All charts
👋 This page displays all the charts available in the python graph gallery.
The vast majority of them are built using matplotlib, seaborn and plotly. But many other python charting libraries are used too.
Click on an image to read the full tutorial with explanation and reproducible code 😍! If you are interested in a particular chart type, please check the classification in the front page.
The most basic density plot one can make with python and seaborn
seaborn densty chart with filled area
vertical seaborn density chart
control density chart color
Changed the bandwidth used to compute the density
Density chart with several displayed groups
Mirror density chart to compare the distribution of 2 variables
Basic contourplot with seaborn.
Basic contourplot with seaborn.
Basic contourplot with seaborn.
Basic 2D histogram with seaborn
Control the color in the 2d histogram.
Black and white, color scale legend.
The most basic area chart one can make with python and matplotlib
Control chart color (Matplotlib)
Control line color
Area chart with small multiple, seaborn
Horizontal barplot, handy to make labels more readable.
Barplot and color customization.
Change the bar stroke colors.
Title and axis labels with Matplotlib.
Custom bar width. Can be handy to illustrate the sample size.
Custom space between bars.
Change bar texture.
More space under X axis to fit long labels.
Customize the bar labels.
Barplot with confidence interval.
Highly customized barplot with colors, legend, labels and more.
Most basic barplot with Python and Seaborn
Make your barplot vertical by swapping the x and y parameters
Learn how to reorder groups in a seaborn barplot
Add error bars on a seaborn barplot
How to build a stacked barplot with Python.
How to build a grouped barplot with Python.
How to build a percent stacked barplot with Python..
Grouped barplot with python and seaborn
Stacked barchart with python and seaborn
Percent stacked barchart with python and seaborn
Basic boxplot. You can quickly read the median, quartiles and outliers of each group.
If you add individual points with jitter, a bimodal distribution appears for group B
If you have a very large dataset, the violin plot is a better alternative than jittering
Let's start basic. The most simple boxplot, based on 3 differents input formats
Everything you need concerning color customization on your boxplot: transparency, palette in use, manual control..
Learn how to highlight a specific group in the dataset to make your point more obvious
If you have group and subgroups, you can build a grouped boxplot
Control the order of groups in the boxplot. It makes the chart more insightful
To avoid hiding information, you can add individual data points with jitter
Since individual data points are hidden, it is a good practice to show the sample size under each box
Customization: border width
Customization: use notch
Customization: box width
If you have both groups and subgroups, you'll be interested in a grouped violin plot
How to read a geoJson file with geopandas and transform it to a geodataframe.
How to read a geoJson file with geopandas and transform it to a geodataframe.
How to read a geoJson file with geopandas and transform it to a geodataframe.
Most basic bubble plot with Python and Matplotlib
Bubble plot customization: color
Bubble plot customization: shape
Bubble plot customization: size
Bubble plot customization: stroke
A bubble chart width 4 dimensions: x, y, size & color
Bubble plot of the gapminder dataset
Most basic chord diagram example from a random matrix.
Basic connected scatterplot with Python and Seaborn.
Basic connected scatterplot with Python and Matplotlib
Connected scatterplot on a split window
How to customize line and markers with Matplotlib
Connected scatterplot to show the evolution of 2 variables
Most basic circle packing
Add colors and labels
control space between bubbles
Correlogram with linear trend displayed on each scatterplot
Apply colors to each group and customized the scatterplot
Customize the distributions displayed on the diagonal
Most basic dendrogram and clustering with Python and Matplotlib
Horizontal dendrogram
Cut the dendrogram at a specific height
Most basic heatmap with dendrogram with seaborn
Change color palette
Learn how to normalize data in the heatmap, avoiding that kind of result
The most basic density plot one can make with python and seaborn
seaborn densty chart with filled area
vertical seaborn density chart
control density chart color
Changed the bandwidth used to compute the density
Density chart with several displayed groups
Mirror density chart to compare the distribution of 2 variables
Density chart with multiple groups overlapped
Density chart with multiple groups overlapped
Density chart with multiple groups overlapped
Basic density chart with python and matplotlib from a vector of data
Read a csv file and plot the density of a column
Add and customize the labels
Customize the donut section colors
Add blank space between each donut section.
How to make the rectangle colors relative to their values.
The heatmap() function and how to apply it to any kind of data input
Plotting only one half of the heatmap
Seaborn heatmap customization: grid
Seaborn heatmap customization: show numbers in cell
Seaborn heatmap customization: hide the color bar
Color customization: how to select other color palettes and more
A heatmap that probably needs to be normalized.
The exact same heatmap after normalization
Most basic heatmap with dendrogram with seaborn
Change color palette
Learn how to normalize data in the heatmap, avoiding that kind of result
The most basic histogram with seaborn. It adds a density on top by default
Play with the bin parameter to control the number of bars
Remove the density chart on top of the seaborn histogram
Add rug on top of the histogram
Customize the rug: color, size and more
Customize the appearance of the density chart displayed on top of the histogram
How to split the chart window to display a boxplot on top of the histogram
Compare the distribution of several groups on the same chart
Use small multiple to compare the distribution of several groups
Histogram can be added around a scatterplot to show each variable distributions
Histogram can be added around a 2d density chart to show x and y variable distributions
Mirror density chart to compare the distribution of 2 variables
How to build a basic line chart with python from any kind of input format
How to customize the matplotlib line plot appearance: width, stroke, color, style..
Line chart with multiple groups
How to customize the matplotlib line plot appearance: width, stroke, color, style..
Line chart with multiple groups
Line chart and small multiple
Line chart and small multiple (variation)
Most basic lollipop. Will get you started with any kind of input format
Basic circle customizations: shape, size, color and more.
Change lollipop baseline position.
Horizontal lollipop chart (more readable labels).
Highlight some specific groups in a lollipop.
Cleveland chart: a lollipop chart with 2 subgroups displayed under each row.
Lollipop chart with conditional colors.
How to build a very basic violin. Shows example based on different input formats.
Make your violin chart horizontal to make labels more readable
Chart appearance: control violin width, stroke width and more
How to manage colors on a python violin plot
Control the groups order, very useful if you want to display them by increasing mean
If you have both groups and subgroups, you'll be interested in a grouped violin plot
Show the number of observations per group
If you have both groups and subgroups, you'll be interested in a grouped violin plot
Venn Diagram with 3 sets.
Customize Venn Diagram: circles, lables and more.
Change Venn diagram background color.
How to control the color of your treemap
How to make the rectangle colors relative to their values.
Most basic stacked area chart
Most basic stacked area chart
Apply seaborn style on the matplotlib stacked area chart
Control area colors
Area chart and small multiple
Basic boxplot. You can quickly read the median, quartiles and outliers of each group.
If you add individual points with jitter, a bimodal distribution appears for group B
If you have a very large dataset, the violin plot is a better alternative than jittering
Most basic scatterplot with seaborn
Add a regression thanks to the fit_reg parameter
Customize markers: color, size, opacity, type and more
Color dots according to a group
Control X and Y axes limits
Control the marker's color according to their coordinates
Add labels manually on your scatterplot
Scatterplot with marginal distributions
Display a math equation on your matplotlib chart
Matplotlib: add horizontal and vertical ablines
Highlight a specific area of the chart with Matplotlib
Add an annotation to highlight a specific marker
Ridgeline chart with python, matplotlib and seaborn.
Most basic radar chart with python.
Radar chart with several displayed values
Build a correlogram with the pairplot function
Most basic scatterplot with Matplotlib
Customize marker features: size, color, stroke, type and more
Add and customize the labels
Customize the pie section colors
Add blank space between each pie section.
Basic parallel coordinate char
A polar chart with small multiple to compare 4 groups
Most basic network chart with Python and NetworkX
Custom network appearance: color, shape, size, links
Control the layout used for the node location
Manage directed and undirected networks by adding arrows
Control the background color of a network chart
Map a continuous or categoric variable to nodes
Map a color to edges
Most basic hexbin map from geoJson with python.
Compute polygon centroid to add labels.
Most basic connection map with Python and Basemap.
Show connection between a few cities with great circles.
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