drewconway

counts = dict((k, F.count(k)) for k in set(F))
counts_items=counts.items()
counts_items=[(b,a) for (a,b) in counts_items]
counts_items.sort()
counts_items.reverse()

for i in counts_items:
    print str(i[0])+' of your friends are already following '+i[1]

drewconway

#!/usr/bin/env python
# encoding: utf-8
"""
TwitterEgoBuilder.py

Created by Drew Conway on 2009-02-23.
Copyright (c) 2009. All rights reserved.

The purpose of this script is to generate a 
NetworkX DiGraph object based on the snowball

drewconway

#!/usr/bin/env python
# encoding: utf-8
"""
exploded_view_3d.py

The purpose of this script is to create an 'exploded view'
of a network in 3D using hierarchical clustering of geodesic 
distances in NetworkX and UbiGraph.

This script is intended as an illustrative proof of concept

drewconway

#!/usr/bin/env python
# encoding: utf-8
"""
Experiments for growing fractal networks

Created by Drew Conway on 2009-05-19.
Copyright (c) 2009. All rights reserved.
"""

import sys

drewconway

import os
import sys
import csv

def get_players(path):
# Returns a list of player names from CSV file
    reader=csv.reader(open(path,'U'),delimiter=',')
    '''
    players=[]
    row_num=0

drewconway

import urllib2

def get_player_profiles(player_list):
# Returns a dict of player profile URLs to be used in the next step
    # Dict will hold player profile pages indexed by player name
    player_profile_urls=dict.fromkeys(player_list)
    for n in player_list:
        names=n.split(' ')
        # Search for the player names at NFL.com to get their individual player profiles, which contain the
        # data we ultimately want.

drewconway

import html5lib
from html5lib import treebuilder

def parse_data(player_urls):
# Returns a dict of player data parse trees indexed by player name
    # Create a dict indexed by player names
    player_data=dict.fromkeys(player_urls.keys())
    # Download player profile data and parse using html5lib
    for name in player_urls.keys():
    # html5lib integrates the easy-to-use BeautifulSoup parse tree using the treebuilders library.

drewconway

def write_data(data,path,new_path):
# Takes data dict and writes new data to a new file
    reader=csv.reader(open(path,'U'),delimiter=',')
    writer=csv.writer(open(new_path,"w"))
    row_num=0
    for row in reader:
        if row_num<1:
        # Keep ther same column headers as before, so we simply
        # re-write the first row.
            writer.writerow(row)

drewconway

# File-Name:       currency_converter.R                 
# Date:            2009-11-17                                
# Author:          Drew Conway                                       
# Purpose:         Convert currency data
# Data Used:       vc_invests.csv
# Packages Used:   foreign,XML
# Output File:     vc_invests_USD.csv
# Data Output:     
# Machine:         Drew Conway's MacBook                         
                                                                    

drewconway

# File-Name:       cpi_oprobit.R                 
# Date:            2009-11-17                                
# Author:          Drew Conway                                       
# Purpose:         Quick ordered probit analysis of the Corruption Perceptions Index 2009
#                   to check for effect of number of surveys used on CPI scores
# Data Used:       corruption_index.csv
#  available here: http://www.drewconway.com/zia/wp-content/uploads/2009/11/corruption_index.csv
# Packages Used:   foreign,Zelig
# Output File:     
# Data Output: