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The Gemika's Magical Guide to Sorting Hogwarts Students using the Decision Tree Algorithm (Part #4)

gerryleonugroho

gerry leo nugroho

Posted on July 17, 2024

The Gemika's Magical Guide to Sorting Hogwarts Students using the Decision Tree Algorithm (Part #4)

4. Unveiling the Mysteries: Data Exploration (EDA) 🔍

Welcome back to the enchanting halls of Hogwarts, dear sorcerers! As we continue our magical journey into the world of data science, it's time to unveil the mysteries hidden within our dataset. In this chapter, we'll embark on a series of explorations that will reveal the secrets of our enchanted scroll (or dataset). Think of this as delving into the depths of the Room of Requirement, where each discovery leads us to greater understanding. ✨🧙‍♂️

4.1 Inspecting First Few Rows

Our first step is to take a glimpse at the first few rows of our dataset, much like opening the Marauder's Map for the first time. This will give us an initial understanding of the structure and contents of our data. And dear sorcerers, if you wished to follow along on this magical journey, make a fork of the dataset from my Github account and download the dataset to your local machine from this magical address.



# Inspecting the first few rows of the dataset
dataset_path = 'data/hogwarts-students.csv'  # Path to our dataset
hogwarts_df = pd.read_csv(dataset_path)
print(hogwarts_df.head())
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name  gender  age   origin                      specialty  \
0      Harry Potter    Male   11  England  Defense Against the Dark Arts   
1  Hermione Granger  Female   11  England                Transfiguration   
2       Ron Weasley    Male   11  England                          Chess   
3      Draco Malfoy    Male   11  England                        Potions   
4     Luna Lovegood  Female   11  Ireland                      Creatures   

        house blood_status  pet wand_type              patronus  \
0  Gryffindor   Half-blood  Owl     Holly                  Stag   
1  Gryffindor  Muggle-born  Cat      Vine                 Otter   
2  Gryffindor   Pure-blood  Rat       Ash  Jack Russell Terrier   
3   Slytherin   Pure-blood  Owl  Hawthorn                   NaN   
4   Ravenclaw   Half-blood  NaN       Fir                  Hare   

  quidditch_position         boggart                 favorite_class  \
0             Seeker        Dementor  Defense Against the Dark Arts   
1                NaN         Failure                     Arithmancy   
2             Keeper          Spider                         Charms   
3             Seeker  Lord Voldemort                        Potions   
4                NaN      Her mother                      Creatures   

   house_points  
0         150.0  
1         200.0  
2          50.0  
3         100.0  
4         120.0
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As we peer into these rows, we see a variety of features such as student names, house affiliations, and various traits. Each row is a story, each column a chapter. We might notice, for example, that Harry, Hermione, and Ron are all in Gryffindor, characterized by their bravery and determination. This initial inspection helps us understand the scope and scale of our dataset.

4.2 Checking Dataset Features

Next, we delve deeper into the columns of our DataFrame, much like how Hermione would meticulously study her textbooks. Each column represents a different feature of our students, from their house to their magical abilities.



# Displaying the columns of the dataset
print(hogwarts_df.columns)
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As the magic spell finished its wizardry, the previous magical spell reveal the following hidden artifacts.



Index(['name', 'gender', 'age', 'origin', 'specialty', 'house', 'blood_status',
       'pet', 'wand_type', 'patronus', 'quidditch_position', 'boggart',
       'favorite_class', 'house_points'],
      dtype='object')
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# Displaying the how many rows and columns in the dataset
print(hogwarts_df.shape)
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And you're guessing correctly sorcerers, the dataset consists of 52 rows and 14 columns.✨🌟



(52, 14)
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Let us explore these features, each as significant as a spell component in a well-crafted incantation:

  • Name: The given name of our witch or wizard, from the illustrious Harry Potter to the enigmatic Luna Lovegood. 🌟
  • Gender: Whether they are a young wizard or witch, reflecting the diversity of Hogwarts.
  • Age: Their age at the time of sorting, for even the youngest students have their place in the castle's storied history.
  • Origin: The place they hail from, be it the rolling hills of England, the rugged highlands of Scotland, or the enchanting isles of Ireland. 🏞️
  • Specialty: Their area of magical expertise, such as Potions, Transfiguration, or Defense Against the Dark Arts, much like Professor Snape’s mastery of the subtle art of potion-making.
  • House: The revered house to which they belong — Gryffindor, Hufflepuff, Ravenclaw, or Slytherin — each with its own rich traditions and values.
  • Blood Status: Whether they are Pure-blood, Half-blood, or Muggle-born, a detail that, while significant in the wizarding world, never diminishes their magical potential.
  • Pet: Their chosen magical companion, be it an owl, a cat, or a toad, reminiscent of Harry's loyal Hedwig or Hermione's clever Crookshanks. 🦉🐈
  • Wand Type: The wood and core of their wand, the very tool of their magical prowess.
  • Patronus: The form their Patronus takes, a magical manifestation of their innermost self, like Harry's proud stag or Snape's ethereal doe. 🦌
  • Quidditch Position: Their role in the beloved wizarding sport, whether Seeker, Chaser, Beater, or Keeper, or perhaps no position at all.
  • Boggart: The form their Boggart takes, a glimpse into their deepest fears.
  • Favorite Class: The subject they excel in or enjoy the most, akin to Hermione's love for Arithmancy or Neville's talent in Herbology.
  • House Points: Points they have contributed to their house, reflecting their achievements and misadventures alike.

With this compendium of magical features, we craft our dataset with the precision of a spell-wright composing a new enchantment. Each character's details are meticulously recorded, ensuring that our data is as rich and detailed as the tapestry of Hogwarts itself.🧙‍♂️🏰

By examining these features, we gain a deeper understanding of the dataset's richness, much like a wizard learning about the different properties of magical creatures. As we assemble this treasure trove of information, we prepare ourselves for the next step in our magical journey—transforming these attributes into the foundations upon which our Decision Tree algorithm will cast its spell. Let us proceed, dear sorcerers, for the magic is only just beginning.✨🧙‍♂️

4.3 Inspecting Data Types

With a clear understanding of our features, we now turn our attention to the data types. This step is akin to examining the ingredients of a potion, ensuring each component is appropriate for its intended use.



# Checking the data types of each column
print(hogwarts_df.dtypes)
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And in return, the previous magic spell would yield us, dear sorcerers the following incarnations.



name                   object
gender                 object
age                     int64
origin                 object
specialty              object
house                  object
blood_status           object
pet                    object
wand_type              object
patronus               object
quidditch_position     object
boggart                object
favorite_class         object
house_points          float64
dtype: object
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Wow, would you look at that, we've just discovered a lot of data types inconsistencies within the dataset. The data types had told us whether each column contains numerical values, text, or other forms of data. For instance, Age should be a numerical type, while Name and House are text (or string) types. Ensuring these types are correct is crucial for our subsequent analyses and visualizations.

4.4 Incorrect Data Type

Occasionally, we may find discrepancies in the data types, much like finding a rogue ingredient in a potion. Correcting these mismatches is essential to ensure the accuracy of our spells (or analyses). So let's just spin our wands (should I say Jupyter Lab), and try to fix them this time.



# Converting data types if necessary
# First, let's check the columns again to identify the correct names
print(hogwarts_df.columns)
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Index(['name', 'gender', 'age', 'origin', 'specialty', 'house', 'blood_status',
       'pet', 'wand_type', 'patronus', 'quidditch_position', 'boggart',
       'favorite_class', 'house_points'],
      dtype='object')
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Among one of the requirements to perform the magical data sorcery tasks, is that you need to have a clean dataset that is by its naming convention is easy to follow and easy to work with at the same time. Now, let's try to change the data types according to its nature, by means to have an easier dataset to navigate with according to our next enchanted upcoming magical spells.



# Assuming we identified 'age' as the correct column name for age
hogwarts_df['age'] = pd.to_numeric(hogwarts_df['age'], errors='coerce')  # Ensure Age is numeric

# Ensuring 'gender' is categorical
hogwarts_df['gender'] = hogwarts_df['gender'].astype('category')  # Ensure Gender is categorical

# Ensuring 'specialty' is categorical
hogwarts_df['specialty'] = hogwarts_df['specialty'].astype('category')  # Ensure specialty is categorical

# Ensuring 'house' is categorical
hogwarts_df['house'] = hogwarts_df['house'].astype('category')  # Ensure house is categorical

# Ensuring 'blood_status' is categorical
hogwarts_df['blood_status'] = hogwarts_df['blood_status'].astype('category')  # Ensure blood_status is categorical

# Ensuring 'pet' is categorical
hogwarts_df['pet'] = hogwarts_df['pet'].astype('category')  # Ensure pet is categorical

# Ensuring 'wand_type' is categorical
hogwarts_df['wand_type'] = hogwarts_df['wand_type'].astype('category')  # Ensure wand_type is categorical

# Ensuring 'quidditch_position' is categorical
hogwarts_df['quidditch_position'] = hogwarts_df['quidditch_position'].astype('category')  # Ensure quidditch_position is categorical

# Ensuring 'favorite_class' is categorical
hogwarts_df['favorite_class'] = hogwarts_df['favorite_class'].astype('category')  # Ensure favorite_class is categorical
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By casting these spells, we ensure that each column is of the appropriate type, ready for further exploration and manipulation. This step is much like Snape meticulously adjusting the ingredients of a complex potion to achieve the perfect brew. Now, once we've done the previous spell, the Jupyter would yield us the following updated results.

Now let's verify the previous spell has done it magical course towards our dataset by invoking the following spell again.



# Verify the data types after conversion
print(hogwarts_df.dtypes)
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name                    object
gender                category
age                      int64
origin                  object
specialty             category
house                 category
blood_status          category
pet                   category
wand_type             category
patronus                object
quidditch_position    category
boggart                 object
favorite_class        category
house_points           float64
dtype: object
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4.5 Spells and Charms to Convert Data Types

In case you dear sorcerers are wondering how many data types pandas is capable of supporting, following are all the list of them and ways to manipulate them in orders.

Data Type Description Example Values Conversion Method
int64 Integer values 1, 2, 3, -5, 0 pd.to_numeric(df['column'])
float64 Floating point numbers 1.0, 2.5, -3.4, 0.0 pd.to_numeric(df['column'])
bool Boolean values True, False df['column'].astype('bool')
object String values 'apple', 'banana', '123' df['column'].astype('str')
datetime64[ns] Date and time values '2024-07-17', '2023-01-01 12:00' pd.to_datetime(df['column'])
timedelta[ns] Differences between datetimes '1 days 00:00:00', '2 days 03:04:05' pd.to_timedelta(df['column'])
category Categorical data 'A', 'B', 'C' df['column'].astype('category')

4.6 Reinvestigate The Data Type in The Dataset

Having ensured the correctness of our data types, it's time to take a more comprehensive look at our dataset. This step is akin to casting a revealing charm over a hidden room, allowing us to see everything at once.



# Displaying a summary of the entire data types
print(hogwarts_df.info())
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By previewing the whole dataset, we gain a holistic view of its structure, contents, and summary statistics. This comprehensive overview helps us identify any remaining inconsistencies or areas that require further attention, much like a careful sweep of the castle grounds to ensure everything is in order, as the following results.



<class 'pandas.core.frame.DataFrame'>
RangeIndex: 52 entries, 0 to 51
Data columns (total 14 columns):
 #   Column              Non-Null Count  Dtype   
---  ------              --------------  -----   
 0   name                52 non-null     object  
 1   gender              52 non-null     category
 2   age                 52 non-null     int64   
 3   origin              52 non-null     object  
 4   specialty           52 non-null     category
 5   house               52 non-null     category
 6   blood_status        52 non-null     category
 7   pet                 27 non-null     category
 8   wand_type           52 non-null     category
 9   patronus            50 non-null     object  
 10  quidditch_position  10 non-null     category
 11  boggart             52 non-null     object  
 12  favorite_class      51 non-null     category
 13  house_points        50 non-null     float64 
dtypes: category(8), float64(1), int64(1), object(4)
memory usage: 6.8+ KB
None
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4.7 Detailed Summary of Dataset

And here's the interesting part, how one sorcerers may see thing from a high level overview, while this time the spell would give us the following information about the dataset. It's a bit statistical for sure, but fear not dear sorcerers, as you scroll forward, you'll notice couple of other stunning facts around Hogwarts students.



print(hogwarts_df.describe(include='all'))  # Providing a detailed summary of the dataset
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                name gender        age   origin specialty       house  \
count             52     52  52.000000       52        52          52   
unique            52      2        NaN        9        24           6   
top     Harry Potter   Male        NaN  England    Charms  Gryffindor   
freq               1     27        NaN       35         7          18   
mean             NaN    NaN  14.942308      NaN       NaN         NaN   
std              NaN    NaN   2.492447      NaN       NaN         NaN   
min              NaN    NaN  11.000000      NaN       NaN         NaN   
25%              NaN    NaN  13.250000      NaN       NaN         NaN   
50%              NaN    NaN  16.000000      NaN       NaN         NaN   
75%              NaN    NaN  17.000000      NaN       NaN         NaN   
max              NaN    NaN  18.000000      NaN       NaN         NaN   

       blood_status  pet wand_type       patronus quidditch_position  boggart  \
count            52   27        52             50                 10       52   
unique            4    9        28             15                  5       11   
top      Half-blood  Owl       Fir  Non-corporeal             Seeker  Failure   
freq             25   11         4             34                  5       40   
mean            NaN  NaN       NaN            NaN                NaN      NaN   
std             NaN  NaN       NaN            NaN                NaN      NaN   
min             NaN  NaN       NaN            NaN                NaN      NaN   
25%             NaN  NaN       NaN            NaN                NaN      NaN   
50%             NaN  NaN       NaN            NaN                NaN      NaN   
75%             NaN  NaN       NaN            NaN                NaN      NaN   
max             NaN  NaN       NaN            NaN                NaN      NaN   

       favorite_class  house_points  
count              51     50.000000  
unique             21           NaN  
top            Charms           NaN  
freq                8           NaN  
mean              NaN    119.200000  
std               NaN     54.129097  
min               NaN     10.000000  
25%               NaN     72.500000  
50%               NaN    125.000000  
75%               NaN    160.000000  
max               NaN    200.000000
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From the summary, we can infer several interesting points:

  • House Distribution: Gryffindor has the highest count with 18 students, showing its prominence.
  • Age: The average age of students is around 14.94 years, with the youngest being 11 and the oldest 18.
  • Gender: The dataset includes 27 males and 25 females, showing a fairly balanced gender distribution.
  • Blood Status: Half-bloods are the most common, with 25 occurrences, indicating a diverse student body.
  • Wands and Pets: There are 28 unique wand types and 9 different pet types, reflecting the unique personalities and preferences of the students.
  • Quidditch: Only a few students play Quidditch, with Seeker being the most common position.
  • Favorite Class: Charms is the most favored class among students, with 8 mentions.
  • House Points: The average house points are 119.2, with a standard deviation of 54.13, indicating a wide range of performance.

4.8 Preview the whole Dataset

For the curious mind that their thoughts flew as fast as their broomstick, here's the magic spell to display the whole values within the dataset.



# Displaying a summary of the entire dataset
print(hogwarts_df.to_string())
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                    name  gender  age     origin                      specialty        house blood_status        pet   wand_type              patronus quidditch_position         boggart                 favorite_class  house_points
0           Harry Potter    Male   11    England  Defense Against the Dark Arts   Gryffindor   Half-blood        Owl       Holly                  Stag             Seeker        Dementor  Defense Against the Dark Arts         150.0
1       Hermione Granger  Female   11    England                Transfiguration   Gryffindor  Muggle-born        Cat        Vine                 Otter                NaN         Failure                     Arithmancy         200.0
2            Ron Weasley    Male   11    England                          Chess   Gryffindor   Pure-blood        Rat         Ash  Jack Russell Terrier             Keeper          Spider                         Charms          50.0
3           Draco Malfoy    Male   11    England                        Potions    Slytherin   Pure-blood        Owl    Hawthorn                   NaN             Seeker  Lord Voldemort                        Potions         100.0
4          Luna Lovegood  Female   11    Ireland                      Creatures    Ravenclaw   Half-blood        NaN         Fir                  Hare                NaN      Her mother                      Creatures         120.0
5     Neville Longbottom    Male   11    England                      Herbology   Gryffindor   Pure-blood       Toad      Cherry         Non-corporeal                NaN   Severus Snape                      Herbology          70.0
6          Ginny Weasley  Female   11    England  Defense Against the Dark Arts   Gryffindor   Pure-blood        Owl         Yew                 Horse             Chaser      Tom Riddle  Defense Against the Dark Arts         140.0
7         Cedric Diggory    Male   15    England                      Quidditch   Hufflepuff   Pure-blood        NaN         Ash         Non-corporeal             Seeker         Failure  Defense Against the Dark Arts         160.0
8              Cho Chang  Female   14   Scotland                         Charms    Ravenclaw   Half-blood        Owl       Hazel                  Swan             Seeker         Failure                         Charms         110.0
9          Severus Snape    Male   16    England                        Potions    Slytherin   Half-blood        NaN         Elm                   Doe                NaN     Lily Potter                        Potions          90.0
10      Albus Dumbledore    Male   17    England                Transfiguration   Gryffindor   Half-blood    Phoenix       Elder               Phoenix                NaN  Ariana's death                Transfiguration         200.0
11    Minerva McGonagall  Female   16   Scotland                Transfiguration   Gryffindor   Half-blood        Cat         Fir                   Cat                NaN         Failure                Transfiguration         190.0
12   Bellatrix Lestrange  Female   15    England                      Dark Arts    Slytherin   Pure-blood        NaN      Walnut                   NaN            Azkaban         Dueling                             80           NaN
13      Nymphadora Tonks  Female   14      Wales                 Metamorphmagus   Hufflepuff   Half-blood        Owl  Blackthorn                  Wolf                NaN         Failure  Defense Against the Dark Arts         130.0
14           Remus Lupin    Male   16    England  Defense Against the Dark Arts   Gryffindor   Half-blood        Dog     Cypress         Non-corporeal                NaN       Full Moon  Defense Against the Dark Arts         150.0
15          Sirius Black    Male   16    England                Transfiguration   Gryffindor   Pure-blood        Owl    Chestnut                   Dog             Beater       Full Moon  Defense Against the Dark Arts         140.0
16       Horace Slughorn    Male   16    England                        Potions    Slytherin   Half-blood        NaN       Cedar         Non-corporeal                NaN         Failure                        Potions         100.0
17       Filius Flitwick    Male   17    England                         Charms    Ravenclaw   Half-blood        NaN    Hornbeam         Non-corporeal                NaN         Failure                         Charms         180.0
18         Pomona Sprout  Female   16    England                      Herbology   Hufflepuff   Pure-blood        Cat        Pine         Non-corporeal                NaN         Failure                      Herbology         170.0
19      Helena Ravenclaw  Female   17   Scotland                         Charms    Ravenclaw   Pure-blood        NaN       Rowan         Non-corporeal                NaN      Her mother                         Charms         160.0
20     Godric Gryffindor    Male   17    England                        Dueling   Gryffindor   Pure-blood        NaN       Sword                  Lion                NaN         Failure                        Dueling         200.0
21      Helga Hufflepuff  Female   17      Wales                      Herbology   Hufflepuff   Pure-blood        NaN       Cedar         Non-corporeal                NaN         Failure                      Herbology         190.0
22      Rowena Ravenclaw  Female   17   Scotland                         Charms    Ravenclaw   Pure-blood        NaN       Maple                 Eagle                NaN         Failure                         Charms         180.0
23     Salazar Slytherin    Male   17    England                      Dark Arts    Slytherin   Pure-blood        NaN       Ebony               Serpent                NaN         Failure                      Dark Arts         200.0
24         Molly Weasley  Female   16    England               Household Charms   Gryffindor   Pure-blood        Owl        Pine         Non-corporeal                NaN         Failure               Household Charms          80.0
25        Arthur Weasley    Male   16    England               Muggle Artifacts   Gryffindor   Pure-blood        NaN    Hornbeam         Non-corporeal                NaN         Failure                 Muggle Studies          60.0
26         Lucius Malfoy    Male   16    England                      Dark Arts    Slytherin   Pure-blood        Owl         Elm         Non-corporeal                NaN         Failure                      Dark Arts          90.0
27       Narcissa Malfoy  Female   15    England                        Potions    Slytherin   Pure-blood        NaN    Hawthorn         Non-corporeal                NaN         Failure                        Potions          70.0
28       Pansy Parkinson  Female   11    England                         Gossip    Slytherin   Pure-blood        Cat       Birch         Non-corporeal                NaN         Failure                         Gossip          40.0
29        Vincent Crabbe    Male   11    England                       Strength    Slytherin   Pure-blood        NaN         Oak         Non-corporeal                NaN         Failure                       Strength          50.0
30         Gregory Goyle    Male   11    England                       Strength    Slytherin   Pure-blood        NaN       Alder         Non-corporeal                NaN         Failure                       Strength          50.0
31            Lily Evans  Female   11    England                         Charms   Gryffindor  Muggle-born        NaN      Willow                   Doe                NaN         Failure                         Charms         150.0
32          James Potter    Male   11    England                        Dueling   Gryffindor   Pure-blood        Owl      Walnut                  Stag             Chaser         Failure                        Dueling         160.0
33       Peter Pettigrew    Male   11    England                 Transformation   Gryffindor   Half-blood        Rat         Ash         Non-corporeal                NaN         Failure                 Transformation          30.0
34     Gilderoy Lockhart    Male   15    England                  Memory Charms    Ravenclaw   Half-blood        NaN      Cherry         Non-corporeal                NaN         Failure                  Memory Charms          70.0
35      Dolores Umbridge  Female   15    England                      Dark Arts    Slytherin   Half-blood        Cat     Hemlock         Non-corporeal                NaN         Failure                      Dark Arts          60.0
36        Newt Scamander    Male   17    England              Magical Creatures   Hufflepuff   Half-blood  Demiguise    Chestnut         Non-corporeal                NaN         Failure                      Creatures         160.0
37        Tina Goldstein  Female   17        USA                          Auror   Hufflepuff   Half-blood        Owl         Ash         Non-corporeal                NaN         Failure  Defense Against the Dark Arts         140.0
38     Queenie Goldstein  Female   17        USA                    Legilimency    Ravenclaw   Half-blood        Owl     Cypress         Non-corporeal                NaN         Failure                    Legilimency         130.0
39        Jacob Kowalski    Male   17        USA                         Baking   Hufflepuff       No-mag        NaN       Birch         Non-corporeal                NaN         Failure                         Baking          10.0
40     Theseus Scamander    Male   17    England                          Auror   Gryffindor   Half-blood        Dog       Elder         Non-corporeal                NaN         Failure  Defense Against the Dark Arts         150.0
41        Leta Lestrange  Female   16    England                        Potions    Slytherin   Pure-blood        Cat       Ebony         Non-corporeal                NaN         Failure                        Potions         100.0
42                Nagini  Female   18  Indonesia                 Transformation    Slytherin   Half-blood      Snake        Teak         Non-corporeal                NaN         Failure                 Transformation          90.0
43           Grindelwald    Male   18     Europe                      Dark Arts    Slytherin   Pure-blood        NaN       Elder         Non-corporeal                NaN         Failure                      Dark Arts         200.0
44      Bathilda Bagshot  Female   17    England               History of Magic    Ravenclaw   Half-blood        Cat      Willow         Non-corporeal                NaN         Failure                            NaN           NaN
45  Aberforth Dumbledore    Male   17    England                  Goat Charming   Gryffindor   Half-blood       Goat         Oak         Non-corporeal                NaN         Failure                  Goat Charming          70.0
46     Ariana Dumbledore  Female   14    England                       Obscurus   Gryffindor   Half-blood        NaN         Fir         Non-corporeal                NaN         Failure                       Obscurus          20.0
47           Victor Krum    Male   17   Bulgaria                      Quidditch   Durmstrang   Pure-blood        NaN    Hawthorn         Non-corporeal             Seeker         Failure                      Quidditch         180.0
48        Fleur Delacour  Female   17     France                         Charms  Beauxbatons   Half-blood        NaN    Rosewood         Non-corporeal                NaN         Failure                         Charms         140.0
49    Gabrielle Delacour  Female   14     France                         Charms  Beauxbatons   Half-blood        NaN       Alder         Non-corporeal                NaN         Failure                         Charms          80.0
50         Olympe Maxime  Female   17     France                       Strength  Beauxbatons   Half-blood        NaN         Fir         Non-corporeal                NaN         Failure                       Strength         110.0
51        Igor Karkaroff    Male   18     Europe                      Dark Arts   Durmstrang   Half-blood        NaN         Yew         Non-corporeal                NaN         Failure                      Dark Arts          90.0
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Once we've manipulated the data types from the dataset, it's time to save the existing dataset, so that it'd be ready for our next set of adventures.



hogwarts_df.to_csv('data/hogwarts-students-01.csv')
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Now dear sorcerers, once you've invoked the previous spell, you may check within your data directory that there's a new CSV file already with the name of hogwarts-students-01.csv which will be utilizing through out the next of our magical journey onward.

4.9 Gemika's Pop-Up Quiz: Unveiling the Mysteries

Gemika's Pop-Up Quiz: Unveiling the Mysteries

And now, young wizards and witches, my son Gemika Haziq Nugroho appears with a sparkle in his eye and a quiz at the ready. Are you prepared to test your newfound knowledge and prove your prowess in data exploration?

  1. What function do we use to display the first few rows of a DataFrame?
  2. Why is it important to check the data types of each column in our dataset?
  3. How can we convert a column to a numeric type if it's not already?

Answer these questions with confidence, and you will demonstrate your mastery of the initial steps in data exploration. With our dataset now fully understood and prepared, we are ready to dive even deeper into its mysteries. Onward, to greater discoveries! 🌟✨🧙‍♂️

By now, you should feel like a true data wizard, ready to uncover the hidden patterns and secrets within any dataset. Let us continue our journey with confidence and curiosity, for there is much more to discover in the magical world of data science! 🌌🔍

💖 💪 🙅 🚩
gerryleonugroho
gerry leo nugroho

Posted on July 17, 2024

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