snewcomer

// 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'];

snewcomer

"
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,

snewcomer

import pandas as pd
from datetime import datetime

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

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

# clean dtstart

snewcomer

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]

snewcomer

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

snewcomer

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

snewcomer

# 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

snewcomer

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;