Scott Newcomer snewcomer

Let's Make A Dropdown

Here, we intend a component that can be used for something like a user menu in a nav. Maybe you've heard it called "popover" or something like that.

It is not meant to be a select element, or used in a <form>.

Goals:

	SELECT nspname \|\| '.' \|\| relname AS "relation",
	pg_size_pretty(pg_total_relation_size(C.oid)) AS “total_size”
	FROM pg_class C
	LEFT JOIN pg_namespace N ON (N.oid = C.relnamespace)
	WHERE nspname NOT IN ('pg_catalog', 'information_schema')
	AND C.relkind <> 'i'
	AND nspname ~ '^pg_toast'
	ORDER BY pg_total_relation_size(C.oid) DESC
	LIMIT 150;

	# Import module for data manipulation
	import pandas as pd
	# Import module for linear algebra
	import numpy as np
	# Import module for Fuzzy string matching
	from fuzzywuzzy import fuzz, process
	# Import module for regex
	import re
	# Import module for iteration
	import itertools

	import pandas as pd
	import numpy as np
	from datetime import datetime, time
	from sklearn.linear_model import Lasso, Ridge, LogisticRegression
	from sklearn.metrics import classification_report, confusion_matrix
	import matplotlib.pyplot as plt

	from sklearn.linear_model import LinearRegression
	from sklearn.model_selection import train_test_split
	from sklearn.metrics import r2_score, mean_squared_error, roc_auc_score

	import pandas as pd
	import numpy as np
	from datetime import datetime, time

	#import matplotlib.pyplot as plt

	from sklearn.linear_model import LinearRegression
	from sklearn.model_selection import train_test_split
	from sklearn.metrics import r2_score, mean_squared_error
	from sklearn.ensemble import RandomForestClassifier

	import pandas as pd
	import numpy as np
	from datetime import datetime
	import matplotlib.pyplot as plt

	cal = pd.read_csv('cal.csv')

	# clean summary
	cal = cal.query("summary not in ('Mid-week Meditation', 'Coffee Chat: Meet our New Hires!', 'PED Lunch & Learn Hold', 'AskPeople')")
	cal = cal[cal['summary'].str.contains("Company Holiday") == False]

	"
	with table_stats as (
	select psut.relname,
	psut.n_live_tup,
	1.0 * psut.idx_scan / greatest(1, psut.seq_scan + psut.idx_scan) as index_use_ratio
	from pg_stat_user_tables psut
	order by psut.n_live_tup desc
	),
	table_io as (
	select psiut.relname,

	// NOTE: DO NOT DISABLE THIS ON YOUR OWN!
	// This lint is disabled here knowing that the contract for this
	// helper is that no untrusted content is passed in. If you disable
	// this lint and use htmlSafe elsewhere, you could risk opening up an
	// XSS vulnerability.

	// eslint-disable-next-line name-xss/avoid-xss-risks
	import { htmlSafe } from '@ember/template';

	const DEFAULT_SAFE_TAGS = ['strong', 'em', 'b', 'i', 'u', 'br'];

	// The global revision clock. Every time state changes, the clock increments.
	let $REVISION = 0;

	// The current dependency tracker. Whenever we compute a cache, we create a Set
	// to track any dependencies that are used while computing. If no cache is
	// computing, then the tracker is null.
	let CURRENT_TRACKER = null;

	// Storage represents a root value in the system - the actual state of our app.
	class Storage {