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List comprehensions

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Jordi Gómez
Jordi Gómez

The document discusses list comprehensions in Python. List comprehensions provide a concise way to create lists by applying operations to elements in an iterable. They improve readability, compactness, and execution speed compared to traditional loops. However, list comprehensions are not suitable when list elements involve complex data structures or computations.

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List Comprehensions in Python Jordi Gomez Bao
Lists in Python ● From some elements ○ mylist = [0,1,2,3,4] ● From a function ○ mylist = range(0,5) ● From an iterable ○ mylist = list(myiterable) ● From a list comprehension ○ mylist = [i | for i in myiterable | if condition] ● From a generator expression ○ mylist = list(i | for i in myiterable | if condition)
List Comprehension Syntaxis to make new lists where each element is the result of some operations applied to each member of another iterable. http://docs.python.org/2/tutorial/datastructures.html mylist = [] for i in range(0,5): mylist.append(i) mylist [0, 1, 2, 3, 4] mylist = [i for i in range(0,5)] mylist [0, 1, 2, 3, 4] squarelist = [] for i in range(0,5): if i%2==0: squarelist.append(i*i) squarelist [0, 4, 16] squarelist = [i*i for i in range(0,5)if i%2==0] squarelist [0, 4, 16]
Advantages ● Better ○ Readability ○ Compactness ○ Expressivity ● Faster execution timeit.timeit('loop_matrix(100)', 'from matrix import loop_matrix', number=1000) 1.660736083984375 timeit.timeit('list_comprehension_matrix(100)', 'from matrix import list_comprehension_matrix', number=1000) 1.0217621326446533 import random def loop_matrix(size): matrix = [] rows = columns = range(0, size) for i in rows: row = [] for j in columns: row.append(i*j) matrix.append(row) return matrix def list_comprehension_matrix(size): rows = columns = range(0, size) return [[i*j for j in columns] for i in rows] matrix.py
Spelling Corrector import re, collections NWORDS = train(words(file('big.txt').read())) alphabet = 'abcdefghijklmnopqrstuvwxyz' def words(text): return re.findall('[a-z]+', text.lower()) def train(features): model = collections.defaultdict(lambda: 1) for f in features: model[f] += 1 return model def edits1(word): splits = [(word[:i], word[i:]) for i in range(len(word) + 1)] deletes = [a + b[1:] for a, b in splits if b] transposes = [a + b[1] + b[0] + b[2:] for a, b in splits if len(b)>1] replaces = [a + c + b[1:] for a, b in splits for c in alphabet if b] inserts = [a + c + b for a, b in splits for c in alphabet] return set(deletes + transposes + replaces + inserts) def known_edits2(word): return set(e2 for e1 in edits1(word) for e2 in edits1(e1) if e2 in NWORDS) def known(words): return set(w for w in words if w in NWORDS) def correct(word): candidates = known([word]) or known(edits1(word)) or known_edits2(word) or [word] return max(candidates, key=NWORDS.get) http://norvig.com/spell-correct.html
Conclusion ● List with elements computed from expression ● Better (readability, expressivity and compactness) and faster (execution) ● Pythonic way ● Not possible to use them when elements ○ involve other data structures ○ are computed by complex rules

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List comprehensions

  • 2. Lists in Python ● From some elements ○ mylist = [0,1,2,3,4] ● From a function ○ mylist = range(0,5) ● From an iterable ○ mylist = list(myiterable) ● From a list comprehension ○ mylist = [i | for i in myiterable | if condition] ● From a generator expression ○ mylist = list(i | for i in myiterable | if condition)
  • 3. List Comprehension Syntaxis to make new lists where each element is the result of some operations applied to each member of another iterable. http://docs.python.org/2/tutorial/datastructures.html mylist = [] for i in range(0,5): mylist.append(i) mylist [0, 1, 2, 3, 4] mylist = [i for i in range(0,5)] mylist [0, 1, 2, 3, 4] squarelist = [] for i in range(0,5): if i%2==0: squarelist.append(i*i) squarelist [0, 4, 16] squarelist = [i*i for i in range(0,5)if i%2==0] squarelist [0, 4, 16]
  • 4. Advantages ● Better ○ Readability ○ Compactness ○ Expressivity ● Faster execution timeit.timeit('loop_matrix(100)', 'from matrix import loop_matrix', number=1000) 1.660736083984375 timeit.timeit('list_comprehension_matrix(100)', 'from matrix import list_comprehension_matrix', number=1000) 1.0217621326446533 import random def loop_matrix(size): matrix = [] rows = columns = range(0, size) for i in rows: row = [] for j in columns: row.append(i*j) matrix.append(row) return matrix def list_comprehension_matrix(size): rows = columns = range(0, size) return [[i*j for j in columns] for i in rows] matrix.py
  • 5. Spelling Corrector import re, collections NWORDS = train(words(file('big.txt').read())) alphabet = 'abcdefghijklmnopqrstuvwxyz' def words(text): return re.findall('[a-z]+', text.lower()) def train(features): model = collections.defaultdict(lambda: 1) for f in features: model[f] += 1 return model def edits1(word): splits = [(word[:i], word[i:]) for i in range(len(word) + 1)] deletes = [a + b[1:] for a, b in splits if b] transposes = [a + b[1] + b[0] + b[2:] for a, b in splits if len(b)>1] replaces = [a + c + b[1:] for a, b in splits for c in alphabet if b] inserts = [a + c + b for a, b in splits for c in alphabet] return set(deletes + transposes + replaces + inserts) def known_edits2(word): return set(e2 for e1 in edits1(word) for e2 in edits1(e1) if e2 in NWORDS) def known(words): return set(w for w in words if w in NWORDS) def correct(word): candidates = known([word]) or known(edits1(word)) or known_edits2(word) or [word] return max(candidates, key=NWORDS.get) http://norvig.com/spell-correct.html
  • 6. Conclusion ● List with elements computed from expression ● Better (readability, expressivity and compactness) and faster (execution) ● Pythonic way ● Not possible to use them when elements ○ involve other data structures ○ are computed by complex rules