<jupyter_start><jupyter_text>The histogram looks a little fishy. The general shape of the histogram is not surprising, but the limits are. We don't even make it to $150K here, so c-suite salaries are likely excluded.<jupyter_code># now, a bar chart of average salary by title.
helper_df = data_df.groupby(['title'])['salary'].mean()
helper_df.plot.bar()<jupyter_output><empty_output><jupyter_text>This definitely looks fishy. The average salaries are all really tightly clustered. There also doesn't seem to be much point to advancement. For example, Assistant Engineer, Engineer, and Senior Engineer all make about the same on average. With that, let's take a look behind the curtain.<jupyter_code>punchline = pd.read_sql("SELECT * FROM employees INNER JOIN salaries ON employees.emp_no = salaries.emp_no INNER JOIN titles ON employees.emp_title = titles.title_id WHERE employees.emp_no = 499942", connection)
print(punchline)<jupyter_output>emp_no emp_title  birth_date first_name last_name sex   hire_date  emp_no  \
0  499942     e0004  1963-01-10      April  Foolsday   F  1997-02-10  499942   

   salary title_id             title  
0   40000    e0004  Technique Leader
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