The Gemika's Magical Guide to Sorting Hogwarts Students using the Decision Tree Algorithm (Part #3)
Tags: machinelearning, ai, python, harrypotter
Published: 2024-07-07 00:16:25

## 3. Exploring the Enchanted Dataset 🌟

![Exploring the Enchanted Dataset](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/58bljs6vasmdpd2pvkql.jpg)

Welcome back, young **wizards and witches**! As we gather around the glowing hearth of the **Gryffindor** common room, it is time to delve into the heart of our magical quest: the enchanted dataset. Imagine this dataset as a map of the wizarding world, filled with secrets and mysteries waiting to be uncovered. Each row is a character, each column a spell, and together they tell the story of our beloved Hogwarts.

### **3.1 Introduction to the Dataset**

In the **wizarding world of data science**, our dataset is akin to the ancient scrolls stored in the Restricted Section of the Hogwarts Library. This particular dataset holds information about various **Hogwarts students**, their traits, and the houses they belong to. Much like the Sorting Hat, we will use this data to uncover patterns and predict future house placements. But first, let us familiarize ourselves with the contents of this magical scroll.

### **3.2 Loading Libraries in Python**

Before we can reveal the secrets of our dataset, we must first gather our magical tools. In the realm of data science, these tools come in the form of Python libraries. Think of them as our spell books, each containing powerful incantations that will help us manipulate and visualize our data. We will summon these libraries using the following spells:

```python
# Importing the necessary libraries for our magical journey
import pandas as pd  # For data manipulation
import numpy as np  # For numerical operations
import matplotlib.pyplot as plt  # For data visualization
import seaborn as sns  # For advanced data visualization

# Ensuring our charts are in line with the Hogwarts aesthetic
sns.set(style="whitegrid")
```

### **3.3 Reading the Dataset into a Pandas DataFrame**

With our spell books at the ready, it is time to conjure the dataset into a form we can work with. Using the mystical powers of *[pandas](https://pandas.pydata.org/)*, we will transform the dataset into a DataFrame, much like Professor McGonagall transfigures a desk into a pig. This DataFrame will be our primary tool for exploring and manipulating the data.

```python
# Reading the enchanted dataset into a Pandas DataFrame
dataset_path = '/mnt/data/hogwarts-students.csv'  # Path to our dataset
hogwarts_df = pd.read_csv(dataset_path)

# Displaying the first few rows of the dataset to get a glimpse of its contents
print(hogwarts_df.head())
```

*Ah, look at that!* The first few rows of our DataFrame appear before us like the Marauder's Map, revealing the names, traits, and house placements of our fellow students. Each row tells a unique story, and together, they form the tapestry of Hogwarts.

### **3.4 Gemika's Pop-Up Quiz: Exploring the Enchanted Dataset**

And now, dear reader, my son **Gemika Haziq Nugroho** appears with a twinkle in his eye and a quiz in hand. He has prepared a series of questions to test your knowledge and ensure you are ready to proceed. Are you prepared to face the challenge?

1. **What Python library is used to read the dataset into a DataFrame?**
2. **How do you display the first few rows of a DataFrame?**
3. **What is the purpose of the `sns.set(style="whitegrid")` command?**

Answer these questions correctly, and you will have proven your understanding of the enchanted dataset. Only then can we proceed to uncover the deeper mysteries that lie within. With our dataset unveiled and our understanding tested, we are now ready to embark on the next phase of our journey. The secrets of Hogwarts await, and with our wands and wisdom, we shall uncover them all. Onward, to adventure and discovery! 🌟✨🧙‍♂️