Day 33: Pokémon Data with Pandas

Install the libraries

Open VS Code and your python-class folder. Open the integrated terminal (View → Terminal) and run this once:

python3 -m pip install pandas matplotlib

The terminal will scroll for a bit. When you see the prompt come back, you’re done.

Move the CSV into your project folder

Drag pokemon.csv from your Downloads folder into your python-class folder. Your code will look for the CSV right next to your Python file.

Create a new Python file

In VS Code, go to File → New File and save it as pokemon_stats.py inside python-class. Type this in:

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("pokemon.csv")
print(df.head())

Run the file (▶ Play button). You should see the first 5 rows of the dataset printed in the terminal.

Compute mean, median, min, and max

Add these lines to the bottom of your file and run it again:

print("Mean attack:   ", df["attack"].mean())
print("Median attack: ", df["attack"].median())
print("Min attack:    ", df["attack"].min())
print("Max attack:    ", df["attack"].max())

Each line picks the attack column with df["attack"], then asks pandas for one number.

Mean

The average. Add up all the numbers, then divide by how many there are.

Median

The middle number when the values are sorted from smallest to largest.

Get all the stats at once

Pandas has a shortcut that prints a full summary in one line:

print(df["attack"].describe())

Run it. You’ll see count, mean, std, min, the quartiles, and max — all stacked up.

Try other columns

Replace "attack" with "hp", "defense", or "speed". Run the file each time and notice how the numbers change.

Histogram of attack stats

Add this to the bottom of pokemon_stats.py:

df["attack"].hist(bins=20)
plt.title("Attack Stat Distribution")
plt.xlabel("Attack")
plt.ylabel("Number of Pokémon")
plt.show()

Run the file. A new window should pop up showing a histogram — a bar plot that groups Pokémon by their attack value.

Histogram

A plot that shows how often values fall into different ranges. Use it to see the spread of one number column.

Close the plot window before running the file again, or your computer will fill up with plot windows.

Bar chart by type

A histogram works for numbers. For categories like type1 (grass, fire, water, …) we use a bar chart. Replace the histogram code with this:

df["type1"].value_counts().plot(kind="bar")
plt.title("Pokémon Count by Primary Type")
plt.xlabel("Type")
plt.ylabel("Count")
plt.show()

value_counts() counts how many Pokémon have each type1 value, and .plot(kind="bar") draws it.

Bar chart

A plot that compares counts or totals across different categories.

Stretch: scatter plot

If you have time, try this one. It compares two columns at once — every Pokémon becomes one dot:

df.plot.scatter(x="attack", y="defense")
plt.title("Attack vs. Defense")
plt.show()

What patterns do you see? Are high-attack Pokémon usually also high-defense?