Filtering Data (Pandas)

🟨🐍 Technique Card: Filtering Data using Pandas (practical)

🧰 What You’ll Learn

How to filter a table of data (DataFrame) using Pandas so you can work with just the parts you care about — like countries in a certain continent.

---

🧠 Key Concept

Filtering means picking only the rows in a table (called a DataFrame) that match something — like rows where the “Continent” is “Europe”.

---

🛠️ How to Do It

import pandas as pd

# Read in the data
df = pd.read_csv("countries.csv")

# See what the table looks like
print(df.head())

# Filter rows where the continent is europe
europe_df = df[df["continent"] == "europe"]

# Show the first few rows of the result
print(europe_df.head())

---

🗂️ What’s Going On?

• df["continent"] == "europe" checks each row to see if it's in europe.
• df[...] gives you just the rows where that is True.
• You can store this in a new DataFrame like europe_df.

---

🌏 You Could Try

✅ Filtering by a different continent:

asia_df = df[df["continent"] == "asia"]

✅ Filtering by more than one condition:

# countries in asia with gini less than 35
equal_asia_df = df[(df["continent"] == "asia") & (df["gini"] < 35)]

✅ Using | for OR instead of &:

# countries in asia OR europe
asia_or_europe_df = df[(df["continent"] == "asia") | (df["continent"] == "europe")]

---

🎯 Challenge Ideas

• Filter for countries with a GINI of more than 40 - what do you notice about them?
• Find all the countries in a continent of your own choice.
• Filter to see only countries in europe with a GINI of less than 30.

---

🧩 Vocabulary Boost

• DataFrame – A table of data in Pandas.
• Filter – Picking rows that match a condition.
• Boolean – True or False — used in filtering.

---

🔗 Linked Cards

• 🐍 Technique Card: Reading CSV Files with Pandas (practical)
• 🐍 Technique Card: Pandas and Matplotlib Bar Charts (practical)
• 🐍 Technique Card: Understanding Pandas Series and Dataframes (deeper)
• 📊 Technique Card: Working With CSV Files (practical)
• 📊 Technique Card: Telling Stories With Data (deeper)

---

📹 Video Link - Filtering Data

---

📹 Video Link - Filtering With Multiple Conditions

---