Skip to content

A common problem when creating models to generate business value from data is that the datasets can be so large that it can take days for the model to generate predictions. Ensuring that your dataset is stored as efficiently as possible is crucial for allowing these models to run on a more reasonable timescale without having to reduce the size of the dataset.

You've been hired by a major online data science training provider called Training Data Ltd. to clean up one of their largest customer datasets. This dataset will eventually be used to predict whether their students are looking for a new job or not, information that they will then use to direct them to prospective recruiters.

You've been given access to customer_train.csv, which is a subset of their entire customer dataset, so you can create a proof-of-concept of a much more efficient storage solution. The dataset contains anonymized student information, and whether they were looking for a new job or not during training:

ColumnDescription
student_idA unique ID for each student.
cityA code for the city the student lives in.
city_development_indexA scaled development index for the city.
genderThe student's gender.
relevant_experienceAn indicator of the student's work relevant experience.
enrolled_universityThe type of university course enrolled in (if any).
education_levelThe student's education level.
major_disciplineThe educational discipline of the student.
experienceThe student's total work experience (in years).
company_sizeThe number of employees at the student's current employer.
company_typeThe type of company employing the student.
last_new_jobThe number of years between the student's current and previous jobs.
training_hoursThe number of hours of training completed.
job_changeAn indicator of whether the student is looking for a new job (1) or not (0).
# Import necessary libraries
import pandas as pd

# Load the dataset
ds_jobs = pd.read_csv("customer_train.csv")

# View the dataset
ds_jobs.head()
# Create a copy of ds_jobs for transforming
ds_jobs_transformed = ds_jobs.copy()

# Start coding here. Use as many cells as you like!

1. Exploratory data analysis

print("--- Informações Iniciais do DataFrame ---")
ds_jobs.info()

print() 

print("--- Uso de Memória Inicial ---")
print(ds_jobs.memory_usage().sum(), "bytes")
# Loop para inspecionar cada coluna do seu DataFrame
print("--- Analisando conteúdo das colunas ---\n")
print("-" * 30)

for column in ds_jobs_transformed.columns:
    
    # Conta a quantidade de valores únicos na coluna atual
    num_unique_values = ds_jobs_transformed[column].nunique()
    
    # Imprime o nome da coluna e a contagem para sua análise
    print(f"Coluna: '{column}'")
    print(f"Número de valores únicos: {num_unique_values}")
    
    # Facilita a identificação de categorias.
    if num_unique_values < 25:
        print(f"Valores: {ds_jobs_transformed[column].unique()}")
    
    # Imprime o nome da coluna e a contagem para sua análise
    print(f"Tipo: '{ds_jobs_transformed[column].dtype}'")
    
    # Um separador para manter a organização
    print("-" * 30)
# Criar listas de colunas para cada categoria de transformação

2. Converting integers, floats, and unordered categories

# Criar listas de colunas para cada transformação
booleans = ['relevant_experience', 'job_change']
integers = ['student_id', 'training_hours']
floats = ['city_development_index']
nom_categories = ['city', 'gender', 'enrolled_university', 'major_discipline', 'company_type']

# Prepara a coluna relevant_experience para ser transformada em booleano
cat_to_bool_dict = {'No relevant experience':0, 'Has relevant experience':1}
ds_jobs_transformed['relevant_experience'] = ds_jobs_transformed['relevant_experience'].astype('category')
ds_jobs_transformed['relevant_experience'] = ds_jobs_transformed['relevant_experience'].cat.rename_categories(new_categories=cat_to_bool_dict) 

# Converte as colunas categoricas que só tem 2 fatores em booleanos
ds_jobs_transformed[booleans] = ds_jobs_transformed[booleans].astype('bool')

# Converte colunas de números inteiros e racionáis para os tipos especificados
ds_jobs_transformed[integers] = ds_jobs_transformed[integers].astype("int32")
ds_jobs_transformed[floats] = ds_jobs_transformed[floats].astype("float16")

# Converte colunas de objeto para categoria nominal
ds_jobs_transformed[nom_categories] = ds_jobs_transformed[nom_categories].astype('category')

3. Converting ordered categories

# Criar listas de colunas para cada transformação e listas contendo as ordens das categorias
ord_categories = [
    'enrolled_university', 
    'education_level', 
    'experience', 
    'company_size', 
    'last_new_job'
]
category_order_lists = [
    ['no_enrollment', 'Part time course', 'Full time course'],
    ['Primary School', 'High School', 'Graduate', 'Masters', 'Phd'],
    ['<1', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '>20'],
    ['<10', '10-49', '50-99', '100-499', '500-999', '1000-4999', '5000-9999', '10000+'],
    ['never', '1', '2', '3', '4', '>4']
]

# Loop para repetir operações de criação de categorias ordenadas
for i in range(len(ord_categories)):
    ds_jobs_transformed[ord_categories[i]] = ds_jobs_transformed[ord_categories[i]].astype('category')
    ds_jobs_transformed[ord_categories[i]] = ds_jobs_transformed[ord_categories[i]].cat \
    .set_categories(new_categories=category_order_lists[i], ordered=True)

4. Filtering on ordered categorical columns

# Estabelecendo o primeiro critério solicitado
condition1 = ds_jobs_transformed['experience'] >= '10'

# Estabelecendo o primeiro critério solicitado
condition2 = ds_jobs_transformed['company_size'] > '500-999'

# Filtrando o dataframe de acordo com ambos os critérios
ds_jobs_transformed = ds_jobs_transformed[(condition1)&(condition2)]
print("--- Informações do DataFrame após transformações ---")
ds_jobs_transformed.info()

print() 

print("--- Uso de Memória Inicial ---")
print(ds_jobs_transformed.memory_usage().sum(), "bytes")