GJMcClintock

### This is a quick path to slim deployment by running changed models and their dependencies
### It will NOT pick up changes in macros, so be mindful of that
### It also will not handle seeds, but you can add that file extension if you need (we use Sigma input tables instead)

name: dbt Merge
on:
  workflow_dispatch:
  push:
    branches:
      - main

GJMcClintock

CREATE OR REPLACE VIEW dim_date AS

-- Change the 2009-12-31 to be one day PRIOR to when you want this to start. 
-- The week_of_month and week_of_quarter will be null until the first Monday
-- You may wish to start earlier to allow all needed dates to have proper counting

WITH series AS (
    SELECT x AS id,
     date_add('day', x, DATE '2012-12-31') AS dim_date
     FROM UNNEST(sequence(1, DATE_DIFF('day', DATE '2012-12-31', DATE '2050-01-01'), 1)) t(x)

GJMcClintock

Introduction to College Football Data Using Python

If you are reading this - you enjoy college football. Or you may just enjoy working with different types of data. Hopefully, you're like me and like both. I am self-taught in Python, so I am by no means the end all be all expert of how to approach things, but if I can figure it out, you can to.

This guide is a fork of Parker Flemings (@statsowar on Twitter) guide "Introduction to College Football Data with R and cfbscrapR". We will go through the same excercises he did, simply with the Python equivalent to his R masterpiece.

If you have any questions - or see any errors - shoot me a tweet @GJMcClintock. This is very pragmagtically done - if it works, it works. None of what I presently deal with is very resource intensive, so I am sure there are much more efficient ways to get the job done. If you have suggestions on tha

GJMcClintock

import pandas as pd
import requests
import json
from tqdm import tqdm as tqdm
from scipy.stats import norm
import itertools
import urllib3
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

# %% Get Games