{ "source_file": "0930", "source_platform": "Medium", "author": "Serafeim Loukas, PhD", "title_listed": "Beautiful Boxplots With Statistical Significance Annotation", "keywords": "", "domain": "数据处理", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/beautiful-boxplots-with-statistical-significance-annotation-e1b314927fc5/", "publish_date": "2020-07-09", "corpus_stage": "", "notes": "", "id": "8091ed0ebd9b", "collected_at": "2026-10-02T08:20:10", "title": "Beautiful Boxplots With Statistical Significance Annotation | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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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.\nI 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.\n* * *\n## The dataset\nWe 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.\nFor reference, here are pictures of the **three flowers species** :\nLearn 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.\n![Figure made by the author.](../images/000.png)\nFigure made by the author.\nFor 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._\n* * *\n * **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.\n\n\n* * *\n## Working example in Python\n**Step 1** : Let's **load the data** and sub-select the desired **2 flower classes** :\ntext\n\n```\nfrom sklearn.datasets import load_iris\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport numpy as np\n```\n\ntext\n\n```\n# Load the Iris dataset\nX = load_iris().data\ny = load_iris().target\nfeature_names = load_iris().feature_names\nclasses_names = load_iris().target_names\n```\n\ntext\n\n```\n# Use only 2 classes for this example\nmask = y!=2\nX,y = X[mask,:], y[mask]\n```\n\ntext\n\n```\n# Get the remained class names\nclasses_names[[0,1]]\n# 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)?\nFor this, we need to look at the **t-values.**\ntext\n\n```\nprint(all_t)\n[-10.52098626754911, 9.454975848128596, -39.492719391538095, -34.08034154357719]\n```\n\ntext\n\n```\nprint(feature_names)\n['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n```\n\n**Interpretation:**\n * **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).**\n * **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).**\n\n\n**Reminder** : _feature _names = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']._\n * 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).**\n\n\n* * *\n**Step 5** : **Check the t-test results**\ntext\n\n```\nprint(np.count_nonzero(np.array(feature_names)[np.array(all_p) < 0.05]))\n# 4\n```\n\n**Interpretation:** We can see that there is a statistically significant difference in **all 4 features** between **setosa** and **versicolor** classes.\n* * *\n**Step 6** : Here is the magic. Let's create some **beautiful** **boxplots** and **annotate** them with the **estimated** **statistical** **significance**.\ntext\n\n```\n# renaming so that class 0 will appear as setosa and class 1 as versicolor\ndf_long.loc[df_long.Group==0, 'Group'] = classes_names[0]\ndf_long.loc[df_long.Group==1, 'Group'] = classes_names[1]\n```\n\ntext\n\n```\n# Boxplots\nfig, axes = plt.subplots(2,2, figsize=(14,10), dpi=100)\naxes = axes.flatten()\n```\n\ntext\n\n```\nfor idx, feature in enumerate(feature_names):\n 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])\n #* tick params\n axes[idx].set_xticklabels([str(feature)], rotation=0)\n axes[idx].set(xlabel=None)\n axes[idx].set(ylabel=None)\n axes[idx].grid(alpha=0.5)\n axes[idx].legend(loc=\"lower right\", prop={'size': 11})\n\n #*set edge color = black\n for b in range(len(ax.artists)):\n ax.artists[b].set_edgecolor('black')\n ax.artists[b].set_alpha(0.8)\n\n #* statistical tests\n x1, x2 = -0.20, 0.20\n y, h, col = df_long[df_long.Feature == feature][\"Value\"].max()+1, 2, 'k'\n axes[idx].plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col)\n axes[idx].text((x1+x2)*.5, y+h, \"statistically significant\", ha='center', va='bottom', color=col)\n```\n\ntext\n\n```\nfig.suptitle(\"Significant feature differences between setosa and versicolor classes/groups\", size=14, y=0.93)\nplt.show()\n```\n\n![Figure produced by the author.](../images/001.png)\nFigure produced by the author.\n## Conclusions\nAs 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).**\nOn 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.\n_These observations can be also verified by looking at boxplots._\nThat's all folks! Hope you liked this article!\n* * *\n## Stay tuned & support this effort\nIf you liked and found this article useful, **follow** me to be able to see all my new posts.\nQuestions? Post them as a comment and I will reply as soon as possible.\n* * *\n**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.\n* * *\n## Latest posts\n> [**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)\n> [**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)\n> [**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)\n> [**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)\n> [**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)\n> [**How Scikit-Learn's StandardScaler works**](https://towardsdatascience.com/how-and-why-to-standardize-your-data-996926c2c832)\n* * *\n## Get in touch with me\n * **LinkedIn** : \n * **ResearchGate** : \n * **EPFL** **profile** : \n * **Stack** **Overflow** : \n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[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/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Beautiful Boxplots With Statistical Significance Annotation\nSuper short tutorial for boxplots with significance annotation in Python\n[Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\nJuly 9, 20206 min read\n## Introduction & Motivation\nI 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.\nI 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.\n* * *\n## The dataset\nWe 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.\nFor reference, here are pictures of the **three flowers species** :\nLearn 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.\n![Figure made by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/1_3_b0-hu6xUKLeFAsRUtCg.png)\nFigure made by the author.\nFor 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._\n* * *\n * **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.\n\n\n* * *\n## Working example in Python\n**Step 1** : Let's **load the data** and sub-select the desired **2 flower classes** :\ntext\n\n```\nfrom sklearn.datasets import load_iris\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport numpy as np\n```\n\ntext\n\n```\n# Load the Iris dataset\nX = load_iris().data\ny = load_iris().target\nfeature_names = load_iris().feature_names\nclasses_names = load_iris().target_names\n```\n\ntext\n\n```\n# Use only 2 classes for this example\nmask = y!=2\nX,y = X[mask,:], y[mask]\n```\n\ntext\n\n```\n# Get the remained class names\nclasses_names[[0,1]]\n# 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)?\nFor this, we need to look at the **t-values.**\ntext\n\n```\nprint(all_t)\n[-10.52098626754911, 9.454975848128596, -39.492719391538095, -34.08034154357719]\n```\n\ntext\n\n```\nprint(feature_names)\n['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n```\n\n**Interpretation:**\n * **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).**\n * **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).**\n\n\n**Reminder** : _feature _names = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']._\n * 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).**\n\n\n* * *\n**Step 5** : **Check the t-test results**\ntext\n\n```\nprint(np.count_nonzero(np.array(feature_names)[np.array(all_p) < 0.05]))\n# 4\n```\n\n**Interpretation:** We can see that there is a statistically significant difference in **all 4 features** between **setosa** and **versicolor** classes.\n* * *\n**Step 6** : Here is the magic. Let's create some **beautiful** **boxplots** and **annotate** them with the **estimated** **statistical** **significance**.\ntext\n\n```\n# renaming so that class 0 will appear as setosa and class 1 as versicolor\ndf_long.loc[df_long.Group==0, 'Group'] = classes_names[0]\ndf_long.loc[df_long.Group==1, 'Group'] = classes_names[1]\n```\n\ntext\n\n```\n# Boxplots\nfig, axes = plt.subplots(2,2, figsize=(14,10), dpi=100)\naxes = axes.flatten()\n```\n\ntext\n\n```\nfor idx, feature in enumerate(feature_names):\n 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])\n #* tick params\n axes[idx].set_xticklabels([str(feature)], rotation=0)\n axes[idx].set(xlabel=None)\n axes[idx].set(ylabel=None)\n axes[idx].grid(alpha=0.5)\n axes[idx].legend(loc=\"lower right\", prop={'size': 11})\n\n #*set edge color = black\n for b in range(len(ax.artists)):\n ax.artists[b].set_edgecolor('black')\n ax.artists[b].set_alpha(0.8)\n\n #* statistical tests\n x1, x2 = -0.20, 0.20\n y, h, col = df_long[df_long.Feature == feature][\"Value\"].max()+1, 2, 'k'\n axes[idx].plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col)\n axes[idx].text((x1+x2)*.5, y+h, \"statistically significant\", ha='center', va='bottom', color=col)\n```\n\ntext\n\n```\nfig.suptitle(\"Significant feature differences between setosa and versicolor classes/groups\", size=14, y=0.93)\nplt.show()\n```\n\n![Figure produced by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/1nVu-LWv8dt4coLQwtT_J5A.png)\nFigure produced by the author.\n## Conclusions\nAs 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).**\nOn 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.\n_These observations can be also verified by looking at boxplots._\nThat's all folks! Hope you liked this article!\n* * *\n## Stay tuned & support this effort\nIf you liked and found this article useful, **follow** me to be able to see all my new posts.\nQuestions? Post them as a comment and I will reply as soon as possible.\n* * *\n**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.\n* * *\n## Latest posts\n> [**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)\n> [**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)\n> [**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)\n> [**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)\n> [**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)\n> [**How Scikit-Learn's StandardScaler works**](https://towardsdatascience.com/how-and-why-to-standardize-your-data-996926c2c832)\n* * *\n## Get in touch with me\n * **LinkedIn** : \n * **ResearchGate** : \n * **EPFL** **profile** : \n * **Stack** **Overflow** : \n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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