ryancollingwood

from typing import Dict
import re
from dataclasses import dataclass, field
import sqlparse

@dataclass
class SQLColumnLineage():
    sql: str
    target_column: str
    aliased_columns: Dict[str, str] = field(default_factory=lambda: dict())

ryancollingwood

from typing import List
from pydantic import BaseModel

class CharacterClass(BaseModel):
  name: str
  is_ranged: bool
  is_magic: bool

class Item(BaseModel):
  name: str

ryancollingwood

def flatten_json_object_to_lines(obj, remove_keys=None, line_prefix=None, add_key_index=True):
    ''' 
        flattens a JSON parsed obj into multiple lines
        Last Element [-1] in each line is the value
        Elements [0:-1] is the path to that value
        line_prefix if specified will prefix a value to the begining of each outputted line
        add_key_index will add 0-based index position when encoutering lists or tuples
        remove_keys if a key (any point in the path) matches then remove it
        adapted from: https://thispointer.com/python-how-to-iterate-over-nested-dictionary-dict-of-dicts/
    '''

ryancollingwood

<div id="main">
    	<div class="fof">
        		<h1>Error 404</h1>
    	</div>
</div>

ryancollingwood

import arcade

"""
A silly demo with the python arcade library
"""

SCREEN_WIDTH = 640
SCREEN_HEIGHT = 480

HALF_SQUARE_WIDTH = 2.5

ryancollingwood

import pandas as pd
# docs can be found at http://gspread.readthedocs.io/en/latest/
import gspread
from oauth2client.service_account import ServiceAccountCredentials


def get_googlesheet_worksheet(json_keyfile_name, sheet_name, worksheet_name):
    scope = ['https://spreadsheets.google.com/feeds']
    creds = ServiceAccountCredentials.from_json_keyfile_name(json_keyfile_name, scope)
    

ryancollingwood

# Credit for this: Nicholas Swift
# as found at https://medium.com/@nicholas.w.swift/easy-a-star-pathfinding-7e6689c7f7b2
from warnings import warn
import heapq

class Node:
    """
    A node class for A* Pathfinding
    """

ryancollingwood

import pandas as pd
import numpy as np

def compare_two_dfs(input_df_1, input_df_2):
    
    # explicitly calling fillna with ""
    # as if you've used np.nan it has the 
    # property of nevery being able to be equals
    # i.e. `np.nan == np.nan` will always be False
    df_1, df_2 = input_df_1.copy().fillna(""), input_df_2.copy().fillna("")    

ryancollingwood

import requests
import yaml
from bravado_core.spec import Spec
from bravado_core.validate import validate_schema_object, validate_object
from jsonschema.exceptions import ValidationError

# get a formatted swagger url for yaml definition
def get_swagger_yaml_url(organisation, api, version):
    return "https://api.swaggerhub.com/apis/{}/{}/{}/swagger.yaml".format(organisation ,api, version)

ryancollingwood

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Diagnostics;
using System.IO;
using Newtonsoft.Json;
using Newtonsoft.Json.Linq;

namespace GoogleDocsLauncher