José de Jesus Filho jjesusfilho

Python Zeep SOAP Client

First, we'll look at the SOAP URL and see what Prefixes, Global Elements, Global Types, Bindings, and Services are provided by the SOAP Service.

You can do this by running zeep as a CLI tool.

export WSDL_URL="http://www.dneonline.com/calculator.asmx?WSDL"
python -m zeep $WSDL_URL

UPDATE: I have baked the ideas in this file inside a Python CLI tool called pyds-cli. Please find it here: https://github.com/ericmjl/pyds-cli

How to organize your Python data science project

Having done a number of data projects over the years, and having seen a number of them up on GitHub, I've come to see that there's a wide range in terms of how "readable" a project is. I'd like to share some practices that I have come to adopt in my projects, which I hope will bring some organization to your projects.

Disclaimer: I'm hoping nobody takes this to be "the definitive guide" to organizing a data project; rather, I hope you, the reader, find useful tips that you can adapt to your own projects.

Disclaimer 2: What I’m writing below is primarily geared towards Python language users. Some ideas may be transferable to other languages; others may not be so. Please feel free to remix whatever you see here!

PSQL

Magic words:

psql -U postgres

Some interesting flags (to see all, use -h or --help depending on your psql version):

-E: will describe the underlaying queries of the \ commands (cool for learning!)
-l: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)

	// save to windows-user directory

	linters: with_defaults(object_name_linter = NULL,
	object_length_linter(50),
	commented_code_linter = NULL,
	object_usage_linter = NULL,
	line_length_linter(120),
	cyclocomp_linter = cyclocomp_linter(50))

	library(pdftools)
	library(tidyverse)
	library(janitor)

	pdf_file <- "https://github.com/ropensci/tabulizer/raw/master/inst/examples/data.pdf"

	df <- pdf_data(pdf_file)[[1]]

	# Table-ize ----

	DROP TABLE IF EXISTS matrices;
	CREATE TABLE matrices (
	matrix_id int NOT NULL, -- unique identifier for each matrix
	i int NOT NULL, -- row number
	j int NOT NULL, -- column number
	val decimal NOT NULL, -- the value in this cell
	PRIMARY KEY (matrix_id, i, j)
	);

	-- insert sparse representation of

	FROM node
	RUN mkdir -p /usr/src/app
	COPY index.js /usr/src/app
	EXPOSE 8080
	CMD [ "node", "/usr/src/app/index" ]

	# case_when factory: ----------------------------------------------
	create_case_when <- function(..., vars = "x") {
	fun_fmls <- purrr::map(rlang::set_names(vars), ~ rlang::missing_arg())
	fun_body <- substitute({
	for (name in var) {
	symb <- rlang::eval_bare(rlang::sym(name))
	var <- rlang::eval_tidy(rlang::enquo(symb))
	assign(name, var)
	}
	forms <- purrr::map(formulas, rlang::`f_env<-`, value = environment())

	library(caret)
	library(tm)

	# Training data.
	data <- c('Cats like to chase mice.', 'Dogs like to eat big bones.')
	corpus <- VCorpus(VectorSource(data))

	# Create a document term matrix.
	tdm <- DocumentTermMatrix(corpus, list(removePunctuation = TRUE, stopwords = TRUE, stemming = TRUE, removeNumbers = TRUE))

	library(shiny)
	library(httr)

	# OAuth setup --------------------------------------------------------

	# Most OAuth applications require that you redirect to a fixed and known
	# set of URLs. Many only allow you to redirect to a single URL: if this
	# is the case for, you'll need to create an app for testing with a localhost
	# url, and an app for your deployed app.