raybuhr

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

raybuhr

# coding: utf-8
from slacker import Slacker
import pandas as pd

def pull_messages(slack_api_token, channel_name, from_time=None):
    """
    slack_api_token can be registered at https://api.slack.com/tokens.
    from_time assumes unix_timestamp, such as 1456709957.004145
        -- mysql can convert between unix and posix time easily
        with the function FROM_UNIXTIME so best to just keep that 

raybuhr

#!/bin/bash

wget --mirror \
       --recursive \
       --convert-links \
       --no-parent \
       --domains  learning.oreilly.com \
       --random-wait \
       --adjust-extension \
       --header='cookie: cd_user_id=...; BrowserCookie=...; timezoneoffset=21600; csrfsafari=...; sign-on-email="..."; sessionid=...; logged_in=y;' \

raybuhr

re: dbt-labs/dbt-core#559

New Adapter Information Sheet

The following questions are intended to provide information about a warehouse, for use in building a new dbt adapter. Different data warehouses have pretty different semantics, so some of these questions may be more applicable than others. Where possible, please provide as much detail as you can!

Please copy this questionnaire to a GitHub gist, then fill out answers to the questions to the best of your ability. Where you're done, please post a link to the public gist in the relevant issue. If the issue does not already exist, please create it first.

Questions

raybuhr

Loops

Loops are a concept for repeat an action on each item in a collection. In day to day life, you might think of this like brushing your teeth -- for each tooth in your mouth scrub with toothbrush and a little bit of toothpaste.

For Loops

The basic format for looping in python is usually taught like this:

>>> for number in range(5):

raybuhr

the tl;dr of https://medium.com/dunder-data/minimally-sufficient-pandas-a8e67f2a2428

select a column of data, use brackets df['column_name']

select rows of data, use .loc or datetime index df['2019-01-01':'2019-02-28' ]

• if performance is primary concern, using numpy array instead of Pandas

use read_csv and it's many arguments for reading files

use .isna method to filter NaN rows

raybuhr

raybuhr

## This is my variation on Chase's first pass
## https://gist.github.com/chasemc/1a6978d500aa47268ecaf9f99719af61

library(htmltab)
library(reshape2)
library(ggplot2)

data_url <- "https://web.archive.org/web/20200513204922/http://covidtracking.com/data/state/illinois"
a <- htmltab(doc = data_url, which = 4)
a$date <- as.Date(a$Date, "%a %b %d %Y")

raybuhr

import argparse
import os
import pathlib
import sys
from time import sleep
from bs4 import BeautifulSoup
import pandas as pd
import requests

raybuhr

[sqlfluff]
verbose = 0
nocolor = False
dialect = postgres
templater = jinja
rules = None
exclude_rules = None
recurse = 0
output_line_length = 80
runaway_limit = 10