Being a data analyst is one thing… being a Pokémon data analyst is another. Imagine trying to calculate trends when your dataset includes:

  • Pikachu’s electricity output ⚡
  • Charmander’s flame length 🔥
  • Bulbasaur’s photosynthesis efficiency 🌿
  • Magikarp’s… well, jumping height?

Morning

  • Coffee ☕
  • Open SQL, Python, and Pokédex CSV all at once.
  • Notice that 42 Pikachu entries have null for Thunderbolt Damage. Panic ensues.

Midday

  • Trying to create a scatter plot of Charmander flame length vs. battle victories.
  • Realize half the values are “over 9000” because someone added anime exaggeration.
  • Normalize the data, pray no one notices.

Afternoon

  • Merge datasets: Pokémon stats + battle history + gym locations.
  • Debug error: Magikarp’s jump height is in meters, Pikachu’s in volts…
  • Cry internally.

Evening

  • Make a dashboard: top 10 Pokémon by total damage.
  • Coworker asks: “Why is Magikarp number 3?”
  • Me: Data speaks louder than logic.

Being a Pokémon data analyst requires patience, creativity, and the ability to explain to your manager why “Ditto can transform into any Pokémon, so yes, it counts as everyone.”

Remember: behind every clean Pokémon dataset, there’s a data analyst silently praying that Team Rocket doesn’t corrupt the database.