Python Module Usage Stats – Feb 2011

Here are the top 30 “base modules”, ordered by number of PyPI projects importing them. These results are based on 11,204 packages download from PyPI. Explanations, full results and code to generate them are available below.


(click to enlarge)

Full results are available (see Methodology to understand what they mean exactly).


Some interesting tidbits and comparisons:

  • It seems django has gained “some popularity”. Zope is very high up on the list, and plone is at 42 with 907 projects importing it.
  • The number of projects importing unittest is somewhat depressing, especially relative to setuptools which is impressive. That might be because setuptools is somewhat a prerequisite to appear on PyPI (practically speaking), while unittest is not. (Edit: corrected by Michael Foord in a comment)
  • optparse with 1875 vs. getopt with 515.
  • cPickle with 690 vs. pickle with 598.
  • simplejson with 760 vs. json with 593.

I invite you all to find out more interesting pieces of information by going over the results. I bet there’s a lot more knowledge to be gained from this.


Back in 2007 I wrote a small script that counted module imports in python code. I used it to generate statistics for Python modules. A week or two ago I had an idea to repeat that experiment – and see the difference between 2007 and 2011. I also thought of a small hypothesis to test: since django became very popular, I’d expect it to be very high up on the list.

I started working with my old code, and decided that I should update it. Looking for imports in Python code is not as simple as it seems. I considered using the tokenize and parser modules, but decided against that. Using parser would make my code version dependent and by the time I thought of tokenize, I had the complicated part already worked out. By the complicated part I mean of course the big regexps I used ;)


Input: PyPI and a source distribution of the Python2.7 standard library. I wrote a small script ( to fetch python modules. It does it by reading the PyPI index page, and then using easy_install to fetch each module. Since currently there are a bit less than 13k modules in PyPI, this took some time.

Parsing: I wrote a relatively simple piece of code to find “import x” and “from x import y” statements in code. This is much more tricky than it seems: statements such as “from x import a,b”, “from . import bla” and

from bla import \

should all be supported. In order to achieve uniformity, I converted each import statement to a series of dotted modules. So for example, “import a.b” will yield “a” and “a.b”, and “from b import c,d” will yield “b”, “b.c”, and “b.d”.

Processing: I created three result types:

  1. total number of imports
  2. total number of packages importing the module
  3. total number of packages importing the module, only for the first module mentioned in a dotted module name, e.g. not “a.b”, only “a”.

I believe the third is the most informative, although there are interesting things to learn from the others as well.

Code: Full code is available. Peer reviews and independent reports are welcome :)

Cryptography Math

10 Awesome Theorems & Results

When I look back at various mathematical courses I took, most have at least one theorem that I really liked. Usually I like it because the proof has a surprising trick, sometimes it’s because of the unexpected conclusion, or maybe the unintuitive feel to it. In other cases it’s just the elegance of the proof, or the result itself.
Without further ado, here’s a selection of my favorite theorems, in no particular order:

1. Linear Algebra: the Cayley Hamilton theorem. When I first grokked the fact that you can substitute matrices for the variables in polynomials, I was awestruck. Then I learned that you can define eA by using a Taylor series, but the fun doesn’t stop there. Using the Eigenvalues you can greatly simplify the calculation, and it all “works out the same” (i.e., if A=P-1DP and D is diagonal, then p(A) = P-1p(D)P. This works also for Jordan forms). Also, since you can show that complex numbers are isomorphic to the 2×2 matrices of the form [[a, b], [-b, a]], and that the calculations were exactly the same – well, everything “fell into place for me”. At the time it seemed to be one of the joys of Mathematics.

2. Calculus: the Bolzano-Weierstrass Theorem. One of the first non trivial results you learn in calculus, I originally learned the version that says: “Every bounded infinite set has a limit point”, and its proof was a bit more elegant in my eyes than the proof of the Wikipedia version. I liked it so much that one time when I was in boot camp in the service, I worked it out again just to keep my mind working. Good times.

3. Probability: The elegant result of V(x) = E(V(x|y)) + V(E(x|y)). Just the sight of it makes one sigh with contentedness, and the result itself is very nice.

4. Calculus, again: Stokes’ theorem and its friends. Very useful and non intuitive, in layman’s terms it says that you can reason about what happens in an area just by knowing about its perimeter.

5. Numerical Analysis: Richardson Extrapolation: one of the most elegant forms of bootstrapping, you start with a simple approximation method as a building block, and at the end you get a very strong high-quality approximation.

6. Computability: The Parameter theorem. Especially elegant, it basically gives the mathematical definition of the “bind” function for function parameters. In simple terms it uses the source code of a function f(x, y), to find the source code of a function g(y) such that g(y) = f(a, y) for some a. The nice thing about it is that it works only on source code, without calling the function themselves.
This theorem had the added bonus that once I grokked it, the test in computability was very, very easy :)

7. Functional analysis: here it’s a relatively minor result that I ended up remembering distinctly: Given z1.. zn which are linearly independent in E, show that there exists a d such that for each w1…wn that follow ||wi – zi|| < d for each i, are also linearly independent. The footnote says that such a finite, linearly independent group is called stable. When visualizing I think of it this way: given a such a group, kick it. As long as you don’t kick it too strongly – it will stay linearly independent. Now that’s stable.

8. Mathematical Logic: The Compactness theorem: “a set of first-order sentences has a model if and only if every finite subset of it has a model”. One direction is almost trivial, but the other is deep. When studying for the test in this course, I remember being stuck for days on an exercise that required the use of this theorem. Once I fully understood the method of its use, it became a favorite.
(By the way, the exercise was the following: Let G a countable group of first order statements, and p a first order statement. Show that if p is true in every countable model of G, than G |= p.)

9. Cryptography: I’ve learned a bit of cryptography on my own before taking the cryptography course. When I did though, two methods were especially memorable: The first was the “Meet in the Middle” attack. Not to be confused with “Man in the Middle”, this method allows one to attack symmetric ciphers constructed by repeatedly applying a simpler cipher. This known plaintext attack got its name from its method of operation: the attacker calculates all possible decryptions the ciphertext and stores them in a lookup table. Then, he calculates all encryptions of the plaintext and looks them up in that lookup table. Once a result is found – the combination of the encryption and the decryption keys used is the final key of the composed cipher.

10. The second cryptography result that I liked was secret sharing. Trivial secret sharing is so simple, and yet effective, that when I first learned it I thought: “how come I didn’t think of this before?”.

There are obviously many more elegant theorems, some of which I’ve learned in my studies. I sure hope to learn a few more. Still, these are special. As a highschool math teacher once told us about the Pythagorean theorem: “I want you to remember the proof even if I wake you in the middle of the night”. The theorems in this short list come close to that ideal.

Now I wonder – what are your favorite theorems?

Programming Philosophy

Beautiful Code

A few days ago, @edensh mentioned in Facebook beautiful code, and many people gave examples of assembly, while I was thinking of Python.

That got me thinking: what is beautiful code for me?

So here are my criteria for beautiful code:

  1. Readable (also visually pretty)
  2. Concise
  3. Does something non trivial (usually in an unexpectedly short manner)
  4. Good (solves the problem, efficiently)

If we consider code to be an implementation of a solution to a problem, than 3 & 4 usually apply to the solution, while 1 & 2 apply to the code itself. This brings me to why I like Python:

  1. Code is more readable. Specifically, I can still still easily understand code I wrote years ago. Also, Python’s zen encourages you to write readable code. For example “explicit is better than implicit” directly applies to readability.
  2. Python is visually appealing, although I guess that’s a matter of opinion :)
  3. Python almost always allows me to express my solutions easily & succinctly, whereas with other languages (C, C++, Java) I have to fight to “get my point across”.
  4. Python almost always has the right data structures to implement my solutions efficiently.

With that in mind, it’s clear to me now how assembly code can be beautiful.

Note that I didn’t mention C#, Ruby or Haskell. I don’t have much experience with these languages, but from what I’ve seen so far, it seems to me that these languages may help you write beautiful code. Of these, Haskell is probably going to be the first language I’ll learn – I think it will be the most educating experience, although I’m pretty sure others will argue with me regarding Haskell’s readability :)

Now, My question to you is: what do you think makes code beautiful?