Efficient Python part 3: Gaining efficiency

Welcome to the third part out of four in the series on how to write more efficient python code. In the first part, we checked the very basics: Pythonic code, built-in functions, and just a bit on how numpy can improve speed. In the second part we learned how to measure which code is faster and enable comparison of efficiency. As a recap, the series comprehends:

• Efficient Python Part 1: Start with the Basics
• Efficient Python Part 2: Tools for Evaluating Your Code
• Efficient Python Part 3: Increasing code performance
• Efficient Python Part 4: Optimisation for Pandas

Combining, counting and iterating

let’s say we have a list os pokemons and we want to combine:

names = ['Bulbasaur','Charmander','Squirtle']
hps = [45,39,44]
combined = []
for i,pokemon in enumerate(names):
  combined.append((pokemon,hps[i]))
print(combined)

But this is not elegant and efficient.
Then, use Zip:

names = ['Bulbasaur','Charmander','Squirtle']
hps = [45,39,44]
combined_zip = zip(names, hps)
# combined zip is packed, then use * to unpack
combined_zip_list [*combined_zip]
print(combined_zip_list)

Collection Module