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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()

Project Instructions

The Head Data Scientist at Training Data Ltd. has asked you to create a new DataFrame called ds_jobs_transformed that stores the data in customer_train.csv much more efficiently. Specifically, follow these preprocessing requirements:

  1. Columns with only two categories must be stored as Booleans (bool).
  2. Columns containing only integers must be stored as 32-bit integers (int32).
  3. Columns containing floats must be stored as 16-bit floats (float16).
  4. Columns with nominal categorical data must be stored using the category data type.
  5. Columns with ordinal categorical data must be stored as ordered categories,
    not mapped to numerical values, with an order that reflects the natural ranking of the column.

Additionally, filter the DataFrame to include only students who:

  • Have 10 or more years of experience, and
  • Have worked at companies with at least 1000 employees,

since the recruiter base is better suited for more experienced professionals at large enterprise companies.

💡 After completing the transformation, comparing .info() or .memory_usage() of ds_jobs and ds_jobs_transformed should show a substantial reduction in memory usage.

# Load the dataset and create a copy
ds_jobs = pd.read_csv("customer_train.csv")
ds_jobs_transformed = ds_jobs.copy()
ds_jobs_transformed.head()
ds_jobs_transformed.info()
ds_jobs_transformed.columns
# EDA to help identify ordinal, nominal, and two-factor categories
for col in ds_jobs.select_dtypes("object").columns:
    print(ds_jobs_transformed[col].value_counts(), '\n')
# Create a dictionary of columns containing ordered categorical data
ordered_cats = {
    'enrolled_university': ['no_enrollment', 'Part time course', 'Full time course'],
    'education_level': ['Primary School', 'High School', 'Graduate', 'Masters', 'Phd'],
    'experience': ['<1'] + list(map(str, range(1, 21))) + ['>20'],
    'company_size': ['<10', '10-49', '50-99', '100-499', '500-999', '1000-4999', '5000-9999', '10000+'],
    'last_new_job': ['never', '1', '2', '3', '4', '>4']
}
# Create a mapping dictionary of columns containing two-factor categories to convert to Booleans
two_factor_cats = {
    'relevant_experience': {'No relevant experience': False, 'Has relevant experience': True},
    'job_change': {0.0: False, 1.0: True}
}
# Loop through DataFrame columns to efficiently change data types
for col in ds_jobs_transformed:
    
    # Convert two-factor categories to bool
    if col in ['relevant_experience', 'job_change']:
        ds_jobs_transformed[col] = ds_jobs_transformed[col].map(two_factor_cats[col])
    
    # Convert integer columns to int32
    elif col in ['student_id', 'training_hours']:
        ds_jobs_transformed[col] = ds_jobs_transformed[col].astype('int32')
    
    # Convert float columns to float16
    elif col == 'city_development_index':
        ds_jobs_transformed[col] = ds_jobs_transformed[col].astype('float16')
    
    # Convert columns containing ordered categorical data to ordered categories using dict
    elif col in ordered_cats.keys():
        category = pd.CategoricalDtype(ordered_cats[col], ordered=True)
        ds_jobs_transformed[col] = ds_jobs_transformed[col].astype(category)
    
    # Convert remaining columns to standard categories
    else:
        ds_jobs_transformed[col] = ds_jobs_transformed[col].astype('category')
# Filter students with 10 or more years experience at companies with at least 1000 employees
ds_jobs_transformed = ds_jobs_transformed[(ds_jobs_transformed['experience'] >= '10') & (ds_jobs_transformed['company_size'] >= '1000-4999')]
ds_jobs_transformed.head()
ds_jobs_transformed.info()

Step 3: Verify memory usage

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