Skip to main content

Python: To format or to concatenate

A while ago a kindly reader pointed out that Python's string .format method is, after all, a function, and carries with it some over head. I have some inner loop code that I could stand to run a little faster and I was looking for a way to speed things up without losing readability. In one part of the loop I was creating a string using the format statement. I wondered if I could speed things up by changing that to a concat.

So I first tested it out on some toy code:

def str_format(size=int(1e6)):
  for n in range(size):
    a = 'hi {:d} {:d} {:d}'.format(n, n+1, n+2)
  return a


def str_concat(size=int(1e6)):
  for n in range(size):
    a = 'hi ' + str(n) + str(n+1) + str(n+2)
  return a

In [448]: %timeit str_concat()
1 loops, best of 3: 996 ms per loop

In [449]: %timeit str_format()
1 loops, best of 3: 1.26 s per loop

So, the plain python concat is faster than the elegant way. This held with one variable or two variables too. This probably has to do with the complexity of the .format function, which offsets even the extra calls to str







Comments

Popular posts from this blog

A note on Python's __exit__() and errors

Python's context managers are a very neat way of handling code that needs a teardown once you are done. Python objects have do have a destructor method ( __del__ ) called right before the last instance of the object is about to be destroyed. You can do a teardown there. However there is a lot of fine print to the __del__ method. A cleaner way of doing tear-downs is through Python's context manager , manifested as the with keyword. class CrushMe: def __init__(self): self.f = open('test.txt', 'w') def foo(self, a, b): self.f.write(str(a - b)) def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): self.f.close() return True with CrushMe() as c: c.foo(2, 3) One thing that is important, and that got me just now, is error handling. I made the mistake of ignoring all those 'junk' arguments ( exc_type, exc_val, exc_tb ). I just skimmed the docs and what popped out is that you need to return True or...

Store numpy arrays in sqlite

Use numpy.getbuffer (or sqlite3.Binary ) in combination with numpy.frombuffer to lug numpy data in and out of the sqlite3 database: import sqlite3, numpy r1d = numpy.random.randn(10) con = sqlite3.connect(':memory:') con.execute("CREATE TABLE eye(id INTEGER PRIMARY KEY, desc TEXT, data BLOB)") con.execute("INSERT INTO eye(desc,data) VALUES(?,?)", ("1d", sqlite3.Binary(r1d))) con.execute("INSERT INTO eye(desc,data) VALUES(?,?)", ("1d", numpy.getbuffer(r1d))) res = con.execute("SELECT * FROM eye").fetchall() con.close() #res -> #[(1, u'1d', <read-write buffer ptr 0x10371b220, size 80 at 0x10371b1e0>), # (2, u'1d', <read-write buffer ptr 0x10371b190, size 80 at 0x10371b150>)] print r1d - numpy.frombuffer(res[0][2]) #->[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] print r1d - numpy.frombuffer(res[1][2]) #->[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] Note that for work where data ty...