Author Archives

Yan Holtz

Hi! I am Yan Holtz, passionate about data analysis and data visualization. I create the R and the Python graph galleries and hope it helps you!

https://www.linkedin.com/in/yan-holtz-2477534a/?ppe=1

Bad chart

This page is dedicated to the dangerous feature of boxplots. A boxplot summarizes the distribution of a numeric variable for several groups. The problem is than summarizing also means loosing information, and that can become a mistake. If…

2D density plot

#134 How to avoid overplotting with python

Yan Holtz

Overplotting is one of the most common problem in dataviz. When your dataset is big, dots of your scatterplot tend overlap, and your graphic becomes unreadable. This problem is illustrated by the scatterplot beside, realised with Matplotlib (code…

Lineplot

#125 Small multiples for line chart

Yan Holtz

Let’s say you want to realise a line chart with several lines, one for each group of your dataset. Adding all of them on the same plot can quickly lead to a spaghetti plot, and thus provide a…

Lineplot

#124 Spaghetti plot

Yan Holtz

A Spaghetti plot is a line plot with many lines displayed together. The problem is that it is really hard to read, and thus provide few insight about the data. This is well documented here.…

Lineplot

#123 Highlight a line in line plot

Yan Holtz

In order to avoid the creation of a spaghetti plot, it is a good practice to highlight the group(s) that interests you the most in your line plot. It allows the reader to understand your point quickly,…

Matplotlib

#199 Matplotlib style sheets

Yan Holtz

The matplotlib library comes with several built in styles. It is very easy to use them, and allows to improve the quality of your work. To apply a style to your plot, just add: plt.style.use(“my style”). Here is an example that…

Dendrogram

#405 Dendrogram with heatmap and coloured leaves

Yan Holtz

The chart #404 describes in detail how to do a dendrogram with heatmap using seaborn. I strongly advise to read it before doing this chart. Once you understood how to study the structure of your population,…

Dendrogram

#404 Dendrogram with heat map

Yan Holtz

When you use a dendrogram to display the result of a cluster analysis, it is a good practice to add the corresponding heatmap. It allows you to visualise the structure of your entities (dendrogram), and to understand if this structure…

Dendrogram

#402 Color dendrogram labels

Yan Holtz

Using graph #400 and #401 you should be able to build a dendrogram and customise most of its features. But now, you probably want to compare the structure you get with your expectations. In this example we will…

Dendrogram

#401 Customised dendrogram

Yan Holtz

The chart #400 gives the basic steps to realise a dendrogram from a numeric matrix. Here, let’s describe a few customisation that you can easily apply to your dendrogram.

Dendrogram

#400 Basic Dendrogram

Yan Holtz

This page aims to describe how to realise a basic dendrogram with Python. To realise such a dendrogram, you first need to have a numeric matrix. Each line represent an entity (here a car). Each column is…

Radar

#392 Use faceting for Radar chart

Yan Holtz

The charts #390 and #391 show how to make a radar chart with one or few individuals. However, it is not advised to display to many entities on the same spider plot: it would result in an unreadable plot. Instead,…

Radar

#391 Radar chart with several individuals

Yan Holtz

This example follows the chart #390 where only one individual was plotted on the radar chart. Once you understood the method, it is quite easy to apply it to more individuals. Note that if you have more…

Radar

#390 Basic radar chart

Yan Holtz

A radar chart displays the value of several numerical variables for one or several entities. Here is an example of a simple one, displaying the values of 5 variables for only one individual. To my knowledge, there…

Map

#313 Bubble map with Folium

Yan Holtz

This page describes how to add bubbles to your folium map. It is done using the folium.Circle function. Each bubble has a size related to a specific value. Note that if you zoom on the map, the circle will get…

Map

#312 Add markers on folium map

Yan Holtz

This page describes how to add markers to your folium map. It is done using the folium.Marker function. Note that you can custom the popup window of each marker with any html code!

Map

#315 A world map of #surf tweets

Yan Holtz

During 300 days, I harvested every tweet containing the hashtags #surf, #kitesurf and #windsurf. Here is a map showing the localisation of these tweets. This projects is explained more in detail in the blog of the R graph gallery. This…

Map

#292 Choropleth map with Folium

Yan Holtz

Here is an example of a choropleth map made using the Folium library. This example comes directly from the (awesome) documentation of this library. Note that you need 2 elements to build a chloropleth map. i/ A shape file in…

2D density plot

#86 Avoid overlapping in scatterplot with 2D density

Yan Holtz

Consider the scatterplot on the left hand side of this figure. A lot of dots overlap and make the figure hard to read. Even worse, it is impossible to determine how many data points are in each position. In…

2D density plot

#82 Marginal plot with Seaborn

Yan Holtz

A marginal plot allows to study the relationship between 2 numeric variables. The central chart display their correlation. It is usually a scatterplot, a hexbin plot, a 2D histogram or a 2D density plot. The marginal charts,…

2D density plot

#85 Density plot with Matplotlib

Yan Holtz

Let’s consider that you want to study the relationship between 2 numerical variables with a lot of points. Then you can consider the number of points on each part of the plotting area and thus calculate a 2D kernel density…

2D density plot

#84 Hexbin plot with matplotlib

Yan Holtz

A Hexbin plot is useful to represent the relationship of 2 numerical variables when you have a lot of data point. Instead of overlapping, the plotting window is split in several hexbins, and the number of points per hexbin is…

#372 3D PCA result

Yan Holtz

3D scatterplots can be useful to display the result of a PCA, in the case you would like to display 3 principal components. Here is an example showing how to achieve it. Note that the 3 reds…

#370 3D Scatterplot

Yan Holtz

The mplot3D toolkit of Matplotlib allows to easily create 3D scatterplots. Note that most of the customisations presented in the Scatterplot section will work in 3D as well. The result can be a bit disappointing since each…

#371 Surface plot

Yan Holtz

3D plots are awesome to make surface plots. In a surface plot, each point is defined by 3 points: its latitude, its longitude, and its altitude (X, Y and Z). Thus, 2 types of input are possible. i/ A rectangular…

#342 Animation on 3D plot

Yan Holtz

It is possible to create a 3D object with python. See the dedicated section. Once this is done, we can make evolute the angle of view (‘camera position’) and use each image to make an animation. Once more, the image…

Correlogram

#111 Custom correlogram

Yan Holtz

The graph #110 showed how to make a basic correlogram with seaborn. This post aims to explain how to improve it. It is divided in 2 parts: how to custom the correlation observation (for each pair of numeric variable), and…

Correlogram

#110 Basic Correlation matrix with Seaborn

Yan Holtz

Seaborn allows to make a correlogram or correlation matrix really easily. Correlogram are awesome for exploratory analysis: it allows to quickly observe the relationship between every variable of your matrix. It is easy to do it…

Animation

#341 Python Gapminder Animation

Yan Holtz

The TED talk of Hans Rosling on ‘Developing Country’ is probably one of the most famous dataviz ever. It shows the evolution of life Expectancy and GDP per capita of several countries through an animation. (Data found in the gapminder…

Animation

#340 Scatterplot animation

Yan Holtz

The trick used to make animated plots is always the same: realise a set of several images, and display them one after another in a .gif file with Image Magick. Here I do a loop where each iteration make a…

seaborn

#106 Seaborn style on matplotlib plot

Yan Holtz

Matplotlib allows to make absolutely any type of chart, but its style does not look very great. It is possible to benefit the seaborn library style really easily: just the load the seaborn library before your plot!

seaborn

#104 Seaborn Themes

Yan Holtz

The Seaborn python library is well known for its grey background and its general styling. However, note that a few other built in style are available: darkgrid, white grid, dark, white and ticks. Here is how to call them:

seaborn

#101 Make a color palette with Seaborn

Yan Holtz

The post #196 describes how to pick up a single color with matplotlib or seaborn. This post aims to describe a few color palette that are provided, and to explain how to call them in a Seaborn plot. There are…

seaborn

#100 Calling a color with seaborn

Yan Holtz

Calling a color with seaborn works exactly the same way than with matplotlib. Thus, see the dedicated page that gives extensive explanations. However, here is a list of the available colors if you want to call them by their…

Matplotlib

#197 Available color palettes with Matplotlib

Yan Holtz

The post #196 describes how to pick up a single color when working with python and matplotlib. This post aims to describe a few color palette that are provided, and thus make your life easier when plotting several color. There…

Matplotlib

#196 Select one color with matplotlib

Yan Holtz

This section aims to describe how to set a color with the matplotlib library of python. These techniques also work for most of the python libraries, since most of them are build on top of matplotlib. Note that another page…

Matplotlib

#194 Split the graphic window with subplot

Yan Holtz

It can be really useful to split your graphic window in several parts, in order to display several charts in the same time. With matplotlib, this is done through the subplot function. It is possible to display your graphics in…

Map

#310 Basic map with markers

Yan Holtz

This page aims to explain how to add marker on a map made with the base map python library. It supposes you know how to make a basic map with base map, and that you have a pandas data frame…

Map

#300 Draw a connection line with great circle

Yan Holtz

This page describe how to add a connection line between 2 places on a map with python and the basemap library. Here we represent the connection between New York and London. Note that the line is not straight: it is…

Map

#286 Boundaries provided in Basemap

Yan Holtz

The basemap library (closely linked with matplotlib) contains a database with several boundaries. Thus, it is easy to represent the limits of countries, states and counties, without having to load a shape file. Here is how to show these 3…

Map

#285 Use a background layer

Yan Holtz

Instead of colouring your map with uniform colors, you can use layer for your background. Here are a few examples showing the layers that are available using python and basemap.

Map

#284 Different types of map projection

Yan Holtz

The earth being a sphere, it has always been tricky to represent it in 2 dimensions. Several projections exist, all with pro and con, and a few of them are implemented in matplotlib. Pick the one you need!

Map

#283 Set bounding box in basemap

Yan Holtz

Charts #281 and #282 show how to make a world map with desired colors. But what if you want to look at a specific region of the globe? When you initialise the map, you can determine a specific…

Map

#282 Custom appearance of basemap

Yan Holtz

The graph #281 shows how to draw a basic map with basemap. Here, I show how to improve its appearance. Each element that you draw on the map (boundary, continent, coast lines…) can be customised. Moreover, note that it is…

Map

#281 Basic map with basemap

Yan Holtz

Here is the most basic map you can do with the basemap library of python. It allows to understand the basic use of this library. Always start by initialising the map with the Basemap() function. Then, add the elements your…

Network

#325 Map colour to the edges of a Network

Yan Holtz

This chart follows the chart #324 where we learned how to map a color to each nodes of a network. This time, we suppose that we have a feature for each edge of our network. For example, this feature can…

Network

#327 Network from correlation matrix

Yan Holtz

This page explains how to draw a correlation network: a network build on a correlation matrix. Suppose that you have 10 individuals, and know how close they are related to each other. It is possible to represent…

Network

#326 Background colour of network chart

Yan Holtz

You can change the background colour of your network chart with fig.set_facecolor(). Note that you need to add fig.get_facecolor if you want to keep your background colour for your png.

Network

#324 Map a color to network nodes

Yan Holtz

A common task is to color each node of your network chart following a feature of your node (we call it mapping a color). It allows to display more information in your chart. There are 2 possibilities: 1/ The feature…

Network

#323 Directed or Undirected network

Yan Holtz

Network charts can be split in 2 main categories: directed and undirected networks. If it is directed, there is a notion of flow between the 2 nodes, thus leaving a place to go somewhere else. Like money goes from…

Network

#322 Network layout possibilities

Yan Holtz

Chart #320 and #321 explain how to realise a basic network chart and custom its appearance. The next step is to control the layout of your network. There is actually an algorithm that calculate the most optimal position of each…

Network

#321 Custom NetworkX graph appearance

Yan Holtz

The chart #320 explain how to realise a basic network chart. Now, let’s have a look to the arguments that allows to custom the appearance of the chart. The customisations are separated in 3 main categories: nodes, node labels and…

Network

#320 Basic Network from pandas data frame

Yan Holtz

This example is probably the most basic network chart you can realise. A network chart is constituted by nodes. These nodes are interconnected by edges. So a basic format is a data frame where each line describes…

Matplotlib

#220 Sankey diagram with Matplotlib

Yan Holtz

The Matplotlib library has a module that allows to make basic Sankey Diagrams. This is an example coming from the corresponding documentation where you will find more examples of this type. I strongly advise you to have…

Matplotlib

#193 Annotate matplotlib chart

Yan Holtz

Matplotlib

#192 About Matplotlib margins

Yan Holtz

Here are a few thoughts concerning margins management in a Matplotlib chart. As you can see on the left chart, expanding the margins of your plot can be necessary to make the axis labels fully readable. This is easy fix…

Matplotlib

#191 Custom axis on matplotlib chart

Yan Holtz

This page describes several customisations you can apply on the axis of your matplotlib chart. These examples are applied on the X axis but can naturally be imitated for the Y axis!

Matplotlib

#190 Custom Matplotlib title

Yan Holtz

This page aims to explain how to add a title to your python chart, and how to customise it. This is done through the title function of Matplotlib.

Barplot

#10 Barplot with number of observation

Yan Holtz

This page explains how to display the number of observation per group on a barplot. Just use the text function of matplotlib to add the label one by one in a loop. If you need to add the…

Barplot

#7 Custom barplot layout

Yan Holtz

Here are 3 examples of customisation you could probably need for your barplots.

Map

#288 Map background with folium

Yan Holtz

Folium is a python library allowing to call the Leaflet.js Javascript library. It allows you to manipulate your data with python and map them using the power of leaflet! It is really easy to call a map using this library.…

Bubble

#272 Map a color to bubble plot

Yan Holtz

A bubble plot is a scatterplot with a third dimension: the size of the markers. It is even possible to add a fourth dimension using colors. this is possible using the ‘cmap‘ argument of…

Bubble

#271 Custom your bubble plot

Yan Holtz

The chart #270 describes how to draw a basic bubble plot with matplotlib. This page aims to describe how to custom several features of your bubble plot.

Bubble

#270 Basic Bubble plot

Yan Holtz

A bubble plot is very close to a scatterplot. With Matplotlib, we will construct them using the same scatter function. However, we will use the ‘s‘ argument to map a third numerical variable to the size…

CircularPlot

#231 Chord diagram with Bokeh

Yan Holtz

This example is a chord diagram that comes from the Bokeh Documentation.

Matplotlib

#255 Percentage stacked area chart

Yan Holtz

A percentage stacked area chart is very close from a classic stacked area chart. However, values are normalised to make in sort that the sum of each group is 100 at each position on the X…

Panda

#254 Pandas Stacked area chart

Yan Holtz

With pandas, the stacked area charts are made using the plot.area() function. Each column of your data frame will be plotted as an area on the chart.

Matplotlib

#253 Control the color in stacked area chart

Yan Holtz

Once you understand how to create a stacked area chart and which baseline option to choose, it’s highly recommended to custom the colors of your chart. Here I propose 2 solutions: the first consists to use a common color palette,…

Matplotlib

#252 Baseline options for stacked area chart

Yan Holtz

The stackplot function of Matplotlib allows to make stacked area chart. It provides a baseline argument that allow to custom the position of the areas around the baseline. Four possibilities exist, and are represented here. This…

Matplotlib

#251 Stacked area chart with seaborn style

Yan Holtz

The graph #250 describes how to realise a stacked area chart with matplotlib. As usual, you can benefit the seaborn style just by loading the library at the beginning of your code: …

Matplotlib

#250 Basic Stacked Area Chart

Yan Holtz

This page describes how to realise a stacked area chart using the Matplotlib library of python. This is done using the stackplot() function. There are 2 ways to use this function, as described below:…

Area

#243 Area chart with white grid

Yan Holtz

Another example of area chart using the white grid seaborn style. This chart has been found on stack overflow, proposed by mwaskom.

Area

#242 Area chart and faceting

Yan Holtz

I believe that area charts are especially useful when used through faceting. It allows to quickly find out the different patterns existing in the data. This example relies on a pandas data frame. We have 2 numerical variables (year and…

Area

#241 Improve area chart

Yan Holtz

The chart #240 describes how to make a basic area chart. This page aims to describe a few customisation you can apply to your area chart. It is highly advised to change the default colour of matplotlib. Moreover, it is…

Area

#240 basic area chart

Yan Holtz

An area chart can easily be done using python and matplotlib. Note that there are 2 main functions allowing to draw them. I advise to use the fill_between function that allows easier customisation. The stackplot function…

Matplotlib

#174 Change background colour of Venn diagram

Yan Holtz

This is just a short example showing how to custom the background colour of your Venn diagram.

Matplotlib

#173 Elaborated Venn diagram

Yan Holtz

This plot comes from the Matplotlib documentation. I think it describes very well the possibilities offered by the matplotlib library in term of Venn diagram. Thanks to the Matplotlib team for providing such a tool…

Matplotlib

#172 Custom Venn diagram

Yan Holtz

The charts #170 and #171 show how to make 2 and 3 groups Venn diagrams respectively. This page aims to give a few keys to custom these graphics. The code of the 3 above examples is provided. The first…

Matplotlib

#171 Basic Venn diagram with 3 groups

Yan Holtz

Here is a basic example of a Venn diagram with 3 groups realised using the Matplotlib library.

Matplotlib

#170 Basic Venn diagram with 2 groups

Yan Holtz

There are 2 main methods to make a Venn diagram with the matplotlib library, leading to the same result. The first consists to give directly the size of your group and their intersection. The second one consists…

Lollipop

#185 Lollipop plot with conditional color

Yan Holtz

This page explain how to have several colors on the same lollipop plot, and how to make this color depends the Y value. Here the color is blue if the value is under 0, orange if…

Lollipop

#184 Lollipop plot with 2 groups

Yan Holtz

This variation of lollipop chart is really useful if you have 2 observations for each groups. Instead of displaying the values of both groups one beside each other, show them on the same line and represent…

Lollipop

#183 Highlight a group in lollipop

Yan Holtz

The graphic #182 shows and explains how to make a vertical lollipop plot. In this chart we add a useful feature for storytelling: highlight a group that specifically interests you with a bigger size and another color!…

Lollipop

#182 Vertical lollipop plot

Yan Holtz

When a lollipop plot shows the relationship between a numerical and a categorical variable. I find it better to order the groups on a decreasing order and represent them vertically. Here is how the code works,…

Lollipop

#181 Custom lollipop plot

Yan Holtz

The graph #180 explains how to make a lollipop plot with Matplotlib, whatever the shape of your data. This page aims to describe the customization you can apply to the 3 main parts: the stem, the markers and the baseline. Note…

Lollipop

#180 Basic lollipop plot

Yan Holtz

This page aims to explain the basic tricks allowing to realize a lollipop plot with matplotlib. Here is a first example with 2 numerical variables, one for each axis. A lollipop plot can be created 1: using…

2D density plot

#83 basic 2D Histograms with matplotlib

Yan Holtz

This page is dedicated to 2D histograms made with matplotlib, through the hist2D function. 2D histograms are useful when you need to analyse the relationship between 2 numerical variables that have a huge number of values. It avoids the over…

2D density plot

#80 Contour plot with seaborn

Yan Holtz

Here are 3 contour plots made using the seaborn python library. You have to provide 2 numerical variables as input (one for each axis). The function will calculate the kernel density estimate and represent it as a contour plot or…

Panda

#150 Parallel plot with pandas

Yan Holtz

Here is an example of a basic parallel plot using the pandas library

Donut

#163 Donut plot with subgroups

Yan Holtz

Here is a donut plot with 3 groups and several subgroups for each. You can set the position of the 2 circle levels using the radius and width options. Then, the idea is to attribute a color palette for each…

Donut

#162 Change background of donut plot

Yan Holtz

Here is a tip to change the background color of your donut chart with matplotlib.

Donut

#161 Custom matplotlib donut plot

Yan Holtz

This page aims to describe how to improve the features of your basic donut plot. It shows how to custom color, labels, and wedges. These first line of code are common to every next examples…

Donut

#160 Basic donut plot

Yan Holtz

Here is a basic donut (?doughnut) plot made using the matplotlib library. The trick here is to make a pieplot and add a white circle in the middle of it. Note that another option would be…

TreeMap

#202 Treemap with colors mapped on values

Yan Holtz

Here is a tip to map a color on the values of each group of a treemap.

TreeMap

#201 Control the color of Treemap

Yan Holtz

This page aims to describe the customization you can apply on a treemap. You can easily change the color of each group using the following code:

TreeMap

#200 Basic Treemap with python

Yan Holtz

Here is a basic treemap example. It relies on the squarify library. This treemap only has one group level. The input is just a set of numeric value. Each square will be as big as…

Wordcloud

#262 Worcloud with specific shape

Yan Holtz

You can use an external image as a mask to give a specific shape to your wordcloud. You need to have the image in your current directory and give it to the wordcloud function. …

Wordcloud

#261 Custom python wordcloud

Yan Holtz

The chart #260 aims to describe how to perform a basic wordcloud. This page shows how to customize it. Here are a few tips. Note that each chart always starts the same way: …

Wordcloud

#260 Basic wordcloud

Yan Holtz

Wordclouds can easily be done in python using the wordcloud library. Here is a really basic example

Panda

#140 Basic pieplot with panda

Yan Holtz

Here is a first pieplot example using python and the panda library. Warning! Pieplots are a highly unadvised way to represent data. Read the intro of the pieplot section to learn more. …

Connected Scatter

#132 Basic connected scatterplot

Yan Holtz

Connected scatterplots are just a mix between scatterplots and linecharts. They are made with the plot function of matplotlib. If you want to custom them, just check the scatter and line sections! …

Matplotlib

#131 Custom a Matplotlib scatterplot

Yan Holtz

Matplotlib allows to make scatter plots with python using the plot function. This page aims to provide a few elements of customization. For every example, we need a few libraries and to create a dataset:

Matplotlib

#130 Basic Matplotlib scatterplot

Yan Holtz

This is a basic scatterplot example made with the plot function of Matplotlib. Once you understood how this works, see plot #131 for more customization.

Lineplot

#122 Multiple lines chart

Yan Holtz

Graphics #120 and #121 show you how to create a basic line chart and how to apply basic customization. This posts explains how to make a line chart with several lines. Each line represents a set of values, for example…

Lineplot

#121 Line chart customization

Yan Holtz

The graph #120 explains how to create a linechart. This post aims to show how to customize it, feature per feature. To realize the following examples, we first need to import libraries and create data:

Lineplot

#120 Line chart with matplotlib

Yan Holtz

A line chart or line graph is a type of chart which displays information as a series of data points called ‘markers’ connected by straight line segments. It is similar to a scatter plot except that the measurement points are…

seaborn

#58 Show number of observation on violinplot

Yan Holtz

It is a good practice to specify the number of observation for each group of your violinplot. Indeed, a group with 10 observation can look the same as a group with 10000, what can highly…

seaborn

#55 Control order of groups in violinplot | seaborn

Yan Holtz

Here are 2 tips to order your seaborn violinplot.

seaborn

#54 Grouped violinplot

Yan Holtz

If you have one numerical variable, several groups, and subgroups, you probably need to make a grouped violinplot. Note that you can use faceting as well to solve this kind of dataset. …

seaborn

#53 Control color of seaborn violinplot

Yan Holtz

Color is probably the first feature you want to control on your seaborn violinplot. Here I give 4 tricks to control it:

seaborn

#52 Custom seaborn violinplot

Yan Holtz

You can custom some features of seaborn violinplots. Here are 2 examples showing how to change linewidth (left) and general width of each group (right).

seaborn

#51 Horizontal violinplot

Yan Holtz

Once you know how to make a violinplot with seaborn, it is quite straightforward to turn it horizontal.

seaborn

#50 Basic violinplot and input formats

Yan Holtz

This post aims to describe how to realize a basic violinplot. It explains how your input must be formated and which function of seaborn you need to use. Three input formats exist to draw a violinplot:

Density

#74 Density plot of several variables

Yan Holtz

Sometimes it is useful to plot the distribution of several variables on the same plot to compare them. This is possible using the kdeplot function of seaborn several times:

Density

#73 Control bandwidth of seaborn density plot

Yan Holtz

These 2 density plots have been made using the same data. Only the bandwidth changes from 0.5 on the left to 0.05 on the right. This is controlled using the bw argument of the kdeplot function (seaborn library).

Density

#72 Horizontal density plot

Yan Holtz

Once you understood how to build a basic density plot with seaborn, it is really easy to turn it horizontal.

Density

#71 Density plot with shade | seaborn

Yan Holtz

Once you understood how to build a basic density plot with seaborn, it is really easy to add a shade under the line:

Density

#70 Basic density plot with seaborn

Yan Holtz

With seaborn, a density plot is made using the kdeplot function. As input, density plot need only one numerical variable. See how to use this function below:

Heatmap

#94 Use normalization on seaborn heatmap

Yan Holtz

Sometimes, a normalization step is necessary to find out patterns in your heatmap. Check the left heatmap: an individual has higher values than others. Thus, he absorbs all the color variation: his column appears yellow and the rest of the…

Heatmap

#92 Control color in seaborn heatmaps

Yan Holtz

Once you understood how to make a heatmap with seaborn and how to make basic customization, you probably want to control the color palette. This is a crucial step since the message provided by your heatmap can be different following…

Heatmap

#91 Customize seaborn heatmap

Yan Holtz

The graph #90 explains how to make a heatmap from 3 different input formats. In this post, I describe how to customize the appearance of these heatmaps. These 4 examples start by importing libraries and making a data frame:

Heatmap

#90 Heatmaps with various input format

Yan Holtz

This post explains how to make heatmaps with python and seaborn. Three main types of input exist to plot heatmap, let’s study them one by one.

Scatterplot

#46 Add text annotation on scatterplot

Yan Holtz

Scatterplot

#45 Control color of each marker | seaborn

Yan Holtz

The graph #41 shows how to custom the features of markers and the #43 shows how to map a categorical value to a color. But sometimes, you need to have a more precise control on…

Scatterplot

#44 Control axis limits of plot | seaborn

Yan Holtz

Control the limits of the X and Y axis of your plot using the matplotlib function plt.xlim and plt.ylim.

Scatterplot

#43 Use categorical variable to color scatterplot | seaborn

Yan Holtz

Once you understood how to make a basic scatterplot with seaborn and how to custom shapes and color, you probably want the color corresponds to a categorical variable (a group). This is possible using the hue argument: it’s here that…

Scatterplot

#42 Custom linear regression fit | seaborn

Yan Holtz

You can custom the appearance of the regression fit proposed by seaborn. In this example, color, transparency and width are controlled through the line_kws={} option.

Scatterplot

#41 Control marker features

Yan Holtz

Once you understood how to plot a basic scatterplot with seaborn, you probably want to custom the appearance of your markers. You can custom color, transparency, shape and size. Here is how to do it: Control shape List of available…

Scatterplot

#40 Basic scatterplot | seaborn

Yan Holtz

Using seaborn, scatterplots are made using the regplot() function. Here is an example showing the most basic utilization of this function. You have to provide at least 2 lists: the positions of points on the X and Y axis. By…

Boxplot

#38 Show number of observation on boxplot

Yan Holtz

Boxplot is an amazing way to study distributions. However, it can be useful to display the number of observation for each group since this info is hidden under boxes.

Boxplot

#36 Add jitter over boxplot | seaborn

Yan Holtz

Boxplot is an amazing way to study distributions. However, note that different type of distribution can be hidden under the same box. Thus, it is highly advised to display every observations over your boxplot, to be…

Boxplot

#35 Control order of boxplot

Yan Holtz

Boxplots often give more information if you order group in a specific order. This is feasible with seaborn. Here are 2 examples explaining the 2 main needs you can have:

Boxplot

#34 Grouped Boxplot

Yan Holtz

Grouped boxplot are used when you have a numerical variable, several groups and subgroups. It is easy to realize one using seaborn.Y is your numerical variable, x is the group column, and hue is the subgroup…

Boxplot

#33 Control colors of boxplot | seaborn

Yan Holtz

This post gives 5 tips to manage the color of your seaborn boxplot:

Non classé

#32 Custom boxplot appearance | seaborn

Yan Holtz

This post aims to describe 3 customizations you can apply to your seaborn boxplot:

Boxplot

#31 Horizontal boxplot with seaborn

Yan Holtz

It is quite straight forward to turn your boxplot horizontal with seaborn. You can switch your x and y attributes, or use the option ‘orient=”h”‘

Boxplot

#30 Basic Boxplot with Seaborn

Yan Holtz

This page aims to explain how to plot a basic boxplot with seaborn. Boxplot are made using the … boxplot() function! Three types of input can be used to make a boxplot:

Histogram

#25 Histogram with several variables | Seaborn

Yan Holtz

If you have several numeric variables and want to visualize their distributions together, you have 2 options: plot them on the same axis (left), or split your windows in several parts (faceting, right). The first option is nicer if you…

Histogram

#24 Histogram with a boxplot on top | seaborn

Yan Holtz

This chart is mainly based on seaborn but necessitates matplotlib as well, to split the graphic window in 2 parts. If you need to learn how to custom individual charts, visit the histogram and boxplot sections.

Histogram

#23 Vertical Histogram

Yan Holtz

It is quite straightforward to make your histogram vertical with seaborn, just add vertical=True as an option.

Histogram

#21 Control rug and density on seaborn histogram

Yan Holtz

By default, the displot function of seaborn plots an histogram with a density curve (see graph #20). You can easily remove the density using the option kde=”False”. You can also control the presence of rugs using rug=”True”. You can custom…

Histogram

#20 Basic histogram | Seaborn

Yan Holtz

With Seaborn, histograms are made using the distplot function. You can call the function with default values (left), what already gives a nice chart. Do not forget to play with the number of bins using the ‘bins’ argument. It is…

Area

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