t = Table(rows=[
[uid(), "broccoli", "vegetable"],
[uid(), "turnip", "vegetable"],
[uid(), "asparagus", "vegetable"],
[uid(), "banana", "fruit" ],
])
print t.rows[0] # A list of rows.
print t.columns[1] # A list of columns, where each column is a list of values.
print
# Columns can be manipulated directly like any other Python list.
# This can be slow for large tables. If you need a fast way to do matrix math,
# use numpy (http://numpy.scipy.org/) instead.
# The purpose of Table is data storage.
t.columns.append([
"green",
"purple",
"white",
"yellow"
])
# Save as a comma-separated (unicode) text file.
t.save("food.txt")
# Load a table from file.
t = Table.load("food.txt")
pprint(t, truncate=50, padding=" ", fill=".")
# This example demonstrates how table values can be grouped.
t = Table(rows=[
# 0-ID 1-NAME 2-TYPE 3-COLOR
[uid(), "broccoli", "vegetable", "green"],
[uid(), "turnip", "vegetable", "purple"],
[uid(), "asparagus", "vegetable", "white"],
[uid(), "banana", "fruit", "yellow"],
[uid(), "orange", "fruit", "orange"]
])
g = t.copy(columns=[2, 0]) # A copy with only the type and id columns.
g = g.group(0, COUNT) # Group by type, count rows per type.
# Group functions: FIRST, LAST, COUNT, MAX, MIN, SUM, AVG, STDEV.
pprint(g)
print
# This will group by type and concatenate all names per type:
g = t.copy(columns=[2, 1])
g = g.group(0, function=lambda list: "/".join(list))
pprint(g)
print
# This will group by type, count the id's per type, and concatenate all names per type.
# Each column is given a different grouping function.
# For the column one whose values is grouped, simply use FIRST.
g = t.copy(columns=[2, 0, 1])
g = g.group(0, function=(FIRST, COUNT, lambda list: "/".join(list)))
g.columns[1].sort() # Sort by count.
# "X IS MORE IMPORTANT THAN Y"
# Here is a rough example of how to build a web miner.
# It mines comparative statements from Yahoo! and stores the results in a table,
# which can be saved as a text file for further processing later on.
# Pattern matching also works with Sentence objects from the MBSP module.
# MBSP's parser is much more robust (but also slower).
#from MBSP import Sentence, parse
q = '"more important than"' # Yahoo search query
p = "NP (VP) more important than NP" # Search pattern.
p = Pattern.fromstring(p)
t = Table()
engine = Yahoo(license=None)
for i in range(1): # max=10
for result in engine.search(q, start=i+1, count=100, cached=True):
s = result.description
s = plaintext(s)
s = Sentence(parse(s))
for m in p.search(s):
a = m.constituents(constraint=0)[-1] # Left NP.
b = m.constituents(constraint=5)[ 0] # Right NP.
t.append((
a.string.lower(),
b.string.lower()))
pprint(t)
print
print len(t), "results."
# This example demonstrates how table values can be grouped.
t = Table(rows=[
# 0-ID 1-NAME 2-TYPE 3-COLOR
[uid(), "broccoli", "vegetable", "green" ],
[uid(), "turnip", "vegetable", "purple"],
[uid(), "asparagus", "vegetable", "white" ],
[uid(), "banana", "fruit", "yellow"],
[uid(), "orange", "fruit", "orange"]
])
g = t.copy(columns=[2,0]) # A copy with only the type and id columns.
g = g.group(0, COUNT) # Group by type, count rows per type.
# Group functions: FIRST, LAST, COUNT, MAX, MIN, SUM, AVG, STDEV.
pprint(g)
print
# This will group by type and concatenate all names per type:
g = t.copy(columns=[2,1])
g = g.group(0, function=lambda list: "/".join(list))
pprint(g)
print
# This will group by type, count the id's per type, and concatenate all names per type.
# Each column is given a different grouping function.
# For the column one whose values is grouped, simply use FIRST.
g = t.copy(columns=[2,0,1])
g = g.group(0, function=(FIRST, COUNT, lambda list: "/".join(list)))
g.columns[1].sort() # Sort by count.
from pattern.table import Table, pprint
# "X IS MORE IMPORTANT THAN Y"
# Here is a rough example of how to build a web miner.
# It mines comparative statements from Yahoo! and stores the results in a table,
# which can be saved as a text file for further processing later on.
# Pattern matching also works with Sentence objects from the MBSP module.
# MBSP's parser is much more robust (but also slower).
#from MBSP import Sentence, parse
q = '"more important than"' # Yahoo search query
p = "NP (VP) more important than NP" # Search pattern.
p = Pattern.fromstring(p)
t = Table()
engine = Yahoo(license=None)
for i in range(1): # max=10
for result in engine.search(q, start=i + 1, count=100, cached=True):
s = result.description
s = plaintext(s)
s = Sentence(parse(s))
for m in p.search(s):
a = m.constituents(constraint=0)[-1] # Left NP.
b = m.constituents(constraint=5)[0] # Right NP.
t.append((a.string.lower(), b.string.lower()))
pprint(t)
print
print len(t), "results."
# It can be saved as a CSV text file that is both human/machine readable.
# See also: examples/01-web/03-twitter.py
# Supported values that are imported and exported correctly:
# str, unicode, int, float, bool, None
# For other data types, custom encoder and decoder functions can be used.
t = Table(rows=[
[uid(), "broccoli", "vegetable"],
[uid(), "turnip", "vegetable"],
[uid(), "asparagus", "vegetable"],
[uid(), "banana", "fruit"],
])
print t.rows[0] # A list of rows.
print t.columns[1] # A list of columns, where each column is a list of values.
print
# Columns can be manipulated directly like any other Python list.
# This can be slow for large tables. If you need a fast way to do matrix math,
# use numpy (http://numpy.scipy.org/) instead.
# The purpose of Table is data storage.
t.columns.append(["green", "purple", "white", "yellow"])
# Save as a comma-separated (unicode) text file.
t.save("food.txt")
# Load a table from file.
t = Table.load("food.txt")
pprint(t, truncate=50, padding=" ", fill=".")