Project: Customer Analytics- Preparing Data for Modeling

Two data scientists working on a dashboard.

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

ds_jobs=pd.read_csv("customer_train.csv")
ds_jobs.shape

(19158, 14)

ds_jobs.head(4)

	student_id	city	city_development_index	gender	relevant_experience	enrolled_university	education_level	major_discipline	experience	company_size	company_type	last_new_job	training_hours	job_change
0	8949	city_103	0.920	Male	Has relevant experience	no_enrollment	Graduate	STEM	>20	NaN	NaN	1	36	1
1	29725	city_40	0.776	Male	No relevant experience	no_enrollment	Graduate	STEM	15	50-99	Pvt Ltd	>4	47	0
2	11561	city_21	0.624	NaN	No relevant experience	Full time course	Graduate	STEM	5	NaN	NaN	never	83	0
3	33241	city_115	0.789	NaN	No relevant experience	NaN	Graduate	Business Degree	<1	NaN	Pvt Ltd	never	52	1

ds_jobs.memory_usage().sum()

ds_jobs.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 19158 entries, 0 to 19157
Data columns (total 14 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   student_id              19158 non-null  int64  
 1   city                    19158 non-null  object 
 2   city_development_index  19158 non-null  float64
 3   gender                  14650 non-null  object 
 4   relevant_experience     19158 non-null  object 
 5   enrolled_university     18772 non-null  object 
 6   education_level         18698 non-null  object 
 7   major_discipline        16345 non-null  object 
 8   experience              19093 non-null  object 
 9   company_size            13220 non-null  object 
 10  company_type            13018 non-null  object 
 11  last_new_job            18735 non-null  object 
 12  training_hours          19158 non-null  int64  
 13  job_change              19158 non-null  int64  
dtypes: float64(1), int64(3), object(10)
memory usage: 2.0+ MB

Task 1

Columns containing integers must be stored as 32-bit integers (int32).

int_columns = ds_jobs.select_dtypes(include='int64').columns.tolist()
int_columns

['student_id', 'training_hours', 'job_change']

ds_jobs[int_columns]=ds_jobs[int_columns].astype('int32')
ds_jobs.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 19158 entries, 0 to 19157
Data columns (total 14 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   student_id              19158 non-null  int32  
 1   city                    19158 non-null  object 
 2   city_development_index  19158 non-null  float64
 3   gender                  14650 non-null  object 
 4   relevant_experience     19158 non-null  object 
 5   enrolled_university     18772 non-null  object 
 6   education_level         18698 non-null  object 
 7   major_discipline        16345 non-null  object 
 8   experience              19093 non-null  object 
 9   company_size            13220 non-null  object 
 10  company_type            13018 non-null  object 
 11  last_new_job            18735 non-null  object 
 12  training_hours          19158 non-null  int32  
 13  job_change              19158 non-null  int32  
dtypes: float64(1), int32(3), object(10)
memory usage: 1.8+ MB

Task 2

Columns containing floats must be stored as 16-bit floats (float16).

float_columns = ds_jobs.select_dtypes(include='float64').columns.tolist()

ds_jobs[float_columns]=ds_jobs[float_columns].astype("float16")

ds_jobs.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 19158 entries, 0 to 19157
Data columns (total 14 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   student_id              19158 non-null  int32  
 1   city                    19158 non-null  object 
 2   city_development_index  19158 non-null  float16
 3   gender                  14650 non-null  object 
 4   relevant_experience     19158 non-null  object 
 5   enrolled_university     18772 non-null  object 
 6   education_level         18698 non-null  object 
 7   major_discipline        16345 non-null  object 
 8   experience              19093 non-null  object 
 9   company_size            13220 non-null  object 
 10  company_type            13018 non-null  object 
 11  last_new_job            18735 non-null  object 
 12  training_hours          19158 non-null  int32  
 13  job_change              19158 non-null  int32  
dtypes: float16(1), int32(3), object(10)
memory usage: 1.7+ MB

Task 3

Columns containing nominal categorical data must be stored as the category data type.

ds_jobs.columns

Index(['student_id', 'city', 'city_development_index', 'gender',
       'relevant_experience', 'enrolled_university', 'education_level',
       'major_discipline', 'experience', 'company_size', 'company_type',
       'last_new_job', 'training_hours', 'job_change'],
      dtype='object')

ds_jobs.head(6)

	student_id	city	city_development_index	gender	relevant_experience	enrolled_university	education_level	major_discipline	experience	company_size	company_type	last_new_job	training_hours	job_change
0	8949	city_103	0.919922	Male	Has relevant experience	no_enrollment	Graduate	STEM	>20	NaN	NaN	1	36	1
1	29725	city_40	0.775879	Male	No relevant experience	no_enrollment	Graduate	STEM	15	50-99	Pvt Ltd	>4	47	0
2	11561	city_21	0.624023	NaN	No relevant experience	Full time course	Graduate	STEM	5	NaN	NaN	never	83	0
3	33241	city_115	0.789062	NaN	No relevant experience	NaN	Graduate	Business Degree	<1	NaN	Pvt Ltd	never	52	1
4	666	city_162	0.767090	Male	Has relevant experience	no_enrollment	Masters	STEM	>20	50-99	Funded Startup	4	8	0
5	21651	city_176	0.764160	NaN	Has relevant experience	Part time course	Graduate	STEM	11	NaN	NaN	1	24	1

nominal_cat_columns=['city', 'gender','enrolled_university', 'major_discipline', 'company_type']

ds_jobs[nominal_cat_columns]=ds_jobs[nominal_cat_columns].astype("category")
ds_jobs.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 19158 entries, 0 to 19157
Data columns (total 14 columns):
 #   Column                  Non-Null Count  Dtype   
---  ------                  --------------  -----   
 0   student_id              19158 non-null  int32   
 1   city                    19158 non-null  category
 2   city_development_index  19158 non-null  float16 
 3   gender                  14650 non-null  category
 4   relevant_experience     19158 non-null  object  
 5   enrolled_university     18772 non-null  category
 6   education_level         18698 non-null  object  
 7   major_discipline        16345 non-null  category
 8   experience              19093 non-null  object  
 9   company_size            13220 non-null  object  
 10  company_type            13018 non-null  category
 11  last_new_job            18735 non-null  object  
 12  training_hours          19158 non-null  int32   
 13  job_change              19158 non-null  int32   
dtypes: category(5), float16(1), int32(3), object(5)
memory usage: 1.1+ MB

Task 4

Columns containing ordinal categorical data must be stored as ordered categories, and not mapped to numerical values, with an order that reflects the natural order of the column.

ds_jobs["education_level"].unique()

array(['Graduate', 'Masters', 'High School', nan, 'Phd', 'Primary School'],
      dtype=object)

education_order=['Primary School','High School','Graduate', 'Masters',  'Phd', 'nan' ]

ds_jobs["education_level"]=pd.Categorical(ds_jobs["education_level"],ordered=True,categories=education_order)

ds_jobs.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 19158 entries, 0 to 19157
Data columns (total 14 columns):
 #   Column                  Non-Null Count  Dtype   
---  ------                  --------------  -----   
 0   student_id              19158 non-null  int32   
 1   city                    19158 non-null  category
 2   city_development_index  19158 non-null  float16 
 3   gender                  14650 non-null  category
 4   relevant_experience     19158 non-null  object  
 5   enrolled_university     18772 non-null  category
 6   education_level         18698 non-null  category
 7   major_discipline        16345 non-null  category
 8   experience              19093 non-null  object  
 9   company_size            13220 non-null  object  
 10  company_type            13018 non-null  category
 11  last_new_job            18735 non-null  object  
 12  training_hours          19158 non-null  int32   
 13  job_change              19158 non-null  int32   
dtypes: category(6), float16(1), int32(3), object(4)
memory usage: 978.9+ KB

ds_jobs["relevant_experience"].unique()
order=['No relevant experience','Has relevant experience']
ds_jobs["relevant_experience"]=pd.Categorical(ds_jobs["relevant_experience"],ordered=True,categories=order)

ds_jobs["enrolled_university"].unique()
order=['no_enrollment','Part time course','Full time course','NaN']
ds_jobs["enrolled_university"]=pd.Categorical(ds_jobs["enrolled_university"],ordered=True,categories=order)

order=['<1','1','2', '3',  '4','5','6', '7',  '8','9','10','11','12', '13',  '14','15','16', '17',  '18','19', '20','>20','nan' ]

ds_jobs["experience"].unique()

array(['>20', '15', '5', '<1', '11', '13', '7', '17', '2', '16', '1', '4',
       '10', '14', '18', '19', '12', '3', '6', '9', '8', '20', nan],
      dtype=object)

order=['<1','1','2', '3',  '4','5','6', '7',  '8','9','10','11','12', '13',  '14','15','16', '17',  '18','19', '20','>20','nan' ]

ds_jobs["experience"]=pd.Categorical(ds_jobs["experience"],ordered=True,categories=order)

ds_jobs.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 19158 entries, 0 to 19157
Data columns (total 14 columns):
 #   Column                  Non-Null Count  Dtype   
---  ------                  --------------  -----   
 0   student_id              19158 non-null  int32   
 1   city                    19158 non-null  category
 2   city_development_index  19158 non-null  float16 
 3   gender                  14650 non-null  category
 4   relevant_experience     19158 non-null  category
 5   enrolled_university     18772 non-null  category
 6   education_level         18698 non-null  category
 7   major_discipline        16345 non-null  category
 8   experience              19093 non-null  category
 9   company_size            13220 non-null  object  
 10  company_type            13018 non-null  category
 11  last_new_job            18735 non-null  object  
 12  training_hours          19158 non-null  int32   
 13  job_change              19158 non-null  int32   
dtypes: category(8), float16(1), int32(3), object(2)
memory usage: 717.9+ KB

ds_jobs["company_size"].unique()

array([nan, '50-99', '<10', '10000+', '5000-9999', '1000-4999', '10-49',
       '100-499', '500-999'], dtype=object)

order=['<10','10-49','50-99', '100-499',  '500-999','1000-4999','5000-9999','10000+','nan']

ds_jobs["company_size"]=pd.Categorical(ds_jobs["company_size"],ordered=True,categories=order)

ds_jobs.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 19158 entries, 0 to 19157
Data columns (total 14 columns):
 #   Column                  Non-Null Count  Dtype   
---  ------                  --------------  -----   
 0   student_id              19158 non-null  int32   
 1   city                    19158 non-null  category
 2   city_development_index  19158 non-null  float16 
 3   gender                  14650 non-null  category
 4   relevant_experience     19158 non-null  category
 5   enrolled_university     18772 non-null  category
 6   education_level         18698 non-null  category
 7   major_discipline        16345 non-null  category
 8   experience              19093 non-null  category
 9   company_size            13220 non-null  category
 10  company_type            13018 non-null  category
 11  last_new_job            18735 non-null  object  
 12  training_hours          19158 non-null  int32   
 13  job_change              19158 non-null  int32   
dtypes: category(9), float16(1), int32(3), object(1)
memory usage: 587.3+ KB

ds_jobs["last_new_job"].unique()

array(['1', '>4', 'never', '4', '3', '2', nan], dtype=object)

order=['never','1','2', '3',  '4','>4','nan']

ds_jobs["last_new_job"]=pd.Categorical(ds_jobs["last_new_job"],ordered=True,categories=order)
ds_jobs.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 19158 entries, 0 to 19157
Data columns (total 14 columns):
 #   Column                  Non-Null Count  Dtype   
---  ------                  --------------  -----   
 0   student_id              19158 non-null  int32   
 1   city                    19158 non-null  category
 2   city_development_index  19158 non-null  float16 
 3   gender                  14650 non-null  category
 4   relevant_experience     19158 non-null  category
 5   enrolled_university     18772 non-null  category
 6   education_level         18698 non-null  category
 7   major_discipline        16345 non-null  category
 8   experience              19093 non-null  category
 9   company_size            13220 non-null  category
 10  company_type            13018 non-null  category
 11  last_new_job            18735 non-null  category
 12  training_hours          19158 non-null  int32   
 13  job_change              19158 non-null  int32   
dtypes: category(10), float16(1), int32(3)
memory usage: 456.7 KB

ds_jobs['company_size'].cat.categories[5]

'1000-4999'

x=ds_jobs['experience']>=ds_jobs['experience'].cat.categories[10]
y = ds_jobs['company_size']>=ds_jobs['company_size'].cat.categories[5]

ds_jobs_clean = ds_jobs[x & y]

ds_jobs_clean.head(10)

	student_id	city	city_development_index	gender	relevant_experience	enrolled_university	education_level	major_discipline	experience	company_size	company_type	last_new_job	training_hours	job_change
9	699	city_103	0.919922	NaN	Has relevant experience	no_enrollment	Graduate	STEM	17	10000+	Pvt Ltd	>4	123	0
12	25619	city_61	0.913086	Male	Has relevant experience	no_enrollment	Graduate	STEM	>20	1000-4999	Pvt Ltd	3	23	0
31	22293	city_103	0.919922	Male	Has relevant experience	Part time course	Graduate	STEM	19	5000-9999	Pvt Ltd	>4	141	0
34	26494	city_16	0.910156	Male	Has relevant experience	no_enrollment	Graduate	Business Degree	12	5000-9999	Pvt Ltd	3	145	0
40	2547	city_114	0.925781	Female	Has relevant experience	Full time course	Masters	STEM	16	1000-4999	Public Sector	2	14	0
47	25987	city_103	0.919922	Other	Has relevant experience	no_enrollment	Graduate	STEM	19	10000+	Public Sector	4	52	1
104	1180	city_16	0.910156	Male	Has relevant experience	no_enrollment	Graduate	STEM	12	5000-9999	Pvt Ltd	1	52	0
108	25349	city_16	0.910156	Male	Has relevant experience	no_enrollment	Graduate	STEM	>20	1000-4999	Pvt Ltd	>4	28	0
115	20576	city_97	0.924805	Male	Has relevant experience	no_enrollment	Graduate	STEM	>20	1000-4999	Pvt Ltd	>4	19	0
130	3921	city_36	0.893066	NaN	No relevant experience	no_enrollment	Phd	STEM	>20	1000-4999	Public Sector	>4	4	0

ds_jobs_clean.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2201 entries, 9 to 19143
Data columns (total 14 columns):
 #   Column                  Non-Null Count  Dtype   
---  ------                  --------------  -----   
 0   student_id              2201 non-null   int32   
 1   city                    2201 non-null   category
 2   city_development_index  2201 non-null   float16 
 3   gender                  1821 non-null   category
 4   relevant_experience     2201 non-null   category
 5   enrolled_university     2185 non-null   category
 6   education_level         2184 non-null   category
 7   major_discipline        2097 non-null   category
 8   experience              2201 non-null   category
 9   company_size            2201 non-null   category
 10  company_type            2144 non-null   category
 11  last_new_job            2184 non-null   category
 12  training_hours          2201 non-null   int32   
 13  job_change              2201 non-null   int32   
dtypes: category(10), float16(1), int32(3)
memory usage: 76.3 KB

DataCamp Codes

import pandas as pd

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

# Copy the DataFrame for cleaning
ds_jobs_clean = ds_jobs.copy()

# Create a dictionary of columns containing ordered categorical data
ordered_cats = {
    'relevant_experience': ['No relevant experience', 'Has relevant experience'],
    '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']
}

# Loop through DataFrame columns to efficiently change data types
for col in ds_jobs_clean:
    
    # Convert integer columns to int32
    if ds_jobs_clean[col].dtype == 'int':
        ds_jobs_clean[col] = ds_jobs_clean[col].astype('int32')
    
    # Convert float columns to float16
    elif ds_jobs_clean[col].dtype == 'float':
        ds_jobs_clean[col] = ds_jobs_clean[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_clean[col] = ds_jobs_clean[col].astype(category)
    
    # Convert remaining columns to standard categories
    else:
        ds_jobs_clean[col] = ds_jobs_clean[col].astype('category')
        
# Filter students with 10 or more years experience at companies with at least 1000 employees
ds_jobs_clean = ds_jobs_clean[(ds_jobs_clean['experience'] >= '10') & (ds_jobs_clean['company_size'] >= '1000-4999')]