"Doing Math with Python" by Amit Saha: Book Review
Note: No Starch Press has sent me a copy of this book for review purposes.
SHORT VERSION: Doing Math with Python is well written and introduces topics in a nice, mathematical way. I would recommend it for new users of SymPy.
Doing Math with Python by Amit Saha is a new book published by No Starch Press. The book shows how to use Python to do high schoollevel mathematics. It makes heavy use of SymPy in many chapters, and this review will focus mainly on those parts, as that is the area I have expertise in.
The book assumes a basic understanding of programming in Python 3, as well as the mathematics used (although advanced topics are explained). No prior background in the libraries used, SymPy and matplotlib, is assumed. For this reason, this book can serve as an introduction them. Each chapter ends with some programming exercises, which range from easy exercises to more advanced ones.
The book has seven chapters. In the first chapter, "Working with numbers",
basic mathematics using pure Python is introduced (no SymPy yet). It should be
noted that Python 3 (not Python 2) is required for this book. One of the
earliest examples in the book (3/2 == 1.5
) will not work correctly without
it. I applaud this choice, although I might have added a more prominent
warning to wary users. (As a side note, in the appendix, it is recommended to
install Python via Anaconda, which I
also applaud). This chapter also introduces the fractions
module, which
seems odd since sympy.Rational
will be implicitly used for rational numbers
later in the text (to little harm, however, since SymPy automatically converts
fractions.Fraction
instances to sympy.Rational
).
In all, this chapter is a good introduction to the basics of the mathematics of Python. There is also an introduction to variables and strings. However, as I noted above, one should really have some background with basic Python before reading this book, as concepts like flow control and function definition are assumed (note: there is an appendix that goes over this).
Chapters 2 and 3 cover plotting with matplotlib and basic statistics, respectively. I will not say much about the matplotlib chapter, since I know only basic matplotlib myself. I will note that the chapter covers matplotlib from a (high school) mathematics point of view, starting with a definition of the Cartesian plane, which seems a fitting choice for the book.
Chapter 3 shows how to do basic statistics (mean, median, standard deviation,
etc.) using pure Python. This chapter is clearly meant for pedagogical
purposes for basic statistics, since the basic functions mean
, median
,
etc. are implemented from scratch (as opposed to using numpy.mean
or the
standard library statistics.mean
). This serves as a good introduction to
more Python concepts (like collections.Counter
) and statistics.
Note that the functions in this chapter assume that the data is the entire
population, not a sample. This is mentioned at the beginning of the chapter,
but not elaborated on. For example, this leads to a different definition of
variance than what might be seen elsewhere (the calculate_variance
used in
this chapter is statistics.pvariance
, not statistics.variance
).
It is good to see that a numerically stable definition of variance is used
here (see PEP 450 for more
discussion on this). These numerical issues show why it is important to use a
real statistics library rather than a home grown one. In other words, use this
chapter to learn more about statistics and Python, but if you ever need to do
statistics on real data, use a statistics library like statistics
or
numpy
. Finally, I should note that this book appears to be written against
Python 3.3, whereas statistics
was added to the Python standard library in
Python 3.4. Perhaps it will get a mention in future editions.
Chapter 4, "Algebra and Symbolic Math with SymPy" starts the introduction to SymPy. The chapter starts similar to the official SymPy tutorial in describing what symbolics is, and guiding the reader away from common misconceptions and gotchas. The chapter does a good job of explaining common gotchas and avoiding antipatterns.
This chapter may serve as an alternative to the official tutorial. Unlike the official tutorial, which jumps into higherlevel mathematics and broader usecases, this chapter may be better suited to those wishing to use SymPy from the standpoint of high school mathematics.
My only gripes with this chapter, which, in total, are minor, relate to printing.

The typesetting of the pretty printing is inconsistent and, in some cases, incorrect. Powers are printed in the book using superscript numbers, like
x²
However, SymPy prints powers like
2 x
even when Unicode pretty printing is enabled. This is a minor point, but it may confuse users. Also, the output appears to use ASCII pretty printing (mixed with superscript powers), for example
x² x³ x⁴ x⁵ x +  +  +  +  2 3 4 5
Most users will either get MathJax printing (if they are using the Jupyter notebook), or Unicode printing, like
2 3 4 5 x x x x x + ── + ── + ── + ── 2 3 4 5
Again, this is a minor point, but at the very least the correct printing looks better than the fake printing used here.

In line with the previous point, I would recommend telling the user to start with
init_printing()
. The function is used once to change the order of printing to revlex (for series printing). There is a link to the tutorial page on printing. That page goes into more depth than is necessary for the book, but I would recommend at least mentioning to always callinit_printing()
, as 2D printing can make a huge difference over the defaultstr
printing, and it obviates the need to callpprint
.
Chapter 5, "Playing with Sets and Probability" covers SymPy's set objects
(particularly FiniteSet
) to do some basic set theory and probability. I'm
excited to see this in the book. The sets module in SymPy is relatively new,
but quite powerful. We do not yet have an introduction to the sets module in
the SymPy tutorial. This chapter serves as a good introduction to it (albeit
only with finite sets, but the SymPy functions that operate on infinite sets
are exactly the same as the ones that operate on finite sets). In all, I don't
have much to say about this chapter other than that I was pleasantly surprised
to see it included.
Chapter 6 shows how to draw geometric shapes and fractals with matplotlib. I again won't say much on this, as I am no matplotlib expert. The ability to draw leaf fractals and Sierpiński triangles with Python does look entertaining, and should keep readers enthralled.
Chapter 7, "Solving Calculus Problems" goes into more depth with SymPy. In
particular, assumptions, limits, derivatives, and integrals. The chapter
alternates between symbolic formulations using SymPy and numeric
calculations (using evalf
). The numeric calculations are done both for
simple examples and more advanced things (like implementing gradient descent).
One small gripe here. The book shows that
from sympy import Symbol
x = Symbol('x')
if (x + 5) > 0:
print('Do Something')
else:
print('Do Something else')
raises TypeError
at the evaluation of (x + 5) > 0
because its truth value
cannot be determined. The solution to this issue is given as
x = Symbol('x', positive=True)
if (x + 5) > 0:
print('Do Something')
else:
print('Do Something else')
Setting x
to be positive via Symbol('x', positive=True)
is correct, but
even in this case, evaluating an inequality may still raise a TypeError
(for
example, if (x  5) > 0
). The better way to do this is to use (x + 5).is_positive
. This would require a bit more discussion, especially since
SymPy uses a threevalued logic for assumptions, but I do consider "if
<symbolic inequality>" to be a SymPy antipattern.
I like Saha's approach in this chapter of first showing unevaluated forms
(Limit
, Derivative
, Integral
), and then evaluating them with
doit()
. This puts users in the mindset of a mathematical expression being a
formula which may or may not later be "calculated". The opposite approach,
using the function forms, limit
, diff
, and integrate
, which evaluate if
they can and return an unevaluated object if they can't, can be confusing to
new users in my experience. A common new SymPy user question is (some form of)
"how do I evaluate an expression?" (the answer is doit()
). Saha's approach
avoids this question by showing doit()
from the outset.
I also like that this chapter explains the gotcha of math.sin(Symbol('x'))
,
although I personally would have included this earlier in the text.
(Side note: now that I look, these are both areas in which the official tutorial could be improved).
Summary
This book is a good introduction to doing math with Python, and, for the chapters that use it, a good basic introduction to SymPy. I would recommend it to anyone wishing to learn SymPy, but especially to anyone whose knowledge of mathematics may preclude them from getting the most out of the official SymPy tutorial.
I imagine this book would work well as a pedagogical tool, either for math teachers or for selflearners. The exercises in this book should push the motivated to learn more.
I have a few minor gripes, but no major issues.
You can purchase this book from the No Starch Press website, both as a print book or an ebook. The website also includes a sample chapter (chapter 1), code samples from the book, and exercise solutions.
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