Tutorial Description

Welcome to the Tutorial 5 on "Pandas Filtering". Here's a brief overview of what you can expect in this tutorial:

Introduction to the Dataset

We'll start with a glance at a dataset detailing vessel specifications and sale information. The dataset contains various columns such as:

• Saledate, Vesselname, IMOnumber, BuildDate, Dwt, Builder, SalePrice, and others.

Importing Libraries and Dataset

• Understand how to use Python's pandas library to import and manipulate data. Here, you'll see the potential of handling large datasets with just a few lines of code.

Data Cleaning

• Learn how to handle missing values, detect outliers, and reformat data to make it consistent and analysis-ready.

Exploratory Data Analysis (EDA)

Explore the dataset to discover some patterns. We'll be looking at:

• Top vessel builders and countries
• Price distribution of vessels
• And more!

Advanced Filtering

• Learn to filter data based on specific conditions, such as filtering vessels with a specific Dwt range and grain capacity.