Generating example data

foo.py

# These are the "true" slopes and intercepts that I made up.
set_parameters = (
    (('no degree', 'CA', 'Riverside County'), (5000, 1250)),
    (('no degree', 'IL', 'Cook County'), (6500, 1150)),
    (('no degree', 'IL', 'Lake County'), (7000, 1350)),
    
    (('degree', 'CA', 'Riverside County'), (6000, 1250)),
    (('degree', 'IL', 'Cook County'), (7500, 1150)),
    (('degree', 'IL', 'Lake County'), (8000, 1350))
)

# Go through each definition above and generate a fake time series
rows = []
for set_parameter, N in zip(set_parameters, data_points):
  key, parameters = set_parameter
  population_rows = [{
      'degree': key[0],
      'state': key[1],
      'county': key[2],
      'month_index': i,
      # Create time series data and add some noise to make it realistic
      'salary': i*parameters[0] + parameters[1] + np.random.normal(loc=0, scale=500) 
  } for i in range(N)]
  rows += population_rows

salary_df = pd.DataFrame(rows)