# Python Data Science and Analytics.

# Data Science is a field in computer science that is dedicated to analyzing patterns in raw data using

# techniques like Artificial Intelligence (AI), Machine Learning (ML), mathematical functions, and

# statistical algorithms.

# Pattern is a web mining module for the Python programming language.

# It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural

# language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning

# (vector space model, clustering, SVM), network analysis and <canvas> visualization.

# pattern.graph

# The pattern.graph module provides a graph data structure that represents relations between nodes (e.g., terms, concepts).

# Graphs can be exported as HTML <canvas> animations (demo). In the example below, more central nodes

# (= more incoming traffic) are colored in blue.

from pattern.web import Bing, plaintext

from pattern.en import parsetree

from pattern.search import search

from pattern.graph import Graph

g = Graph()

for i in range(10):

for result in Bing().search('"more important than"', start=i+1, count=50):

s = r.text.lower()

s = plaintext(s)

s = parsetree(s)

p = '{NP} (VP) more important than {NP}'

for m in search(p, s):

x = m.group(1).string # NP left

y = m.group(2).string # NP right

if x not in g:

g.add_node(x)

if y not in g:

g.add_node(y)

g.add_edge(g[x], g[y], stroke=(0,0,0,0.75)) # R,G,B,A

g = g.split()[0] # Largest subgraph.

for n in g.sorted()[:40]: # Sort by Node.weight.

n.fill = (0, 0.5, 1, 0.75 * n.weight)

g.export('test', directed=True, weighted=0.6)