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Bringing it all together: Pokémon z-scores

A list of 720 Pokémon has been loaded into your session as names. Each Pokémon's corresponding Health Points is stored in a NumPy array called hps. You want to analyze the Health Points using the z-score to see how many standard deviations each Pokémon's HP is from the mean of all HPs.

The below code was written to calculate the HP z-score for each Pokémon and gather the Pokémon with the highest HPs based on their z-scores:

poke_zscores = []

for name,hp in zip(names, hps):
    hp_avg = hps.mean()
    hp_std = hps.std()
    z_score = (hp - hp_avg)/hp_std
    poke_zscores.append((name, hp, z_score))

highest_hp_pokemon = []

for name,hp,zscore in poke_zscores:
    if zscore > 2:
        highest_hp_pokemon.append((name, hp, zscore))

指示1 / 3

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  • Use NumPy to eliminate the for loop used to create the z-scores.
  • Then, combine the names, hps, and z_scores objects together into a list called poke_zscores2.