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import os, sys; sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
from pattern.db import Datasheet, INTEGER, STRING
from pattern.db 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 Datasheet class offers a useful matrix (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
# A Datasheet can have headers: a (name, type)-tuple for each column.
# In this case, imported columns will automatically map values to the defined type.
# Supported values that are imported and exported correctly:
# str, unicode, int, float, bool, Date, None
# For other data types, custom encoder and decoder functions can be used.
ds = Datasheet(rows=[
[uid(), "broccoli", "vegetable"],
[uid(), "turnip", "vegetable"],
[uid(), "asparagus", "vegetable"],
[uid(), "banana", "fruit"],
], fields=[
("id", INTEGER), # Define the column headers.
("name", STRING),
("type", STRING)
])
print ds.rows[0] # A list of rows.
print ds.columns[1] # A list of columns, where each column is a list of values.
print ds.name
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.
ds.columns.append([
"green",
"purple",
"white",
"yellow"
], field=("color", STRING))
# Save as a comma-separated (unicode) text file.
ds.save("food.txt", headers=True)
# Load a table from file.
ds = Datasheet.load("food.txt", headers=True)
pprint(ds, truncate=50, padding=" ", fill=".")
print
print ds.fields
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