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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:

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

Clarify and agree on the scope of the system

User cases (description of sequences of events that, taken together, lead to a system doing something useful)
- Who is going to use it?
- How are they going to use it?

	# The MIT License (MIT)
	# Copyright (c) 2016 Vladimir Ignatev
	#
	# 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:
	#

	#!/usr/bin/env python

	import argparse
	import sys

	import jinja2
	import markdown

	TEMPLATE = """<!DOCTYPE html>
	<html>

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R to python data wrangling snippets

System Design Cheatsheet

Basic Steps

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