Using Requests and Beautiful Soup, with the most recent Beautiful Soup 4 docs.
Install our tools (preferably in a new virtualenv):
pip install beautifulsoup4
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']) |
""" | |
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 | |
# 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 |
Using Requests and Beautiful Soup, with the most recent Beautiful Soup 4 docs.
Install our tools (preferably in a new virtualenv):
pip install beautifulsoup4