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:
| Column | Description |
|---|---|
student_id | A unique ID for each student. |
city | A code for the city the student lives in. |
city_development_index | A scaled development index for the city. |
gender | The student's gender. |
relevant_experience | An indicator of the student's work relevant experience. |
enrolled_university | The type of university course enrolled in (if any). |
education_level | The student's education level. |
major_discipline | The educational discipline of the student. |
experience | The student's total work experience (in years). |
company_size | The number of employees at the student's current employer. |
last_new_job | The number of years between the student's current and previous jobs. |
training_hours | The number of hours of training completed. |
job_change | An indicator of whether the student is looking for a new job (1) or not (0). |
# Start your code here!
import pandas as pd
import numpy as np
# Load the dataset
ds_jobs = pd.read_csv("customer_train.csv")
# Copy the DataFrame for cleaning
ds_jobs_clean = ds_jobs.copy()
ordinal_columns = ['relevant_experience', 'enrolled_university', 'education_level', 'experience', 'last_new_job', 'company_size']
nominal_columns = ['city', 'gender', 'company_type', 'major_discipline']
for column in ds_jobs_clean.columns:
if ds_jobs_clean[column].dtype == 'int64':
ds_jobs_clean[column] = ds_jobs_clean[column].astype('int32')
elif ds_jobs_clean[column].dtype == 'float64':
ds_jobs_clean[column] = ds_jobs_clean[column].astype('float16')
elif column in nominal_columns:
ds_jobs_clean[column] = ds_jobs_clean[column].astype('category')
#Building a function to return the appropriate category list
def get_cat_order(col):
cat_order_list = []
if col=='relevant_experience':
cat_order_list = ['No relevant experience', 'Has relevant experience']
elif col=='enrolled_university':
cat_order_list = ['no_enrollment', 'Part time course', 'Full time course']
elif col=='education_level':
cat_order_list = ['Primary School', 'High School', 'Graduate', 'Masters', 'Phd']
elif col=='experience':
cat_order_list = ['<1', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '>20']
elif col=='last_new_job':
cat_order_list = ['never', '1', '2', '3', '4', '>4']
elif col=='company_size':
cat_order_list = ['<10', '10-49', '50-99', '100-499', '500-999', '1000-4999', '5000-9999', '10000+']
return cat_order_list
for column in ds_jobs_clean.columns:
if column in ordinal_columns:
cat_list = get_cat_order(column)
ds_jobs_clean[column] = pd.Categorical(ds_jobs_clean[column], categories=cat_list, ordered=True)
#Filtering the dataframe based on the mentioned changes
# exp_filter = get_cat_order('experience')[10:] # >=10 years
# cmp_size_filter = get_cat_order('company_size')[5:] # >= 1000 employees
# ds_jobs_clean = ds_jobs_clean[(ds_jobs_clean['experience'].isin(exp_filter)) &
# (ds_jobs_clean['company_size'].isin(cmp_size_filter))]
ds_jobs_clean = ds_jobs_clean[(ds_jobs_clean['experience']>='10') & (ds_jobs_clean['company_size']>='1000-4999')]ds_jobs_clean[ds_jobs_clean['company_size']=='5000-9999']ds_jobs_clean.info()