatter, line), how values compare across categories (bar), how a single variable is distributed (histogram, box, violin, ECDF), and how parts make up a whole or sit on a map (pie, treemap, choropleth). Most are a single Plotly Express call away.

Type	Example	Description
Scatter Plot	`import plotly.express as px` `fig = px.scatter(df, x='col1', y='col2')`	• Visualizes relationships between two continuous variables • supports color, size, and hover customization.
Line Chart	`fig = px.line(df, x='date', y='value')`	• Displays trends over time or continuous data • ideal for time-series with automatic date formatting.
Bar Chart	`fig = px.bar(df, x='category', y='count')`	• Represents categorical data using rectangular bars • supports grouped, stacked, and horizontal orientations.
Histogram	`fig = px.histogram(df, x='values', nbins=30)`	• Shows distribution of a single continuous variable by binning values • automatically calculates counts.
Box Plot	`fig = px.box(df, x='group', y='values')`	• Displays five-number summary (min, Q1, median, Q3, max) plus outliers • useful for comparing distributions.
Violin Plot	`fig = px.violin(df, x='group', y='values')`	• Combines box plot with kernel density estimation • reveals distribution shape more clearly than box plots.
Strip Chart	`fig = px.strip(df, x='group', y='values')`	• Plots individual data points as a jitter strip over categories • useful when sample size is small enough to show every point.
ECDF Plot	`fig = px.ecdf(df, x='values')`	• Empirical cumulative distribution function • shows the fraction of data at or below each value without binning.
Heatmap	`import plotly.graph_objects as go` `fig = go.Figure(data=go.Heatmap(z=matrix))`	• Visualizes matrix data using color gradients • commonly used for correlation matrices and 2D density.
Density Heatmap	`fig = px.density_heatmap(df, x='col1', y='col2')`	• Aggregates data into a 2D grid and colors by count or aggregate • shows joint distribution of two continuous variables.
Density Contour	`fig = px.density_contour(df, x='col1', y='col2')`	• Draws contour lines over the 2D density of data points • similar to density heatmap but as isolines.
Pie Chart	`fig = px.pie(df, names='category', values='amount')`	• Shows part-to-whole relationships as slices • supports donut variant with `hole` parameter.
Sunburst Chart	`fig = px.sunburst(df, path=['level1', 'level2'], values='val')`	• Displays hierarchical data as concentric rings • interactive drill-down by clicking segments.
Treemap	`fig = px.treemap(df, path=['level1', 'level2'], values='val')`	• Represents hierarchical data using nested rectangles • size and color encode different metrics.
Choropleth Map	`fig = px.choropleth(df, locations='country', color='value')`	• Creates geographic outline maps with regions colored by data values • uses built-in country/state boundaries.
Scatter Mapbox	`fig = px.scatter_mapbox(df, lat='lat', lon='lon')`	• Plots points on Mapbox tile maps • requires Mapbox token for advanced styles.
Scatter Map (MapLibre)	`fig = px.scatter_map(df, lat='lat', lon='lon')`	• Plots points on MapLibre tile maps • newer alternative to scattermapbox — no Mapbox token required.
3D Scatter	`fig = px.scatter_3d(df, x='x', y='y', z='z')`	• Creates three-dimensional scatter plots with rotation and zoom • ideal for multivariate exploration.
Contour Plot	`fig = go.Figure(data=go.Contour(z=matrix))`	• Shows isolines of a 3D surface on a 2D plane • useful for visualizing functions of two variables.
Sankey Diagram	`fig = go.Figure(data=go.Sankey(node=nodes, link=links))`	• Visualizes flow between nodes with weighted connections • commonly used for process flows and transfers.
Funnel Chart	`fig = px.funnel(df, x='values', y='stage')`	• Displays progressive reduction through sequential stages • typical for sales pipelines and conversion funnels.
WebGL Scatter	`fig = go.Figure(data=go.Scattergl(x=x, y=y))`	• Uses WebGL rendering for scatter plots with millions of points • drop-in replacement for `go.Scatter` when performance matters.

Table 2: Plotly Specialized Chart Types

Beyond the basics, Plotly ships purpose-built charts for specific domains. Candlestick, OHLC, and waterfall charts are the language of finance; Gantt timelines and funnels belong to project and sales analytics; and SPLOM, parallel coordinates, radar, and ternary plots are the heavy machinery of multivariate exploration when three or more dimensions need to be on screen at once.

Type	Example	Description
Candlestick Chart	`fig = go.Figure(data=go.Candlestick(` `x=df['date'], open=df['open'],` `high=df['high'], low=df['low'], close=df['close']))`	• Displays OHLC financial data as colored candle bodies with wicks • green/red coloring indicates price direction.

The rest of this section is part of the full cheat sheet

Table 3: Plotly Express vs Graph Objects

Plotly gives you two ways to build the same figure, and knowing when to switch between them is the key to working efficiently. Express (px) gets you a polished chart in one line, Graph Objects (go) trades that brevity for total control, and — because every Express figure is a Graph Object underneath — you can start with one and layer in the other.

Approach	Example	Description
Plotly Express (px)	`import plotly.express as px` `fig = px.scatter(df, x='a', y='b')`	• High-level interface with sensible defaults • creates complete figures in one line for common charts.

The rest of this section is part of the full cheat sheet

Table 4: Plotly Figure Customization

Once a figure exists, almost all refinement happens through a handful of update_* and add_* methods. update_layout reshapes the figure as a whole while update_traces and update_xaxes/update_yaxes target the data and axes; the add_* calls overlay extra traces, annotations, reference lines, and shaded regions to draw the eye where it matters.

Method	Example	Description
update_layout	`fig.update_layout(title='My Plot', xaxis_title='X')`	Modifies global figure properties like title, axes, margins, and background in one call.

The rest of this section is part of the full cheat sheet

Table 1: Plotly Core Chart Types

Type	Example	Description
Scatter Plot	`import plotly.express as px` `fig = px.scatter(df, x='col1', y='col2')`	• Visualizes relationships between two continuous variables • supports color, size, and hover customization.
Line Chart	`fig = px.line(df, x='date', y='value')`	• Displays trends over time or continuous data • ideal for time-series with automatic date formatting.
Bar Chart	`fig = px.bar(df, x='category', y='count')`	• Represents categorical data using rectangular bars • supports grouped, stacked, and horizontal orientations.
Histogram	`fig = px.histogram(df, x='values', nbins=30)`	• Shows distribution of a single continuous variable by binning values • automatically calculates counts.
Box Plot	`fig = px.box(df, x='group', y='values')`	• Displays five-number summary (min, Q1, median, Q3, max) plus outliers • useful for comparing distributions.
Violin Plot	`fig = px.violin(df, x='group', y='values')`	• Combines box plot with kernel density estimation • reveals distribution shape more clearly than box plots.
Strip Chart	`fig = px.strip(df, x='group', y='values')`	• Plots individual data points as a jitter strip over categories • useful when sample size is small enough to show every point.
ECDF Plot	`fig = px.ecdf(df, x='values')`	• Empirical cumulative distribution function • shows the fraction of data at or below each value without binning.
Heatmap	`import plotly.graph_objects as go` `fig = go.Figure(data=go.Heatmap(z=matrix))`	• Visualizes matrix data using color gradients • commonly used for correlation matrices and 2D density.
Density Heatmap	`fig = px.density_heatmap(df, x='col1', y='col2')`	• Aggregates data into a 2D grid and colors by count or aggregate • shows joint distribution of two continuous variables.
Density Contour	`fig = px.density_contour(df, x='col1', y='col2')`	• Draws contour lines over the 2D density of data points • similar to density heatmap but as isolines.
Pie Chart	`fig = px.pie(df, names='category', values='amount')`	• Shows part-to-whole relationships as slices • supports donut variant with `hole` parameter.
Sunburst Chart	`fig = px.sunburst(df, path=['level1', 'level2'], values='val')`	• Displays hierarchical data as concentric rings • interactive drill-down by clicking segments.
Treemap	`fig = px.treemap(df, path=['level1', 'level2'], values='val')`	• Represents hierarchical data using nested rectangles • size and color encode different metrics.
Choropleth Map	`fig = px.choropleth(df, locations='country', color='value')`	• Creates geographic outline maps with regions colored by data values • uses built-in country/state boundaries.
Scatter Mapbox	`fig = px.scatter_mapbox(df, lat='lat', lon='lon')`	• Plots points on Mapbox tile maps • requires Mapbox token for advanced styles.
Scatter Map (MapLibre)	`fig = px.scatter_map(df, lat='lat', lon='lon')`	• Plots points on MapLibre tile maps • newer alternative to scattermapbox — no Mapbox token required.
3D Scatter	`fig = px.scatter_3d(df, x='x', y='y', z='z')`	• Creates three-dimensional scatter plots with rotation and zoom • ideal for multivariate exploration.
Contour Plot	`fig = go.Figure(data=go.Contour(z=matrix))`	• Shows isolines of a 3D surface on a 2D plane • useful for visualizing functions of two variables.
Sankey Diagram	`fig = go.Figure(data=go.Sankey(node=nodes, link=links))`	• Visualizes flow between nodes with weighted connections • commonly used for process flows and transfers.
Funnel Chart	`fig = px.funnel(df, x='values', y='stage')`	• Displays progressive reduction through sequential stages • typical for sales pipelines and conversion funnels.
WebGL Scatter	`fig = go.Figure(data=go.Scattergl(x=x, y=y))`	• Uses WebGL rendering for scatter plots with millions of points • drop-in replacement for `go.Scatter` when performance matters.

Table 2: Plotly Specialized Chart Types

Type	Example	Description
Candlestick Chart	`fig = go.Figure(data=go.Candlestick(` `x=df['date'], open=df['open'],` `high=df['high'], low=df['low'], close=df['close']))`	• Displays OHLC financial data as colored candle bodies with wicks • green/red coloring indicates price direction.

Table 3: Plotly Express vs Graph Objects

Approach	Example	Description
Plotly Express (px)	`import plotly.express as px` `fig = px.scatter(df, x='a', y='b')`	• High-level interface with sensible defaults • creates complete figures in one line for common charts.

Table 4: Plotly Figure Customization

Method	Example	Description
update_layout	`fig.update_layout(title='My Plot', xaxis_title='X')`	Modifies global figure properties like title, axes, margins, and background in one call.