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Sign in[Submit an Article](https://write.insightmediagroup.io) [Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/) * * * [Write For TDS](https://towardsdatascience.com/submissions/) [LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search [Data Science](https://towardsdatascience.com/category/data-science/) # Beautiful Boxplots With Statistical Significance Annotation Super short tutorial for boxplots with significance annotation in Python [Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/) July 9, 20206 min read ## Introduction & Motivation I always remember myself reading some nice scientific publications where the authors would have created some nice boxplots with statistical annotations. In most of these cases, a statistical test had been used to determine whether there was a statistically significant difference in the mean value of a specific feature between different groups. I have now managed to create some custom python code to do exactly this: produce beautiful boxplots with statistical annotations integrated. In this short article, I just show how to create such beautiful boxplots in Python. * * * ## The dataset We will use the Iris Dataset as we have done in all my previous posts. The dataset contains **four** **features** (length and width of sepals and petals) of **50** samples of **three** **species** of Iris (Iris **setosa** , Iris **virginica** and Iris **versicolor**). The dataset is often used in data mining, classification and clustering examples and to test algorithms. For reference, here are pictures of the **three flowers species** : Learn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps. ![Figure made by the author.](../images/000.png) Figure made by the author. For this short tutorial, we will be only using **2** out of the 3 classes i.e. the **setosa** and **versicolor** classes. _This is done only for the sake of simplicity._ * * * * **NEW** : After a great deal of hard work and staying behind the scenes for quite a while, we're excited to now offer our expertise through a platform, the "[**Data Science Hub**](https://www.patreon.com/TheDataScienceHub\))" on Patreon (). This hub is our way of providing you with **bespoke consulting services** and comprehensive **responses to all your inquiries** , ranging from Machine Learning to strategic data analytics planning. * * * ## Working example in Python **Step 1** : Let's **load the data** and sub-select the desired **2 flower classes** : text ``` from sklearn.datasets import load_iris import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import numpy as np ``` text ``` # Load the Iris dataset X = load_iris().data y = load_iris().target feature_names = load_iris().feature_names classes_names = load_iris().target_names ``` text ``` # Use only 2 classes for this example mask = y!=2 X,y = X[mask,:], y[mask] ``` text ``` # Get the remained class names classes_names[[0,1]] # array(['setosa', 'versicolor'], dtype=' But how can we know if the mean of g1 (group 1: setosa) was significantly greater or smaller than the mean of g2 (group 2: versicolor)? For this, we need to look at the **t-values.** text ``` print(all_t) [-10.52098626754911, 9.454975848128596, -39.492719391538095, -34.08034154357719] ``` text ``` print(feature_names) ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'] ``` **Interpretation:** * **If the t-value is** _**positive**_**( >0) then the mean of g1 (group 1: setosa) was significantly **_**greater**_**than the mean of g2 (group 2: versicolor).** * **If the t-value is** _**negative**_**( <0) then the mean of g1 (group 1: setosa) was significantly **_**smaller**_**than the mean of g2 (group 2: versicolor).** **Reminder** : _feature _names = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']._ * We can **conclude** that **only the mean value of** _**sepal width**_**of g1 (setosa) was statistically greater than the mean value of** _**sepal width**_**of g2 (versicolor).** * * * **Step 5** : **Check the t-test results** text ``` print(np.count_nonzero(np.array(feature_names)[np.array(all_p) < 0.05])) # 4 ``` **Interpretation:** We can see that there is a statistically significant difference in **all 4 features** between **setosa** and **versicolor** classes. * * * **Step 6** : Here is the magic. Let's create some **beautiful** **boxplots** and **annotate** them with the **estimated** **statistical** **significance**. text ``` # renaming so that class 0 will appear as setosa and class 1 as versicolor df_long.loc[df_long.Group==0, 'Group'] = classes_names[0] df_long.loc[df_long.Group==1, 'Group'] = classes_names[1] ``` text ``` # Boxplots fig, axes = plt.subplots(2,2, figsize=(14,10), dpi=100) axes = axes.flatten() ``` text ``` for idx, feature in enumerate(feature_names): ax = sns.boxplot(x="Feature", hue="Group", y="Value", data = df_long[df_long.Feature == feature], linewidth=2, showmeans=True, meanprops={"marker":"*","markerfacecolor":"white", "markeredgecolor":"black"}, ax=axes[idx]) #* tick params axes[idx].set_xticklabels([str(feature)], rotation=0) axes[idx].set(xlabel=None) axes[idx].set(ylabel=None) axes[idx].grid(alpha=0.5) axes[idx].legend(loc="lower right", prop={'size': 11}) #*set edge color = black for b in range(len(ax.artists)): ax.artists[b].set_edgecolor('black') ax.artists[b].set_alpha(0.8) #* statistical tests x1, x2 = -0.20, 0.20 y, h, col = df_long[df_long.Feature == feature]["Value"].max()+1, 2, 'k' axes[idx].plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col) axes[idx].text((x1+x2)*.5, y+h, "statistically significant", ha='center', va='bottom', color=col) ``` text ``` fig.suptitle("Significant feature differences between setosa and versicolor classes/groups", size=14, y=0.93) plt.show() ``` ![Figure produced by the author.](../images/001.png) Figure produced by the author. ## Conclusions As we can see from the statistical tests, we can **conclude** that **only the mean value of** _**sepal width**_**of group 1 (setosa) was statistically greater than the mean value of** _**sepal width**_**of group 2 (versicolor).** On the other hand, the mean value of **sepal _length, petal length and petal width** of the **Setosa** group_ was **statistically** **smaller** than the **mean** **value** of the **Versicolor** group. _These observations can be also verified by looking at boxplots._ That's all folks! Hope you liked this article! * * * ## Stay tuned & support this effort If you liked and found this article useful, **follow** me to be able to see all my new posts. Questions? Post them as a comment and I will reply as soon as possible. * * * **NEW** : We offer our **expertise** through a platform, the "[**Data Science Hub**](https://www.patreon.com/TheDataScienceHub\))" on Patreon (). This hub is our way of providing you with **bespoke consulting services** and comprehensive **responses to all your inquiries** , ranging from Machine Learning to strategic data analytics planning. * * * ## Latest posts > [**Time-Series Forecasting: Predicting Stock Prices Using Facebook's Prophet Model**](https://towardsdatascience.com/time-series-forecasting-predicting-stock-prices-using-facebooks-prophet-model-9ee1657132b5) > [**ROC Curve Explained using a COVID-19 hypothetical example: Binary & Multi-Class Classification...**](https://towardsdatascience.com/roc-curve-explained-using-a-covid-19-hypothetical-example-binary-multi-class-classification-bab188ea869c) > [**Support Vector Machines (SVM) clearly explained: A python tutorial for classification problems...**](https://towardsdatascience.com/support-vector-machines-svm-clearly-explained-a-python-tutorial-for-classification-problems-29c539f3ad8) > [**PCA clearly explained - How, when, why to use it and feature importance: A guide in Python**](https://towardsdatascience.com/pca-clearly-explained-how-when-why-to-use-it-and-feature-importance-a-guide-in-python-7c274582c37e) > [**Everything you need to know about Min-Max normalization in Python**](https://towardsdatascience.com/everything-you-need-to-know-about-min-max-normalization-in-python-b79592732b79) > [**How Scikit-Learn's StandardScaler works**](https://towardsdatascience.com/how-and-why-to-standardize-your-data-996926c2c832) * * * ## Get in touch with me * **LinkedIn** : * **ResearchGate** : * **EPFL** **profile** : * **Stack** **Overflow** : ## Register Now to Continue Reading ### Unlock Towards Data Science with a Free Account All articles on Towards Data Science are available to registered users. 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