def METHOD_NAME(pdf_path, question):
    # Read table from PDF file
    df1 = shared.read_table_from_pdf(pdf_path, 1)
    # Select the desired rows
    df1 = df1[19:61]
    # Set the column names from the 6th row
    df1.columns = df1.iloc[5]
    # Remove unnecessary rows
    df1 = df1[3:]
    # Drop the 2nd and 3rd columns
    df1 = df1.drop(df1.columns[[1, 2]], axis=1)
    # Rename the columns
    df1.columns = [question, "Increase", "Have no effect", "Decrease", "Not sure"]
    # Remove "%" from the values
    df1 = df1.replace("%", "", regex=True)
    # Convert columns to numeric values
    df1[df1.columns[1:]] = df1[df1.columns[1:]].apply(pd.to_numeric)
    # Drop rows with missing values
    df1 = df1.dropna(axis=0)
    # Reset the index
    df1.reset_index(inplace=True, drop=True)
    # List of occupations
    df1occupations = [
        "Accountants",
        "Artists",
        "Child care workers",
        "Computer programmers",
        "Customer service agents",
        "Data scientists",
        "Medical doctors",
        "Engineers",
        "Graphic designers",
        "Journalists",
        "Judges",
        "Lawyers",
        "Librarians",
        "Market research analysts",
        "Manufacturing workers",
        "Real estate agents",
        "Retail sales workers",
        "Social workers",
        "Teachers",
        "Truck drivers",
    ]
    # Add occupations as a new column
    df1[question] = df1occupations
    return df1