Michele Lanning mpdo2017

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Python - comparing JSON data structures

A function compare_json_data(source_data_a,source_data_b), accepting structures populated with data loaded from json.load() and comparing for equality.

Example

$ ./compare.py 
Compare JSON result is: True

Common Options

-#, --progress-bar Make curl display a simple progress bar instead of the more informational standard meter.

-b, --cookie <name=data> Supply cookie with request. If no =, then specifies the cookie file to use (see -c).

-c, --cookie-jar <file name> File to save response cookies to.

Here's a little walkthrough of how Yannick and I are using feature branches and pull requests to develop new features and adding them to the project. Below are the steps I take when working on a new feature. Hopefully this, along with watching the process on Github, will serve as a starting point to having everyone use a similar workflow.

Questions, comments, and suggestions for improvements welcome!

Start with the latest on master

When starting a new feature, I make sure to start with the latest and greatest codebase:

git checkout master

Nice answer on stackoverflow to the question of when to use one or the other content-types for POSTing data, viz. application/x-www-form-urlencoded and multipart/form-data.

“The moral of the story is, if you have binary (non-alphanumeric) data (or a significantly sized payload) to transmit, use multipart/form-data. Otherwise, use application/x-www-form-urlencoded.”

Matt Bridges' answer in full:

The MIME types you mention are the two Content-Type headers for HTTP POST requests that user-agents (browsers) must support. The purpose of both of those types of requests is to send a list of name/value pairs to the server. Depending on the type and amount of data being transmitted, one of the methods will be more efficient than the other. To understand why, you have to look at what each is doing

	import tableauserverclient as TSC
	import pandas as pd
	from io import StringIO

	class Tableau_Server(object):

	"""docstring for ClassName"""
	def __init__(self,username, password,site_id,url, https = False):
	super().__init__() # http://stackoverflow.com/questions/576169/understanding-python-super-with-init-methods

	import org.apache.spark.SparkContext
	import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
	import org.apache.spark.sql.types._
	import org.apache.spark.sql.{Column, Row, SQLContext}

	/***
	* UDAF combining maps, overriding any duplicate key with "latest" value
	* @param keyType DataType of Map key
	* @param valueType DataType of Value key
	* @param merge function to merge values of identical keys

	<?xml version='1.0' encoding='utf-8' ?>
	<!--This is the basics - platform (windows or mac)and version number-->
	<workbook source-platform='mac' version='9.0' xmlns:user='http://www.tableausoftware.com/xml/user'>
	<!-- build 9000.15.0318.1720 -->
	<!--No idea what this for -->
	<preferences>
	<preference name='ui.encoding.shelf.height' value='250' />
	<preference name='ui.shelf.height' value='250' />
	</preferences>
	<!--This is everything you need to know about datasources -->