hengrumay

Census Tracts of New York City

Here at PyData NYC, I heard a tutorial of how to use numpy and iPython notebooks. In a previous gist, I wrote drew all the zip codes of the Bronx in d3.js

This would be great for reproducing inforgraphics like Educational Attainment in New York City -- Brooklyn which looks a bit like a jigsaw puzzle:

Where to Obtain the Data

What

Roll your own iPython Notebook server with Amazon Web Services (EC2) using their Free Tier.

What are we using? What do you need?

An active AWS account. First time sign-ups are eligible for the free tier for a year
One Micro Tier EC2 Instance
With AWS we will use the stock Ubuntu Server AMI and customize it.
Anaconda for Python.
Coffee/Beer/Time

	import string
	import nltk
	from nltk.tokenize import RegexpTokenizer
	from nltk.corpus import stopwords
	import re

	def preprocess(sentence):
	sentence = sentence.lower()
	tokenizer = RegexpTokenizer(r'\w+')
	tokens = tokenizer.tokenize(sentence)

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8" />
	<title>parseTable</title>
	</head>
	<body>
	<table>
	<thead>
	<tr>

	require(MASS)
	require(ggplot2)
	require(scales)
	require(gridExtra)

	pca <- prcomp(iris[,-5],
	center = TRUE,
	scale. = TRUE)

	prop.pca = pca$sdev^2/sum(pca$sdev^2)

	from collections import namedtuple

	import numpy as np
	import scipy.stats as st

	TtestResults = namedtuple("Ttest", "T p")

	def t_welch(x, y, tails=2):
	"""
	Welch's t-test for two unequal-size samples, not assuming equal variances

	from sklearn import linear_model
	from scipy import stats
	import numpy as np


	class LinearRegression(linear_model.LinearRegression):
	"""
	LinearRegression class after sklearn's, but calculate t-statistics
	and p-values for model coefficients (betas).
	Additional attributes available after .fit()

	#!/usr/bin/env python
	# -- coding: utf-8 --

	#
	# Test that I passed on codility.com for TopTal company
	#


	# Task #1
	def binary_gap(N):

	#!/usr/bin/env python
	# -- coding: utf-8 --

	'''
	Logistic Regression

	References :
	- Jason Rennie: Logistic Regression,
	http://qwone.com/~jason/writing/lr.pdf