Day 19 of 30Real Python & Data

🐼Pandas — DataFrames

🎯 Today's objective

Load and explore tabular data like a spreadsheet in code.

📖 Key concepts

DataFrame = rows × columns table
pd.read_csv() loads data
.head(), .info(), .describe()
Select columns df["col"], filter df[df.col > x]

💻 Try this code

import pandas as pd
df = pd.DataFrame({"name": ["A","B","C"], "score": [88, 71, 95]})
print(df.head())
print(df["score"].mean())
print(df[df["score"] > 80])

👥 Learn it your way

🧒 Young Explorers

Pandas is a spreadsheet you control with code — sort, filter, and total in one line.

Your task

Build a DataFrame of 4 students & marks; print the topper and the class average.

💼 Professionals

Learn indexing (loc/iloc), groupby, and handling missing values (NaN).

Your task

Load any CSV, group by a category, and compute mean of a numeric column.

🤖 AI connection

Real ML starts in Pandas: loading, cleaning, and shaping data into the rows/columns a model expects. ~80% of ML work is this data wrangling.

✅ Quick check

What does df.head() show?

The first few rows of the DataFrame.

How do you load a CSV?

pd.read_csv("file.csv")