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62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
5 years ago
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from __future__ import print_function
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from __future__ import unicode_literals
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from builtins import str, bytes, dict, int
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import os
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import sys
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
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from pattern.db import Datasheet, INTEGER, STRING
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from pattern.db import uid, pprint
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# The main purpose of the pattern module is to facilitate automated processes
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# for (text) data acquisition and (linguistical) data mining.
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# Often, this involves a tangle of messy text files and custom formats to store the data.
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# The Datasheet class offers a useful matrix (cfr. MS Excel) in Python code.
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# It can be saved as a CSV text file that is both human/machine readable.
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# See also: examples/01-web/03-twitter.py
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# A Datasheet can have headers: a (name, type)-tuple for each column.
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# In this case, imported columns will automatically map values to the defined type.
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# Supported values that are imported and exported correctly:
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# str, unicode, int, float, bool, Date, None
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# For other data types, custom encoder and decoder functions can be used.
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ds = Datasheet(rows=[
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[uid(), "broccoli", "vegetable"],
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[uid(), "turnip", "vegetable"],
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[uid(), "asparagus", "vegetable"],
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[uid(), "banana", "fruit"],
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], fields=[
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("id", INTEGER), # Define the column headers.
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("name", STRING),
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("type", STRING)
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])
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print(ds.rows[0]) # A list of rows.
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print(ds.columns[1]) # A list of columns, where each column is a list of values.
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print(ds.name)
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print("")
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# Columns can be manipulated directly like any other Python list.
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# This can be slow for large tables. If you need a fast way to do matrix math,
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# use numpy (http://numpy.scipy.org/) instead.
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# The purpose of Table is data storage.
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ds.columns.append([
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"green",
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"purple",
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"white",
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"yellow"
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], field=("color", STRING))
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# Save as a comma-separated (unicode) text file.
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ds.save("food.txt", headers=True)
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# Load a table from file.
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ds = Datasheet.load("food.txt", headers=True)
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pprint(ds, truncate=50, padding=" ", fill=".")
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print("")
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print(ds.fields)
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