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Gemika 使用決策樹演算法對霍格華茲學生進行排序的神奇指南（第 7 部分）

王林
發布： 2024-08-07 19:09:31
原創
1056 人瀏覽過

7.施展一熱編碼咒語？ ✨

當我們深入數據魔法的深處時，空氣中瀰漫著羊皮紙的氣味和魔法能量的劈啪聲。今天，我們將探索一種令人著迷的技術，稱為One-Hot Encoding。想像一下，將一個頑皮的小精靈，以其充滿活力的個性和不可預測的天性，轉變為魔法地圖上的一系列精確坐標。親愛的讀者，這就是One-Hot Encoding的本質。 ?‍♂️✨

正如麥格教授的魔杖可以將茶杯變成臘腸犬一樣，這個咒語可以將分類資料（那些拒絕符合數值計算的討厭的標籤）轉變為我們的模型可以理解的格式。可以將其視為將一群混亂的赫夫帕夫、雷文克勞、葛萊分多和史萊哲林變成由 1 和 0 組成的整齊網格，每個網格代表一個特定的學院。 ?✨

透過 One-Hot 編碼，我們為每個獨特的類別建立新列，並用 1 和 0 填充它們以指示存在或不存在。這就像將頑皮的家養小精靈分類到指定的房間中，確保每個小精靈都有自己的空間。到本章結束時，您將能夠像經驗豐富的魔咒大師一樣充滿信心地施展該咒語，將您的數據從糾結的結轉變為組織精美的掛毯。 ?✨

7.1 分類資料：排序帽子和魔法標籤

在數據科學的迷人領域，數字起舞和模式顯露出來，我們遇到了一種奇怪的信息，稱為分類數據。與數字對應不同，這些數據點並不代表數量，而是代表不同的類別或組別。

想像一下霍格華茲的分院帽，這是一個古老的智慧魔法物品，可以將學生分配到他們正確的學院。葛萊分多學院、赫夫帕夫學院、雷文克勞學院和史萊哲林學院都是分類資料的範例。它們代表著具有獨特特徵的不同群體，就像房屋本身一樣。同樣，學生選擇的寵物類型——忠誠的貓頭鷹、咕嚕咕嚕的貓或脾氣暴躁的蟾蜍——也屬於分類數據的範疇。

分類資料就像在物體上放置神奇的標籤，幫助我們區分和分類它們。就像草藥學學生會根據不同植物的特性對其進行仔細分類一樣，我們使用分類資料來排序和理解資料集中的不同元素。透過理解這些神奇的標籤，我們可以解鎖隱藏的模式並施展強大的咒語（分析）來揭開數據的秘密。讓我們尋求一些進一步的知識，房子柱下面有什麼價值，親愛的巫師們，請施展你的魔杖。 ?✨

# Displaying the unique categories in the 'House' column
unique_houses = hogwarts_df['house'].unique()
print(f"Unique Houses: {unique_houses}")
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Unique Houses: ['Gryffindor' 'Slytherin' 'Ravenclaw' 'Hufflepuff' 'Durmstrang' 'Beauxbatons']
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理解這些類別至關重要，因為我們神奇的演算法（或模型）需要知道如何解釋這些數據。然而，這些演算法常常難以處理非數字數據，因為它們更適合處理數字。這就是 One-Hot Encoding 的魔力發揮作用的地方。

7.2 使用 One-Hot 編碼轉換分類資料

想像一下我們的霍格華茲學生記錄，裡面充滿了迷人的細節，例如房子、魔杖類型和最喜歡的科目。這些品質就像神奇的符文一樣，承載著獨特的能量。然而，我們出色的數據模型雖然能夠實現驚人的壯舉，但無法直接破解這些印記。我們必須將它們轉化為它們能夠理解的語言——數字。

輸入One-Hot Encoding咒語，這是一個強大的咒語，揭示了每個分類變數隱藏的本質。這就像在隱藏的密室上施展Lumos咒語，照亮每個角落和縫隙。只要輕輕一揮編碼棒，我們就能將每個類別轉換為自己的獨立列。例如，如果一名學生屬於葛萊芬多，那麼 1 將神奇地出現在葛萊分多欄中，而其他學院欄則保持黑色。

這種轉換類似於創建一個神奇的掛毯，其中每個線程代表一個類別。透過將這些線索編織在一起，我們創建了豐富而詳細的學生畫像，準備好透過我們的資料模型進行分析。就好像我們賦予我們的模型透過複方果汁飲用者的眼睛看世界的能力，體驗每個學生的獨特視角。讓我們繼續嘗試對我們的第一列或功能進行 One-Hot 編碼，首先嘗試性別列。 

# Importing necessary libraries
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
from IPython.display import display, HTML

# Assuming hogwarts_df is already defined and contains the 'gender' column

# Applying One-Hot Encoding to the 'gender' column
encoder = OneHotEncoder(sparse_output=False)  # Updated parameter name
encoded_data = encoder.fit_transform(hogwarts_df[['gender']])

# Converting the encoded data into a DataFrame and attaching it to the original dataset
encoded_df = pd.DataFrame(encoded_data, columns=encoder.get_feature_names_out(['gender']))
hogwarts_df = pd.concat([hogwarts_df, encoded_df], axis=1)

# Dropping the original 'gender' column as it's now encoded
hogwarts_df.drop('gender', axis=1, inplace=True)

# Displaying the transformed DataFrame in a scrollable pane
html = hogwarts_df.head(5).to_html() # Convert DataFrame to HTML
scrollable_html = f"""
<div style="height: 300px; overflow: auto;">
    {html}
</div>
"""
display(HTML(scrollable_html))
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name    age origin  specialty   house   blood_status    pet wand_type   patronus    quidditch_position  boggart favorite_class  house_points    gender_Female   gender_Male
0   Harry Potter    11  England Defense Against the Dark Arts   Gryffindor  Half-blood  Owl Holly   Stag    Seeker  Dementor    Defense Against the Dark Arts   150.0   0.0 1.0
1   Hermione Granger    11  England Transfiguration Gryffindor  Muggle-born Cat Vine    Otter   Seeker  Failure Arithmancy  200.0   1.0 0.0
2   Ron Weasley 11  England Chess   Gryffindor  Pure-blood  Rat Ash Jack Russell Terrier    Keeper  Spider  Charms  50.0    0.0 1.0
3   Draco Malfoy    11  England Potions Slytherin   Pure-blood  Owl Hawthorn    Non-corporeal   Seeker  Lord Voldemort  Potions 100.0   0.0 1.0
4   Luna Lovegood   11  Ireland Creatures   Ravenclaw   Half-blood  Owl Fir Hare    Seeker  Her mother  Creatures   120.0   1.0 0.0
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And if you scroll to the right, you might notice that the dataset now has additional two columns, the gender_Female and the gender_Male on top of the existing one, while dropping the original gender column that was there previously.

7.3 The Two Great Treasures of the Data Realm ?✨

In the grand tapestry of the wizarding world of data, there exist two primary categories of magical artifacts: Structured Data and Unstructured Data. These are the building blocks of our enchanting spells and powerful potions.

Categorical Data is akin to a well-organized Herbology garden, where every plant (data point) has its rightful place. It's like a neatly filled Hogwarts student record, with columns for names, houses, and wand types, all aligned in perfect order. Structured data is a wizard's delight, easily understood and manipulated with a flick of the wand (or a few lines of code). ?✨

On the other hand, Numerical Data is a sprawling Forbidden Forest, filled with magical creatures (data points) roaming freely. It's like a collection of owls' letters, each with its own unique style and format. This data can be as diverse as the stars in the night sky, ranging from social media posts to news articles, images, and even spoken words. While it holds immense potential, taming this wild magic requires special spells and a keen eye for patterns. ??

  1. Categorical Data (Qualitative Data):

    • Definition: Categorical data refers to information that can be sorted into distinct groups or categories based on qualitative characteristics, rather than numerical values.
    • Types:
      • Nominal Data: This type includes categories without any specific order (e.g., gender, hair color). It is often used for labeling variables without providing a numerical value.
      • Ordinal Data: This type has a defined order or ranking (e.g., customer satisfaction ratings, economic class ratings, movie ratings). The differences between the ranks may not be equal.
    • Examples: Gender, race, color, and types of products.
    • Analysis: Categorical data is typically analyzed using frequency counts, bar graphs, and pie charts. It does not support arithmetic operations like addition or averaging.

  2. Numerical Data (Quantitative Data):

    • Definition: Numerical data consists of values that can be measured and expressed numerically, allowing for mathematical operations.
    • Types:
      • Discrete Data: Countable values (e.g., number of students in a class).
      • Continuous Data: Measurable quantities that can take any value within a range (e.g., height, weight).
    • Analysis: Numerical data can be analyzed using various statistical methods, including mean, median, mode, and standard deviation.

7.4 Transforming Text into Numbers: The Magic of One-Hot Encoding ✨

Imagine a world where numbers and words could converse, where the language of magic flowed seamlessly from one to the other. This is the realm of one-hot encoding, a powerful spell that transforms the mysterious world of text into the concrete world of numbers.

Just as a skilled Herbologist categorizes plants by their properties, one-hot encoding sorts textual data into distinct categories. Consider the Sorting Hat, which assigns students to houses based on their unique qualities. Similarly, one-hot encoding creates separate columns for each category, with values of 0 or 1 indicating whether a data point belongs to that category or not.

For instance, if you have a column representing the houses of Hogwarts students (Gryffindor, Slytherin, Ravenclaw, and Hufflepuff), one-hot encoding would conjure four new columns, one for each house. A value of 1 in the Gryffindor column would signify a student belonging to that house, while other columns would be filled with 0s. This numerical representation allows our magical models to understand and process textual information with ease. With that being said, let's try to transform the remaining of the columns in our dataset, and see what we have to work on. ?

# Importing necessary libraries
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
from IPython.display import display, HTML

# Assuming hogwarts_df is already defined and contains the necessary columns
columns_to_encode = [
    'origin', 'specialty', 'blood_status', 'pet', 'wand_type', 'patronus', 'quidditch_position', 'boggart', 'favorite_class'
]

# Creating an instance of OneHotEncoder
encoder = OneHotEncoder(sparse_output=False)

# List to hold encoded DataFrames
encoded_dfs = []

# Applying One-Hot Encoding to each column and storing the result
for column in columns_to_encode:
    encoded_data = encoder.fit_transform(hogwarts_df[[column]])
    encoded_df = pd.DataFrame(encoded_data, columns=encoder.get_feature_names_out([column]))
    encoded_dfs.append(encoded_df)

# Concatenating all encoded DataFrames into one
encoded_df_combined = pd.concat(encoded_dfs, axis=1)

# Concatenating the encoded DataFrame with the original DataFrame
hogwarts_df = pd.concat([hogwarts_df, encoded_df_combined], axis=1)

# Dropping the original columns that were encoded
hogwarts_df.drop(columns=columns_to_encode, inplace=True)

# Displaying the transformed DataFrame in a scrollable pane
html = hogwarts_df.head(5).to_html()  # Convert DataFrame to HTML
scrollable_html = f"""
<div style="height: 300px; overflow: auto;">
    {html}
</div>
"""
display(HTML(scrollable_html))
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name    age house   house_points    gender_Female   gender_Male origin_Bulgaria origin_England  origin_Europe   origin_France   origin_Indonesia    origin_Ireland  origin_Scotland origin_USA  origin_Wales    specialty_Auror specialty_Baking    specialty_Charms    specialty_Chess specialty_Creatures specialty_Dark Arts specialty_Defense Against the Dark Arts specialty_Dueling   specialty_Goat Charming specialty_Gossip    specialty_Herbology specialty_History of Magic  specialty_Household Charms  specialty_Legilimency   specialty_Magical Creatures specialty_Memory Charms specialty_Metamorphmagus    specialty_Muggle Artifacts  specialty_Obscurus  specialty_Potions   specialty_Quidditch specialty_Strength  specialty_Transfiguration   specialty_Transformation    blood_status_Half-blood blood_status_Muggle-born    blood_status_No-mag blood_status_Pure-blood pet_Cat pet_Demiguise   pet_Dog pet_Goat    pet_Owl pet_Phoenix pet_Rat pet_Snake   pet_Toad    wand_type_Alder wand_type_Ash   wand_type_Birch wand_type_Blackthorn    wand_type_Cedar wand_type_Cherry    wand_type_Chestnut  wand_type_Cypress   wand_type_Ebony wand_type_Elder wand_type_Elm   wand_type_Fir   wand_type_Hawthorn  wand_type_Hazel wand_type_Hemlock   wand_type_Holly wand_type_Hornbeam  wand_type_Maple wand_type_Oak   wand_type_Pine  wand_type_Rosewood  wand_type_Rowan wand_type_Sword wand_type_Teak  wand_type_Vine  wand_type_Walnut    wand_type_Willow    wand_type_Yew   patronus_Cat    patronus_Doe    patronus_Dog    patronus_Eagle  patronus_Hare   patronus_Horse  patronus_Jack Russell Terrier   patronus_Lion   patronus_Non-corporeal  patronus_Otter  patronus_Phoenix    patronus_Serpent    patronus_Stag   patronus_Swan   patronus_Wolf   quidditch_position_Azkaban  quidditch_position_Beater   quidditch_position_Chaser   quidditch_position_Keeper   quidditch_position_Seeker   boggart_Ariana's death  boggart_Dementor    boggart_Dueling boggart_Failure boggart_Full Moon   boggart_Her mother  boggart_Lily Potter boggart_Lord Voldemort  boggart_Severus Snape   boggart_Spider  boggart_Tom Riddle  favorite_class_Arithmancy   favorite_class_Baking   favorite_class_Charms   favorite_class_Creatures    favorite_class_Dark Arts    favorite_class_Defense Against the Dark Arts    favorite_class_Dueling  favorite_class_Goat Charming    favorite_class_Gossip   favorite_class_Herbology    favorite_class_Household Charms favorite_class_Legilimency  favorite_class_Memory Charms    favorite_class_Muggle Studies   favorite_class_Obscurus favorite_class_Potions  favorite_class_Quidditch    favorite_class_Strength favorite_class_Transfiguration  favorite_class_Transformation
0   Harry Potter    11  Gryffindor  150.0   0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1   Hermione Granger    11  Gryffindor  200.0   1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2   Ron Weasley 11  Gryffindor  50.0    0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3   Draco Malfoy    11  Slytherin   100.0   0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
4   Luna Lovegood   11  Ravenclaw   120.0   1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
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7.5 Discretizing the Numerical Values: Uncovering the Numerical Data) ✨

In the magical realm of data science, where numbers hold secrets and patterns dance in the shadows, there exists a particularly enchanting spell: one-hot encoding. This spell is a transfiguration charm, capable of transforming seemingly ordinary text into a numerical language that our magical computers can understand. Imagine a bustling Diagon Alley, filled with shops selling wands, cauldrons, and robes of every color. Each shop has a unique name, a string of letters that defines its identity. Now, picture these shop names as magical creatures, wild and untamed. To harness their power for our spells, we must transform them into something more manageable – numbers.

One-hot encoding is the spell that accomplishes this feat. It takes each unique shop name and creates a separate magical dimension (column) for it. Within these dimensions, we cast a binary spell, assigning a value of 1 to the shop that exists in that dimension and 0 to all others. It's like creating a magical grid, where each shop has its own spotlight moment. With this transformation, our once chaotic collection of shop names becomes an orderly array of numbers, ready to be analyzed and explored.?✨

7.5.1 Converting Columns with Numerical Values

To convert a numerical column with values ranging from 100 to 200 into a more machine learning-friendly format using one-hot encoding, you typically need to discretize the numerical values into categorical bins first. Here’s how to do it step by step:

You can create bins (categories) for the numerical values. For example, you might define bins like this:

  • 100-120
  • 121-140
  • 141-160
  • 161-180
  • 181-200

7.5.2 Assign Categories

Next, assign each numerical value to its corresponding bin. For example:

Original Value Category
100 100-120
110 100-120
125 121-140
145 141-160
165 161-180
180 161-180
200 181-200

7.5.3 One-Hot Encode the Categories

Now, you can apply one-hot encoding to the categorical column. Each category will be represented as a binary vector:

Original Value 100-120 121-140 141-160 161-180 181-200
100 1 0 0 0 0
110 1 0 0 0 0
125 0 1 0 0 0
145 0 0 1 0 0
165 0 0 0 1 0
180 0 0 0 1 0
200 0 0 0 0 1

By discretizing the numerical values into categories and applying one-hot encoding, you transform the original numerical data into a format that machine learning algorithms can process effectively. This method captures the categorical nature of the data while preserving the information contained in the original numerical values.

import pandas as pd
from sklearn.preprocessing import OneHotEncoder
from IPython.display import display, HTML

# Step 1: Define bins and labels
bins = [100, 120, 140, 160, 180, 200]  # Define the bin edges
labels = ['hp_100_120', 'hp_121_140', 'hp_141_160', 'hp_161_180', 'hp_181_200']  # Define the bin labels

# Step 2: Create a new categorical column based on the bins
hogwarts_df['house_category'] = pd.cut(hogwarts_df['house_points'], bins=bins, labels=labels, right=True)

# Step 3: One-hot encode the categorical column
hogwarts_df_encoded = pd.get_dummies(hogwarts_df, columns=['house_category'], prefix='', prefix_sep='')

# Replace True with 1 and False with 0
hogwarts_df_encoded = hogwarts_df_encoded.replace({True: 1, False: 0})

# Drop the house_points column
hogwarts_df_encoded.drop('house_points', axis=1, inplace=True)

# Displaying the transformed DataFrame in a scrollable pane
html = hogwarts_df_encoded.head(5).to_html()  # Convert DataFrame to HTML & # Display first 5 rows in a scrollable pane
scrollable_html = f"""
<div style="height: 300px; overflow: auto;">
    {html}
</div>
"""
display(HTML(scrollable_html))
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name    age house   gender_Female   gender_Male origin_Bulgaria origin_England  origin_Europe   origin_France   origin_Indonesia    origin_Ireland  origin_Scotland origin_USA  origin_Wales    specialty_Auror specialty_Baking    specialty_Charms    specialty_Chess specialty_Creatures specialty_Dark Arts specialty_Defense Against the Dark Arts specialty_Dueling   specialty_Goat Charming specialty_Gossip    specialty_Herbology specialty_History of Magic  specialty_Household Charms  specialty_Legilimency   specialty_Magical Creatures specialty_Memory Charms specialty_Metamorphmagus    specialty_Muggle Artifacts  specialty_Obscurus  specialty_Potions   specialty_Quidditch specialty_Strength  specialty_Transfiguration   specialty_Transformation    blood_status_Half-blood blood_status_Muggle-born    blood_status_No-mag blood_status_Pure-blood pet_Cat pet_Demiguise   pet_Dog pet_Goat    pet_Owl pet_Phoenix pet_Rat pet_Snake   pet_Toad    wand_type_Alder wand_type_Ash   wand_type_Birch wand_type_Blackthorn    wand_type_Cedar wand_type_Cherry    wand_type_Chestnut  wand_type_Cypress   wand_type_Ebony wand_type_Elder wand_type_Elm   wand_type_Fir   wand_type_Hawthorn  wand_type_Hazel wand_type_Hemlock   wand_type_Holly wand_type_Hornbeam  wand_type_Maple wand_type_Oak   wand_type_Pine  wand_type_Rosewood  wand_type_Rowan wand_type_Sword wand_type_Teak  wand_type_Vine  wand_type_Walnut    wand_type_Willow    wand_type_Yew   patronus_Cat    patronus_Doe    patronus_Dog    patronus_Eagle  patronus_Hare   patronus_Horse  patronus_Jack Russell Terrier   patronus_Lion   patronus_Non-corporeal  patronus_Otter  patronus_Phoenix    patronus_Serpent    patronus_Stag   patronus_Swan   patronus_Wolf   quidditch_position_Azkaban  quidditch_position_Beater   quidditch_position_Chaser   quidditch_position_Keeper   quidditch_position_Seeker   boggart_Ariana's death  boggart_Dementor    boggart_Dueling boggart_Failure boggart_Full Moon   boggart_Her mother  boggart_Lily Potter boggart_Lord Voldemort  boggart_Severus Snape   boggart_Spider  boggart_Tom Riddle  favorite_class_Arithmancy   favorite_class_Baking   favorite_class_Charms   favorite_class_Creatures    favorite_class_Dark Arts    favorite_class_Defense Against the Dark Arts    favorite_class_Dueling  favorite_class_Goat Charming    favorite_class_Gossip   favorite_class_Herbology    favorite_class_Household Charms favorite_class_Legilimency  favorite_class_Memory Charms    favorite_class_Muggle Studies   favorite_class_Obscurus favorite_class_Potions  favorite_class_Quidditch    favorite_class_Strength favorite_class_Transfiguration  favorite_class_Transformation   hp_100_120  hp_121_140  hp_141_160  hp_161_180  hp_181_200
0   Harry Potter    11  Gryffindor  0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0   0   1   0   0
1   Hermione Granger    11  Gryffindor  1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0   0   0   0   1
2   Ron Weasley 11  Gryffindor  0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0   0   0   0   0
3   Draco Malfoy    11  Slytherin   0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0   0   0   0   0
4   Luna Lovegood   11  Ravenclaw   1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1   0   0   0   0
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7.6 Transforming Numbers into Magical Categories: Discretizing Age ?‍♀️✨

Imagine a bustling Hogwarts Express, filled with students of all ages. From wide-eyed first-years to wise-cracking seventh-years, each student possesses a unique blend of magic and mischief. To better understand these young wizards and witches, we can transform their ages from precise numbers into magical categories.

This process, known as discretization, is like sorting mischievous house-elves into their designated chores. Instead of treating age as a continuous spectrum, we group students into specific age ranges or bins. Picture these bins as enchanted compartments on the Hogwarts Express, each holding students of similar age.

By discretizing age, we unlock a new dimension of magical insight. We can explore how different age groups perform in classes, participate in Quidditch, or even their preferred wand type. It's like casting a revealing charm on our data, uncovering hidden patterns and trends that would otherwise remain obscured. So, let's transform those numbers into magical categories and discover the enchanting secrets hidden within the age of our Hogwarts students! ?✨

import pandas as pd
from IPython.display import display, HTML

# Step 1: Define bins and labels for the age column
bins = [10, 12, 14, 16, 18]  # Define the bin edges
labels = ['age_11', 'age_12', 'age_13', 'age_14']  # Define the bin labels

# Step 2: Create a new categorical column based on the bins
hogwarts_df_encoded['age_category'] = pd.cut(hogwarts_df_encoded['age'], bins=bins, labels=labels, right=True)

# Step 3: One-hot encode the categorical column
hogwarts_df_encoded_age = pd.get_dummies(hogwarts_df_encoded, columns=['age_category'], prefix='', prefix_sep='')

# Replace True with 1 and False with 0 (not necessary here since get_dummies already returns integers)
hogwarts_df_encoded_age = hogwarts_df_encoded_age.replace({True: 1, False: 0})

# Drop the age column
hogwarts_df_encoded_age.drop('age', axis=1, inplace=True)

# Displaying the transformed DataFrame in a scrollable pane
html = hogwarts_df_encoded_age.head(5).to_html()  # Convert DataFrame to HTML & Display first 5 rows in a scrollable pane
scrollable_html = f"""
<div style="height: 300px; overflow: auto;">
    {html}
</div>
"""
display(HTML(scrollable_html))
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7.7 Order in the Chaos: The Magic of Naming Conventions ? ✨

Imagine a bustling Hogwarts library, books overflowing from shelves, each with its own peculiar title. Without a proper cataloguing system, finding a specific spellbook would be like searching for a needle in a haystack, wouldn't it? In the realm of data, our columns are like these books. They hold valuable information, but without a clear and consistent naming system, they can quickly become a chaotic mess. This is where the magic of naming conventions comes into play.

Just as a librarian organizes books by subject, author, and title, we must impose order upon our columns. A well-crafted naming convention is like a powerful sorting spell, grouping similar columns together and making them easily identifiable. For example, using prefixes like 'student_', 'subject_', or 'score_' can instantly clarify the column's purpose.

By adopting a clear and consistent naming convention, you'll transform your data from a chaotic jumble into a well-organized magical library. This not only improves readability but also streamlines data manipulation and analysis. So, next time you're wrangling with data, remember the importance of a strong naming convention. It's the first step towards unlocking the hidden treasures within your dataset! ✨

import pandas as pd
from IPython.display import display, HTML

# Assuming hogwarts_df_encoded is already defined and contains the necessary columns

# Manipulate column names
hogwarts_df_encoded_age.columns = hogwarts_df_encoded_age.columns.str.lower().str.replace(' ', '_').str.replace('-', '-').str.replace("'", "")

# Display the transformed DataFrame in a scrollable pane
html = hogwarts_df_encoded_age.head(5).to_html()  # Convert first 5 rows to HTML
scrollable_html = f"""
<div style="height: 300px; overflow: auto;">
    {html}
</div>
"""
display(HTML(scrollable_html))  # Display first 5 rows in a scrollable pane
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name    house   gender_female   gender_male origin_bulgaria origin_england  origin_europe   origin_france   origin_indonesia    origin_ireland  origin_scotland origin_usa  origin_wales    specialty_auror specialty_baking    specialty_charms    specialty_chess specialty_creatures specialty_dark_arts specialty_defense_against_the_dark_arts specialty_dueling   specialty_goat_charming specialty_gossip    specialty_herbology specialty_history_of_magic  specialty_household_charms  specialty_legilimency   specialty_magical_creatures specialty_memory_charms specialty_metamorphmagus    specialty_muggle_artifacts  specialty_obscurus  specialty_potions   specialty_quidditch specialty_strength  specialty_transfiguration   specialty_transformation    blood_status_half-blood blood_status_muggle-born    blood_status_no-mag blood_status_pure-blood pet_cat pet_demiguise   pet_dog pet_goat    pet_owl pet_phoenix pet_rat pet_snake   pet_toad    wand_type_alder wand_type_ash   wand_type_birch wand_type_blackthorn    wand_type_cedar wand_type_cherry    wand_type_chestnut  wand_type_cypress   wand_type_ebony wand_type_elder wand_type_elm   wand_type_fir   wand_type_hawthorn  wand_type_hazel wand_type_hemlock   wand_type_holly wand_type_hornbeam  wand_type_maple wand_type_oak   wand_type_pine  wand_type_rosewood  wand_type_rowan wand_type_sword wand_type_teak  wand_type_vine  wand_type_walnut    wand_type_willow    wand_type_yew   patronus_cat    patronus_doe    patronus_dog    patronus_eagle  patronus_hare   patronus_horse  patronus_jack_russell_terrier   patronus_lion   patronus_non-corporeal  patronus_otter  patronus_phoenix    patronus_serpent    patronus_stag   patronus_swan   patronus_wolf   quidditch_position_azkaban  quidditch_position_beater   quidditch_position_chaser   quidditch_position_keeper   quidditch_position_seeker   boggart_arianas_death   boggart_dementor    boggart_dueling boggart_failure boggart_full_moon   boggart_her_mother  boggart_lily_potter boggart_lord_voldemort  boggart_severus_snape   boggart_spider  boggart_tom_riddle  favorite_class_arithmancy   favorite_class_baking   favorite_class_charms   favorite_class_creatures    favorite_class_dark_arts    favorite_class_defense_against_the_dark_arts    favorite_class_dueling  favorite_class_goat_charming    favorite_class_gossip   favorite_class_herbology    favorite_class_household_charms favorite_class_legilimency  favorite_class_memory_charms    favorite_class_muggle_studies   favorite_class_obscurus favorite_class_potions  favorite_class_quidditch    favorite_class_strength favorite_class_transfiguration  favorite_class_transformation   hp_100_120  hp_121_140  hp_141_160  hp_161_180  hp_181_200  age_11  age_12  age_13  age_14
0   Harry Potter    Gryffindor  0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0   0   1   0   0   1   0   0   0
1   Hermione Granger    Gryffindor  1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0   0   0   0   1   1   0   0   0
2   Ron Weasley Gryffindor  0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0   0   0   0   0   1   0   0   0
3   Draco Malfoy    Slytherin   0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0   0   0   0   0   1   0   0   0
4   Luna Lovegood   Ravenclaw   1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1   0   0   0   0   1   0   0   0
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And once we've satisfied with the results, let's go ahead and save the current dataset to make the next enchanting journey more easier to navigate.

hogwarts_df_encoded_age.to_csv('data/hogwarts-students-03.csv', index=False)
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7.8 Mapping the Magical Connections: Uncovering Hidden Relationships

Imagine a vast enchanted forest, teeming with magical creatures and extraordinary plants. Each creature and plant possesses unique qualities, and some may share hidden connections. To unveil these mysterious bonds, we must cast a spell of correlation. A correlation matrix is like a magical map, guiding us through this enchanted forest. Each point on the map represents a different creature or plant, and the lines connecting them reveal the strength of their relationship. A thick, vibrant line signifies a strong connection, while a thin, faint line indicates a weaker bond.

As we explore this magical map, we search for patterns and trends. Are certain creatures often found near specific plants? Do particular plants thrive in the company of others? By deciphering these connections, we can uncover hidden knowledge about the forest and its inhabitants. Just as a skilled Herbologist studies the interactions between plants, we, as data wizards, uncover the hidden relationships between variables. With the correlation matrix as our guide, we embark on a thrilling adventure to explore the magical tapestry of our data! ?️✨

# Importing necessary libraries
import pandas as pd
from IPython.display import display, HTML

# Assuming hogwarts_df is already defined and contains the necessary columns

# Selecting only numerical columns
numerical_df = hogwarts_df_encoded_age.select_dtypes(include=['number'])

# Calculating the correlation matrix
correlation_matrix = numerical_df.corr()

# Displaying the correlation matrix in a scrollable pane
correlation_html = correlation_matrix.to_html()  # Convert correlation matrix to HTML
scrollable_correlation_html = f"""
<div style="height: 300px; overflow: auto;">
    {correlation_html}
</div>
"""
display(HTML(scrollable_correlation_html))  # Display correlation matrix in a scrollable pane
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The Gemika

And to make our analysis easier, let's just go ahead and save the correlation matrix into a tabular format.

correlation_matrix.to_csv('data/correlation-matrix.csv')
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7.9 Potion Ingredients: Verifying Our Data Types ?✨

Just as a skilled potioneer carefully examines their ingredients before brewing a powerful concoction, we data scientists must meticulously inspect our data types. These data types are like the magical properties of our ingredients, determining how they will react in our spells (algorithms).

Imagine our dataset as a cauldron brimming with magical elements. Each element, be it a student's age, house, or wand type, has a specific form or essence - its data type. These types can be as diverse as the magical creatures of the Forbidden Forest: numbers (like the count of house points), text (like a student's name), dates (like the founding year of Hogwarts), and more.

Mismatched data types can lead to disastrous results, like a potion exploding or a spell backfiring. That's why we must cast a discerning eye over our data, ensuring each element is of the correct type. It's like checking if a Mandrake root is truly a root and not a disguised Goblin! Only then can we confidently proceed with our magical data transformations. ?‍♀️✨

# Importing necessary libraries
import pandas as pd
from IPython.display import display, HTML

# Assuming hogwarts_df is already defined and contains the necessary columns

# Setting display options to show all columns and prevent truncation
pd.set_option('display.max_columns', None)  # Show all columns
pd.set_option('display.expand_frame_repr', False)  # Prevent truncation in output

# Checking the data types of each column
data_types_df = hogwarts_df_encoded_age.dtypes.to_frame(name='Data Type')  # Convert data types to a DataFrame

# Displaying the data types in a scrollable pane
data_types_html = data_types_df.to_html()  # Convert DataFrame to HTML
scrollable_data_types_html = f"""
<div style="height: 150px; overflow: auto;">
    {data_types_html}
</div>
"""
display(HTML(scrollable_data_types_html))  # Display data types in a scrollable pane
登入後複製
    Data Type
name    object
house   object
gender_female   float64
gender_male float64
origin_bulgaria float64
origin_england  float64
origin_europe   float64
origin_france   float64
origin_indonesia    float64
origin_ireland  float64
origin_scotland float64
origin_usa  float64
origin_wales    float64
specialty_auror float64
specialty_baking    float64
specialty_charms    float64
specialty_chess float64
specialty_creatures float64
specialty_dark_arts float64
specialty_defense_against_the_dark_arts float64
specialty_dueling   float64
specialty_goat_charming float64
specialty_gossip    float64
specialty_herbology float64
specialty_history_of_magic  float64
specialty_household_charms  float64
specialty_legilimency   float64
specialty_magical_creatures float64
登入後複製

7.10 Gemika's Pop-Up Quiz: Spotting the Trends

And now, dear apprentices, Gemika Haziq Nugroho has prepared a quiz to test your understanding of this powerful encoding spell. Are you ready to decode the mysteries of One-Hot Encoding?

  • What is categorical data, and why is it important in data analysis?
  • How does One-Hot Encoding transform categorical data for machine learning models?
  • Why do we drop the original categorical column after applying One-Hot Encoding?

Answer these questions to prove your mastery over the art of data transformation. As we continue our journey through the magical world of data science, remember that each spell we learn brings us closer to unveiling the secrets of our enchanted dataset. ??‍♂️✨

With these newfound skills, you're well-equipped to handle categorical data in any dataset. The path to becoming a master data wizard is full of wonder and discovery, and with each step, we draw closer to the heart of the magical data that surrounds us. Let us press on, eager to learn and ready to explore! ??

以上是Gemika 使用決策樹演算法對霍格華茲學生進行排序的神奇指南（第 7 部分）的詳細內容。更多資訊請關注PHP中文網其他相關文章！

來源：dev.to
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