text: import pandas as pd
  import numpy as np
  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import r2_score, mean_squared_error
  
  
  df = pd.read_csv('./survey_results_public.csv')
  schema = pd.read_csv('./survey_results_schema.csv')
  
  ## How To Break Into the Field
  # Solution to Question 1
  def get_description(schema, column_name):
      '''
      INPUT - schema - pandas dataframe with the schema of the developers survey
              column_name - string - the name of the column you would like to know about
      OUTPUT -
              desc - string - the description of the column
      '''
      desc = list(schema[schema['Column'] == column_name]['Question'])[0]
      return desc
  
  descrips = set(get_description(schema, col) for col in df.columns)
  
  # Solution to Question 4
  higher_ed = lambda x: 1 if x in ("Master's degree", "Doctoral", "Professional degree") else 0
  
  df['HigherEd'] = df["FormalEducation"].apply(higher_ed)
  higher_ed_perc = df['HigherEd'].mean()
  
  
  # Solution to Question 6
  sol = {'Everyone should get a higher level of formal education': False,
         'Regardless of formal education, online courses are the top suggested form of education': True,
         'There is less than a 1% difference between suggestions of the two groups for all forms of education': False,
         'Those with higher formal education suggest it more than those who do not have it': True}
  