I have a df that looks like this:

df = pd.DataFrame({
    'job_title':['Senior Data Scientist', 'Junior Data Analyst', 'Data Engineer Intern', 'Data Engieneer/Architect', 'Data Analyst/Visualisation'],
    'location':['Berlin', 'Frankfurt', 'Munich','Frankfurt', 'Munich'],
    'job_desc':['something something Python, R, Tableau something', 'something R and SQL',
                 'something Power Bi, Excel and Python','something Power Bi, Excel and Python somthing', 'Power BI and R something']})
        

My objective is to now plot the necessary skills that appear in the job description in job_desc column depending on the job title in job_title. Whats important is that the job titles in 'job_title' need to somehow filtered according to the three roles I mention below.

My idea was to do the following:

1. create sub data frames according to the job title for Data Scientist, Data Analyst and Data Engineer
2. creating new dfs from the ones I created that count the skills in the job_desc
3. plot the skills in a bar plot with three sub bar plot according to the role

To do this I have done the following:

1.)

# creating sub datasets according to the three roles above to look further into the different skillset

# data analyst dataset
dfa = df[df['job_title'].str.contains('Data Ana')]

# data scientist dataset
dfs = df[df['job_title'].str.contains('Data Sci')]

# data engineer dataset
dfe = df[df['job_title'].str.contains('Data Eng')]

2.) Here I created a loop and stored the obtained information in a nested dictionary. At first I tried to directly store the data from the loop in new data frames, but I read