Simon Thompson simonthompson99

Merging two very different branches

git checkout master to be in master

git merge <branch> to merge in the other branch, will tell you that can't auto-merge

git mergetool brings up a vim window with:

+--------------------------------+

FUNCTION	DESCRIPTION
index()	Method returns index (row labels) of the DataFrame
insert()	Method inserts a column into a DataFrame
add()	Method returns addition of dataframe and other, element-wise (binary operator add)
sub()	Method returns subtraction of dataframe and other, element-wise (binary operator sub)
mul()	Method returns multiplication of dataframe and other, element-wise (binary operator mul)
div()	Method returns floating division of dataframe and other, element-wise (binary operator truediv)
unique()	Method extracts the unique values in the dataframe
nunique()	Method returns count of the unique values in the dataframe

git stash list see what is in the stash list
git stash show -p see full diff of the stash (add specific stash, otherwise will be most recent)

git stash stash all changes to tracked files and revert to HEAD, add -m <message> to give an accompany description, and can specify some filepaths to limit the stash to
git stash -p go through hunk by hunk and decided what to stash

	import tempfile
	import zipfile

	# make archive.zip in temp directory
	tmpdir = tempfile.mkdtemp()
	zip_fn = os.path.join(tmpdir, 'archive.zip')
	zip_obj = zipfile.ZipFile(zip_fn, 'w')

	for f in files:
	zip_obj.write(f, os.path.basename(f)) # add file to archive, second argument is the structure to be represented in zip archive, i.e. this just makes flat strucutre

	# case when
	from sqlalchemy import case
	d = s.query(config_db.ClinicalIndication.clinical_indication_uid,
	case([(config_db.ClinicalIndication.clinical_indication_code.startswith('M'), 'C'),
	(config_db.ClinicalIndication.clinical_indication_code.startswith('R'), 'R')],
	else_ = None).label('programme')).all()

	# convert query to pandas df
	import pandas as pd
	pd.DataFrame(q.all(), columns = ['col1', 'col2'])

	read_in_xlsx <- function(f) {
	# read in an Excel file into a dataframe
	require(openxlsx)
	d <- read.xlsx(f)
	cat(paste0(f, " - ", nrow(d), "Rx", ncol(d), "C\n"))
	return(d)
	}

	-- Index from query
	psql metrics -h 10.1.24.39 -p 5433 -U cdt_user

	-- Index from local
	psql metrics -h localhost -p 5441 -U cdt_user

	-- local postgres
	psql metrics -h localhost -p 5432 -U cdt_user

	import pandas as pd
	raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
	'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'],
	'age': [42, 52, 36, 24, 73],
	'preTestScore': [4, 24, 31, 2, 3],
	'postTestScore': [25, 94, 57, 62, 70]}
	df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])

	from tempfile import NamedTemporaryFile
	import webbrowser

	base_html = """
	<!doctype html>
	<html><head>
	<meta http-equiv="Content-type" content="text/html; charset=utf-8">
	<script type="text/javascript" src="https://ajax.googleapis.com/ajax/libs/jquery/2.2.2/jquery.min.js"></script>
	<link rel="stylesheet" type="text/css" href="https://cdn.datatables.net/1.10.16/css/jquery.dataTables.css">
	<script type="text/javascript" src="https://cdn.datatables.net/1.10.16/js/jquery.dataTables.js"></script>