Python Lists

Python Data Structures

Copying

Deep copying

import copy
deep_copy = copy.deepcopy([[1,2,3], [4,5,6]])

Shallow copying

list([[1,2,3], [4,5,6]])

Flatten a list of lists

list(
itertools.
chain.from_iterable([[3,5,7], [0,6], [0, 5, 8]]))

lazily get all the values

def from_iterable(iterables):
    # chain.from_iterable(['ABC', 'DEF']) --> A B C D E F
    for it in iterables:
        for element in it:
            yield element

Deep copying

array.array

• start off with list
• use array.array if you run out of space
  • constrained to one type => less space
  • thin wrapper on C arrays
  • typically for interfacing C code
• still a dynamic list
  • same operations

from array import array
arr = array('f', (1.0, 1.5))

array.array vs np.array

• array.array is way more lightweight
• np does a ton more stuff
  • numerical calc

Sets

• frozenset
• collections.Counter

>>> loot = {'sword': 1, 'bread': 3}
>>> inventory.update(loot)
>>> inventory
Counter({'bread': 3, 'sword': 1})
>>> more_loot = {'sword': 1, 'apple': 1}
>>> inventory.update(more_loot)
>>> inventory
Counter({'bread': 3, 'sword': 2, 'apple': 1})

>>> len(inventory)
3 # Unique elements
>>> sum(inventory.values())
6 # Total no. of elements

Counter

from collections import Counter

votes = ["Person C", "Person A", "Person B", "Person A"] * 100

c = Counter(votes)

>>> c.most_common()
[('Person A', 200), ('Person C', 100), ('Person B', 100)]

for name, count in c.most_common():
    percent = int(count * 100 / c.total())
    print(f"{name}: {percent}%")

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Last update: 2023-04-24