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A solution to last week’s challenge can be found here.
For this challenge, imagine you are a senior-level professional researching the job market.
The first dataset contains information about the job market in data analysis. The second dataset contains the country codes and the full names of the countries.
The experience is categorized into 4 levels:
- EN: Entry-level/Junior
- MI: Mid-level/Intermediate
- SE: Senior-level/Expert
- EX: Executive-level/Director
Remote ratio correspondence:
- If Remote_ratio = 0, the job is onsite.
- If Remote_ratio = 50, the job is hybrid.
- If Remote_ratio = 100, the job is remote.
Question 1
Taking into consideration the average salary, find the 5 best opportunities for a senior-level professional working full-time, 100% remote, and the countries where these opportunities can be found.
Only consider job titles that appear more than once and salary in USD.
Question 2
How do the remote, hybrid, and onsite ratios vary from 2020 to 2022? Build a graph to show your results.
Hints
- Append the country name in the dataset using the company_location_country_code field.
- Change your data types accordingly.
It's 100% Remote here at Aimpoint Digital. Solution attached,
Adding my solution in the Python Tool,
from ayx import Alteryx
import plotly.express as px
#################################
df = Alteryx.read('#1')
country_mapper = Alteryx.read('#2')
#################################
job_count_mask = df.groupby('job_title')['job_title'].transform('count') > 1
df1 = df[job_count_mask]
#################################
mask = (df1.employment_type=='FT') & (df1.remote_ratio=='100') & (df1.experience_level=='SE')
df1 = df1[mask]
#################################
df1 = df1.join(country_mapper.set_index('country_code'), on='company_location_country_code')
#################################
df1['salary_in_usd'] = df1.salary_in_usd.astype(int)
#################################
df1.groupby(['country_name', 'job_title'], as_index=False)['salary_in_usd'].mean().nlargest(5, 'salary_in_usd')
#################################
df2 = df.groupby(['work_year', 'remote_ratio'], as_index=False)['salary'].count().rename(columns={'salary':'count'})
#################################
df2['remote_ratio'] = df2.remote_ratio.map({'0': 'Onsite', '50': 'Hybrid', '100': 'Remote' })
#################################
df2 = df2.sort_values('remote_ratio').rename(columns={'remote_ratio':'Work Type', 'count':'Count', 'work_year':'Year'})
#################################
px.bar(df2, x='Work Type', y='Count', color='Year', barmode='group')
Challenge 333
I had to do some debugging because I wasn't getting the right answer at first. I left my debugging steps in so that you can see where I made an adjustment.
