from pattern.table import Table
from pattern.table import uid, pprint
# The main purpose of the pattern module is to facilitate automated processes
# for (text) data acquisition and (linguistical) data mining.
# Often, this involves a tangle of messy text files and custom formats to store the data.
# The Table class offers a useful datasheet (cfr. MS Excel) in Python code.
# 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",
import os, sys; sys.path.append(os.path.join("..", "..", ".."))
from pattern.table import Table
from pattern.table import uid, pprint, COUNT, FIRST
# 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.
import os, sys
sys.path.insert(0, os.path.join("..", "..", ".."))
from pattern.table import Table
from pattern.table import uid, pprint, COUNT, FIRST
# 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
from pattern.table import Table
from pattern.table import uid, pprint
# The main purpose of the pattern module is to facilitate automated processes
# for (text) data acquisition and (linguistical) data mining.
# Often, this involves a tangle of messy text files and custom formats to store the data.
# The Table class offers a useful datasheet (cfr. MS Excel) in Python code.
# 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"])
