Alexander Goida xtrmstep

Test Name	Null Hypothesis	p-value Criteria	Limitations	Use Cases
Shapiro-Wilk Test	The data is normally distributed.	If `p > 0.05`,

Technique	Purpose
Z-Score	Centers data to mean = 0, std dev = 1; for Gaussian data or regression-based models.
Min-Max	Scales data to a specific range (e.g., [0, 1]); for bounded input in neural networks.
Log Transformation	Compresses large values and reduces skewness; for data with exponential growth patterns.
Robust Scaling	Rescales using median and IQR; for datasets with many outliers.

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SENDING METRICS TO STATSD WITH A SHELL ONE-LINER

NOTE: From here

Etsy’s great post about tracking every release and has some good tips about tracking releases with statsd and graphite (including some essential graphite config tweaks). I was wondering how to do this from within a shell script, and I had to dig through lots of StatsD code and examples to find this snippet. I forget where I eventually found it, and thought it’d make it easier to find.

Deploy scripts are just one place where a concise and safe way to record a metric/event in important. From Etsy’s blog, using vertical lines to represent distinct events (code deployments) to give more context to the login trends:

Sending a metric from the command line, with netcat or curl, is just one bit of ‘glue’ that is essential for pulling t

	import json

	import fastavro
	from fastavro.schema import load_schema


	def json_to_avro(json_file_path, avro_file_path, schema_file_path, compression='deflate'):
	try:
	schema = load_schema(schema_file_path)
	except Exception as e:

	DO
	$do$
	declare
	r record;
	query_cmd text;
	begin
	for r in select table_name from information_schema.tables where table_schema = 'public' and table_name like 'prefix%'
	loop
	query_cmd := format('delete from %s where CONDITION', r.table_name);
	-- raise notice '%', query_cmd;

	// usage:
	// 'http://www.website.com/'.urlQueryParameter('id', 2) => http://www.website.com/?id=2
	// 'http://www.website.com/?type=1'.urlQueryParameter('id', 2) => http://www.website.com/?type=1&id=2

	String.prototype.isString = true;
	String.prototype.urlQueryParameter = function(key, value) {
	var uri = this;
	if (uri.isString) {
	var regEx = new RegExp("([?\|&])" + key + "=.*?(&\|$)", "i");
	var separator = uri.indexOf('?') !== -1 ? "&" : "?";

	function odump(object, depth, max) {
	depth = depth \|\| 0;
	max = max \|\| 2;
	if (depth > max) return false;
	var indent = "";
	for (var i = 0; i < depth; i++) indent += " ";
	var output = "";
	for (var key in object) {
	output += "n" + indent + key + ": ";
	switch (typeof object[key]) {

	files = [
	"file://path"
	]
	df = spark.read.json(files)
	catalyst_plan = df._jdf.queryExecution().logical()
	df_size_read = spark._jsparkSession.sessionState().executePlan(catalyst_plan).optimizedPlan().stats().sizeInBytes()

	# source: https://stackoverflow.com/a/50156142/2833774

	def flatten(schema, prefix=None):
	fields = []
	for field in schema.fields:
	name = prefix + '.' + field.name if prefix else field.name
	alias_name = name.replace(".", "__")
	dtype = field.dataType
	if isinstance(dtype, pst.ArrayType):
	dtype = dtype.elementType