Pandas for Data Handling

While NumPy excels at numerical operations, Pandas is designed for working with tabular data - the format most real-world data comes in. If you've ever worked with spreadsheets, Pandas will feel familiar.

DataFrames: Your Data Home

A DataFrame is like a spreadsheet in Python - rows and columns with labels.

Loading Data from Files

In real ML projects, you'll load data from CSV files, databases, or APIs.

Exploring Your Data

Before training a model, you must understand your data.

Selecting Data for ML

Handling Missing Data

Real-world data often has missing values. ML models can't handle NaN values.

Converting Categorical Data

ML models need numbers, but real data often has categories.

Preparing Data for ML

Practice: Prepare a Dataset

Key Takeaways

• DataFrames are the standard format for tabular data
• Use head(), describe(), info() to explore data
• Handle missing values with fillna() or dropna()
• Convert categorical data with get_dummies() or label encoding
• Split into features (X) and target (y) before training
• Convert to NumPy arrays with .values for sklearn

Next, we'll learn scikit-learn - the library that makes ML accessible!