Plotly and Dask Cheat Sheet

Updated 2026-04-27

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. 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

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.

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.