For this project we'll help a recruitment agency and their candidates for the role of data analyst to find their ideal job based on technical skills. To archive this, we'll follow these steps:
Each step will be handled for a different Python function, let's prepare our environment and then define the functions, then we'll use them to create two customized recommendations for ficticional candidates.
import pandas as pd
import re
import os
from selenium import webdriver
from bs4 import BeautifulSoup
pd.set_option("display.max_colwidth", None)
pd.set_option('display.max_columns', None)
If we want to help candidates find their ideal job we need to start by knowing what available offers are out there, so we'll look into one of the most popular sites nowadays, indeed.com.
To gather relevant information from this site, we need our function to perform the following steps:
Let's define the function. Note that we are defining three of the function arguments with default values. Also, note that we are using a try - except block for the section handling the second request, meaning that if any error is encontered during the execution we'll just print the impacted id for our knowledge and move forward with the next one without interruption. Finally, note that for now we will only focus on indeed.com, meaning that the jobs are based on the United States.
def indeed_jobs(keyword, location = '', pages = 1, age = 30):
job_dict = dict()
q_val = 'q='+'+'.join(keyword.split())
l_val = '&l='+location if location != '' else ''
fromage_val = '&fromage='+str(age)
start_val = '&start='
for page in range(1, pages+1):
url = 'https://www.indeed.com/jobs?'+q_val+l_val+fromage_val+start_val+str((page*10)-10)
dr = webdriver.Chrome()
dr.get(url)
bs = BeautifulSoup(dr.page_source,"html")
for block in bs.find_all('script'):
if '"jobKeysWithTwoPaneEligibility":' in str(block):
job_ids= eval(re.search('"jobKeysWithTwoPaneEligibility":({[\w\"\"\,\:]+\})', str(block)).group(1).replace('true', 'True'))
for job_id in job_ids.keys():
try:
job_detail_url = 'https://www.indeed.com/viewjob?jk='+job_id
dr.get(job_detail_url)
bs_jd = BeautifulSoup(dr.page_source,"html")
job_title = bs_jd.find('h1').find('span').text
job_description = bs_jd.find('div',attrs={'id':'jobDescriptionText'}).text.strip()
job_dict[job_id] = {'url': job_detail_url, 'title':job_title, 'description': job_description}
except:
print(f'Job: {job_id} failed - Ignoring')
continue
dr.close()
return job_dict
Having gathered raw data about a series of job postings, we want to come up with a practical way to summarize it in a way that we can recomend job postings to our candidates without having to read the whole post and decide which of our candidates could be a good fit, in other words, we want to make automated recomendations. To archieve this, we need a funtions that performs the following steps:
To make bullet number two a viable approach to tackle our challenge, we need to be able to find common skills for a role and then identify the words (strings) used to descibe them, for example, "databases" is a skill, and words like "SQL", "PSQL", "sequel", "Postgres", etc., are directly related to this skill, so a job posting saying: "1+ years sxperience in SQL Server is preferred" will be categorized as requiring the databases skill for having a word associated wih the skill. So our funtion will require a second input, which is a dicionary having skils as keys and lists of associated words as values.
Let's define the function.
def scan_jobs(job_dict, skill_dict):
job_df = pd.DataFrame({'Title':[], 'URL':[]})
for skill in skill_dict.keys():
job_df.insert(len(job_df.columns), skill, [], False)
for job in job_dict.keys():
title = job_dict[job]['title']
url = job_dict[job]['url']
job_df_row = {'Title':[title], 'URL':[url]}
for skill in skill_dict.keys():
job_df_row[skill] = False
for keyword in skill_dict[skill]:
if keyword.lower() in job_dict[job]['description'].lower():
job_df_row[skill] = True
job_df = pd.concat([job_df, pd.DataFrame(job_df_row)], ignore_index = True)
job_df.drop_duplicates(inplace=True)
return job_df
Now its time to give value to our summarized dataframe by sharing with our candidates fitting job posibilities. We will archive this goal by defining a function that will:
Let's define the function
def html_communication(cadidate_name, candidate_profile, job_df, skill_dict, position):
summary = pd.melt(job_df, value_vars=skill_dict.keys(), var_name = 'Skills', value_name='Count').groupby('Skills').sum().sort_values('Count', ascending=False).head(10)
summary['%'] = round((summary['Count'] / result.shape[0]) * 100,2)
candidate_recomendation = job_df
candidate_skills = ['Title', 'URL']
for skill in candidate_profile:
if candidate_profile[skill] == True:
candidate_skills.append(skill)
candidate_recomendation = candidate_recomendation[candidate_skills]
candidate_recomendation['total'] = candidate_recomendation.iloc[:, 2:].sum(axis = 1)
candidate_recomendation = candidate_recomendation.sort_values('total', ascending=False).head(5)
Job_table = '<table><tr><th>Job Title</th><th>Link</th></tr>'
for indx, row in candidate_recomendation[['Title', 'URL']].iterrows():
title = row[0]
url = row[1]
table_row = f'<tr><td>{title}</td><td><a href="{str(url)}" target="_blank">Apply here!</a></td></tr>\n'
Job_table += table_row
Job_table += '</table>'
top_skill_table = '<table><tr><th>Skill</th><th>Percentage</th></tr>'
for indx, row in summary.iterrows():
skill = indx
percentage = row[1]
table_row = f'<tr><td>{skill}</td><td>{percentage}%</td></tr>\n'
top_skill_table += table_row
top_skill_table += '</table>'
style_tag = '''<style>
div{
margin: auto;width: 90%;
padding: 10px;
font-family: Arial;
}
table {
font-family: arial, sans-serif;
border-collapse: collapse;
width: 100%;
}
td, th {
border: 1px solid #dddddd;
text-align: left;
padding: 8px;
}
tr:nth-child(even) {
background-color: #dddddd;
}
@media (min-width: 650px) {
div{
margin: auto;width: 50%;
}
}
</style>'''
output_html = f'''<!doctype html>
<html lang="en">
<head>
<title>Your Daily Job Recommendation</title>
<meta name="viewport" content="width=device-width,initial-scale=1">
</head>
{style_tag}
<body>
<div>
<h1>Hello {cadidate_name}!</h1>
<h2>Are you ready to take the next step?</h2>
<p>We've found positions that fit your profile:</p>
{Job_table}
<h2>Do you know?</h2>
<p>As per our last analysis, these are the top 10 skill demanded for {position}:
{top_skill_table}
</div>
</body>
</html>'''
return output_html
It's time to test our solution. First, we need to gather raw data from indeed, let's define the parameters we'll use to get data about data analyst jobs, let's focus on posts having up to 30 days, data from 150 indeed pages and for now we won't worry about the location.
position = 'Data Analyst'
pages = 150
age = 30
Let's run the first function saving the outcome on a variable called "job_dict".
job_dict = indeed_jobs(position, pages = pages, age = age)
Job: 41b53f4529a4b14c failed - Ignoring Job: 56f150b103d4e40c failed - Ignoring Job: 2c317bb86965da31 failed - Ignoring
Let's print the first job captured to show as an example.
job_dict[list(job_dict.keys())[0]]
{'url': 'https://www.indeed.com/viewjob?jk=f69950156bbdac29',
'title': 'Data Analyst',
'description': "At Alpine IQ, we're passionate about delivering innovative and effective software solutions to our clients. Our commitment to data-driven decision-making drives our success, and we're looking for a Data Analyst to join our team. This is an exciting opportunity for individuals looking to launch their career in the dynamic world of Software as a Service (SaaS) and make a significant impact in a fast-growing company.\n\n At Alpine IQ, the Data Analyst is a pivotal role, tasked with unlocking the power of data to drive insights for our Sales and Customer Success teams. With a keen eye for detail and a passion for numbers, you will be responsible for designing and maintaining dashboards that provide actionable insights into our sales processes, customer journeys, and success metrics. If you're driven by the challenge of converting complex data into compelling business narratives, we would love to hear from you.\n General Duties & Responsibilities\n Develop, Implement and Maintain a Comprehensive Ticketing System:\n\nArchitect a ticketing system within our productivity management platform to streamline, prioritize and manage incoming data requests.\nIntegrate a robust data definition framework and incorporate discovery questions, ensuring precise and meaningful report generation for each request.\n\nDashboard Design & Maintenance:\n\nDesign, develop, and update intuitive dashboards for the Sales and Customer Success teams using tools like Tableau, Power BI, or Looker.\nEnsure dashboards provide real-time, accurate, and relevant insights.\n\nData Management:\n\nExtract, clean, and analyze data from various sources to ensure its integrity and relevance.\nCollaborate with the engineering team for seamless data integration.\n\nCollaboration with Sales Customer Success Teams and Marketing teams:\n\nEngage regularly with the Sales Customer Success and Marketing teams to comprehend their data requirements and challenges.\nOffer strategic insights to optimize sales performance and elevate customer success outcomes.\n\nContinuous Learning and Optimization:\n\nStay abreast with emerging trends in data analysis and visualization.\nAdvocate for enhancements in data collection and interpretation methodologies.\n\nReporting and Insights Communication:\n\nCraft regular reports for different stakeholders, from team leads to executive members.\nPresent data-backed recommendations to Alpine IQ's leadership team.\n\nSkills & Competencies\n\nMastery of data visualization tools like Tableau, Power BI, or Looker.\nStrong analytical and problem-solving skills.\nAbility to translate data into actionable business insights.\nExceptional communication skills, both written and verbal.\nProficient in SQL and understanding of data warehouse structures.\nTeam player with the ability to function in a cross-functional environment.\n\nPreferred Experience & Education\n\nBachelor's degree in Data Science, Statistics, Business, or a related field.\n2-4 years of experience in a data analyst role, preferably within a SaaS company.\nProven experience in designing and maintaining business dashboards.\nFamiliarity with the SaaS industry and its key metrics would be a plus.\nExperience in Hubspot CRM"}
Now let's create our dictionary for the skills we are considering relevant for the role of data analyst and that our candidates may or may not have. These dictionaries are the objects we can always revisit, this is because they can continually change as new technologies become popular or obselete. Also onsider that given that our function will work with subtrings a single keyword may match with multiple items on the job description, for example the substring "Statistic" will match with "Statistic", "Statistical", "Statisticts", etc., but for this same reason, we also need to be careful of keywords that can be taken out of context, for example words like "Excel", the Microsoft tool, to be considered as part of the word "excellent", which we expect to see on many postings like: "excellent communication skills are required...", for this reason, the definition bellow include commas, extra characters or spaces that allow us to limit those mistakes as much as possible, for example, we are using "MS Access" to avoid including the expression "to access".
skill_dict = {'Databases': ['SQL', 'Transact-SQL', 'Sequel', 'Database', 'MySQL', 'Postgres', 'PostgreSQL', 'DB2','Oracle DB','OracleDB', 'MS Access', 'MSAccess', 'Microsoft Access'],
'Non-Relational Databases':['MongoDB'],
'Python': ['Python', 'Jupyter'],
'R (Programming Language)': [' R ', ', R,', ' R,', ' R.',',R,'],
'JavaScript': ['JavaScript'],
'Java':['Java ', 'Java,', 'Java.'],
'PHP':['PHP'],
'HTML':['HTML'],
'Spreadsheets':['Excel ', 'Excel,','Excel.', 'spreadsheet', 'Google Sheets'],
'VBA':['VBA'],
'SAS':['SAS'],
'SAP':[' SAP ', ', SAP', ',SAP,', ' SAP.'],
'SPSS':['SPSS'],
'Tableau':['Tableau'],
'Power BI':['Power BI', 'PowerBI'],
'Qlik': ['Qlik'],
'Power Point':['Power Point', 'PowerPoint'],
'ETL':['ETL', 'ELT'],
'Databricks':['Data bricks', 'Databricks'],
'Snowflake':['Snowflake'],
'Google Analytics': ['Google Analytics'],
'Statisticts/Mathematics':['Statistic','Math'],
'Linux':['Linux', 'Ubuntu', 'Debian', 'CentOS', 'Red Hat', 'RedHat','RHEL'],
'Version Control Systems': [' Git ',' Git,', ',Git,',' Git.', 'GitHub', 'GitLab']}
let's run the second function saving the outcome into a variable called "result".
result = scan_jobs(job_dict, skill_dict)
C:\Users\perez\AppData\Local\Temp\ipykernel_17384\4000243932.py:17: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype. job_df = pd.concat([job_df, pd.DataFrame(job_df_row)], ignore_index = True)
Let's print the first couple lines of the resulting output. As a refresher, the 1s and 0s indicate that at least one word related to the skill was identified regardless of how many there were, these values represent a true/false outcome and is not to be considered a count.
result.head()
| Title | URL | Databases | Non-Relational Databases | Python | R (Programming Language) | JavaScript | Java | PHP | HTML | Spreadsheets | VBA | SAS | SAP | SPSS | Tableau | Power BI | Qlik | Power Point | ETL | Databricks | Snowflake | Google Analytics | Statisticts/Mathematics | Linux | Version Control Systems | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Data Analyst | https://www.indeed.com/viewjob?jk=f69950156bbdac29 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 1 | Data Analyst | https://www.indeed.com/viewjob?jk=98d14a169483ded8 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2 | Data Analyst | https://www.indeed.com/viewjob?jk=2b79c482e030df68 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 3 | Data Analyst I | https://www.indeed.com/viewjob?jk=138bd3c14de00e5f | 1.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4 | Data Analyst | https://www.indeed.com/viewjob?jk=0ab722268e5631e5 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
As for the algorithm we do not expect to see any null values, let's confirm that
result.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 916 entries, 0 to 915 Data columns (total 26 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Title 916 non-null object 1 URL 916 non-null object 2 Databases 916 non-null float64 3 Non-Relational Databases 916 non-null float64 4 Python 916 non-null float64 5 R (Programming Language) 916 non-null float64 6 JavaScript 916 non-null float64 7 Java 916 non-null float64 8 PHP 916 non-null float64 9 HTML 916 non-null float64 10 Spreadsheets 916 non-null float64 11 VBA 916 non-null float64 12 SAS 916 non-null float64 13 SAP 916 non-null float64 14 SPSS 916 non-null float64 15 Tableau 916 non-null float64 16 Power BI 916 non-null float64 17 Qlik 916 non-null float64 18 Power Point 916 non-null float64 19 ETL 916 non-null float64 20 Databricks 916 non-null float64 21 Snowflake 916 non-null float64 22 Google Analytics 916 non-null float64 23 Statisticts/Mathematics 916 non-null float64 24 Linux 916 non-null float64 25 Version Control Systems 916 non-null float64 dtypes: float64(24), object(2) memory usage: 193.2+ KB
result.to_csv('results.csv', index=False)
Find the complete results table here
For our knowledge, let's see the observed frecuencies per skill, remember that we are providing this information to our candidates as well (as a top 10).
summary = pd.melt(result, value_vars=skill_dict.keys(), var_name = 'Skills', value_name='Count').groupby('Skills').sum().sort_values('Count', ascending=False)
summary['Percentage'] = round((summary['Count'] / result.shape[0]) * 100,2)
summary
| Count | Percentage | |
|---|---|---|
| Skills | ||
| Databases | 596.0 | 65.07 |
| Statisticts/Mathematics | 399.0 | 43.56 |
| Spreadsheets | 374.0 | 40.83 |
| Tableau | 252.0 | 27.51 |
| Python | 232.0 | 25.33 |
| Power BI | 217.0 | 23.69 |
| SAS | 131.0 | 14.30 |
| R (Programming Language) | 122.0 | 13.32 |
| Power Point | 111.0 | 12.12 |
| ETL | 78.0 | 8.52 |
| SAP | 40.0 | 4.37 |
| Snowflake | 39.0 | 4.26 |
| HTML | 30.0 | 3.28 |
| SPSS | 27.0 | 2.95 |
| VBA | 26.0 | 2.84 |
| Qlik | 25.0 | 2.73 |
| Java | 21.0 | 2.29 |
| JavaScript | 18.0 | 1.97 |
| Databricks | 16.0 | 1.75 |
| Version Control Systems | 14.0 | 1.53 |
| PHP | 12.0 | 1.31 |
| Linux | 12.0 | 1.31 |
| Google Analytics | 10.0 | 1.09 |
| Non-Relational Databases | 3.0 | 0.33 |
summary.rename(index={'R (Programming Language)': 'R', 'Statisticts/Mathematics':'Stats/Maths', 'Non-Relational Databases':'NR-Databases', 'Version Control Systems':'VCS', 'Google Analytics': 'G. Analytics'}).plot.bar(y='Percentage',title = f'Most demanded skills for the role of: {position}', rot=35, fontsize = 8, figsize = (10, 5)).grid(axis='y')
As new jobs are posted, it is expected that our analysis shows some variation each time it is executed, however, in almost all cases Databases and Statistics will come up on top, there is no question, every data professional should have a strong foundation working with tabular data and should feel confortable using this data to perform calculations using an SQL management system or Spreadsheets. Popular tools such as Tableau and Power BI are nice complement to our toolbox, finally being able to to automate tasks using a flexible and powerful programming language like Pyhon will help anyone to be more attractive for most companies.
Now, let's use our third and last function to transform our jobs into recommendations for the candidates. Given that we defined a set of skills as a dictionary, we should define a candidate's profile in the same way, replacing the list of associated words with a single True or False value, where True represents that the candidate has the specific skill or feels confortable with it.
Let's create two fictional profiles:
Laura: she has experience working with Excel including VBA to automate some tasks, SAS, and she also has a strong knowledge of Tableau and Power BI to create dashboards and visualizations.
Laura_profile = {'Databases': False,
'Non-Relational Databases':False,
'Python':False,
'R (Programming Language)': False,
'JavaScript':False,
'Java':False,
'PHP':False,
'HTML':False,
'Spreadsheets':True,
'VBA':True,
'SAS':True,
'SAP':False,
'SPSS':False,
'Tableau':True,
'Power BI':True,
'Qlik': False,
'Power Point':False,
'ETL':False,
'Databricks':False,
'Snowflake':False,
'Google Analytics':False,
'Statisticts/Mathematics':False,
'Linux':False,
'Version Control Systems': False}
Robert: He has experience working with SQL, programming languages such as R and Python, GitHub as versioning control system, Linux distributions such as Ubuntu and he has also some experience with Tableau and Power BI.
Robert_profile = {'Databases': True,
'Non-Relational Databases':False,
'Python':True,
'R (Programming Language)': True,
'JavaScript':False,
'Java':False,
'PHP':False,
'HTML':False,
'Spreadsheets':True,
'VBA':False,
'SAS':False,
'SAP':False,
'SPSS':False,
'Tableau':False,
'Power BI':True,
'Qlik': False,
'Power Point':False,
'ETL':False,
'Databricks':False,
'Snowflake':False,
'Google Analytics':False,
'Statisticts/Mathematics':False,
'Linux':True,
'Version Control Systems': True}
Finally, let's create the custom HTML communication for our two candidates and save the output into a file.
Laura_email = html_communication('Laura', Laura_profile, result, skill_dict, position)
Robert_email = html_communication('Robert', Robert_profile, result, skill_dict, position)
C:\Users\perez\AppData\Local\Temp\ipykernel_17384\1074182653.py:11: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy candidate_recomendation['total'] = candidate_recomendation.iloc[:, 2:].sum(axis = 1) C:\Users\perez\AppData\Local\Temp\ipykernel_17384\1074182653.py:11: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy candidate_recomendation['total'] = candidate_recomendation.iloc[:, 2:].sum(axis = 1)
Let's print Laura's communication as an example.
print(Laura_email)
<!doctype html>
<html lang="en">
<head>
<title>Your Daily Job Recommendation</title>
<meta name="viewport" content="width=device-width,initial-scale=1">
</head>
<style>
div{
margin: auto;width: 90%;
padding: 10px;
font-family: Arial;
}
table {
font-family: arial, sans-serif;
border-collapse: collapse;
width: 100%;
}
td, th {
border: 1px solid #dddddd;
text-align: left;
padding: 8px;
}
tr:nth-child(even) {
background-color: #dddddd;
}
@media (min-width: 650px) {
div{
margin: auto;width: 50%;
}
}
</style>
<body>
<div>
<h1>Hello Laura!</h1>
<h2>Are you ready to take the next step?</h2>
<p>We've found positions that fit your profile:</p>
<table><tr><th>Job Title</th><th>Link</th></tr><tr><td>Staff Data Analyst, Credit Analytics</td><td><a href="https://www.indeed.com/viewjob?jk=918ad336af7e09ae" target="_blank">Apply here!</a></td></tr>
<tr><td>Data Analyst Mid to Senior</td><td><a href="https://www.indeed.com/viewjob?jk=b4bf6ec3e4a1a893" target="_blank">Apply here!</a></td></tr>
<tr><td>Data Analyst (full-time)</td><td><a href="https://www.indeed.com/viewjob?jk=8ed44fc2b94856ce" target="_blank">Apply here!</a></td></tr>
<tr><td>Data Analyst</td><td><a href="https://www.indeed.com/viewjob?jk=14f020e039a577ed" target="_blank">Apply here!</a></td></tr>
<tr><td>Data Analyst - Defense</td><td><a href="https://www.indeed.com/viewjob?jk=094f3784e07bdd98" target="_blank">Apply here!</a></td></tr>
</table>
<h2>Do you know?</h2>
<p>As per our last analysis, these are the top 10 skill demanded for Data Analyst:
<table><tr><th>Skill</th><th>Percentage</th></tr><tr><td>Databases</td><td>65.07%</td></tr>
<tr><td>Statisticts/Mathematics</td><td>43.56%</td></tr>
<tr><td>Spreadsheets</td><td>40.83%</td></tr>
<tr><td>Tableau</td><td>27.51%</td></tr>
<tr><td>Python</td><td>25.33%</td></tr>
<tr><td>Power BI</td><td>23.69%</td></tr>
<tr><td>SAS</td><td>14.3%</td></tr>
<tr><td>R (Programming Language)</td><td>13.32%</td></tr>
<tr><td>Power Point</td><td>12.12%</td></tr>
<tr><td>ETL</td><td>8.52%</td></tr>
</table>
</div>
</body>
</html>
Finally let's save the files, let's include link for those two bellow.
f = open("Laura.html", "w")
f.write(Laura_email)
f.close()
f = open("Robert.html", "w")
f.write(Robert_email)
f.close()
We had a lot of fun with this excercise, right? however there are clear limitations and areas where we can improve beyond this demo. here are just a few:
I would definetively consider the bullets above and more in a real implementation, but as of now, I feel happy with the results and patterns observed, which I think do reflect the reality and skills needed from people working as Data Analyst.
Thanks for reading!!
find more about my job on my public portfolio