Day 2 — Mastering DataFrames

Introduction

Welcome back to Day 2 of our Machine Learning adventure! After exploring the basics, it’s time to delve deeper into one of the pillars of ML: DataFrames. These are the building blocks for data manipulation and analysis, critical for any budding ML engineer. Today, we’ll learn how to create, explore, clean, and manipulate DataFrames using Python’s Pandas library.

Understanding DataFrames

A DataFrame is essentially a table with rows and columns, similar to an Excel spreadsheet. In ML, it’s the go-to structure for handling data. Let’s start by installing Pandas, the powerhouse Python library:

pip install pandas

Creating Your First DataFrame

DataFrames can be created from various sources, but let’s start simple:

import pandas as pd
data = {'Name': ['Anna', 'Brian', 'Catherine'],
        'Age': [28, 34, 22],
        'City': ['Boston', 'Seattle', 'Denver']}
df = pd.DataFrame(data)
print(df)

This snippet creates a DataFrame from a dictionary. Easy, right?

Data Exploration

Understanding your data is key. Pandas offers several methods:

Viewing Data