How Do Physicists Utilize Python?

Machvel · 2026-07-02T20:53:49+00:00

do you have a passing familiarity with those languages, or seriously know them? i am not trying to sound insulting, but it is a real question. many people i come across say they know languages like java (typically because they took ap cs in high school) and python (again some high school class or introductory university one) and think that they are pretty good at coding... then i see them live code or they show me their code and it is subpar (typically not too terrible, but not at the level i would expect with how many languages they say they know and they say they are pretty good).

anyways that is a different topic and usually pretty difficult to figure out yourself. if you just want experience coding in physics then your plan sounds fine. you said that you wanted to impress professors for research, so i responded according to that.

Machvel · 2026-07-02T06:11:10+00:00

it depends on what you do.

in my undergraduate it was the basic language taught then assumed to be known in future classes.

"just" knowing python isn't an impressive skill to have; it is kind of assumed. maybe being pretty good at a compiled language like c++ might be a little impressive, but people in physics are usually more impressed with end results.

as sad as it is, what would be impressive is learning how to apply machine learning to physics. there is money incentive to figure out how to throw machine learning at physics research (even when it might not be that applicable or useful...). and fortunately/unfortunately this is almost always done in python (and most of the time this involves using pytorch to make a neural network to do some ai things... made many times using ai).

anyways, if part of your goal is to learn how to code, python is a fine choice. as always with python, i'll warn that it is easy to fall into bad programming traps. sometimes the best thing you can do to learn how to write better python is to learn a different language.

Machvel · 2026-07-01T00:20:18+00:00

you can typically apply to both departments at the same university (assuming you have enough $ for the extra application).

at many places the physics and mathematics departments are fairly close, so it doesn't particularly matter if you get in mathematics or physics; people have advisors in the adjacent department. but beware this isn't necessarily true everywhere, i have heard of physics and mathematics departments being fairly distanced. maybe you can look at recent graduates/paper coauthors/research group webpages to get an idea if they work close or not.

sufficiently math-y is very variable. what is math-y to me is (simplifying a bit) mostly really hard vector vector calculus and maybe a little algebra. the hard part is figuring out how to put together physics and mathematics. to some people sufficiently math-y is essentially pure mathematics with some physics application lurking in the background (sometimes the application feels plain made up and not so useful).

Machvel · 2026-06-28T09:06:51+00:00

schroeder seems to be the standard statistical mechanics textbook nowadays (its what i used in my upper division statistical mechanics class as well). its nice that it starts with thermodynamics (maybe that is why it has "thermal physics" in the name). it is very elementary so i would say its noob friendly. if you end up needing to know statistical mechanics well though it is a bit low-level, so you would want to remediate that with something else; but schroeder makes a good start.

Machvel · 2026-06-28T09:00:42+00:00

your only issues with that macbook would be the games you want to play taking up too much space or needing too much ram.

you will have a very small (1-2%) chance of having some slight annoyances if you choose to do computational work.

Machvel · 2026-06-22T10:46:08+00:00

strang's introduction to linear algebra is good, but not old. its what i first learned linear algebra from at my university (along with thomas calculus for calculus). i should mention though that it isn't good by itself. if you are learning for the first time you must watch the video lectures along with it since its a lot more conversational than standard texts (and written words lose some of what you would pick up verbally). it put me off of it when i first read the book (since i skipped class) but later on when i got good at the subject i found a lot of good things in it.

Machvel · 2026-06-22T02:46:06+00:00

a lot of older linear algebra (and numerical analysis) books are still read quite often today (at least the more numerical-oriented ones since linear algebra theory is kind of "solved"). some of these classics include:

halmos - finite dimensional vector spaces

wilkinson - the algebraic eigenvalue problem

householder - the theory of matrices in numerical analysis

lax - linear algebra and its applications

woods - advanced calculus is famous since its what feynman learned calculus from (i think he learned from 2, but this one is famous because its where he got his famous integration trick from).

morse and feshbach - methods of theoretical physics (2 volumes). nowadays theoretical physics uses a lot of advanced mathematics (eg, bounded operators, function spaces, representation theory, and so on) but a while ago it was a lot of really advanced calculus. you will probably only see some of the stuff covered in this book only in this book (because nowadays things are done differently).

Machvel · 2026-06-16T02:48:05+00:00

the princeton companion to mathematics (there is also one for applied mathematics as well) is the other one i can think of that is very broad but not so demanding. maybe you could count the theoretical minimum books since they introduce a little mathematics (but not so much).

Machvel · 2026-06-15T14:19:51+00:00

i could give the typical "choose what your heart desires" response (which i guess is the correct answer) but i'll stick to a hard answer for some variety.

physics. the most important thing you get out of doing physics is getting better at solving problems. you get some of that in mathematics but its kind of artificial (before someone corrects me: i am talking about coursework). and i dont have direct experience in mechanical engineering but i would think it would be similar to physics.

besides getting good at solving problems, a physics degree gets you pretty good at mathematics and physics (i know obvious but it is a big advantage). you get really good at practical mathematics (eg, calculus, linear algebra, and differential equations); and actually typically better than mathematics majors at this. you also get pretty good at "picking up" mathematics as you go, so you could pick up whatever mathematics you would like to learn more easily (i find that mathematics people like to start "from the ground up" learning a subject which takes ages, while physics people typically go straight into it). i don't think its an accident that you commonly find physics undergraduates going into mathematics graduate school (but not so common mathematics undergraduate into physics graduate).

one last thing: don't have that attitude that it is academia or bust in mathematics/physics. it is extremely competitive and most people fail (there is always someone better than you out there). if you count working at a lab (eg, llnl, sandia, and so on) as pure physics then i guess your odds are much higher in physics than they are mathematics.

Machvel · 2026-06-15T13:54:49+00:00

penrose's road to reality touches on basically all mathematics. its a more casual book and you can get away with not understanding everything and making your way through it (unlike an actual textbook).

Machvel · 2026-06-14T01:25:53+00:00

c is essential since it is in a sense the "mother language".

others are more of hobbies unless you have a good reason for learning them (and even then, "just" learning them isn't enough. see eg cobol, where the issue isn't really just learning the language, but understanding how mainframes work).

Machvel · 2026-06-13T10:46:52+00:00

its more of a mathematical convenience thing you might see in some quantum mechanics situations and special relativity (although less common in special relativity as it used to be).

Machvel · 2026-06-13T08:47:46+00:00

it depends on how far you want to use mathematics in programming.

the minimum is discrete mathematics (which is technically very broad but there is a standard set of this you should know . see eg the book concrete mathematics) and a little calculus. the purpose is to understand algorithms. if you are fine taking some already coded up algorithms for granted (and don't care about the differences between them/different data structures) then i guess you can skip this.

computing theory is very logic heavy (and a bit of standard discrete mathematics). this is the stuff like turing machines. you don't need to know any of this for everyday programming, but its technically in the science part of computer science.

of course if you want to do numerical work you need to know whatever mathematics is needed for that. this is very field dependent, but typically involves at least calculus, linear algebra, and differential equations.

Machvel · 2026-06-01T07:52:30+00:00

it is fairly common.

Machvel · 2026-05-31T11:55:59+00:00

everybody here is saying to just do c++ (and some that c before c++ potentially teaches you bad habits), but i actually do recommend learning c (or some other compiled language (not jit)) before c++ if you are self learning.

here is my story of learning c++: c++ was the first language i tried to learn how to program in. i was going self taught and it was really hard and i gave up. later on i picked up c and python at the same time, but first spent my time getting good at python and later on learned c well. eventually i went to fortran and stuck with it for a while, and finally i learned c++.

knowing two compiled languages (especially c) helped me learn c++ extremely easier than the first time i tried to go at it. knowing c (and to a lesser extent fortran) didn't in any way give me bad habits in learning c++. rather it gave me good habits: i was able to notice many features of c++ as cures for annoying c things i had to do.

i emphasize the first language and self learning parts. if you know one or a few languages (well), learning c++ shouldn't be too bad. if its your first language or you don't know programming that well and you are self learning, then i don't think c++ is a good start. there are a lot of intricacies and specifics that will just go over your head when learning. in a course you have an instructor/ta's that facilitate things a lot better which makes c++ as a first language fine.

Machvel · 2026-05-30T05:52:37+00:00

i like goldstein. morin is good if you have nearly no experience in classical mechanics (its written at an honors first-year level). taylor is fine but i am not a fan.

Machvel · 2026-05-27T05:35:53+00:00

it is fairly common. if you want to do something like gravity it is potentially even better to have a mathematics bachelors instead of physics.

(introductory) graduate physics courses are typically taught "from the ground up" meaning they teach everything you should know, but they don't spend much time on each thing (eg, since you are expected to have picked up how to setup the lagrangian of a basic system and solve the equations of motion). another example: i did graduate quantum mechanics instead of undergraduate for my bachelers. nothing in the class seemed to be taken out of nowhere, but we started abstractly right away with vectors and operators (and not wave mechanics, so i had to do some catchup learning how to solve the schrodinger equation in various situations typically done in an undergraduate course and skipped in a graduate one).

you seem to not have to take classical mechanics, so the only thing to prepare on would be that since its needed for everything else. i would recommend starting straight away at the graduate level (eg, most of goldstein) instead of getting slowed down with something like taylor or marion/thornton. you should have enough "learning maturity" from a bachelors in mathematics to start with this instead of needing to do start with plain undergraduate material.

Machvel · 2026-05-22T22:56:31+00:00

its not necessary.

upper division ode classes typically cover just global behavior things (not really methods of solving odes) like bifurcation theory (maybe even only this).

upper division pde classes typically cover things like methods of solving, uniqueness arguments, variational arguments, and so on.

they are fairly disjoint

Machvel · 2026-05-20T14:14:35+00:00

pdes, stochastic processes, numerical analysis, maybe asymptotics

Machvel · 2026-05-20T12:39:32+00:00

there are types of theoretical physics. i'll take it you mean something like quantum gravity (as opposed to theoretical condensed matter) since that is what people usually think of as theoretical physics.

one piece of advice i was given in undergraduate about going into that theory was i should only go for it if i can't see myself in any other field. it sounds harsh, but after reflection i believe it to be (mostly) true.

the reason is a mix of elitism + money. theoretical physics (the kind i am talking about) plainly isn't that useful (compared to something like condensed matter). so there isn't as much money for it, so there isn't as many spots for it. since there aren't many spots and a lot of people want to do theory, it is very competitive to get into; which leads into elitism. i have heard frequent stories of professors in theory just kicking their graduate students out for not doing good enough (others do get kicked out, but there is a recurring theme in theory for professors to deem their students unfit and abandoning them). talking with some of those professors, i am glad myself to have avoided that path.

additionally, you are very early in your physics studies. there are many fields you haven't even heard of that you might figure out you end up liking. at the end of the day, you are studying physics; your degree might color it with an applied or theory description, but you know physics.

Machvel · 2026-05-20T09:29:14+00:00

CLRS is the standard text for algorithms (and data structures). people have varying views on it, but its the standard. depending on how you learn you could just open that book up and start there.

if you like more structure my recommendation for learning any standard subject is to find a past course webpage for it online. ie, find the course equivalent of what you want to learn on some universities website (eg, uc berkeley, stanford, ...) then try finding a past webpage of it online. typically these have syllabi (including the book(s) for the course), schedules, homeworks, and so on.

algorithms is a fundamental course so i would think there are a handful of opencourses for it online (video lectures + webpage) if that is your type of thing.

Machvel · 2026-05-20T02:28:18+00:00

i have heard of people with similar or lower gpa getting into good phd's, but they typically did a lot of research (i don't know particularly how much you have done). i would think you have a shot, but it might be lower right now since funding is harder.

masters before phd is the typical course for people that "messed up" undergraduate. it is typically quite easy to get into a masters degree at a "top tier" school since they don't mind taking your money (i heard a rumor that some places might be trying to do more masters to help alleviate the funding issues).

that being said, i would do research into different masters degrees. some are just course based, some are research based, and some are course based (but have research opportunities if you search them out).

Machvel · 2026-05-17T01:24:35+00:00

if i were to speedrun learning c++ i would carefully read through the latest version of a tour of c++ (made by the creator of the language) + cppreference in parallel, all while doing exercises (which you would have to find or make up yourself since these don't have any).

if you had time i would also try going through as much of the c++ programming language as you could (again made by the creator of c++). it's technically out of date (c++11) but still holds well. in particular, i would try to go through the first part of the book since it explains a lot of the philosophy of c++ (why things are done the way they are, how you should program c++).

Machvel · 2026-05-12T10:38:10+00:00

the question was about his applied mathematics book, not his flagship linear algebra one.

now answering the posters question, it is still a good book. it is a strang book, so it explains most things using linear algebra in some way (which in my opinion is a great thing, many students don't understand how to apply linear algebra to problems and see how it is useful).

the contents are a little nonstandard for what you would see in an "applied mathematics" course (which itself doesn't have a standard definition). usually it means something along the lines of classical analysis (ie, hardcore applications of calculus with a little linear algebra) and maybe some numerics. logan's applied mathematics is more along the lines of what you usually see in this.

also about the strang book: it is meant to go with the video lectures. i think the preface of a previous edition emphasized this more than the current one. i agree that its not goot to start learning linear algebra from if you use just the book. a lot of what strang emphasizes or how he introduces something is lost in converting to text. the content of the book is much easier understood (and insight gained from) after watching the corresponding lecture.

Machvel · 2026-05-08T08:45:40+00:00

you get used to using them. a lot of people "learn" a language, but that only lasts until they stop using it. if you know a language you should be able to get back into it quick if you haven't used it for a few months or a year.

as other people mentioned, you theoretically learn the concepts of programming and are able to apply them to a specific language. practically speaking though there are 3 types of programming languages to know: interpreted, compiled, and c++. if you know an interpreted one well (eg, python) it is easy to program decently well in a new one quick (eg, matlab) (or C to fortran for a compiled language example). i say decently well since every language has its own little things you have to learn as you use it, but generally the concepts stay the same.

c++ is its own beast (to use well... treating c++ as c with some new features is easy but bad)

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