urschrei

import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from descartes import PolygonPatch
import fiona
from shapely.geometry import Polygon, MultiPolygon, shape

# We can extract the London Borough boundaries by filtering on the AREA_CODE key
mp = MultiPolygon(
    [shape(pol['geometry']) for pol in fiona.open('data/boroughs/boroughs.shp')
    if pol['properties']['AREA_CODE'] == 'LBO'])

urschrei

""" 
required packages:
numpy
matplotlib
basemap: http://matplotlib.org/basemap/users/installing.html
shapely: https://pypi.python.org/pypi/Shapely
descartes: https://pypi.python.org/pypi/descartes
random

gist:2760470

# Two Matching Methods

# Method 1 - GenMatch()

library(Matching)
covars <- c("sick", "age", "literate", "employment", "public", "urban", "poverty", "owndwell")
X <- as.matrix(collapsed.data[,covars])
W <- collapsed.data$treated_any
g.weights <- GenMatch(Tr=W, X=X, BalanceMatrix=X, estimand="ATT", M=1,print.level=0,max.generations=1,hard.generation.limit=TRUE)
g.weights$matches

bradmontgomery

Web Scraping Workshop

Using Requests and Beautiful Soup, with the most recent Beautiful Soup 4 docs.

Getting Started

Install our tools (preferably in a new virtualenv):

pip install beautifulsoup4