The Python Graph Gallery
A collection of hundreds of charts made with Python, from beginner-friendly basics to stunning dataviz. Each example comes with its reproducible code and explanations. Most of them use matplotlib and seaborn.
The biggest list of python chart examples
Charts are classified by chart type. Each section starts with a foundational tutorial, then goes into the most common customizations.
General Knowledge
Hunting the most beautiful Python charts
Explore our curated collection of the finest Python charts, handpicked for their superior design and accuracy. Go beyond the defaults with chart examples that are both visually stunning and instructive.
Accompanied by in-depth tutorials, this section will please seasoned professionals aspiring to elevate their craft. Featuring the finest charts on the web, we stay ahead of the curve, showcasing the latest tips, tricks, and advanced techniques in data visualization.
Salary evolution
Joseph Barbier
Beyond the choropleth: a polygon map
Sebastiaan Broekema
Salary in the USA
Joseph Barbier
Unemployment rate in Belgium
Koen Van den Eeckhout
Animal adoption evolution
Joseph Barbier
Unemployment rates in the US
Joseph Barbier
HDI distribution in Sao Paulo
Vinicius Oike Reginatto
Evolution of temperature variation
Joseph Barbier
Share of cereals used as animal feeds
Muhammad Azhar
Share of cereals used as animal feeds
Benjamin Nowak
Japan's population evolution
Joseph Barbier
Earthquakes around the world
Joseph Barbier
Density population in Asia
Joseph Barbier
CO2 consumption in Europe
Joseph Barbier
London recycling rates
Lisa Hornung
Evolution of natural disasters
Joseph Barbier
Unemployment rate during COVID-19
Joseph Barbier
Lollipop about music genres
Joseph Barbier
Map with scatter plot on top
Joseph Barbier
Multiple choropleth map
Joseph Barbier
Scatter plot with specific highlights
Joseph Barbier
Dumbell chart
Cédric Scherer
Double heatmap for comparison
Joseph Barbier
Ridgeline with quantiles display
Ansgar Wolsing
Polished table with bubbles
Fortune Uwha
Line chart with word cloud
Joseph Barbier
Mini stacked areas for US states
Erin
Choropleth map with gradient color
Joseph Barbier
Ordered & Mirrored barplot
Lisa Hornung
Ordered & Mirrored barplot
Benjamin Nowak
Scatterplot with grouping, highlighting and annotation
Data Wrapper
Histogram with clean color scale and annotation
INSEE
Area over flexible baseline chart
J. Kühn
Circular Barplot
T. Stadler
Circular Lollipop Plot
C. Scherer
Violin and Boxplot combination
T. Wang
Circle Heatmap
M. Siple
Circular barplot
M. Siple
Line chart with faceting
A. Madjid
Scatterplot with images
Tanya Shapiro
Horizontal Barplot
The Economist
Parallel chart
G. Karamanis
Inline labels
C. Scherer
Line chart from the Economist
The Economist
Area chart from the Economist
The Economist
Lollipop chart
C. Scherer
Line chart with small multiple
O. Medina
Radar chart with matplotlib
T. Wang
Astronaut Scatterplot
C. Thompson
Scatterplot with regression fit
C. O. Wilke
Stacked barchart
G. Fontana
X-men Streamchart
C. Scherer
Scatterplot with text repel
T. Wang
Timeseries and faceting
G. Karamanis
Polar bar chart
nyx-it-up
Hourly heatmap
J. MacKintosh
Line chart & small multiple
G. Fontana
Barplot with annotations and arrows
J. Barbier
Palette color finder
Selecting optimal chart colors can be challenging and time-intensive. The pypalettes library simplifies this process by providing access to over 2,500 color palettes with a single line of code.
Additionally, the Python Graph Gallery features a dedicated page where you can browse all these palettes and preview their appearance on your charts.
Python Graphing with State-of-the-Art Libraries
The python graph gallery relies on the latest and most powerful charting libraries.
Matplotlib
The foundation of Python visualization. Offers a wide array of customizable 2D plots and an extensive set of tools for creating intricate figures and charts.
Tutorial
Seaborn
Built atop Matplotlib, Seaborn elevates data visualization by providing a higher-level interface and stunning default themes.
Tutorial
Pandas
Beyond its powerful data manipulation capabilities, Pandas offers convenient plotting methods, enabling users to visualize data directly from DataFrame and Series objects.
Tutorial
Plotly
Delivering interactive and browser-based visualizations, Plotly allows users to craft visually captivating charts, bridging the gap between static graphs and web-based interactivity.
Tutorial
PlotnineLove beautiful charts?
The Python Graph Gallery complements dataviz-Inspiration.com, a website featuring hundreds of my favorite data visualization projects.
Get some inspiration
Matplotlib Journey is an interactive online course crafted to transform you into a Matplotlib dataviz expert. It provides a clear, big-picture understanding of how data visualization works in Python, empowering you to grasp any example from the gallery with ease.
Finally, Understand Matplotlib.💡 Out of dataviz inspiration?
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