![]() This tutorial will focus on the mathematics I’m most familiar with, i.e., nothing beyond first-year university math. There’s a lot more to SymPy than what we’ll cover here. ![]() Using SymPy and Jupyter: A Quick Start Tutorial I haven’t tested SymPy as extensively yet, but so far have been successful with both Pip and Conda right out of the gate on macOS. In fairness, I’ve since gotten it working on each of those, either using Docker or a native binary installer, but trying to do it via conda or apt-get has not gone well. I could not install SageMath on the first try on any major platform: macOS, Windows, and Linux. However, a more severe limitation of SageMath is that many of the options for installing it are problematic. However, this makes SageMath less appropriate for embedding into a Python application than either SciPy or SymPy. However, I have not yet run benchmarks on this.Īdditionally, because it is abstracted from Python, SageMath probably has the most appeal as a generalized CAS in the mold of a tool like Mathematica. Because many of these are the same C and Fortran libraries that SciPy relies on, Sage may run faster for many operations than SymPy. SageMath is an ambitious project that builds a domain-specific language on top of Python and an assortment of open-source libraries written in C, Fortran, and even LISP. SageMath is another Python Computer Algebra System and the first one that I evaluated in any depth. SymPy, in contrast, is more appropriate for the sort of routine interactive use that a student of mathematics or researcher is likely to need. In general, SciPy works well when you need relatively to embed high-speed math algorithms in Python. In Sage and SymPy, as we’ll see below, they’re defined even more concisely as mathematical expressions. Another noteworthy difference is that, unlike Sage and SymPy, you write mathematical functions in SciPy as simple Python functions that return a result. Unlike SymPy, SciPy is not written entirely in Python. In addition, it imports and extends many of NumPy’s linear algebra functions as well. In Python, I must also give a brief nod to SciPy, which has excellent support for functionality that overlaps SymPy, including solving equations, integration, differentiation, and many other features. Outside of Python, noteworthy players in this space include MatLab (commercial) and Octave (A MatLab-compatible open source tool). (See our practice exercises for Pandas and NumPy if you need a refresher on these). Among Python tools, NumPy and Pandas are well-known tools in this space. Many tools that overlap this category are specialized for high-speed matrix operations, linear algebra, data science, solving systems of linear equations, and the like. Wolfram also hosts a popular freemium site, WolframAlpha, which we discussed in Teach Yourself Math. On the free side, if you mainly need a graphing calculator, the free tool, Desmos, is quite popular. There are several tools in this space, but perhaps the best-known is Wolfram’s Mathematica. Many popular Computer Algebra System tools are closed source (which can be problematic from a “how did you get that result”? perspective). As we move into the tutorial, “Using SymPy and Jupyter,” we’ll share a simple code repository to get you up and running quickly with SymPy and Jupyter Lab so you can check it out for yourself. We began working with SageMath first and shared our experiences with it in Introducing Sage Math: Symbolic Math Software In Python. ![]() ![]() We especially want to zero give a detailed comparison of SymPy to SageMath. In the review section, “SymPy Alternatives,” we’ll compare SymPy to other Python and non-Python tools that are alternatives to SymPy. This article is both a brief review of SymPy and a basic tutorial. For example, you can define mathematical functions in terms of one or more variables, then manipulate them in various ways: solving them, factoring, substituting numbers of other expressions, differentiating them (taking derivatives), and integrating them (calculating definite and indefinite integrals). Symbolic math software tools, also called Computer Algebra Systems (CAS), allow you to work with mathematical equations more or less as you would on paper. It’s free and open source, and because it’s written entirely in Python, it’s easy to install and use. SymPy is a Python library for symbolic mathematics. ![]()
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