{"id":176114,"date":"2024-01-06T23:29:45","date_gmt":"2024-01-06T22:29:45","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=176114"},"modified":"2024-01-06T23:35:52","modified_gmt":"2024-01-06T22:35:52","slug":"sympy-everything-you-need-to-know-about-the-python-symbolic-computation-library","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/sympy-everything-you-need-to-know-about-the-python-symbolic-computation-library","title":{"rendered":"SymPy: everything you need to know about the Python symbolic computation library"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"176114\" class=\"elementor elementor-176114\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e87032b e-flex e-con-boxed e-con\" data-id=\"e87032b\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-05f3526 elementor-widget elementor-widget-heading\" data-id=\"05f3526\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">SymPy is a Python library dedicated to symbolic mathematics. Find out all you need to know about this useful tool for Data Science!<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-520a352 e-flex e-con-boxed e-con\" data-id=\"520a352\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9e9c8d3 elementor-widget elementor-widget-text-editor\" data-id=\"9e9c8d3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><strong>Mathematics<\/strong> is essential in Data Science. Fortunately, there are many Python libraries dedicated to this discipline, such as NumPy, <a href=\"https:\/\/datascientest.com\/en\/scipy-all-about-the-python-machine-learning-library\">SciPy<\/a>, Scikit-Learn and <a href=\"https:\/\/datascientest.com\/en\/step-by-step-guide-installing-tensorflow-with-anaconda-on-macos\">TensorFlow.<\/a><\/p><p>When it comes to mathematical symbols, the most popular Python library is SymPy. Its name stands for &#8220;Symbolic Mathematics in Python&#8221;, and it is part of the SciPy ecosystem alongside <a href=\"https:\/\/datascientest.com\/en\/numpy-the-python-library-in-data-science\">NumPy<\/a>, Pandas and Matplotlib.<\/p><p>\u00a0<\/p><p>\ud83d\udca1Related articles:<\/p><table dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\" data-sheets-root=\"1\"><colgroup><col width=\"656\" \/><\/colgroup><tbody><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Matplotlib: Master Data Visualization in Python &quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/matplotlib-master-data-visualization-in-python\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/matplotlib-master-data-visualization-in-python\" target=\"_blank\" rel=\"noopener\">Matplotlib: Master Data Visualization in Python <\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Python Crash Course: Get started &quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/python-all-you-need-to-know\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/python-all-you-need-to-know\" target=\"_blank\" rel=\"noopener\">Python Crash Course: Get started <\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Mastering Machine Learning in Python: Data-Driven Success &quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/machine-learning-python-where-to-start\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/machine-learning-python-where-to-start\" target=\"_blank\" rel=\"noopener\">Mastering Machine Learning in Python: Data-Driven Success <\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Python Programming for Beginners - Episode 3&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/python-programming-for-beginners-episode-3\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/python-programming-for-beginners-episode-3\" target=\"_blank\" rel=\"noopener\">Python Programming for Beginners &#8211; Episode 3<\/a><\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-391cc32 e-flex e-con-boxed e-con\" data-id=\"391cc32\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1721719 elementor-widget elementor-widget-heading\" data-id=\"1721719\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is SymPy?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bdd6f77 elementor-widget elementor-widget-text-editor\" data-id=\"bdd6f77\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The<strong> SymPy library for Python<\/strong> presents itself as a complete computer algebra system (CAS), while keeping the code simple so that it can be understood and expanded.<\/p><p>Written entirely in Python, it is simple to use, relying solely on mpmath: a pure <a href=\"https:\/\/datascientest.com\/en\/python-programming-for-dummies-episode-1\">Python library for floating-point arithmetic.<\/a><\/p><p>Focused on simplicity and extensibility, it makes no attempt to extend the Python language. Its aim is to enable users to use it alongside other libraries within an interactive environment, or as a <strong>component of a larger system.<\/strong><\/p><p>It can be incorporated into other programs, and modified with custom functions. This library is offered free of charge under the BSD license.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-56e3855 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"56e3855\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover the DataScientest training courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ec726d6 e-flex e-con-boxed e-con\" data-id=\"ec726d6\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c6bc134 elementor-widget elementor-widget-heading\" data-id=\"c6bc134\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How do I use SymPy?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9fe418f elementor-widget elementor-widget-text-editor\" data-id=\"9fe418f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>SymPy<\/strong> is easily installed using the &#8220;pip install sympy&#8221; command. The fundamental object of this library is the symbol.<\/p><p>After importing the sympy package as &#8220;sp&#8221;, create a symbol &#8220;x&#8221; representing an unknown value with the command &#8220;x = sp.symbole(&#8220;x&#8221;)&#8221;. Several symbols can be created, such as &#8220;x&#8221; and &#8220;y&#8221;, depending on the number of unknowns. These symbols can be added, subtracted, multiplied and divided as required.<\/p><p>There are numerous<strong> SymPy<\/strong> functions. For example, sympify() transforms an arbitrary expression into a SymPy expression and converts<strong> standard Python objects.<\/strong><\/p><p>For its part, the evalf() function evaluates a specific numerical expression to a maximum precision of 100 decimal places. It can accept a dictionary object with numerical values as a sub-argument.<\/p><p>The<strong> Lambdify() function<\/strong> converts expressions into Python functions. Indeed, the evalf() method is inefficient for evaluating an expression over a wide range of values.<\/p><p>Lambdify works in a similar way to a lambda function, but translates <strong>SymPy<\/strong> names into the names of the specified numerical library. This is usually <strong>NumPy.<\/strong><\/p><p>Functions can be written in the script text, in the terminal or on a Jupyter notebook. This provides a graphical representation of the calculations.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-603fac0 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"603fac0\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover the DataScientest training courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3def156 e-flex e-con-boxed e-con\" data-id=\"3def156\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7c6518b elementor-widget elementor-widget-heading\" data-id=\"7c6518b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Alternatives to SymPy<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6592517 elementor-widget elementor-widget-text-editor\" data-id=\"6592517\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Many of the <strong>Computer Algebra System (CAS) tools<\/strong> available as alternatives to SymPy are closed-source. This can make it difficult to understand how a result is obtained, and such software is generally expensive.<\/p><p>One of the best-known examples is Wolfram Mathematica. There are also free tools, such as the free Desmos calculator.<\/p><p>The best-known Python tools dedicated to mathematics are NumPy and Pandas. However, while <strong>NumPy and SymPy<\/strong> are two libraries designed for maths, they are very different. NumPy operates numerically, while SymPy works with symbolic expressions. However, it is possible to export a SymPy expression to a<strong> NumPy array<\/strong> using the lambdify function.<\/p><p>Likewise, SciPy offers several functions similar to SymPy, such as equation solving, integration and differentiation. It also offers linear algebra functions similar to NumPy. Unlike <strong>SymPy, SciPy<\/strong> is not written entirely in Python. Another notable difference is that mathematical functions are written as simple Python functions returning a result, rather than as mathematical expressions.<\/p><p>In general, SciPy is ideal for integrating high-speed mathematical algorithms into <strong>Python. SymPy<\/strong>, on the other hand, is more suited to the needs of a mathematics student, researcher or data scientist.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d981418 elementor-widget elementor-widget-image\" data-id=\"d981418\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1000\" height=\"571\" src=\"https:\/\/datascientest.com\/en\/files\/2024\/11\/antivirus-datascientest-2.webp\" class=\"attachment-full size-full wp-image-191380\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2024\/11\/antivirus-datascientest-2.webp 1000w, https:\/\/datascientest.com\/en\/files\/2024\/11\/antivirus-datascientest-2-300x171.webp 300w, https:\/\/datascientest.com\/en\/files\/2024\/11\/antivirus-datascientest-2-768x439.webp 768w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-306ffb1 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"306ffb1\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Getting started in Data Science<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bc5198a elementor-widget elementor-widget-text-editor\" data-id=\"bc5198a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Another <strong>computer algebra system using Python as its programming language is SageMath.<\/strong> It is, however, a much heavier program than SymPy, requiring a download of over a gigabyte.<\/p><p>As well as being compact, SymPy has no dependencies other than Python. It can therefore be used in any environment.<\/p><p>For its part, Sage presents itself as a complete mathematical system, combining all the major open-source systems into a single solution. Using a function invokes the corresponding open-source packages.<\/p><p>This <strong>CAS<\/strong> is based on a dedicated language built on top of Python and a set of open-source libraries written in C, <a href=\"https:\/\/datascientest.com\/en\/demystifying-fortran-a-comprehensive-introduction-to-the-first-programming-language\">Fortran<\/a> or even LISP.<\/p><p>This is not the case with SymPy, which is an independent system, embedding all its functionalities. What makes SymPy so special is that it is a Python library.<\/p><p>SageMath&#8217;s abstraction from Python means it can be used as a general-purpose CAS. <strong>SymPy,<\/strong> on the other hand, is more suited to integration into a Python application.<\/p><p>Many CAS designed for use in interactive environments are difficult to automate and extend. This is not the case with SymPy, which can be used interactively on Python or imported into your own program. Its <a href=\"https:\/\/datascientest.com\/en\/api-the-path-to-seamless-integration\">APIs also simplify extension.<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7170dfc e-flex e-con-boxed e-con\" data-id=\"7170dfc\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d2f122c elementor-widget elementor-widget-heading\" data-id=\"d2f122c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d3e52e6 elementor-widget elementor-widget-text-editor\" data-id=\"d3e52e6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>SymPy<\/strong> is a powerful library dedicated to symbolic mathematics. It enables you to create variables and functions, as well as extend and simplify mathematical statements symbolically or solve equations.<\/p><p>To learn how to <strong>handle Python<\/strong> and the various libraries dedicated to mathematics, you can choose DataScientest training courses. Our various courses include a module dedicated to this <strong>programming language.<\/strong><\/p><p>By following our distance learning courses, you can acquire all the skills you need to work as a <a href=\"https:\/\/datascientest.com\/en\/data-analyst-vs-data-scientis-unveiling-the-key-similarities-and-differences\">Data Analyst, Data Engineer, Data Scientist,<\/a> Machine Learning Engineer or Data Product Manager.<\/p><p>Our courses can be completed via BootCamp or continuing education, and our state-recognized organization is eligible for funding options Discover DataScientest!<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fe0407f elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"fe0407f\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover the DataScientest training courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f5fa884 e-flex e-con-boxed e-con\" data-id=\"f5fa884\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e553081 elementor-widget elementor-widget-html\" data-id=\"e553081\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"What is SymPy?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"The SymPy library for Python presents itself as a complete computer algebra system (CAS), while preserving the simplicity of the code to allow it to be understood and expanded. Written entirely in Python, it's easy to use since it relies solely on mpmath: a pure Python library for floating-point arithmetic.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"What is SymPy for?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"SymPy is used in particular for manipulating mathematical expressions, or for solving university-level mathematical problems. It allows you to build a calculator, and can be used as a Computer Algebra System (CAS). However, it is necessary to manually declare symbolic variables with the Symbol() function.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"How do I use SymPy?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"SymPy is easily installed using the \"pip install sympy\" command. The fundamental object of this library is the symbol. After importing the sympy package as \"sp\", a symbol \"x\" representing an unknown value is created with the command \"x = sp.symbole(\\\"x\\\")\". Several symbols can be created, such as \"x\" and \"y\", depending on the number of unknowns. These symbols can be added, subtracted, multiplied and divided at will.\"\n    }\n  }]\n}\n<\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>SymPy is a Python library dedicated to symbolic mathematics. Find out all you need to know about this useful tool for Data Science! Mathematics is essential in Data Science. Fortunately, there are many Python libraries dedicated to this discipline, such as NumPy, SciPy, Scikit-Learn and TensorFlow. When it comes to mathematical symbols, the most popular [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":176123,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>SymPy: everything you need to know about the Python symbolic computation library<\/title>\n<meta name=\"description\" content=\"SymPy is a powerful tool for symbolic mathematics in Python, and it&#039;s particularly useful in fields like mathematics, physics, engineering, and computer science where symbolic manipulation of mathematical expressions is required.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/sympy-everything-you-need-to-know-about-the-python-symbolic-computation-library\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SymPy: everything you need to know about the Python symbolic computation library\" \/>\n<meta property=\"og:description\" content=\"SymPy is a Python library dedicated to symbolic mathematics. 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