Libraries

Neal Davis edited this page Oct 1, 2016 · 2 revisions

Selected Python Libraries (Modules)

itertools

Iterators are data types which contain other values and are compatible with looping. For instance, you can iterate over a string or a list. We generally do this in sequence or simply over every member of the container, but there are quite a few efficient and sophisticated alternatives. itertools supports many of these; we will focus on only a few of them: combinations, permutations, and accumulate.

itertools.combinations

Use combinations when we want to carry out analysis on each subset of a group. For instance, to obtain each pair in a set:

from itertools import combinations
for pair in combinations( 'ABCD',2 ):
    print( pair )

Note that the order is preserved. The number specifies the number of elements possible combinations should contain.

Examples

The following code generates all possible teams of three partners in a group.

partners = [ 'Sergey', 'Julian', 'Azra',
             'Berenice', 'Ogden', 'Gloria' ]
for group in combinations( partners,3 ):
    print( group )

itertools.permutations

In contrast to itertools.combinations (which preserves order), we use permutations when we want all of the elements in every possible order:

from itertools import permutations
for triplet in permutations( 'ABCD',3 ):
    print( triplet )

Both of these functions tend to be more useful in computer science and informatics than in engineering, but they occasionally come in useful for scientists.

Examples

Consider a network of cities connected by roads.

The following code lets you traverse every city in all possible orders from all possible starting locations:

from itertools import permutations
cities = [ 'St Albans', 'Montford', 'Kramer',
           'Cariston', 'Howardsville', 'Winslow' ]
print( list( permutations( cities,6 ) ) )

itertools.accumulate

If you need to accumulate across all values in a container, one easy and quick way to do it is to use accumulate:

from itertools import accumulate
values = [ 0.3, 0.5, 1.2, -0.6, 0.3, 0.2 ]
cumulative_sums = accumulate( values )
print( list( cumulative_sums ) )

Examples

This code approximately calculates the cumulative probability of the normal distribution:

# calculate values of normal distribution
from math import exp, sqrt, pi
x = [ -3.0, -2.5, -2.0, -1.5, -1.0, -0.5, 0.0,
      0.5, 1.0, 1.5, 2.0, 2.5, 3.0 ]
normal_dist = [ ]
for i in x:
    normal_dist.append( (exp(-0.5*i*i)) / (sqrt(2)*pi) )
# calculate approximate cumulative probability of normal distribution
from itertools import accumulate
cumul_sum = accumulate( normal_dist )
print( list( cumul_sum ) )

Resources

math

string

You can’t perform that action at this time.
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session.
Press h to open a hovercard with more details.