Plotly Express

0.4.1 · active · verified Sat Apr 11

Plotly Express (often imported as `px`) is a high-level Python visualization library, designed as a terse, consistent, and easy-to-learn wrapper for Plotly.py. It simplifies the creation of interactive charts with single-function calls, offering over 30 functions for various plot types, including statistical, scientific, and geographical charts. While `plotly-express` on PyPI is at version 0.4.1, the core functionality is now a built-in module, `plotly.express`, within the main `plotly` library (version 6.x.x+), which is actively developed and maintained.

Warnings

gotcha The standalone `plotly-express` PyPI package (version 0.4.1) is now largely a wrapper that re-exports `plotly.express`, which is a module built directly into the main `plotly` library (versions 4.0 and higher). Users typically install `plotly` directly to get `plotly.express`.
Fix: Prefer `import plotly.express as px` after `pip install plotly`. The `plotly-express` package serves mostly for backward compatibility or explicit clarity.
deprecated Mapbox-based traces (`scatter_mapbox`, `line_mapbox`, `choropleth_mapbox`, `density_mapbox`) are deprecated in `plotly.py` version 6 and will be removed in a future release. They rely on Mapbox tokens and are being replaced.
Fix: Migrate to Maplibre-based traces such as `scatter_map`, `line_map`, `choropleth_map`, and `density_map` which offer similar functionality without Mapbox dependency.
breaking Transforms, which were deprecated in `plotly.py` v5, have been completely removed in `plotly.py` v6. Functionality previously achieved with transforms must now be handled by preprocessing data with a dataframe library.
Fix: Rewrite data manipulation logic using pandas, polars, or other dataframe libraries before passing data to Plotly Express functions.
breaking Several layout attributes for titles (`titlefont`, `titleposition`, `titleside`, `titleoffset`) have been removed in `plotly.py` v6. These were part of the older layout structure.
Fix: Update your code to use the new nested title attributes: `layout.title.font`, `layout.title.position`, `layout.title.side`, and `layout.title.offset`.
gotcha Plotly Express functions are primarily designed for 'tidy' or long-form dataframes. While some 2D Cartesian functions support wide-form data, using wide-form data may not expose all of Plotly Express's flexible parameter mapping capabilities.
Fix: For optimal use and to leverage parameters like `color`, `facet_row`, `animation_frame`, etc., it's often best to transform wide-form data into long-form using methods like `df.melt()` from pandas.
gotcha While Plotly Express offers a high-level API for rapid plotting, it provides less fine-grained control over every plot detail compared to `plotly.graph_objects`. For highly customized or complex interactive figures, direct use of `plotly.graph_objects` might be necessary.
Fix: Start with Plotly Express for initial exploration. If specific customizations are not available, you can obtain the figure object from Plotly Express (e.g., `fig = px.scatter(...)`) and then modify it using `fig.update_layout()` or by adding `plotly.graph_objects` traces.

Install

pip install plotly-express Install plotly-express (re-exports plotly.express)
pip install plotly Install main Plotly library (includes plotly.express)

Imports

plotly.express
```
import plotly.express as px
```

Quickstart

This quickstart demonstrates how to import `plotly.express`, load a sample dataset, create a basic scatter plot with colored data points based on a category, and display the interactive figure.

import plotly.express as px
import pandas as pd

# Use a built-in dataset for a quick example
df = px.data.iris()

# Create a scatter plot
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", title="Iris Sepal Width vs Length")

# Display the figure
fig.show()

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