Python:Plotly 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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