David Yerrington dyerrington

## linear_regression_kfold_cross_validation.py
# k-fold regression
# we need our modules for this:
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import cross_val_score, cross_val_predict
from matplotlib import pyplot as plt

from sklearn import metrics

# Make the plots bigger
plt.rcParams['figure.figsize'] = 10, 10

## pearson_pandas_heatmap.py
import seaborn as sns
import matplotlib, matplotlib.pyplot as plt

# initializing subplots allows changing of font size
fig, ax = plt.subplots()
fig.set_size_inches(12, 8)

# Change font size
matplotlib.rcParams.update({'font.size': 12})

## json_job_miner.py
class usajobs:

    total_pages = 0
    total_results = 0
    per_page = 25
    verify_ssl = False
    search_term = ""
    mined_jobs = []

    headers = {

## auto_patsy_formula.py
columns = ["col1", "col2", "col3", "col4"]
joined_columns = ") + c(".join(columns)
"target ~ c(" + joined_columns[0:len(joined_columns) - 1] + ")"

## 0_reuse_code.js
// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

## foursquare.py
# !pip install foursquare

import foursquare
import pandas as pd

CLIENT_ID = ''   # Input your client id/ client secret here
CLIENT_SECRET = ''
client = foursquare.Foursquare(client_id=CLIENT_ID, client_secret=CLIENT_SECRET)

# grab from bounding box (raw csv):  http://boundingbox.klokantech.com/

## pandas_stateful_logger.py
from sqlalchemy import create_engine
import pandas as pd

class sql_logger:

    sql_file = False
    engine = False
    last_game_id = 0

    def __init__(self, sql_file="crap.db"):

## pandas_select_numeric_objects.py
team['game_id'] = team[team['Gm#'].apply(lambda val: val.isdigit())]['Gm#'].map(int)

## init.sh
# Basic system updates
sudo apt-get update
sudo apt-get install python-pip
sudo apt-get install python-virtualenv
sudo apt-get install python-dev

# Create a directory for our virtualenvs
mkdir virtualenvs
virtualenv data
cd data

## profile_roamer.js
var newwin;
var tabs=[];

function close_win() {
    win = tabs.pop();
    win.close();
}

jQuery( document ).ajaxComplete(function( event,request, settings ) {
	# k-fold regression
	# we need our modules for this:
	from sklearn.linear_model import LinearRegression
	from sklearn.cross_validation import cross_val_score, cross_val_predict
	from matplotlib import pyplot as plt

	from sklearn import metrics

	# Make the plots bigger
	plt.rcParams['figure.figsize'] = 10, 10
	import seaborn as sns
	import matplotlib, matplotlib.pyplot as plt

	# initializing subplots allows changing of font size
	fig, ax = plt.subplots()
	fig.set_size_inches(12, 8)

	# Change font size
	matplotlib.rcParams.update({'font.size': 12})
	class usajobs:

	total_pages = 0
	total_results = 0
	per_page = 25
	verify_ssl = False
	search_term = ""
	mined_jobs = []

	headers = {
	columns = ["col1", "col2", "col3", "col4"]
	joined_columns = ") + c(".join(columns)
	"target ~ c(" + joined_columns[0:len(joined_columns) - 1] + ")"
	// Use Gists to store code you would like to remember later on
	console.log(window); // log the "window" object to the console
	# !pip install foursquare

	import foursquare
	import pandas as pd

	CLIENT_ID = '' # Input your client id/ client secret here
	CLIENT_SECRET = ''
	client = foursquare.Foursquare(client_id=CLIENT_ID, client_secret=CLIENT_SECRET)

	# grab from bounding box (raw csv): http://boundingbox.klokantech.com/
	from sqlalchemy import create_engine
	import pandas as pd

	class sql_logger:

	sql_file = False
	engine = False
	last_game_id = 0

	def __init__(self, sql_file="crap.db"):
	# Basic system updates
	sudo apt-get update
	sudo apt-get install python-pip
	sudo apt-get install python-virtualenv
	sudo apt-get install python-dev

	# Create a directory for our virtualenvs
	mkdir virtualenvs
	virtualenv data
	cd data
	var newwin;
	var tabs=[];

	function close_win() {
	win = tabs.pop();
	win.close();
	}

	jQuery( document ).ajaxComplete(function( event,request, settings ) {