Skip to main content

Recap: Python list indexing is O(1)

Just in case any of you, hearing about how slow and ponderous Python is, were worried about indexing into a list - it's a O(1) operation:

In [936]: big_list = range(int(1e8))
len(b
In [937]: len(big_list)
Out[937]: 100000000

In [938]: %timeint big_list[-1]
ERROR: Line magic function `%timeint` not found.

In [939]: %timeit big_list[-1]
10000000 loops, best of 3: 66.1 ns per loop

In [940]: %timeit big_list[0]
10000000 loops, best of 3: 65.2 ns per loop

In [941]: small_list = range(1000)

In [942]: %timeit small_list[0]
10000000 loops, best of 3: 70.6 ns per loop

In [943]: %timeit small_list[-1]
10000000 loops, best of 3: 69.7 ns per loop

In [944]: %timeit big_list[10000]
10000000 loops, best of 3: 68.2 ns per loop

In [945]: %timeit small_list[100]
10000000 loops, best of 3: 70.2 ns per loop
Labels:

Comments

Post a Comment

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...
Post a Comment
Read more

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...
Post a Comment
Read more