Utilize sys.arg within code.py

code.py

@@ -1,6 +1,6 @@
 from jinja2 import Environment, FileSystemLoader
 import yaml
-
+import sys
 file_loader = FileSystemLoader("templates")
 
 
@@ -118,8 +118,12 @@ def helper_parse_values(self, key, value, table):
 # template = env.get_template("ecomm_template.py.jinja2")
 template.globals['Table'] = Table()
 
+yaml_file = "yaml/job_listings.yaml"
+if sys.argv[1]:
+    yaml_file = f"yaml/{sys.argv[1]}.yaml"
+print(yaml_file)
 # read yaml file
-with open("yaml/job_listings.yaml", "r") as val:
+with open(yaml_file, "r") as val:
     values = yaml.safe_load(val)
     output = template.render(tables=values)
     # write python file

data/jobs.csv

@@ -1,101 +1,101 @@
 id,level,org,title
-1,1,Sales,SE
-2,4,Customer Success,CSM
-3,2,Sales,AE
-4,2,Engineering,SWE
-5,3,Product,UI
+1,2,Product,UX
+2,3,Customer Success,CSM
+3,3,Customer Success,CSE
+4,3,Marketing,Marketing Analyst
+5,3,Sales,SE
 6,1,Engineering,SWE
-7,3,Product,UX
-8,3,Sales,SE
-9,1,Marketing,Marketing Analyst
-10,3,Product,UX
-11,3,Product,UX
-12,2,Engineering,SWE
-13,3,Marketing,Marketing Manager
-14,1,Product,PM
-15,3,Engineering,SWE
-16,4,Product,PM
-17,3,Product,UX
-18,2,Sales,FSR
-19,3,Sales,SE
-20,2,Marketing,Marketing Manager
-21,3,Marketing,Marketing Analyst
-22,2,Engineering,Data Eng
-23,2,Customer Success,CSM
-24,3,Engineering,Data Eng
-25,3,Sales,SE
-26,2,Sales,AE
-27,4,Sales,AE
-28,4,Product,PM
-29,3,Engineering,Data Eng
-30,3,Engineering,SWE
-31,3,Product,PM
-32,2,Customer Success,CSM
-33,2,Product,UI
-34,4,Sales,AE
-35,1,Engineering,SWE
-36,3,Engineering,SWE
-37,2,Sales,SE
-38,3,Engineering,Data Eng
-39,3,Customer Success,CSM
-40,4,Customer Success,TSM
-41,4,Product,UI
-42,4,Customer Success,CSM
-43,2,Product,PM
-44,1,Sales,AE
-45,4,Marketing,Marketing Manager
-46,3,Engineering,Data Eng
-47,3,Product,UX
-48,4,Sales,FSR
-49,2,Sales,SE
-50,2,Customer Success,CSM
-51,3,Customer Success,CSM
-52,2,Product,PM
-53,4,Product,UI
-54,3,Customer Success,TSM
-55,4,Engineering,SWE
-56,2,Marketing,Marketing Manager
-57,2,Marketing,Marketing Manager
-58,4,Customer Success,CSE
-59,2,Customer Success,CSM
-60,2,Customer Success,CSE
-61,2,Marketing,Marketing Manager
-62,4,Engineering,SWE
-63,3,Engineering,Data Eng
-64,4,Engineering,Data Eng
+7,3,Sales,AE
+8,2,Sales,AE
+9,2,Engineering,Data Eng
+10,3,Customer Success,CSM
+11,1,Marketing,Marketing Analyst
+12,1,Engineering,Data Eng
+13,2,Product,PM
+14,1,Engineering,Data Eng
+15,1,Product,PM
+16,2,Sales,AE
+17,1,Engineering,SWE
+18,3,Marketing,Marketing Analyst
+19,1,Engineering,SWE
+20,2,Sales,FSR
+21,4,Marketing,Marketing Analyst
+22,2,Product,PM
+23,4,Sales,AE
+24,3,Product,PM
+25,3,Customer Success,CSE
+26,3,Product,PM
+27,4,Engineering,SWE
+28,3,Engineering,Data Eng
+29,4,Customer Success,CSM
+30,4,Customer Success,CSM
+31,3,Product,UX
+32,4,Sales,FSR
+33,4,Sales,SE
+34,2,Engineering,SWE
+35,3,Product,UX
+36,4,Product,UX
+37,1,Customer Success,CSE
+38,2,Engineering,SWE
+39,2,Sales,AE
+40,4,Sales,SE
+41,2,Marketing,Marketing Manager
+42,4,Engineering,SWE
+43,3,Engineering,Data Eng
+44,4,Customer Success,CSM
+45,3,Product,PM
+46,3,Customer Success,TSM
+47,2,Engineering,SWE
+48,4,Marketing,Marketing Analyst
+49,4,Engineering,SWE
+50,4,Marketing,Marketing Analyst
+51,1,Customer Success,CSM
+52,1,Customer Success,CSE
+53,3,Customer Success,CSE
+54,3,Sales,FSR
+55,2,Sales,FSR
+56,3,Sales,AE
+57,3,Product,PM
+58,1,Sales,SE
+59,1,Sales,FSR
+60,3,Sales,FSR
+61,4,Product,UI
+62,4,Customer Success,CSM
+63,1,Engineering,Data Eng
+64,4,Engineering,SWE
 65,2,Sales,AE
-66,3,Engineering,Data Eng
-67,3,Customer Success,TSM
-68,4,Customer Success,CSE
-69,1,Product,UI
-70,4,Product,UX
-71,4,Engineering,SWE
-72,1,Customer Success,TSM
-73,4,Product,UX
+66,4,Marketing,Marketing Analyst
+67,2,Sales,AE
+68,2,Product,PM
+69,1,Product,UX
+70,1,Sales,SE
+71,3,Product,PM
+72,3,Engineering,Data Eng
+73,2,Customer Success,TSM
 74,1,Product,PM
-75,4,Customer Success,CSM
-76,1,Marketing,Marketing Analyst
-77,3,Product,PM
-78,1,Product,UX
-79,3,Sales,AE
-80,2,Product,UX
-81,4,Engineering,SWE
-82,3,Sales,SE
-83,3,Marketing,Marketing Analyst
-84,4,Engineering,SWE
-85,3,Marketing,Marketing Analyst
-86,2,Marketing,Marketing Analyst
-87,3,Customer Success,CSE
-88,3,Engineering,Data Eng
-89,3,Customer Success,TSM
-90,1,Product,PM
-91,2,Marketing,Marketing Manager
-92,4,Engineering,SWE
-93,4,Customer Success,TSM
-94,2,Customer Success,CSE
-95,2,Product,UI
-96,4,Sales,SE
-97,1,Marketing,Marketing Manager
-98,4,Marketing,Marketing Analyst
-99,3,Marketing,Marketing Analyst
-100,1,Customer Success,CSE
+75,1,Customer Success,TSM
+76,3,Engineering,Data Eng
+77,2,Customer Success,CSM
+78,4,Marketing,Marketing Manager
+79,3,Customer Success,CSE
+80,1,Product,PM
+81,1,Sales,AE
+82,4,Engineering,Data Eng
+83,3,Engineering,Data Eng
+84,3,Product,PM
+85,4,Product,UI
+86,2,Customer Success,CSM
+87,2,Product,UX
+88,4,Product,UX
+89,4,Product,UX
+90,4,Product,UX
+91,3,Customer Success,CSE
+92,2,Product,UX
+93,2,Engineering,Data Eng
+94,2,Engineering,Data Eng
+95,3,Customer Success,CSM
+96,4,Product,PM
+97,4,Engineering,Data Eng
+98,1,Engineering,SWE
+99,3,Marketing,Marketing Manager
+100,1,Customer Success,CSM

fake.py

@@ -4,10 +4,12 @@
 import datetime
 import itertools
 import json
+import typing
 
 from utils import DataUtil
 import faker
 fake = faker.Faker()
+
 jobs_out = []
 @dataclasses.dataclass
 class Jobs(DataUtil.DataUtil):

prev_roles.py

@@ -2,11 +2,11 @@
 import datetime
 import random
 titles_by_org = {
-    "sales": ["AE", "SE", "FSR"],
-    "marketing": ["Marketing Analyst", "Marketing Manager"],
-    "engineering": ["SWE", "Data Eng"],
-    "product": ["PM", "UI", "UX"],
-    "customer_success": ["CSM", "CSE", "TSM"],
+    "Sales": ["AE", "SE", "FSR"],
+    "Marketing": ["Marketing Analyst", "Marketing Manager"],
+    "Engineering": ["SWE", "Data Eng"],
+    "Product": ["PM", "UI", "UX"],
+    "Customer Success": ["CSM", "CSE", "TSM"],
 }
 
 today = datetime.date.today()
@@ -21,5 +21,5 @@
                                 row["hired_date"], '%Y-%m-%d').date())).days
         if days_since_hired / 365 > 6:
             row["title"] = random.choices(
-                [i for i in titles_by_org[row["org"].lower().replace(' ', '_')] if i != row["org"]])[0]
+                [i for i in titles_by_org[row["org"]] if i != row["org"]])[0]
             dict_writer.writerow(row.values())

yaml/open_jobs.yaml

@@ -1,2 +1,22 @@
-Openings:
-  job_id: { type: int }
+Jobs:
+  id: { type: int, primary_key: True }
+  level: { type: int, dist: { min: 1, max: 5 } }
+  org:
+    {
+      type: str,
+      values:
+        ["Sales", "Marketing", "Engineering", "Product", "Customer Success"],
+    }
+  title:
+    {
+      type: str,
+      depends_on: ["org"],
+      mapping:
+        {
+          "Sales": ["AE", "SE", "FSR"],
+          "Marketing": ["Marketing Analyst", "Marketing Manager"],
+          "Engineering": ["SWE", "Data Eng"],
+          "Product": ["PM", "UI", "UX"],
+          "Customer Success": ["CSM", "CSE", "TSM"],
+        },
+    }