{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Kurtis Pykes",
  "title_listed": "3 Aesthetically Pleasing Ways to Represent Numerical Data in Python",
  "keywords": "数据可视化,Python",
  "domain": "数据处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "部分免费/会员墙",
  "url": "https://towardsdatascience.com/3-aesthetically-pleasing-ways-to-represent-numerical-data-in-python-ebc3572bd7db/",
  "publish_date": "2022-03",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "bf08e832b761",
  "collected_at": "2026-10-02T00:08:43",
  "title": "3 Aesthetically Pleasing Ways to Represent Numerical Data in Python | Towards Data Science",
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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# 3 Aesthetically Pleasing Ways to Represent Numerical Data in Python\nThe art of data visualization\n[Kurtis Pykes](https://towardsdatascience.com/author/kurtispykes/)\nMarch 31, 20225 min read\n![Photo by Firmbee.com on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/03/0vb-15AmuZg0Mazix.png)\nPhoto by [Firmbee.com](https://unsplash.com/@firmbee?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nPresenting data in an easy-to-read format is vital for proper interpretation; We often leverage data visualization techniques to arrange data such that the information it contains is clearly obtainable from a visual perspective. This is because it's more natural for us to comprehend visual information - we better recognize trends, patterns, outliers, etc when it's presented to us visually.\nKnowing the optimal way to visualize your data can be challenging. There's several ways to represent a piece of information, but some are more informative than others in certain scenarios. Having a clear question you're trying to answer with the visualization is a step in the right direction. From then on, all you must do is select the graph that emphasizes the information you wish to investigate.\nOur focus for this article will be on numerical data. We're going to cover three common graphs you could use to represent numerical data. We will also cover their usage, how to interpret them, and implement them in Python.\ntext\n\n```\nTable of Contents\n--> The data\n--> Scatter plot \n--> Histogram\n--> Box Plot\n```\n\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 [Data Analyst](https://roadmap.sh/data-analyst) roadmaps.\n## The data\nTo demonstrate some of the graphs we will cover in this article, we've generated a dataset using scikit-learn's `[make_regression](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html)` function.\nOur dataset will consist of one hundred samples and four features of which four are informative. We've also made the standard deviation of the gaussian noise applied to the output ten, and set the `random_state` to twenty-five - but these are just random decisions.\ntext\n\n```\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import make_regression\n```\n\ntext\n\n```\n# Generate a regression problem\nX, y = make_regression(\n    n_samples=100,\n    n_features=4,\n    n_informative=4,\n    noise = 10,\n    random_state=25\n    )\n```\n\ntext\n\n```\n# view first 5 observations\nprint(X[:5])\n```\n\ntext\n\n```\n\"\"\"\narray([[-0.30756146,  0.88820421, -0.50315008,  0.89552899],        [ 0.49830983, -1.35889534, -0.53652108, -1.14732055],        [-0.58739664,  1.02036419,  0.56016947,  0.04803703],        [-0.92340049, -0.56273293,  0.00384206, -0.41429594],        \n[ 0.46763613, -0.01500773,  0.02000411, -0.5443902 ]])\n\"\"\"\n```\n\n## Scatter plot\nScatter plots are a great tool for visualizing relationships between two numerical variables. We can use it to better understand how one variable impacts another if it does. Each dot in the plot is used to represent an individual observation.\ntext\n\n```\n# generate line of best fit\nm, b = np.polyfit(X[:, 2], y, 1)\nbest_fit = m*X + b\n```\n\ntext\n\n```\n# plotting\nplt.subplots(figsize=(8, 5))\nplt.scatter(X[:, 2], y, color=\"blue\")\nplt.plot(X, best_fit, color=\"red\")\nplt.title(\"The impact of feature at index 2 on target label\")\nplt.xlabel(\"Feature at index 2\")\nplt.ylabel(\"Target label\")\nplt.show()\n```\n\n![Image By Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/03/1sSRsNT2MaHPapF7kCJyMUw.png)\n**Image By Author**\nWe are mainly focused on the relationship between two variables. This means the main question you're answering with a scatter plot is whether there's a relationship.\nAs you may know from the relationships you have with your colleagues, friends, and family, not all relationships are the same. If there is a relationship between the two variables, you can begin to dissect the dynamics of the relationship for greater insight into the data.\nSome things you may want to know about your variables are: → The strength of the relationship → Whether the relationship is positive or negative → Whether the relationship is linear or non-linear → If there's any outliers present\nThe image above shows our dataset has a positive, fairly strong, linear relationship. It also doesn't look as though there's any outliers in our data but we can double-check with a [box plot](https://towardsdatascience.com/3-aesthetically-pleasing-ways-to-represent-numerical-data-in-python-ebc3572bd7db/#8461).\n## Histogram\nYou may remember histograms from your high school maths class; Histograms are probably the most commonly used plot for representing statistical data. They're a great way to learn the general distribution of a numerical feature in a dataset.\nIt works by binning the data into various intervals. Each observation is then put into an appropriate interval which can be seen by the height of each bar. The histogram can then be used to learn the density of the data we're working with and how to describe the distribution of the variable.\ntext\n\n```\nplt.subplots(figsize=(8, 5))\nplt.hist(X[:, 0], bins=10) #10 bins is the default\nplt.xlabel(\"Feature at index 0\")\nplt.title(\"Distribution of feature at index 0\")\nplt.ylabel(\"Number of occurences\")\nplt.show()\n```\n\n![Image By Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/03/1Cs6h7Sz3OxrhUcDlK4RZkw.png)\n**Image By Author**\nSome things to pay attention to include the **skewness** and **modality** :\n  * **Skewness** is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean [**Source** : [Wikipedia](https://en.wikipedia.org/wiki/Skewness)]. The skewness of a feature may be defined by one of four types of skew: **positive/right-skew** , **negative/left-skew, symmetric/no skew, or undefined.**\n  * The **modality** describes the number of peaks in a dataset. A dataset may be: **unimodal** which means it has only one prominent peak in the data distribution, **bimodal** which means the distribution has two prominent peaks, **uniform** to say there are no prominent peaks, or **multimodal** to say the distribution has more than two prominent peaks.\n\n\nThe data we've visualized looks as though it's symmetrical and unimodal.\nAnother thing to note is that the bin width we've selected for our histogram is completely random. Adjusting the number of bins used to represent the data can alter the story being told by the histogram: too wide and you lose information, too narrow and it becomes increasingly difficult to determine the overall distribution. The ideal bin width is subjective to the problem.\n## Box plots\nA boxplot is another great graph to gain a better understanding of the spread of the data. They do a great job of highlighting outliers, the interquartile range, and median of a group. In addition, much less space is used so it makes it easier to compare distributions between various groups on the same graph.\ntext\n\n```\nplt.subplots(figsize=(8, 5))\nplt.boxplot(X, vert=False)\nplt.ylabel(\"Features\")\nplt.show()\n```\n\n![Image By Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/03/1XLGAS8SclCYuYh1bqd1VZA.png)\n**Image By Author**\nWe can use our boxplot to determine the skewness of each feature, the outliers, as well as the minimum and maximum of a group.\nIn the visualization of our data, we can determine that all of our features are symmetrical. We can also see that our first and fourth features have outliers present in them which will require further investigation in a real-world scenario.\n* * *\nData visualization is vital for efficiently and effectively sharing insights. It's of immense necessity that our visualizations are easy to understand for the audience they're designed for. A key component to achieve this is by using your graphs to answer questions the audience is interested in learning. While we didn't share a comprehensive list of graphs in this article, you now have a good foundation to get started with sharing good quality visual insights.\n_Thanks for reading._\nIf you enjoy reading stories like this one and wish to support my writing, consider [becoming a Medium member](https://kurtispykes.medium.com/membership). With a $5 a month commitment, you unlock unlimited access to stories on Medium. 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