{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Tirthajyoti Sarkar",
  "title_listed": "Spearman coefficient: Tool for a generalized correlation analysis",
  "keywords": "",
  "domain": "数据处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/spearman-coefficient-tool-for-a-generalized-correlation-analysis-d15b70d4ff1e/",
  "publish_date": "2021-11-01",
  "corpus_stage": "",
  "notes": "",
  "id": "9df7ae5eb3a3",
  "collected_at": "2026-10-02T09:09:51",
  "title": "Spearman coefficient: Tool for a generalized correlation analysis | 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# Spearman coefficient: Tool for a generalized correlation analysis\nLinear relationships are not all a correlation analysis can reveal. We discuss rank-based correlation that is more generalized and...\n[Tirthajyoti Sarkar](https://towardsdatascience.com/author/tirthajyoti/)\nNovember 1, 20214 min read\n### Linear relationships are not all a correlation analysis can reveal. We discuss rank-based correlation that is more generalized and powerful.\n## Beyond linear relationship\nIt's a common data science interview question.\n> Say, _y_ = _x_² + noise, you plot _y_ vs. _x_ and try to determine the correlation coefficient. What will it be?\nMost interviewers won't give you credit if you say anything other than zero. The word correlation, in most circumstances, invokes the concept of linearity or linear relationship between variables.\nHere is a visual illustration of what I meant.\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), [Machine Learning](https://roadmap.sh/machine-learning) and [Data Analyst](https://roadmap.sh/data-analyst) roadmaps.\n![](../images/000.png)\n![](../images/001.png)\n![Image source: Created by the author](../images/002.png)\nImage source: Created by the author\nThis notion of linearity is coming from the definition of a particular correlation coefficient that is most widely used in statistical analysis or in data science - [Pearson's coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).\nIt is named after the famous English statistician Karl Pearson and is given by,\n![](../images/003.png)\nwhere cov(_X_ ,_Y_) is the covariance of two random variables _X_ and _Y_ and the denominator consists of the product of their standard deviations.\n![Karl Pearson, Image source: Wikimedia, Free media to use](../images/004.jpeg)\n**Karl Pearson, Image source:**[**Wikimedia**](https://en.wikipedia.org/wiki/Karl_Pearson#/media/File:Karl_Pearson,_1912.jpg)**, Free media to use**\nPearson's coefficient suffices for a first-order analysis but suffers from outliers and cannot handle nonlinearity by definition. Therefore, there is a need to look beyond the linear correlation analysis.\nFor such generalized correlation analysis, there are a few more tools. Among them, Spearman's coefficient is the most straightforward to understand and calculate. In this article, we will discuss that.\n## Spearman's coefficient\nSpearman's coefficient is a [rank correlation](https://en.wikipedia.org/wiki/Rank_correlation) (a measure of [statistical dependence](https://en.wikipedia.org/wiki/Correlation_and_dependence) between the [rankings](https://en.wikipedia.org/wiki/Ranking) of two random variables). It is named after [Charles Spearman](https://en.wikipedia.org/wiki/Charles_Spearman), an English psychologist, famous for factor analysis among other contributions.\nSo, how is this different from the regular correlation coefficient we are familiar with? Spearman coefficient is different in the sense that **it is not just about the raw numerical values, but about rankings**.\n### What's a rank?\nSo, what is a rank? Here is the [Wikipedia definition](https://en.wikipedia.org/wiki/Ranking),\n\"_A_ _**ranking**_ _is a relationship between a set of items such that, for any two items, the first is either \"ranked higher than\", \"ranked lower than\" or \"ranked equal to\" the second._ \"\nTake two sets of numbers - 1,2,3 and 10,24,102. The exact numerical relationship between these sets may look odd, but they are very similar in a \"one-to-one\" sense if we consider their ranking only. _The second number in each set is greater than the first one and it is lesser than the third number. Two sets are identical from that point of view_. The relative order matters and not the actual numerical values.\nThe Pearson (linear) correlation coefficient of these two sets can be quite different than 1. But the Spearman's coefficient will be a perfect 1.\nFollowing a similar formula like the Pearson coefficient, it is given by,\n![](../images/body-11638033061e6ed200a6.png)\nThe only difference is that all the raw numerical values are replaced by their rank values (i.e., order values 1st, 2nd, 3rd, 50th percentile, etc.).\n### An example with Python\nUsing the Scipy library, it is quite simple to evaluate the Spearman coefficient of two sets of 1-D or 2-D arrays.\nLet's show the stark difference between the two types of correlation with a canonical example,\ntext\n\n```\nimport numpy as np, matplotlib.pyplot as plt\nimport scipy.stats as stat\n```\n\ntext\n\n```\nx = np.linspace(0,10,20)\ny = np.log(1+1/(x+0.1))\n```\n\ntext\n\n```\npearson = stat.pearsonr(x,y)\nspearman = stat.spearmanr(x,y)\n```\n\nIf we check their values, they will be -0.65 and -1.0 respectively. Here is the visual representation,\n![Image source: Created by the author](../images/005.png)\nImage source: Created by the author\nWhy is Spearman's coefficient a perfect -1.0? Because the rank order is exactly identical between the two arrays. As long as the underlying function follows a monotonic (increasing or decreasing) order, this will be true. Obviously, the generating function is a highly nonlinear one (inverse of a logarithm) but that does not impact Spearman's coefficient.\n### Spearman coefficient is more robust to outliers\nIf we introduce some random outliers to the same arrays as above, we will see that Spearman's coefficient is more robust to those outliers as the rank order is disturbed much less than the actual numeric values. Here is a sample result with outliers.\n![Image source: Created by the author](../images/006.png)\nImage source: Created by the author\nThe Pearson's coefficient is disturbed by the outliers (from -0.65 to -0.79), but the Spearman's coefficient hardly moved (from -1.0 to -0.99).\n## Summary\nIn this article, we discussed Spearman's coefficient which is equivalent to the more widely used Pearson's coefficient but for ranked orders of inter-related arrays. It is a special case of more generalized correlation analysis that looks at multiple levels of properties associated with arrays of numbers or sets of values that two random variables can assume.\nIn many cases, going beyond the linear Pearson coefficient can be used such as in the presence of outliers or when rank ordering is more interesting to analyze than raw numerical values. Concepts like Spearman's coefficient will come in handy for data scientists in such scenarios.\n* * *\n_Loved the article? 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We discuss rank-based correlation that is more generalized and...\n[Tirthajyoti Sarkar](https://towardsdatascience.com/author/tirthajyoti/)\nNovember 1, 20214 min read\n### Linear relationships are not all a correlation analysis can reveal. We discuss rank-based correlation that is more generalized and powerful.\n## Beyond linear relationship\nIt's a common data science interview question.\n> Say, _y_ = _x_² + noise, you plot _y_ vs. _x_ and try to determine the correlation coefficient. What will it be?\nMost interviewers won't give you credit if you say anything other than zero. The word correlation, in most circumstances, invokes the concept of linearity or linear relationship between variables.\nHere is a visual illustration of what I meant.\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), [Machine Learning](https://roadmap.sh/machine-learning) and [Data Analyst](https://roadmap.sh/data-analyst) roadmaps.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/11/17F-PzgFuTwKXOtG3aQDROg.png)\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/11/1wneOn0vu8mpX7QozaDY3Yg.png)\n![Image source: Created by the author](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/11/19F6BMOvux_4VKVWTgvcGsQ.png)\nImage source: Created by the author\nThis notion of linearity is coming from the definition of a particular correlation coefficient that is most widely used in statistical analysis or in data science - [Pearson's coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).\nIt is named after the famous English statistician Karl Pearson and is given by,\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/11/1TsADc8EuouHnMsQvNYehGA.png)\nwhere cov(_X_ ,_Y_) is the covariance of two random variables _X_ and _Y_ and the denominator consists of the product of their standard deviations.\n![Karl Pearson, Image source: Wikimedia, Free media to use](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/11/1WloJZodoU6YvhM9lAp6vaA.jpeg)\n**Karl Pearson, Image source:**[**Wikimedia**](https://en.wikipedia.org/wiki/Karl_Pearson#/media/File:Karl_Pearson,_1912.jpg)**, Free media to use**\nPearson's coefficient suffices for a first-order analysis but suffers from outliers and cannot handle nonlinearity by definition. Therefore, there is a need to look beyond the linear correlation analysis.\nFor such generalized correlation analysis, there are a few more tools. Among them, Spearman's coefficient is the most straightforward to understand and calculate. In this article, we will discuss that.\n## Spearman's coefficient\nSpearman's coefficient is a [rank correlation](https://en.wikipedia.org/wiki/Rank_correlation) (a measure of [statistical dependence](https://en.wikipedia.org/wiki/Correlation_and_dependence) between the [rankings](https://en.wikipedia.org/wiki/Ranking) of two random variables). It is named after [Charles Spearman](https://en.wikipedia.org/wiki/Charles_Spearman), an English psychologist, famous for factor analysis among other contributions.\nSo, how is this different from the regular correlation coefficient we are familiar with? Spearman coefficient is different in the sense that **it is not just about the raw numerical values, but about rankings**.\n### What's a rank?\nSo, what is a rank? Here is the [Wikipedia definition](https://en.wikipedia.org/wiki/Ranking),\n\"_A_ _**ranking**_ _is a relationship between a set of items such that, for any two items, the first is either \"ranked higher than\", \"ranked lower than\" or \"ranked equal to\" the second._ \"\nTake two sets of numbers - 1,2,3 and 10,24,102. The exact numerical relationship between these sets may look odd, but they are very similar in a \"one-to-one\" sense if we consider their ranking only. _The second number in each set is greater than the first one and it is lesser than the third number. Two sets are identical from that point of view_. The relative order matters and not the actual numerical values.\nThe Pearson (linear) correlation coefficient of these two sets can be quite different than 1. But the Spearman's coefficient will be a perfect 1.\nFollowing a similar formula like the Pearson coefficient, it is given by,\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/11/156Vqdof7Sz49clBtZMw6Uw.png)\nThe only difference is that all the raw numerical values are replaced by their rank values (i.e., order values 1st, 2nd, 3rd, 50th percentile, etc.).\n### An example with Python\nUsing the Scipy library, it is quite simple to evaluate the Spearman coefficient of two sets of 1-D or 2-D arrays.\nLet's show the stark difference between the two types of correlation with a canonical example,\ntext\n\n```\nimport numpy as np, matplotlib.pyplot as plt\nimport scipy.stats as stat\n```\n\ntext\n\n```\nx = np.linspace(0,10,20)\ny = np.log(1+1/(x+0.1))\n```\n\ntext\n\n```\npearson = stat.pearsonr(x,y)\nspearman = stat.spearmanr(x,y)\n```\n\nIf we check their values, they will be -0.65 and -1.0 respectively. Here is the visual representation,\n![Image source: Created by the author](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/11/10IZoPMFdfUMrvVlOU3cibQ.png)\nImage source: Created by the author\nWhy is Spearman's coefficient a perfect -1.0? Because the rank order is exactly identical between the two arrays. As long as the underlying function follows a monotonic (increasing or decreasing) order, this will be true. Obviously, the generating function is a highly nonlinear one (inverse of a logarithm) but that does not impact Spearman's coefficient.\n### Spearman coefficient is more robust to outliers\nIf we introduce some random outliers to the same arrays as above, we will see that Spearman's coefficient is more robust to those outliers as the rank order is disturbed much less than the actual numeric values. Here is a sample result with outliers.\n![Image source: Created by the author](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/11/1YT0Mb5FueEa3gnFHfhG2rQ.png)\nImage source: Created by the author\nThe Pearson's coefficient is disturbed by the outliers (from -0.65 to -0.79), but the Spearman's coefficient hardly moved (from -1.0 to -0.99).\n## Summary\nIn this article, we discussed Spearman's coefficient which is equivalent to the more widely used Pearson's coefficient but for ranked orders of inter-related arrays. It is a special case of more generalized correlation analysis that looks at multiple levels of properties associated with arrays of numbers or sets of values that two random variables can assume.\nIn many cases, going beyond the linear Pearson coefficient can be used such as in the presence of outliers or when rank ordering is more interesting to analyze than raw numerical values. Concepts like Spearman's coefficient will come in handy for data scientists in such scenarios.\n* * *\n_Loved the article? Become a_[ _**Medium member**_](https://medium.com/@tirthajyoti/membership) _to continue_ _**learning without limits**_ _. I'll receive a portion of your membership fee if you use the following link,__**with no extra cost to you**_ _._\n> [**Join Medium with my referral link - Tirthajyoti Sarkar**](https://medium.com/@tirthajyoti/membership)\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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