Data diver andirey

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs:

	---
	title: "Expense Analyser"
	output:
	flexdashboard::flex_dashboard:
	navbar:
	- { title: "About", href: "https://www.tadge-analytics.com.au", align: right }
	runtime: shiny
	---

	```{r setup, include=FALSE}

	Key Sublime Text 3.2.1 Build 3207
	----- BEGIN LICENSE -----
	Member J2TeaM
	Single User License
	EA7E-1011316
	D7DA350E 1B8B0760 972F8B60 F3E64036
	B9B4E234 F356F38F 0AD1E3B7 0E9C5FAD
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	library(shiny)
	library(shinyauthr)
	library(shinyjs)

	user_base <- data.frame(
	user = c("user1", "user2"),
	password = c("pass1", "pass2"),
	permissions = c("admin", "standard"),
	name = c("User One", "User Two"),
	stringsAsFactors = FALSE

	library(shiny)

	ui <- fluidPage(
	sidebarLayout(
	sidebarPanel(
	tags$a("Go to Panel 1", href = "#panel1"), br(),
	tags$a("Go to Panel 2", href = "#panel2"), br(),
	tags$a("Go to Panel 3", href = "#panel3")
	),
	mainPanel(

	#!/usr/bin/env node

	function execute(command) {
	const exec = require('child_process').exec

	exec(command, (err, stdout, stderr) => {
	process.stdout.write(stdout)
	})
	}

	#!/usr/bin/env python
	'''
	Permission is hereby granted, free of charge, to any person obtaining a
	copy of this software and associated documentation files (the "Software"),
	to deal in the Software without restriction, including without limitation
	the rights to use, copy, modify, merge, publish, distribute, sublicense,
	and/or sell copies of the Software, and to permit persons to whom the
	Software is furnished to do so, subject to the following conditions:

	The above copyright notice and this permission notice shall be included in

	I use the first


	—– BEGIN LICENSE —–
	Michael Barnes
	Single User License
	EA7E-821385
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	C458EA6D 8EA3C286 98D1D650 131A97AB
	AA919AEC EF20E143 B361B1E7 4C8B7F04

	# Using airquality dataset
	data <- airquality
	data[4:10,3] <- rep(NA,7)
	data[1:5,4] <- NA

	# Removing categorical variables
	data <- airquality[-c(5,6)]
	summary(data)

	#-------------------------------------------------------------------------------

	# Load the raw training data and replace missing values with NA
	training.data.raw <- read.csv('train.csv',header=T,na.strings=c(""))

	# Output the number of missing values for each column
	sapply(training.data.raw,function(x) sum(is.na(x)))

	# Quick check for how many different values for each feature
	sapply(training.data.raw, function(x) length(unique(x)))

	# A visual way to check for missing data