Feature engineering function

hr_feature_engineering.py

def create_generation_feature(age_val: int) -> str:
    """
    Function to convert age value onto generation string
    @Args:
      age_val (int): the age value from data frame
      
    Return:
      String output specifies the generation 
    """
    out = ''
    if age_val < 37:
        out = 'Millenials'
        
    elif age_val < 54:
        out = 'Generation X'
        
    elif age_val < 73:
        out = 'Boomers'
        
    else:
        out = 'Silent'
        
    return out


def create_job_hop_index(df: pd.DataFrame, total_exp_col: str, 
                         num_prev_com_col: str) -> (int, float):
    """
    Function to compute the job hopper index and identify the first job or not
    @Args:
      df (pd.DataFrame): pandas data frame
      total_exp_col (str): the name of the column containing total experience
      num_prev_com_col (str): the name of the column containing numbers of previous companies worked
    
    Return:
      The job hopper index
    """
    first_job_ind = np.where(df[num_prev_com_col] == 0, 1, 0)
    job_hop_idx = np.where(df[num_prev_com_col] == 0, 
                           0.,
                           df[total_exp_col] / df[num_prev_com_col]
                          )
    
    return first_job_ind, job_hop_idx

  
def compute_compa_ratio_feature(df: pd.DataFrame, salary_col: str,
                                lookup_df: pd.DataFrame=_MEDIAN_INCOME_LOOKUP, 
                                col_lookup_list: list=_LOOKUP_COL):
    """
    Function to compute the compa-ratio of each employee.
      This is computed by each employee divided by the median salary of the industry
    @Args:
      df: pandas data frame with monthly salary of each employee
      salary_col: a name of pandas data frame storing income
      lookup_df: pandas data frame stored the median of salary
      col_lookup_list: A list of column names
      
    Return:
      pandas data frame
    """
    merge_df = df.reset_index().merge(_MEDIAN_INCOME_LOOKUP, 
                                      on=_LOOKUP_COL,
                                      how="left").set_index('index')
    merge_df['compa_ratio'] = merge_df[salary_col] / merge_df['MedianIncome']
    merge_df.drop(['MedianIncome', 'Count'], axis=1, inplace=True)
    return merge_df