express

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Published May 21, 2024
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In Plotly, the express module is a high-level interface designed to simplify the creation of interactive and visually appealing plots by providing easy-to-use functions for a wide range of chart types, including line charts, scatter plots, bar charts, and more, allowing users to generate complex visualizations with minimal code.

Syntax

import plotly.express as px

express

.area()
Creates an area chart by filling the space under a line plot to visualize trends and cumulative data.
.bar()
Generates a chart representing categorical data with vertical bars.
.box()
Creates a box plot to visualize the distribution of data points through their quartiles.
.density_contour()
Creates a 2D density contour plot that shows how data points are concentrated in a two-dimensional space.
.density_heatmap()
Creates a 2D histogram-based heatmap that visualizes the density of points in a dataset using color intensity.
.ecdf()
Creates ECDF plots, which are used for visualizing the proportion or count of observations that are less than or equal to a given value.
.funnel()
Generates a funnel chart that visualizes the reduction of data in progressive stages.
.histogram()
Creates a histogram, which is a graphical representation of the distribution of a dataset.
.icicle()
Creates an icicle chart, a hierarchical visualization that displays data as nested rectangles, where each level represents a breakdown of the parent category.
.line()
Creates line charts, also known as line plots or line graphs.
.pie()
Creates a pie chart, a circular statistical graphic divided into slices to illustrate numerical proportions.
.scatter()
Creates a scatter plot, which displays data points based on their values on the x and y axes.
.scatter_3d()
Creates a 3D scatter plot to visualize data points across three dimensions (x, y, z) with options for color, size, and hover data.
.strip()
Creates a strip chart, which is a dot plot visualizing the distribution of a numerical variable for one or several groups.
.sunburst()
Generates a sunburst chart to visualize hierarchical data using nested circular sectors.
.treemap()
Returns a visualization of hierarchical data using nested rectangles.
.violin()
Generates a violin plot that displays the distribution of numeric data across different categories.

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